Jahanshah Kabodian

   In this research, a hybrid algorithm called WOA-Q Learning is proposed for real-time task scheduling in Fog-Cloud environments. The algorithm integrates the global exploration capability of the Whale Optimization Algorithm (WOA) with the adaptive decision-making mechanism of Q-Learning, aiming to optimize resource allocation and minimize delay. Simulations conducted in MATLAB across scenarios with 25 to 100 tasks demonstrated that the proposed method outperforms benchmark algorithms such as EDF, PSO, WOA, and QL in four key performance metrics: total delay, energy consumption, deadline miss ratio, and scheduling efficiency. The WOA-Q algorithm achieved up to 25% reduction in total delay, 20% lower energy consumption, and a deadline miss ratio of around 0.05. Although its execution time is slightly higher due to computational complexity, the overall performance improvement justifies the trade-off. The results confirm that combining metaheuristic and reinforcement learning techniques provides an effective and intelligent approach for real-time scheduling, with significant potential for applications in IoT, edge computing, and industrial control systems.          Keywords: Optimization, Real-time scheduling, Cloud-fog environment, Internet of Things      

Abstract Speech signal processing, as one of the key domains in artificial intelligence and data science, has gained significant importance in today's world. This field plays a crucial role in enhancing human-computer interaction. One of the most challenging and fascinating applications in this domain is speech emotion recognition (SER), which finds applications in psychology, customer service, mental health diagnosis, and even information security. Emotions are intense and specific mental activities that can often be expressed through various behaviors, with speech being one of the most prominent. A speech emotion recognition system works by first receiving a person's voice as input and then determining the emotional state present in the speech, such as anger, fear, happiness, neutrality, etc. In this study, an innovative system based on deep learning methods is proposed, leveraging important and diverse speech signal features such as Mel-frequency cepstral coefficients (MFCCs), Mel spectrograms, zero-crossing rate (ZCR), and others. The system utilizes a dual parallel pipeline for emotion recognition. The first pipeline analyzes the temporal sequence of the speech signal features and sends them to a Bi-LSTM network to model temporal dependencies within the data. The second pipeline performs statistical computations on the extracted features, creating a one-dimensional feature vector, which is then fed into a multilayer perceptron (MLP) model. Finally, the outputs of both pipelines are combined and used for emotion recognition in the speech signal. For evaluation, leave-one-speaker-out (LOSO) cross-validation is employed, and two metrics, WAR and UAR, are used. The results on the EmoDB dataset achieved 83.44% WAR and 80.79% UAR, while on the SAVEE dataset, the results were 58.75% WAR and 54.64% UAR. The findings demonstrate that combining temporal and statistical information from speech signals yields higher accuracy compared to single-method approaches. These results highlight the high potential of this method for practical applications.   

In this research, a lowpass-bandpass diplexer with ultra-wide stopband and low insertion loss using hexagon-shaped resonators. The proposed diplexer consists of a bandpass (BPF) and a lowpass filter (LPF), representing the core concept of the proposed design method that aims to concurrently design BPF and LPF. In this proposed design method, the influence of the LPF filter on the BPF's design has been identified through coupling matrix analysis for the first time. Initially, an LPF is designed based on three coupled hexagon-shaped elliptical resonators. Subsequently, a novel model for BPF design, utilizing coupled high-impedance lines, has been introduced. Following this, the BPF model is developed using coupling matrix analysis while considering the impact of LPF resonators. The LPF have a 1.32 GHz cut-off frequency and ultra-wide stopband up to 17.42 GHz. The BPF consisted of four resonators and the hexagon-shaped structure is used instead of low impedance lines. The utilization of hexagon-shaped resonators serves the purpose of enhancing the precision of the coupling effect, aligning with the proposed coupling matrix analysis. Additionally, hexagon-shaped resonators exhibit a greater capacitive effect, leading to a reduction in insertion loss within the pa  and when compared to rectangular-shaped resonators. The BPF has narrow pa  and with center frequency of is 2.25 GHz and 0.31 GHz bandwidth. The measured insertion losses of LPF and BPF are less than 0.75 dB and 0.81 dB, respectively in 60% of pa  ands

   Investigating the seismic behavior of frames braced with CFT columns

Nowadays, social networks have become an inseparable part of people's lives; As far as most people use these networks to express their feelings about all aspects of life. Because understanding and analyzing these emotions has many applications, such as customer relationship management, checking and observing people's mental health, identifying public emotions caused by a national, global or political event, identifying criminals and improving the efficiency of responsive robots, etc.; Identifying the intensity of emotion from the texts of social network users is considered a very practical and important issue. In this project, we are trying to find the most accurate model for predicting the intensity of emotion from social network tweets with the help of different natural language processing methods, using regression-based machine learning models and neural networks. In this research, we first used basic methods such as Bag of Words, Word2Vec, GloVe and TF-IDF and obtained accuracy on the data of the SemEval2018 Task1 EI-Reg competition, then using modern methods such as GPT2 and model Based on BERT, we perform various tests on these data in order to reach the best possible result in terms of Pearson correlation. The result obtained from the methods used, which is a combination method of different models, is equal to 0.82, which compared to the previous works in this research and the teams participating in SemEval competition is the best result.

  The use ofdampers in the connection of steel beams to precast concrete columns has provento be effective in improving the performance of various types of connections.Dampers increase energy dissipation capacity and limit structural damage undersevere seismic loads. The main objective of this research is to employscientific and professional methods based on documented and useful data toaccurately analyze the performance of various dampers in the connection ofsteel beams to precast concrete columns. Comparative analysis of thisconnection with changes in damper parameters and components will be carried outusing numerical methods in finite element software such as Abaqus and itscounterparts. According to the research findings, the ratio of the total energyof the friction damper to the reference model is equal to 1473, for the metaldamper it is equal to 52 and for the combined damper it is equal to 10.Therefore, it is logical to use friction damper in areas with high earthquakeintensity. The final resistance ratio of the friction damper model to thereference model is equal to 7.5, for the model equipped with a metal damper itis equal to 26 and for the combined model it is equal to 60. This means thatcompared to the rest of the models, the hybrid damper will yield later andrequires more force to yield, and it will perform well in areas with strongwinds and to face moderate earthquakes. It will work well.

  Abstract:In general, additional dampers or energy absorbers are usedto reduce the dynamic response of the structure against earthquake load andwind load. The structure of these devices is such that by applying special andspecific deformations and special mechanical actions, they first absorb andthen deplete a huge part of the energy input to the structure due to impact anddynamic loading. The functional behavior of these devices is the reason thatthe energy received by other members from structures should be reduced and weshould not witness many changes in their shape after the earthquake. Ingeneral, the mechanism and performance and energy absorption structures ofthese dampers include 3 general methods of friction and visco-elastic behaviorand the use of the flow properties of stable metals.. Amongthe advantages of these devices, we can point out their use in the improvementand retrofitting of existing structures with a new method. The reason for thisissue is the special shape of these devices as well as their location, whichare generally placed in wind braces. These devices can be easily placed in theexisting structures and may even be replaced if necessary after the loadingtime (earthquake event). Due to the large number of non-resistant and safe structuresagainst earthquakes in our country, Iran, and the need to use new methods inthe design of non-resistant and safe structures due to earthquake loads, whichreduce the dynamic response of structures in a suitable way. Inthis upcoming research, we are looking to find the numerical placement ofrotational friction damper in reinforced concrete frame. For this purpose, twofinal strength parameters and the hysteresis diagram of concrete frame withrotational friction damper were checked after verification under barcyclic ineleven proposed models. and found the optimal numerical model frame accordingto these two parameters, and for this purpose, the dimensions of the frame of aone-storied opening with a length of 5.0 meters and a height of 3.2 meters fromthe middle opening of the seven-story concrete frame and the first floor andthe lowest floor with a dead load of 500 kg/m and live load of 250 kg/m wasselected. After calculating the sliding load of 80,000 kilonewtons forthe frame with a diagonal brace, eleven proposed models were made using theFiliatrat and Cheri method, and at the beginning of the validation work, theframe and damper were each done individually. The support was the onlyrotational friction damper on the column along with the Chevron brace modelwhich had two dampers, the support of the dampers was on the beam, as well asthe single damper cubic panel model, which was supported on the beam, with95,000 Nm of energy, they had the highest energy consumption of the frame. Theultimate strength of the diagonal brace frame has the highest ultimate strengthwith 600,000 newtons of resistance.

AbstractThe aim of non-parallel voice conversion (VC) is to train a voice convertor without relying on paralleldata. Due to the good performance of MaskCycleGAN which is a family of CycleGAN-vc, we usedit as our baseline system here. MaskCycleGAN was an improvement of CycleGAN-vc2, by replacingmel-cepstral features with mel-spectrogram ones, benefited from a mechanism, “filling the frames”(FIF) that make the convertor fill the artificially made missing frames based on neighboring frames.However, this model was not able to capture the inter-channel dependencies. To do so, we proposean attention mechanism integrated in the convertor to help it enhance the sensitivity of the networkto more important features. This application of attention mechanism has a twofold advantage. First,there is no need to define new models, subsequently a vast number of parameters will not be imposedon the network, and second, the process of updating the parameters is done through back propagation.A subjective evaluation of the similarity and naturalness, as well as an objective evaluation shows thatour proposed model outperforms the conditional MaskCycleGAN.

Network traffic classification has been studied for two decades and has been applied to a wide variety of applications, including network traffic management, security in firewalls, and intrusion detection systems. Traditional network traffic classification methods, including port-based methods, deep packet i  ection, and traditional machine learning methods, have been widely used in the past. But due to the dramatic changes that happened in the field of traffic on the Internet, especially the increase in encrypted traffic, as well as the need for these methods to extract features from data streams manually by experts in this field, which was time-consuming, expensive and error-prone. , the accuracy of these methods decreased dramatically. This caused the emergence of newer methods in the field of network traffic classification. Deep learning methods, which are a subset of machine learning science, were able to quickly open their place in this field by automatically extracting features from traffic flows and removing the need for feature extraction by experts, as well as the high accuracy they showed in traffic classification. Also, techniques such as data fusion techniques, as helpful techniques that can be used to further increase accuracy and improve network traffic classification, have come to the aid of these methods. In this research, an attempt has been made to use recurrent deep networks and cumulative cryptography to extract features from the high-level ISCX VPN-non VPN traffic data set. Then, by applying the data integration technique at the feature level, the features extracted from the mentioned networks will be brought to an optimal set of features and finally increase the accuracy in traffic classification. It should be noted that traffic classification is done by the multi-layer perceptron deep network. According to the evaluations, the accuracy of the proposed model in network traffic classification has reached 99.1%.  

ا رشد پلتفرم ها و برنامه هاي كاربردي شبكه هاي اجتماعي آنالين، روزانه مقادير زيادي محتواي متني توسط كاربر به روشهاي مختلف مانند نظرات، تحليلها، اخبار و پيام هاي متني كوتاه ايجاد مي شود. در نتيجه، كاربران اغلب براي استخراج اطالعات مفيد در مورد موضوع مورد بحث اين گونه محتوا را پالش برانگيز ميدانند. امروزه براي 1 استخراج راحتتر اطالعات مفيد از روشي به نام استخراج موضوع استفاده ميكنند. استخراج موضوع با اسستقاده از يك سري محاسبات آماري خالصه يا موضوع اصلي سند مورد نظر را از متن بيرون ميكشد، كه با اين كار ميتوان با مشكالت كمتري به تجزيه و تحليل اسناد پرداخت. در اين پژوهش قصد داريم با استفاده از روشهاي يادگيري عميق همچون)DNN,LSTM )يك شبكه يادگيري عميق جهت استخراج موضوع با دقت بيشتر از كارهاي انجام شده در اين زمينه طراحي كنيم. ديتابيسي كه در اين پژوهش بر روي آن كار خواهيم كرد ديتابيسي متني شامل اخبار است. كه در ابتدا با استفاده از تكنيكهاي پيشپردازش متن )تبديل كردن تمامي حروف موجود در دادههاي متني به »حروف كوچك« )letters Lowercase ،)پاك كردن عالئم نقطهگذاري )Punctuations ،)پاك كردن »كلمات بي اثر« )Stopwords ،)مصدر سازي كلمات)Stemming ) )عمليات نرمل 2 سازي را انجان داديم. از LDA بعنوان روش يادگيري شبكه استفاده ميكنيم به بياني واضح تر شبكه يادگيري عميق بر اساس تكنيك استخراج موضوع LDA كار خواهد كرد. نتيجه اين پژوهش دقت بشتر شبكه يادگيري نسبت به شبكههاي ساخته شده در كارهاي پيشين است كه توانستهايم دقت شبكه بر روي ديتابيس مورد نظر را نسبت به آنها بيشتر كنيم.  

   ABSTRACT Audio conversion aims to change one or more aspects of the speech signal while preserving the speech structure of the signal. One of the subcategories of voice conversion is voice conversion. Voice conversion is a technique to transform the identity of the hidden speaker in the source speech waveform while preserving the linguistic information. The goal of the voice conversion system is to create a conversion function, which converts the same speech features of the language from both source and target speakers. By placing the corresponding features of the target speaker with the corresponding features of the source speaker's speech, and reconstructing these features into the speech wave, voice conversion occurs. Most of the topics of voice conversion revolve around learning the corresponding characteristics of the source and target speakers. In this research, it has been tried to convert the speech wave of the source speaker by separating the signal of the source speaker and the target into the same time segments and convert it into a two-dimensional Mel Spectrum matrix (using the MelGAN vocoder), it prepare the input data and train the network Created, i  ired by Cycle GAN, this transformation function. The MelGAN vocoder has been used to synthesize (transform) the waveform into Mel Spectrum and vice versa (speech waveform). Also, in this research, the data of the voice imitation challenge of 2018 was used. The challenge [50], held every two years, attempts to improve the quality of voice imitation by providing data (in the 2018 series, non-parallel data). In the end, two subjective and objective (realistic) methods have been used to evaluate the final transformation function trained in this research. Existing objective evaluation criteria for voice conversion (VC) are not always relevant to human perception. Therefore, training VC models with such metrics may not effectively improve the naturalness and similarity of the converted speech. In this project, evaluation models based on deep learning have been used to predict human ratings from transformed speech. We adopt convolutional and recurrent neural network models to develop a mean opinion score (MOS) predictor, called MOSnet. Also, the MCD criterion has been used for objective evaluation. Despite years of research, voice imitation systems, and the progress of the transformation function learning process, using different types of neural networks, still have deficiencies in accurately imitating a target speaker spectrally and prosodically and at the same time maintaining speech quality.

  Convolutional neural network has been used in the cuckoo filter for the named data network. First, this cuckoo filter has been made two-dimensional, then a neural network has been used for training. The purpose of this training and learning method is to extract the features of the inserted data and use those features during the data search, which ultimately improves the search speed.

The rapid economic growth in the world has led to anincrease in energy consumption during the last decades. According to recentstudies, the energy consumption of cooling and heating systems in buildings isabout 60%. As a result, any development in thermal system technology to lessenenergy consumption, especially in buildings, is welcomed. Phase changematerials (PCM) with high density for energy storage are one of the mosteffective ways to reduce energy consumption in buildings. In this thesis, usingDesign Builder version 7 software, heat transfer of a 4x4x3 electrical facilityroom located in Tehran city is simulated. Throughout these simulations, thethermal behavior of wall equipped with PCM layers has been examined. The various parametersof wall and PCM considered in the simulations include the thickness,conductivity coefficient, melting temperature of PCM material, and also heatgeneration within the room. The results showed that by increasing the thicknessof PCM, the amount of daily thermal load of the building decreases and as aresult, the thermal performance of the room is improved. By changing thethickness of PCM from 5 to 10 cm, the thermal performance coefficient of PCMincreases by 20-25% depending on the melting temperature of PCM. It is foundthat there is an effective thickness for PCM, after which increasing thethickness has a negligible effect on reducing the thermal load. As anotherresult, it was found that the conductivity coefficient has a small effect on thethermal performance coefficient so that for all the studied cases, the effectof conductivity changes in the range of 0.2 to 2 W/m2 °C on the thermalperformance coefficient is less than 13%. Also, the results showed that anincrease in the internal production of heat, leads to a decrease in the valueof the thermal performance coefficient, and in this case, the phase changematerial has a poor performance.  

  Iran's Standard No. 2800 provides a code for the design of structures against earthquake loads. Due to the fact that mostly, far-field records have been used to prepare the seismic design spectra, in order to consider the destructive effects of near-field earthquakes in the 4th edition of St.2800, the incremental spectral correction coefficient (N) was introduced. In this paper, the accuracy and estimation of the value of this coefficient for 5 structures of special reinforced concrete moment-resisting frame with the number of floors from 3 to 15, are evaluated. Due to the fact that the increase in seismic requirements under the pulses of near-fault earthquakes is not the same for all seismic response parameters, so different correction coefficients can be used to estimate displacement response quantities and force quantities. For this purpose, first, the structures are statically analyzed according to the criteria of Iranian Standard 2800 and are designed according to the criteria of Article 9 of the National Regulations of Iran. Then the dimensions of beams, columns and rebars required by the structures are determined. After that the response spectrum of single-degree of freedom system to a set of records (including 7 far-field records, and 22 near-field records) is calculated, then using incremental dynamic analysis, the seismic response of structures at different seismic intensities is calculated. By calculating the response ratio of structures under near-field records to far-field records, the value of the N-coefficient is calculated. Based on the results, the value of the N-coefficient of the standard spectrum of St.2800 is suitable for estimating the base shear demand of structures, but this coefficient is not accurate enough to estimate the need for lateral drift of structures. In general, the coefficients obtained from elastic and inelastic analyzes for the need for displacement in reinforced concrete flexural frame structures are higher than the values provided by the St.2800. This difference has reached 58% in some structures. It was also observed that there is no regular relationship between the 1st natural period of the structures and the magnitude of the spectral correction coefficient and the magnitude of the spectrum correction decreases with increasing seismic intensity.

به طور معمول يك قاضي بر اساس دانش، تجربه، شخصيت و احساسات خود قضاوت مي‌كند. با افزايش تعداد پرونده‌ها، بررسي اسناد و شواهد به صورت دقيق دشوار است و ممكن است قضاوت‌ها ذهني‌تر شوند. همچنين با افزايش حجم كاري، يك قاضي ممكن است بيش از حد تحت فشار قرار گرفته و نتواند يك قضاوت با كيفيت انجام دهد. پيش بيني حكم دادگاه توسط الگوريتم‌هاي هوش مصنوعي، علاوه بر قضات، مي‌تواند جهت استفاده كارشناسان حقوقي و نيز دادخواهان بسيار مفيد واقع شود. همچنين اين نوع پيش‌بيني مي‌تواند به عنوان يك خدمت مشاوره‌اي آنلاين به آحاد جامعه ارائه شود تا قبل از طرح دعوي در محاكم قضايي و تنظيم دادخواست يا شكواييه، نسبت به نتيجه احتمالي درخواست خود آگاهي يافته و چه بسا همين امر سبب كاهش چشمگير پرونده‌ها و نيز كاهش هزينه‌هاي سرسام‌آور گرفتن وكيل در برخي موارد براي قشر كمتر برخوردار گردد. اين نوع پيش‌بيني همچنين به وكلا و طرفين دعوي كمك مي‌كند كه قبل از رفتن به دادگاه اقدامات لازم را انجام دهند. از ديگر كاربردهاي اين پژوهش مي‌توان كمك به صدور دستور تشكيل دادگاه‌هاي تجديد نظر در صورت مغايرت راي دادگاه بدوي با حكم پيش‌بيني شده توسط مدل هوش مصنوعي اشاره كرد. با وجود آن‌كه متن‌كاوي و كاربردهاي آن به طور گسترده در حوزه‌هاي مختلف مورد استفاده قرار گرفته، اما تنها مطالعات معدودي متن‌كاوي را در زمينه‌هاي قضايي به كار گرفته‌اند. اين پايان‌نامه، اولين پژوهش مدون در حوزه متن‌كاوي اسناد قضايي فارسي مي‌باشد. در اين پايان‌نامه به پيش‌بيني حكم دادگاه در پرونده‌هاي مرتبط با خريد، نگهداري، مخفي كردن يا حمل مواد مخدر با استفاده از تكنيك‌هاي يادگيري ماشين و يادگيري عميق، با بررسي تاثير جنبه احساسات و هيجانات قاضي در شدت حكم صادره، در مجازات‌هاي شلاق، جريمه نقدي و حبس، پرداخته شده‌است. براي اين منظور ابتدا متون و اسناد 6000 پرونده قضايي را پيش‌پردازش نموده، سپس با استفاده از پيكره احساسات و هيجانات NRC، گرايش مثبت يا منفي و نوع هيجان موجود در پرونده‌ها را بررسي و نمره‌گذاري كرديم. در ادامه با روش‌هاي گوناگون يادگيري ماشين و يادگيري عميق، مدلسازي احساسات را انجام داديم كه از ميان روش‌هاي پياده‌سازي شده، روش TFIDF + SVM بيشترين دقت را كسب نمود. سپس به تجزيه و تحليل 8 نوع هيجان موجود در پرونده‌ها پرداخته و به صورت طبقه‌بندي چند برچسبه آن‌ها را مدل‌سازي نموديم كه به صورت ميانگين، الگوريتم TFIDF + SVM بيشترين دقت را داشت. در گام بعد، ميزان مجازات‌هاي در نظر گرفته شده در پرونده‌ها را در دو دسته مخففه و مشدده طبقه‌بندي نموده و به روش‌هاي يادگيري ماشين، يادگيري ماشين جمعي و يادگيري عميق، به مدلسازي آن‌ها اقدام نموديم كه در نهايت از ميان روش‌هاي بررسي شده، در مجازات شلاق روش TFIDF + Adaboost، در مجازات جريمه نقدي روش BERT و در مجازات زندان روش Skipgram + LSTM + CNN، بيشترين دقت را كسب نمودند. در نهايت به منظور تخصيص هر يك از برچسب‌هاي مجازات شلاق، جريمه نقدي و زندان، هر الگوريتمي كه بيشترين دقت را داشت انتخاب نموده و دقت آن را در شرايطي كه داده ما متون قضايي به علاوه نمره احساسات پرونده، متون قضايي به علاوه نمره هيجانات پرونده، متون قضايي به علاوه نمره احساسات و نمره هيجانات پرونده باشد را محاسبه نموديم. نتايج اين پژوهش نشان مي‌دهد كه استفاده از نمره احساسات و هيجانات، باعث افزايش دقت پيش‌بيني حكم دادگاه براي هر سه مجازات مورد بررسي(شلاق، جريمه نقدي، زندان) مي‌گردد. همچنين مجازات شلاق بيشترين تاثير و مجازات زندان كمترين تاثير را از احساسات و هيجانات مي‌گيرد. در ضمن در مجموع احساسات تأثير بيشتري نسبت به هيجانات در پيش‌بيني راي دادگاه دارند. كليدواژه‌ها: پيش‌بيني حكم دادگاه، متن‌كاوي، يادگيري ماشين، يادگيري عميق، تحليل احساسات، تحليل هيجانات   

Every day a large number of comments are published by users on the web, especially on social networks, online review sites in forums and social networks. Due to the huge volume of this data and textual information, their analysis by humans is very difficult, time consuming and practically impossible; so we need a system that can automatically analyze comments. Sentiment analysis is the best solution to this problem. Sentiment analysis is a subset of natural language processing. And it is a process that examines people's concerns, views, and feelings by identifying the positive, negative, and neutral aspects of writing. The corona virus has become a storm on social media. As awareness of this disease increases, messages and posts confirm its existence. The social network Twitter has shown a similar effect to the number of messages related to Covid19. Which has had unprecedented growth in recent times. In this study, the analysis of Twitter Persian messages about the coronavirus was performed using machine learning. The success of machine learning has been discussed in many applications due to its ability to automatically extract features and learn complex patterns. The purpose of this study is to provide a model for analyzing and classifying the Sentiment of Twitter users using machine learning algorithms. In this research, using machine learning algorithms such as decision tree, SVM, logistic regression to approach the emotions of Persian tweets, an acceptable result has been obtained. Similarly, the accuracy of the decision tree algorithm was 83%, the support vector machine 81% and the logistic regression 77%. The decision tree algorithm has the best accuracy. Keywords: Sentiment analysis, Coronavirus pandemic, Twitter social networks, Machine learning.   

   Skin cancer is one of the most common cancers in human societies and its prevalence is increasing dramatically. Melanoma is one of the most dangerous types of skin cancer, and the more the skin lesion grows, the lower the chance of cure. Early detection of cancer plays an important role in its treatment. Definitive treatment of melanoma cancer is possible with early detection. In this dissertation, a new method for diagnosing skin cancer was presented. In this method, two types of discrete and stationary wavelet transform were first applied to the images. A number of statistical features were then extracted from these converted images. Also, various global, local, etc. features were applied to the gray and color surface images. In the next step, to improve the results, the extraction features were combined to obtain the best combination of features that >Keywords: Melanoma Cancer, Discrete Wavelet Transform, Stationary Wavelet Transform, Least Squares Support Vector

AbstractPredicting the stock market is an important and challenging task. Traditional stock market forecasting methods uses only historical stock trading data and related numerical indicators, but with the grows of information about the stock market on the Web, researchers began to use this valuable information to increase the accuracy of stock value forecasting. In many previous studies, only one additional data source has been used to combine with the historical stock data source, which can not show the impact of other information on the stock market price trend properly. And in many studies, they have relied on one learning algorithm, which means that we can not achieve the most accurate forecast for stock value.In this study, by collecting three different stock data sources (historical stock data source, social network data source and daily news data source), we tried to use different aspects affecting stock value in predicting stock value to be more accurate than The traditional way. To do this, we first analyzed the opinions extracted about the stock, from the Twitter social network and the daily news data source extracted from the Reddit news website, using a hybrid opinion mining model, and from this, emotional indicators such as The polarity and subjectivity of each sentence were extracted. Then, by combining these indicators with the historical stock data source, we proceeded to create the final composite data source. Then, by using different >The results of this study showed that in Apple, Cisco and Boeing stocks, the use of information combination has improved the accuracy of stock value forecasting to 65%, and with the analysis of the principal component, this amount reached over 80%, which compared to The traditional method, which is less than 60%, is a good improvement. The experiments also showed that the use of XGBoost >Keywords: stock market prediction, information combination, sentiment Analysis, social network  

In this study, simulation of solar chimney thermal performance in the presence of phase change material (PCM) as thermal energy storage by computational fluid dynamics (CFD) at two thermal powers 1200 W and 800 W for melting process in closed and open heating states and freezing process in Closed and open channels were examined. In closed heating mode, in order to store energy, thermal power is applied by the PCM, and in open channel mode, the heated air is transferred to the environment by thermal evacuation. In order to perform fluid dynamics analysis, the performance of the device with PCM has been investigated using Comsol software. The solar chimney studied in this project consists of three main parts including: PCM chamber, absorber plate and air duct. According to the definition, first the thermal energy is transferred to the fluid and the adsorbent plate and consequently the PCM and also the heat input leads to an increase in the temperature of the fluid inside the duct and is converted into kinetic energy and as a result the fluid flows into the chimney. And causes heat transfer to the environment. The geometry of the solar chimney device is designed with the actual dimensions of the chimney made in the reference. In the next step a PCM system was used. These materials have been used to improve heat transfer inside the solar chimney and have been simulated by CFD. Parameters such as the velocity of the fluid entering the channel, the thermal conductivity of the absorber plate and the thermal conductivity of the PCM have a great effect on the melting time of the PCM and also the specific heat capacity and latent heat of the PCM on energy storage. It affects. The results of simulation of the process of melting the PCM showed that by applying the power of 1200 W of closed and open heating, the time required for melting is 3 hours and 10 minutes and 4 hours and 30 minutes respectively, while in the experimental study these values It is 3 hours, 4 hours and 15 minutes, respectively. Also, the freezing time in the simulation in closed and open air channel mode is 7 hours and 40 minutes, 6 hours and 30 minute   respectively, while in the experimental study, these values ??are 7 hours and 10 minutes, 6 hours and 20 minutes. The outlet temperature of the solar chimney in the simulation is 1200 W in heating mode depending on 69 °C, which is 71 °C in the experimental work. Also at 1200 W in open heating mode the output temperature in the simulation is 51 °C and this value is 49 °C in the experimental study. According to the simulated and laboratory results, it is evident that the PCM system increases the temperature of the exhaust air to heat the environment. Comparison of these results with experimental data confirms the accuracy of this analysis.   

Exploration and production of crude oil is often associated with the formation of water-in-oil (W / O) emulsions, which can cause serious problems for downstream refinery industries. Chemical demulsification by adding demulsifiers is usually the main technique used to dominance the problems associated with the formation of W / O emulsions. In recent years, nanotechnology has been used to accelerate the demulsification process. Utilizing the appropriate nanoparticles significantly reduced the high process costs. Using library studies, in this study, Fe3O4 magnetic nanoparticles were selected and synthesized by electrolysis method and the structure of nanoparticles was investigated using XRD, FE-SEM, FTIR and VSM analyzes. The aim of this study was to evaluate the effect of adding Fe3O4 nanoparticles along with commercial DDH 9855 demolifier to reduce the consumption of the demulsifier and improve the separation of water from Dehloran oil field oil. According to the results, the highest efficiency of water separation was obtained in optimal conditions when the temperature was 40 °C, the concentration of demulsifier was 300 ppm, pH was 6.4, water content was 7.5 ml and the amount of nanoparticles was 0.033 g, 97.83%. Eventually, the nanoparticles used in the demulsification process were reused, and after three times of use, the water separation efficiency dropped by about 15%, which is a very appropriate and negligible reduction, so that the nanoparticles can be reused up to three times and successfully. Also, the effect of settling time as another important parameter in the suspension process during 2 h of data recording was investigated, which increased the efficiency by 66.65%. On the other hand, by examining the effect of adding nanoparticles next to the demulsifier after 5 h of settling time, 14.82% of the isolated water has increased compared to the demulsifier only. On the other hand, the effect of adding nanoparticles next to the demulsifier increased by 15.38% with increasing the amount of nanoparticles compared to the demolifier mode alone, and the settling time decreased by 5 h. Therefore, the results showed that the required settling time is significantly less than the conventional demulsification process.   

   در سالهاي اخير اهميت استفاده از مبدل­هاي الكترونيك قدرت به نحوي زياد شده است كه مقالات متعددي در اين زمينه به چاپ رسيده است. روشهاي متنوعي در كنترل مبدل­هاي قدرت ارائه شده است كه از رايج­ترين روش­ها مي­توان به مدلاسيون بردار فضايي، مدلاسيون پهناي پالس و ... اشاره نمود. در اين پايان نامه از روش كنترل پيش­بين مبتني بر مدل براي كنترل كننده يك اينورتر منبع ولتاژ سه­فاز چهارساق استفاده شده است. روش پيشنهادي در اين پايان­نامه با اعمال قيد كليدزني در طراحي كنترل­كننده باعث كاهش فركانس كليدزني مي­گردد كه تلفات ناشي از كليدزني را كاهش مي­دهد در عين حال باعث بهبود عملكرد سيستم در توان­هاي بالا و كاهش هزينه نهايي ساخت مبدل مي­گردد همچنين با استفاده از روش پيشنهادي در حالت كنترل پيش­بين دو مرحله­اي مي­توان زمان نمونه برداري سيستم را افزايش داد. علاوه­بر­اين مي­توان به مقاوم بودن كنترل­كننده پيشنهادي در تغييرات امپدانس خط اشاره نمود كه اعوجاج هارمونيكي كل سيستم در محدوده قابل قبولي قرار مي­گيرد. كه به‌منظور بررسي عملكرد صحيح روش پيشنهادي حالت‌هاي متعادلي و نامتعادلي سيستم موردمطالعه، در محيط سيمولينك نرم‌افزار متلب شبيه‌سازي گرديده است. واژه‌هاي كليدي: كنترل پيش‌بين ، اينورتر، ، كاهش فركانس كليدزني، قيد كليدزني، امپدانس خط

Aspect-based sentiment classification is one of the most challenging fields in natural language processing. Researchers have used a variety of traditional and machine learning methods. Traditional methods do not make good use of the interaction between data, and we must manually them but deep learning methods, on the other hand, can consider both data specify the feature for widely used in text processing, image processing, and many other fields, and obtain interaction and latent features. Therefore, these methods have recently been networks, attention-based approach, etc. have been introduced for Aspect-based state-of-the-art result. Many deep learning methods such as convolutional sentiment classification, but each has advantages and disadvantages. For to parallelize and extract local features within the text and the attention example, convolutional networks, better than other networks, have the ability approach also has the ability to focus more on the more important parts of the introduced according to the idea of ??extracting the local features of networks sentence. The Burt network was also introduced in 2018 to summarize text in search engines. In this thesis, simple and chain local attention models are using local attention. Then, by applying the attention approach to the lower convolution and more focus on the most important parts with the approach of attention and mapping of words to the vector by Burt network. It can be hoped models, low level and aspect-related features are provided for the upper layer that these networks will cover each other's shortcomings. In the proposed layer, the high-level features are extracted and used for classification. comparable to the superior models in the classification of aspect-based sentiment. Experimental results showed that the proposed models have achieved result  

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists more, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for normalizing stain have been proposed as a pre-processing step in automated pipeline systems. In this thesis, the stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images has been performed based on the Pix2Pix framework derived from conditional generative adversarial networks (cGAN). The proposed approach is called "Stain-to-Stain Translation" (STST). This method learns not only the specific color distribution but also the preserves corresponding histopathological pattern. Also, unlike previous methods that depended on a reference image, this method uses the distribution of all images in the training set for learning. The STST method has achieved significant results, both quantitative and qualitative evaluation, against some of the best methods. Based on the obtained results, it can be shown that STST, besides the very high perceptual similarity between the ground truth and the restained image, outperformed other stain normalization methods examined on the processing time metric. It also in a clinical use-case, namely breast cancer tumor name="_ftnref1" title="">[1]. [1] https://github.com/pegahsalehi/Stain-to-Stain-Translation   

   همخواني ميان شيوه زندگي ساكنين و شيوه سازماندهي فضا ها از ضرورت هاي طراحي خانه است. به اين معني كه سازمان فضايي خانه امكان رخ دادن طيف رويدادهاي زندگي را فراهم كند و براي نيازهاي انسان، در اين سازمان فضايي پاسخ فضايي وجود داشته باشد. به همين دليل زندگي بايد مبناي معماري قرار گيرد و به تبع آن شكل گيرد. اين در حالي است كه در شرايط امروز، تمركز مسئولين معطوف به توسعه ي كمي و انبوه مسكن است و از طرف ديگر معماري خانه بر پايه پاسخ هايي وارداتي و بدون توجه به شكل زندگي زمينه اي كه در ان شكل مي گيرد شكل مي گيرد. اين امر سبب مي شود فضاي زندگي امكان انجام فعاليت هاي بسياري را از ساكنان سلب كند. پاسخگو نبودن خانه به تمامي سطوح نيازهاي ادمي باعث مي شود كه افراد، جواب آن نيازها را در محل ديگري خارج از خانه بجويند كه خود مي تواند در درازمدت به زدگي از خانه و خانواده منجر شود. اين خود، تاثيرات رواني، خانوادگي و اجتماعي بسياري را به دنبال دارد. اين پايان نامه بر ان است كه با شناخت ويژگي هايي از كالبد خانه كه جريان زندگي را مي توانند تقويت كنند به ارائه گزينه هايي براي طراحي در اين جهت منتهي شود. در نهايت بر اساس اين ويژگي ها به طراحي يك مصداق ختم شود. بر اين مبنا نخست با كنكاش در ادبيات موضوع، مفاهيم كيفي مربوط به خانه و الگوهاي كالبدي فضاهاي خانه در منابع مكتوب حوزه معماري استخراج مي گردد. همچنين به تعريف خانواده سالم و رويدادهاي زندگي متناسب با آن در منابع مكتوب حوزه روان شناسي پرداخته مي شود. با در نظر داشتن معيارهاي استخراج شده از متون، در جهت كشف چگونگي ارتباط كالبد با زندگي، 10 خانه در بافت مياني كرمانشاه به عنوان نمونه موردي و به روش كيفي مورد بررسي قرار خواهد گرفت. به اين منظور، از روش مصاحبه ي عميق با ساكنين با حضور در خانه به همراه مشاهده و برداشت از فضاها استفاده مي شود. از طريق تحليل نمونه هاي موردي، تلاش مي شود با شناخت كالبد موجود و رويدادهاي خانواده، به كشف معيارهايي از فضاها كه در تحقق رويدادها موثر بوده پرداخته شود. اين معيارهاي كالبدي در قالب الگوهايي شكلي در مقياس هم نشيني فضاهاي خانه ارائه مي شود. در اين پايان نامه، با بررسي ضرورت مساله، در جهت كشف چگونگي ارتباط كالبد با زندگي، از طريق مطالعه ي منابع در حوزه هاي معماري و روان شناسي، به ارائه پيشينه ي موضوع با طي فرايندي از شناخت مفاهيم كيفي به ارائه الگوهاي كالبدي عام پرداخته مي شود. سپس در جهت شناسايي الگوهاي خاص، به تبيين روش پژوهش پايان نامه با طي فرايندي از شناخت كالبد خانه ها و رويدادهاي جاري در آن ها به شناسايي معيارهاي كالبدي موثر در تحقق رويدادها پرداخته مي شود. در نهايت تحليل داده ها و نتايج، بيان شده و طراحي منتج از آن ها ارائه مي گردد. نتايج حاصل شده، مي توانند به عنوان ابعاد كيفي خانه مورد توجه طراحان قرار گرفته و در تدوين استانداردهاي خانه كاربرد پيدا كنند. همچنين به عنوان ارا ئه ي روشي مناسب در آموزش معماري در جهت رسيدن از مباني به طراحي مورد رجوع باشند.

 Redesigning of ilam's paper recycling plantbased on Attention Restoration Theory Today, stress is one of the most common words we are all familiar with, and it has a negative impact on industrial spaces and their human resources in the face of occupational accidents. This study aimed to investigate Kaplan's theory of attention recovery in Ilam paper recycling plant and the impact of nature on its employees. Based on the assumptions and questions raised in this study, qualitative research method has been used. The statistical population is 100 employees of Ilam industrial town. In order to respond to the research hypotheses, data collection was done using observation and questionnaire to collect field information. Observation was done in two working and non-working days. The two questionnaires consisted of 10 questions, based on a Likert scale and ranged from very low to very high and 7 degrees. The results of this research based on the hypotheses show that: employees spend most of their time indoors and, on the other hand, reduce the fatigue nature of industrial workers, thus indoors in open and factory spaces. Ilam recycling can be used to reduce staff stress and fatigue.

In recent years, internet traffic is growing extremely rapidly with the rapid growth of internet users and the emergence of new applications. As a result, the problem of identifying the applications on the network has become a complex task. The detection and classification of flow patterns and applications on network traffic plays an important role in network security and network management. The purpose of classification is to create a link between packet packets with a particular service or application. The problem with most of the methods is to rely on property extraction by experts. It is difficult and time consuming to find desirable features that lead to high accuracy. In general, most of the traffic classification methods are based on extracted features by an expert on computer networks. These features include port number, packet overhead, packet header and extracted statistical features of flow. The main problem of traffic classification is finding suitable features in traffic network. The process of finding suitable features is time consuming and cost and needs a qualified person to identify and extract these features and to solve these problems, one of the most recent fields in machine learning is deep learning that is based on artificial neural networks and that feature extraction is done in a hierarchical and automatic mode. In this situation, extracting the automatic feature from the expert will be eliminated and the possibility of human errors is reduced. In this work, our solution show that this approach is capable to identify encrypted traffic and surpass the accuracy achieved by almost every classical method in this area of research. We have used Deep Belief Networks and Convolutional Neural Network that can accurately identify and classification on ISCX vpn-nonvpn dataset.

  Abstract Social networks has attracted many users. These social networks have enabled user to connect to each other and share text, image and videos. A social network that allows users to share their location is named a location-based social network. Users can leave their tips on places they have visited and share it with others. User feedback is reflection of how they feel about the places they have visited. In social networks, people are connected to each other’s. One of the issues of these networks is the prediction of communication that may be created between two users in the future. Link prediction is the name chosen for this issue. There are many approaches used to predict links. Network structure information, user information such as their interests and characteristics, and location information that users have visited are used to predict links. User’s sentiment is one of the information that can be used to improve link prediction. Their tips can be analyzed to gain a sentiment for users in location-based social networks. This can provide a new algorithm for link prediction by combining the information of network structure, the information of the places they visited and their sentiments. The algorithm was tested on a foursquare network dataset, and it was found to perform better than one that does not use user sentiment. Therefore, it can be concluded that the role of sentiment is effective in creating new links among users. Keywords: social network, location-based social networks, link prediction, sentiment, location sharing

  Electronic service providers discovered the importance of evaluating their services with market competition. Because the competition is in such a way that any weaknesses in the mindset of customers and the attraction of new customers over a short period can bring the organization into an abyss. In the research literature, we introduced the important concepts of research. In the sequel, we will look at the history of the research and the ongoing efforts. By examining different models, we concluded that the role of the user in different models was not considers sufficiently. In some studies, the user's satisfaction has been overlook unaware that different users have different characters and different tastes. Regardless of these differences, we will not be able to assess accurately the quality of a web service from the user's perspective. Through the Myers-Briggs test, we divided the users into 16 personality categories. Fuzzy was chose as a suitable method because of the close proximity to user interactions. After reviewing some fuzzy methods, Topsis method was select as a suitable method because of high accuracy and unlimited in the number of interviewees and criteria. In Topsis method, we needed to weigh it to the criteria, which used from improve fuzzy AHP method. Finally, we distributed a questionnaire that analyzed the first 60 first questions of personality testing and 42 subsequent questionnaires on quality of web service with Mellat Bank as a well-known Web service. Hundred completed questionnaires filled in for us had remarkable results. As expected, users with different personalities arranged different levels of satisfaction and, in some cases, even contradicted the criteria. In the results of the research, we arrange the criteria for each character as well as the order of the characters according to their satisfaction with the criteria.

  Nowadays different approaches of signal processing are used in many applications due to its potential applicability to a wide range of problems, such as telecommunication and biomedical signals processing. Voice activity detection (VAD) is one of most important signal processing branches in audio signal processor and is used in many telecommunication systems such as Speech compression , speech recognition, upgrade of speech , noise estimation and noise removal. VADs are also used to detect input signals and >For instance in a mobile telecommunication system usually 60% of talk-time includes speech signal, so that the rest of the signal is not informative. To decrease channel capacity and power consumption in this case VADs can be used to submit only pure speech signal. There have been already many studies in this field, however the efficiency of proposed approaches are highly depends on background noise. So that their efficiency may decrease while noise power is quite higher compared to speech power. This current seminar aims to provide an efficient method which is based on the combination of typical detection techniques of the speech versus non-speech blocks, so that the result can be applied for both clean and high SNR environment

  Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal combination of fundamental frequency estimation methods, in noisy signals. In this study, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined both linearly and nonlinearly. These methods are: Autocorrelation, Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F0) of the frame is estimated using the SWIPE method. The optimal coefficients for linear combination are determined using the regularized least squares method with Tikhonov regularization. To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of SDFPE, MFPE, FPE, GPE, VDE, PTE and FFE standard error criteria. The results of the experiments indicate that the linear combination method (on various SNRs) made GPE error 22.98%, VDE error 26.16%, PTE error 9.26%, and FFE error rate of 32.72% (relative) And the nonlinear combining method reduces the GPE error by 30.64%, the VDE error by 33.58%, the PTE error by 9.58%, and the FFE error by 39.86%, as compared to the popular speech frequency extraction methods. 

Classification of Motor Imagery Tasks for Brain Computer Interface Applicatio  

ارائه يك مدل بهينه سازي فرا ابتكاري و استفاده آن در زمانبندي وظايف سيستم هاي ناهمگن

  موضوع بهبود كيفيت صدا امروزه به يكي از موضوعات مهم و اساسي روز تبديل‌شده است .ازاين‌رو بهبود گفتارهاي آغشته به نويز يكي از موضوعات مهم در حوزه پردازش سيگنال است و در موارد بسياري مثل تشخيص صدا، شناسايي احساسات صوتي و...كاربرد دارد. تضعيف نويز به‌نحوي‌كه اختلالي در سيگنال اصلي به وجود نياورد يك چالش مهم براي بهبود صدا محسوب مي‌شود. روش‌هاي مختلفي براي كاهش نويز ارائه‌شده‌اند كه ازجمله روش‌هاي پايه مي‌توان به روش تفريق طيفي ، تبديل موجك، و...ساير موارد اشاره كرد. موضوع تحقيق اين پايان­نامه نيز بررسي نويز موجود در سيگنالِ گفتار، حذف و يا كاهش آن نويز ازسيگنال گفتارِنويزي و ايجاد بهبود در سيگنال‌هاي گفتارِ آغشته به نويز مي­باشد.در اين پايان­نامه دو   روش جديد براي كاهش نويز موجود در سيگنال گفتار نويزي ارائه داده ايم . در روش اول ، يك روش تخمين نويز براي نويزهاي غير ايستان همراه با اعمال تبديل موجك بر روي سيگنال و استفاده از الگوريتم بهينه‌سازي گروه ذرات با رفتار كوانتومي،را به صورت تركيبي با روش Bayesian ارائه داده‌ايم تا نويزهاي موجود در سيگنال نويزي را حذف كند و سيگنال بازيابي شده به سيگنال اصلي نزديك‌تر باشد.در روش دوم نيز با اعمال تبديل موجك بر روي سيگنال و تركيب آن با روش SMPR   روشي جديد براي كاهش نويز ارائه داده ايم. روش­هاي پيشنهادي نسبت به روش‌هاي موردتحقيق در اين پايان­نامه بهتر عمل مي‌كنند و منجر به كاهش نويز از سيگنال با كمترين اعوجاج مي‌شوند.

چكيدهبا توجه به افزايش ترافيك و نياز به پاسخ گويي سريع به درخواست­ها دسته­بندي بسته­ها به يك تكنولوژي مهم و يك چالش در عملكرد مسيرياب­ها تبديل شده است، بخصوص در زمان همگام سازي تصميم گيري خود با سرعت تبادل داده­ها اين موضوع بيشتر نمود پيدا مي كند، يعني سرعت جست و جوي فيلدها با سرعت لينك­هاي انتقال برابر باشد و تا زماني كه سرعت شبكه­ها ثابت نشود كار روي دسته­بندي بسته­ها اهميت خود را حفظ مي­كند. افزايش روزافزون داده­هاي انتقالي و پويا بودن آنها باعث شده راه حل­ها و معماري­هاي سخت­افزاري يا نرم­افزاري متعددي براي اين موضوع ارائه شود. الگوريتم­هاي نرم­افزاري با وجود توسعه­پذيري بالايي كه فراهم مي­كنند اما از سرعت پائيني برخوردارند از طرف ديگر راه­حل­هاي سخت­افزاري سرعت خوبي دارند ولي هزينه بالا و قابليت توسعه­پذيري كمي دارند. از اين رو ارائه روشي براي ايجاد مصالحه بين سخت­افزار و نرم­افزار مورد توجه محققان قرار گرفته است. طبقه بندي بسته­ها يك جستجوي چند فيلدي با سرعت لينك ا انجام مي­دهد.در اين تحقيق ، به منظور رفع مشكلاتي كه در بالا ذكر شد از دو فيلتر بلوم و خارج قسمت استفاده شد و به منظور انطباق روش جستجو با بسته هاي ارسالي در تعداد فيلدهاي موجود در معماري نوين SDN، اين تعداد به 15 فيلد سرايند افزايش يافت. در نهايت با استفاده از ابزارهاي در دسترس از جمله   Intel Platform Power Estimation Tool (IPPET)   معيارهاي مورد نظر براي بررسي قابليت هاي روش ارائه شده استفاده گرديد و از نتايج حاصل از دو فيلتر برتري فيلتر بلوم نسبت به فيلتر خارج قسمت دربرخي معيارها اثبات گرديد به اين صورت كه در مورد زمان مصرفي، سرعت انجام الگوريتم، توان عملياتي و انرژي مصرفي فيلتر بلوم عملكرد بهتري داشته ولي در موارد حافظه مصرفي و نرخ خطاي مثبت فيلتر خارج قسمت عملكرد بهتري دارد.

  In this study, frames with concrete-filled steel tubular (CFST)   columns with sandwich panels were used. The CFST columns, due to the many benefits that it has, are propagation every year. The advantages of steel and concrete are well-known, concrete with good compressive strength, reasonable price for other materials and considerable resistance to fire burning, and steel is a material with high ductility and high strength. The combination of these two materials is a composite material with good properties. In CFST columns, the existence of a steel tube makes it more confinement to the concrete inside it, as well as the existence of concrete inside the steel, preventing the local buckling of the steel and thus increasing the load capacity of the column. Sandwich panel as an effective member is widely used in industrial buildings and structures. The sandwich panel has a good flexible and relatively low weight, with a central core and outer layers attached to the sides of the core. In this study, numerical models have been used to investigate the effect of different parameters on stiffe   , ultimate strength and energy dissipation of the CFST frame with the sandwich panels intermediate. In order to verify the numerical modeling, several experimental tests Wang-2017 (reference 3) were modeled using ABAQUS fainant element   software and the acceptable acceptance of the results of the analyzes confirmed the validity of the modeling. . The results showed that the use of the sandwich panel intermediate reinforced the ultimate strength, structural stiffness and energy dissipation considerably

Rotary Regenerative Air Preheater (RRAPH) is one of the main equipment for energy recovery in the steam boiler of the power plants. In the present study, Ljungstrom air preheater of the Bisotoun Thermal power plant has been investigated with the aim of optimizing its thermal performance. In this regard, with Computational Fluid Dynamics (CFD), three-dimensional simulation of the rotary air preheater was performed to solve the continuity, momentum and energy equations in porous medium. Considering the structure of the plates of the Ljungstrom matrix, the use of the porous medium assumption is acceptable. The results of simulation show acceptable accuracy in comparison with the experimental results which is achieved from Bisetoon power plant data. In this research, the effect of rotational speed on the efficiency of air preheater in variety of loads and mass flow rates for both without leakage and with leakage conditions was investigated. The results of the present study show that the impact of the rotational speed on the performance of RRAPH is in the range of 0.5 to 4 rpm, and after this increase in speed does not have a significant effect on efficiency. The present study also shows that leakage has a significant effect on reducing the efficiency of the RRAPH in all thermal loads and rotational speeds. In the following, the optimum rotary speed was studied in different loads, mass flow rate of air and flue gas. For this purpose, both without leakage and with leakage have been studied. Results show, the efficiency of the power plant was almost constant for various thermal loads, and performance is only increasing with increasing rotating speed. On the other hand, with considering leakage effect, the maximum RRAPH efficiency is related to the power plants nominal load (320 MW). One of the most important and limiting factors in increasing the speed of rotation is the dew point temperature forms acid. Therefore, in this study, this index was extracted for optimal rotary speeds in different thermal loads. In the following, the effect of material change on the efficiency of RRAPH was investigated. According to the results, for both without leakage and with leakage the best thermal performance is related to the stainless steel, which has the lowest thermal diffusivity, lowest thermal performance is related to the copper, which has the highest thermal diffusivity.  

In this thesis, numerical simulation of fluid mixing has been performed for non-Newtonian flow under the effect of electric filed (Electroosmotic flow). This problem is of great importance as a frequent process in advanced and progressive technology of Lab-on-Chips and has numerous applications introduced in medical and biochemical areas. One of the major purposes of this field is to design high performance micromixers so that ideal mixing can be achieved in minimum time and energy consumption. In this study, the effects of governing parameters on mixing performance have been investigated in a flow field consisted of combined electroosmotic and pressure driven flows in presence of physical hurdles and zeta-potential heterogeneities. The simulations have been conducted for 2D geometry using finite element method by means of commercial code COMSOL Multiphysics 5.2a. Nernst-Planck equations have been used for the modeling of electric double layer (EDL) and the distribution of ions. The results indicate that several factors such as dilatant fluid behavior, adverse pressure gradient, zeta-potential heterogeneities as well as height of hurdles can have augmentative effects on the mixing performance. It is found that increasing the length of the hurdles has small effects on mixing performance while the location of the hurdles along the channel hardly changes the mixing quality. It is also seen that the effect of patches’ arrangement on the mixing is mostly depended on the magnitude of the zeta-potentials of the patches. The results showed that among the various effective parameters, the best choice for increasing the mixing quality is to increase the value of zeta-potential of the patches, because the mass flow rate passing the micromixer has no reduction and it is almost constant. This is a key characteristic because any reduction in mass flow rate is undesirable and deteriorates the performance of micromixer.

  يكي از مباحث مهم در پردازش صوت، پردازش فايل‌هايي است كه در آن مخلوطي از گفتار انسان، سكوت و موزيك وجود دارد. به عنوان نمونه مي‌توان به فايل‌هاي ضبط شده از رسانه‌هاي راديويي، تلويزيوني و ماهواره‌اي اشاره كرد كه حاوي سيگنال‌هاي صوتي متنوعي هستند.در برخي از كاربردها مانند كاهش حجم، افزايش كيفيت، شناسايي و كاربردهاي ديگر نياز به جداسازي گفتار انسان و يا به عبارتي حذف سكوت، موزيك و يا نويزهاي محيطي از سيگنال‌هاي صوتي به‌وجود مي‌آيد. سيستم‌هاي جداسازي گفتار را مي‌توان نوعي از سيستم‌هاي شناسايي گفتار انسان و يا سيستم‌هاي دسته‌بندي كننده‌ي سيگنال‌هاي صوتي دانست كه از آنها براي جداسازي، شناسايي و يا نشانه گذاري قسمت‌هايي از سيگنال صوتي كه شامل گفتار انسان است، استفاده مي‌شود.براي انجام عمليات جداسازي گفتار انسان از سيگنال‌هاي صوتي از روش‌ها و رويكرد‌هاي گوناگوني بهره گرفته‌مي‌شود. هدف ما در اينجا ارائه روشي مناسب وكارا   در قسمت استخراج ويژگي (feature extraction) و هم‌چنين در قسمت دستبه‌بندي (classification) با استفاده از الگوريتم‌هاي قدرتمند و پيشنهادي و نوين براي رسيدن به دقت بالا و كارايي بيشتر مي‌باشد.

  Tunneling, tunnel excavation, and the use of underground spaces as one of the most important and widely used tools today are rapidly increasing. Due to the importance of the safety of these spaces, it is essential to have sufficient knowledge and awareness at all stages of the construction and preparation of underground spaces. Selection of a suitable maintenance system for tunnels to achieve a sustainable and safe environment over time is one of the important issues in tunneling. After establishing that the tunnel requires installation of a maintenance system based on sustainability methods, the design phase of the maintenance system should begin. After designing a maintenance system and choosing the right system, by implementation of maintenance system, sustainability is provided. Tunnel maintenance system design methods are divided into three categories: analytical methods, numerical methods, and empirical methods. One of the methods for determining the required buffer of a tunnel is by the Q system method. In this research, it has been tried to determine a suitable maintenance system for tunneling in Q method by using some artificial intelligence methods. For this purpose, firstly, using Pearson analysis methods and Principle Component Analysis (PCA) by    software and then the gamma test method by WinGamma software, the effective parameters have been identified in the Q system and three different models have been selected for obtaining the value of Q. The first and second models have three input parameters and one output parameter, and the third model has four input parameters and one output parameter. Then, using some artificial intelligence methods including neural networks, fuzzy logic, gene expression programming, genetic algorithm and multivariable regressions, the amount of Q is calculated using different models individually. Finally, with respect to the amount of Q obtained from each of the models and the equivalent dimension parameter, the equivalent maintenance system is predicted using the software provided.   The software provides all the parameters required for tunnel buffer, including bolt lengths, bolt distance, and shotcrete thickness for operator. In this study, by using the parameters that have the greatest impact on rock mass index (Q), the value of Q is predicted. In this way, the effect of less important parameters or parameters that are not available or access to them is either costly or time consuming will be eliminated which reduces costs and saves time. After calculating and comparing different results obtained from artificial intelligence methods, it was determined that in the first model, the GRNN type neural network with RMSE value of 0.1 and R2 value of 0.99 is more capable of predicting rock quality index. And then the gene expression planning method with RMSE value of 2.16 and R2 value of 0.90 has more positive outcomes. Also in the second model, the GRNN type neural network with RMSE value of 1.01 and R2 value of 0.97 Has the best performance and then the methods of gene expression planning, genetic algorithm and MLP neural network have nearly similar results. in the third model, the GRNN type neural network with RMSE value of 0.31 and R2 value of 0.99 Has more satisfying results and then gene expression programming with RMSE value of 1.38 and R2 value of 0.95 showed more acceptable outcomes.

هدف از شناسايي گوينده ، تمايز قائل شدن بين افراد از طريق تفاوت در ويژگيهاي گفتار آنهاست. به اين معني افراد نه تنها در ويژگيهايي مانند اثر انگشت و برخي ويژگيهاي شناخته شده از هم قابل تفكيك هستند، بلكه مي­توان از تفاوتهاي ديگري مانند، شكل دستگاه صوتي و ويژگيهايي مثل لحن، لهجه، طرز بيان و ... نيز بهره برد. روشهاي زيادي براي مدل كردن سيگنال صوتي، بصورتي قابل تحليل بوجود آمده­اند. از جمله­ي اين روشها مي­توان به روش مدل مخلوط گوسي و مدل پس­زمينه جهاني استفاده كرد. از اين مدل براي تشكيل ابربردارهاي گوسي استفاده شده است. ابربردارهاي گوسي بردارهايي با بعد ثابت هستند كه از سال 2006، توسط كمپبل تعريف شده­اند. و در سيستمهاي شناسايي گوينده مورد استفاده قرار گرفته­اند. مشكل اين ابربردارها، بعد بالاي آنهاست كه موجب افزايش پيچيدگي محاسباتي شده است. براي مقابله با اين مشكل، از روشهاي كاهش بعد مانند بدست آوردن بردار i-vector مربوط به هرگوينده استفاده شده است. در اين تحقيق مؤلفه­هاي گوسي كه براي مدل كردن i-vectorها استفاده شده اند با توجه به مقدار آماره باوم ولچ مرتبه صفر آنها به دو دسته مؤلفه­هاي كم اهميت و مؤلفه­هاي مؤثر دسته­بندي شده­اند. از هركدام از اين مجموعه­ها عناصري بصورت تصادفي حذف مي­گردد كه تعداد اين عناصر حذفي در دو مجموعه متفاوت است. براي ارزيابي عملكرد سيستم از پايگاه داده TIMIT استفاده شده است. ميانگين خطاي EER روش پيشنهادي نسبت به كمترين مقدار خطاي EER در ساير روشها 56درصد كاهش داشته است.كلمات كليدي: ابربردار، i-vector، نمايش تنك، ماتريس نگاشت، شناسايي گوينده، مدل مخلوط گوسي، مدل پس زمينه جهاني  

High-value chemical production using nanostructure catalysts

Studies to date of the existence of a difference in peoples emotions. This vision for all researchers, particularly developers of computer games is valuable, Because by increasing the touchscreen and an increase in this type of game on our phones, the question arises, "Is touching behavior reflects the mood of the players?" If we can recognize the user’s emotions, according to the emotions of users, game design can control the amount and intensity of the game and to minimize the damaging effects of such games. . In this study, we characterized human touch in time on a touch screen use, So that we can distinguish between emotions and personality of users. In this study, using figures factor in diagnosing mental states were able to carefully 91/90 percent and 97/79 in the best position to do character recognition accuracy. In addition to this we got a result and it is other aspects of the recovery process does not recognize the characters in the parameters may in the algorithms of the parameters of feature in the evaluation of personality dimensions will be deleted when emotions are evaluated. But if consider arousal dimension moods and personality aspects to evaluating mood and personality dimensions also approached carefully 98/52 and will have a positive influence in results.

In recent years iris recognition has attracted the attention of many researchers and it also has been used in many real-world applications. In iris recognition, segmentation phase always has been the one of the challenging problems and it always consumes significant processing time. On the other hand features in a classification task play a major role and as the selected features are good the performance of the classifier can be improved. Particle swarm optimization algorithm is an evolutionary algorithm and it successfully has been used in many optimization problems. We have used this algorithm to select the most appropriate features in an iris recognition task and in this way we have learned a near optimum distance metric. In addition, in this study we have provided an effective and simple method to detect the iris area that could greatly improve iris area detection process speed. To evaluate the proposed method two data sets CASIA Interval and IITD have been tested and the results have been very promising.