Direct current (DC) microgrids have recently attracted more attention from researchers due to their advantages over alternating current (AC) microgrids, such as the absence of transmission losses related to reactive power flow, the absence of harmonic currents, and the simple integration of resources with DC loads. However, the constant exchange of information in the secondary control layer in the cyber environment for voltage recovery and optimal resource management has presented these microgrids with challenges such as high communication costs, the need for high communication bandwidth, and cyber threats. Denial of service (DoS) attacks and false data injection (FDI) attacks are dangerous examples of these threats that can lead the system to instability and even collapse. DoS attacks have extremely destructive effects on the system by temporarily or permanently disrupting access to critical system information. On the other hand, FDI attacks can secretly and noticeably cause harmful disturbances in the system by injecting false data into the transmitted information. In this study, an edge-based event-triggered control structure is proposed as a suitable and optimal alternative for permanent information exchange in second-layer distributed control, which is able to ignore DoS attacks. Based on this structure, information exchange between units occurs only when the sending conditions are met. Also, a decentralized approach based on a multilayer perceptron (MLP) neural network is introduced to detect FDI attacks. The advantages of this approach include high flexibility, reduced information transmission in the cyber environment, reduced complex calculations, and increased information security. Also, a threshold for an FDI attack is determined so that mitigation operations are performed only when the attack occurs. A microgrid consisting of three generating units and a DC voltage bus is simulated under different scenarios to investigate the effectiveness of this study. The results obtained show that the event-triggered (ET) structure is resistant to DoS attacks and the proposed approach is able to detect FDI attacks with high accuracy in the presence of DoS attacks and in the ET communication structure and mitigate its effects. Keywords: Direct Current Microgrids, Denial of Service Attacks, False Data Injection Attacks, Edge-based Event-triggered, Distributed Control, Multilayer Perceptron Neural network

Abstract Today, with the growing complexity of digital circuits and the increasing compactness of manufacturing technologies, the likelihood of failure during both the manufacturing process and the operation of digital circuits has risen. As a result, these products require testing both during production and operation to ensure proper performance. Therefore, producing a high-quality test with a minimal number of test vectors and in the shortest time is crucial. In this thesis, a simulation-based test generation method for combinational circuits is proposed. This method utilizes an approximate criterion called approximate critical path tracing with parallel patterns to evaluate the effectiveness of candidate test vectors. This criterion is based on the traditional critical path tracing method, but introduces an approximate approach for backtracking in fan-outs. By allowing some inaccuracies in the results, this method reduces the complexity of fault simulation compared to traditional methods. The parallel pattern critical path tracing method leverages both fault-level and pattern-level parallelism, resulting in significantly higher simulation speed compared to traditional methods for determining fault coverage. Applying the parallel patterns approximate critical path tracing algorithm to ISCAS85, ISCAS89, and ITC99 benchmark circuits shows that, in over 98% of the circuits, the results strongly correlate with the exact fault coverage index. Additionally, compared to the parallel pattern fault simulation method, this approach is more than 500 times faster. The proposed test generation method, which utilizes the criterion derived from the parallel pattern approximate critical path tracing algorithm to identify effective test vectors, is capable of producing high-quality test vectors quickly. Evaluations on benchmark circuits demonstrate that this method is over 15 times faster than the one using parallel pattern fault simulation, with only a 1% average increase in the number of test vectors.    Key words: Digital circuits testing, Fault coverage, Simulation-based test-pattern generation, Approximate fault coverage index, Critical path tracing.   

   As we know, the structure of electricity networks around the world is changing and evolving. So that distributed production in the form of micro-grid (MG) is expanding. Normally, an MG consists of several scattered production sources, and energy storage systems (batteries) are used for continuity of energy production in new MGs. In such networks, determining the charging and discharging time of the batteries requires having sufficient information about the state of energy production and consumption or the size of the MG bus voltage. Normally, sensors are used in MGs to get information about the above-mentioned things. Using a sensor in any system reduces its reliability. Because there is a possibility of failure in the sensors. For this purpose, the use of sensorless methods to estimate the MG bus voltage creates better conditions in creating more confidence in the MG's reliable performance. Therefore, in this research, an artificial neural network (ANN) model is proposed for MG modeling. Because A   have shown to be capable of modeling non-linear systems. It should be noted that the MG under study in this work consists of a distributed production unit (solar energy) and an energy storage system unit (batteries). In this way, three ANN models have been created to estimate MG bus voltage. So that the first model estimates the output voltage of the photovoltaic array, the second model estimates the current of the storage system, and the third model estimates the MG bus voltage. To evaluate the performance of the created models, the mean square error (MSE) of each model has been calculated. The test results show that the error of the proposed models is very low and close to zero, which means that the models can predict the output with high accuracy. Finally, it should be said that the accuracy of the first model that estimates the voltage of the photovoltaic array is higher than the other two models.

Verifying a person's identity using handwritten signatures is challenging in the presence of a skilled forger, where the forger has access to the person's signature and deliberately tries to imitate it. In offline (static) signature verification, the dynamic information of the linear signature process is lost, and it is difficult to design good feature extractors that can distinguish between genuine signatures and skilled forgeries. A signature is a handwriting of people that has special features and makes each person's signature unique, so a system can be designed to recognize people's signatures and authenticate their identity by means of signatures. One of the machine learning methods that has the appropriate accuracy to detect such projects is convolutional neural networks. In this research, we combined the deep convolutional network model with the federated learning approach, which provides proper accuracy in signature detection. This model recognizes professional forgery signatures with an accuracy of more than 91% and random forgeries with an accuracy of about 97%.  

با توجه به شيوع گسترده بيماري هاي واگيردار در جهان مدل‌هاي رياضي مي‌توانند به پيش‌بيني و كنترل اين پاندمي كمك كنند. پاندمي بيماري نشان داده كه كشورها عليرغم توسعه يافتگي و دسترسي به منابع و تجهيزات در مقابل يك بيماري جديد ناشناخته بايستي زير ساخت‌هاي فيزيكي اعم از بيمارستان‌ها، مراكز بهداشتي درماني و نيروي انساني را در برابر بحران آماده نمايند. اپيدمي ها ممكن است ظرفيت بيمارستاني ارائه خدمات بهداشتي درماني را در هم بشكنند و منابع مادي و انساني، از جمله فضاي بيمارستان، تجهيزات و داروها براي برآوردن تقاضا كافي نباشند، مخصوصاً در مورد اپيدمي كه چندين هفته يا چندين ماه طول بكشد و به ويژه اگر بحران‌هاي همزمان اتفاق افتد، بيمارستان جهت كمك به اقدامات انجام شده در كنترل اپيدمي، بايد بسياري از كاركردها و منابعش را مهار كرده و به طور هماهنگ شده مورد استفاده قرار دهدكه برآوردن اين الزامات مي‌تواند چالش برانگيز باشد. يك اپيدمي نياز به مركز بهداشتي درماني دارد تا اولويت‌هايش را تغيير داده و با روش‌هاي كاري منطبق باشد تا پاسخي سيستميك و هماهنگ به يك موقعيت با   سرعت در حال تغيير بدهد.   بر اساس پيش‌بيني و صحت نتايج مربوطه، آماده‌سازي مراكز درماني در مقابل بحران از جنبه‌هاي مختلف مديريتي، ارتباطات، منابع انساني، تامين تجهيزات، دارو و ساير خدمات تشخيصي، درماني و پشتيباني مورد نياز بيماران، همراهان و كاركنان بهداشت و درمان مهم‌ترين هدف اين تحقيق مي‌باشد كه با مدل‌سازي همه‌گيري بيماري در شرايط مختلف محقق مي‌شود. شبيه سازي با در نظر گرفتن مواردي مانند: تاثيرات اندازه جمعيت، دوره نهفتگي بيماري، دوره بيماري، نرخ آلودگي، مكان‌هاي آلوده بر گسترش بيماري، واكسيناسيون عمومي و ... اجرا و تحليل خواهدشد.

The design of a reconfigurable antenna that is able to change its radiation pattern characteristics at a certain frequency in an organized and reversible manner using control switches such as (semiconductor switches, microelectromechanical switches and varactor switches), and application in the fifth generation of wireless communication networks have The present research method is descriptive-analytical and practical in terms of applying the results. Collecting the required data and information is also done by reading articles, books, using the knowledge of professors and observing the microstrip antennas that have been designed and built in the past, and to analyze and analyze the obtained data and simulate the desired models, high frequency circuit simulation software is used. CST has been used. In the research, a microstrip antenna (printed circuit patch antenna) has been designed to achieve reconfiguration of the radiation pattern. Two capacitors work simultaneously to achieve the radiation pattern reconfiguration operation. This microstrip antenna is i  ired by the design of a circular loop. The basic structure of the loop is changed and the capacitors are integrated into the patch. By changing the capacity of the capacitors in the range of (0.01-10) picofarad, the radiation pattern of the antenna can be changed to six different modes. An electromagnetic model of the proposed antenna is simulated in CST software for numerical analysis and observation of different radiation patterns. The proposed antenna structure has been implemented and built using a material (FR4) substrate with relative transmittance (4.4) and loss tangent (0.02) and thickness (1.6) mm. Antennas have been used since 1886 to send and receive information. They have special characteristics, this divides antennas into two categories, the first category is antennas with fixed characteristics and the second category is tunable antennas, tunable antennas are divided into several categories, the main ones are tunable antennas (frequency, polarization, bandwidth and radiation pattern), these types of antennas are very useful in the technology of the fifth generation of wireless communication systems. One of the achievements of this research is the design and simulation of a microstrip antenna that can reset the radiation pattern by using capacitors as control switches in order to change the surface current and as a result change the radiation pattern of the antenna.   

Abstract  The increasing influence of renewable energy sources, including solar energy, in the production of electrical energy, has increased uncertainties in solving various problems,control and operation of the power system.The increasing influence of renewable energy sources, including solar energy, in the production of electrical energy, has increased uncertainties in solving various problems, control and operation of the power system.The increasing influence of renewable energy sources,including solar energy, in the production of electrical energy, has caused an increase in uncertainties in solving various problems, control and exploitation of the power system.this reason, probabilistic load distribution has become an important tool for investigating the random characteristics of the power system.In this thesis, in order to investigate the amount of losses, considering the unstable behavior of loads in the form of a normal distribution, one of the Monte Carlo probabilistic load distribution methods is used.Two-point, three-point and five-point estimation are used with the help of Matlab software. In the following, the MCS&3PEM algorithm is proposed and the results obtained from it are compared with the Monte Carlo method.Also, in order to check the amount of fuel cost, the PSO algorithm has been used; PSO The obtained results will be evaluated to find the best location of photovoltaic systems, in order to reduce the cost of fuel. Two standard test systems are used to check the said methods. Also, to estimate the amount of solar radiation received in Kermanshah province using daily data obtained from meteorological station, in a period of 6 years, GMDH optimized neural network is proposed. Also, to estimate the amount of solar radiation received in Kermanshah province using daily data obtained from meteorological station, in a period of 6 years, GMDH optimized neural network is proposed.Also, to estimate the amount of solar radiation received in Kermanshah province using the daily data obtained from the weather station, in a period of 6 years, an optimized GMDH neural network is proposed.The results of the research showed that there is a small difference between the radiation values measured at the meteorological station and the radiation obtained by the model, which shows the ability of the model to estimate the radiation. The results of the research showed that there is a small difference between the radiation values measured at the meteorological station and the radiation obtained by the model, which shows the ability of the model to estimate the radiation.The results of the research showed that there is a small difference between the radiation values measured at the meteorological station and the radiation obtained by the model, which indicates the ability of the model to estimate radiation.In order to evaluate different modes of system operation, two scenarios areconsidered. First, all the loads are fed only by the main grid, and losses and production costs are expected to be high. And second, the photovoltaic system is connected to the system. Based on the results,In order to evaluate different modes of system operation, two scenarios are considered. First, all the loads are fed only by the main grid, and losses and production costs are expected to be high. And second, the photovoltaic system is connected to the system. Based on the result،In order to evaluate different modes of systemoperation, two scenarios are considered. First, all the loads are fed only by the main grid and the losses and production costs are expected to be high. And second, the photovoltaic system is connected to the system. Based on the It was found that when the photovoltaic system is connected to the system, the amount of power loss and production cost   is reducedKeywords:

The world around us is full of images. Pictures are documents that, by recording a moment, become the narrator of a world of words. City cameras create, record, and store thousands of traffic images every second. Proper processing of these images can help train models based on deep learning. Such models are used in object recognition and image captioning and will be used in cases such as voice assistants and self-driving cars. In this thesis, a method is introduced to convert traffic images into their descriptions. The presented description is based on prominent objects from images and deep learning and includes three basic steps. In the first stage, data processing and methods such as data augmentation are performed on training images. In the second step, appropriate features are extracted from the images. For this purpose, four deep neural networks named VGGNet, EfficientNetB0, InceptionV3, and ResNet50 have been investigated to extract image features. According to the number of layers in the architecture of each of these deep neural networks, the fine-tuning technique has been applied to improve the accuracy of detecting traffic objects. In the third step, two neural networks, LSTM and Transformer, have been used to convert image features into text. Finally, the optimal solution will be introduced, which will significantly increase the quality of the output sentences. In total, two methods were introduced. Based on the Transformer network, the second method showed better accuracy than the first. The MS-COCO dataset was used to evaluate the proposed methods. For this purpose, a subset including 8,000 images and ten classes of traffic objects in the MS-COCO dataset has been separated and pre-processed. The accuracy of the model introduced in the BLEU evaluation criteria is 65.3595%.

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.

امروزه با ورود منابع تجديدپذير به شبكه توزيع موضوعي به نام ريز شبكه شكلگرفته كه اين موضوع بخشي ازتحقيقات را به خود تخصيص داده است. ريزشبكه داراي مزيتهاي بسيار زيادي ازجمله طبيعي و اقتصادي و غيره است. اگرچه ريزشبكه داراي مزاياي بسيار زيادي است، اما ريز شبكه چالشهاي متفاوتي را نيز ايجاد كرده است. يكي از چالشهاي مهم ريزشبكه موضوع حفاظت از ريزشبكه است، كه موضوع موردبحث اين پاياننامه نيز هست. چون ريزشبكه در دو مد كاري ايزوله و متصل به شبكه كار ميكند، به همين علت بر اندازه جريان اتصال كوتاه به شدت تأثير ميگذارد. در مد كاري متصل به شبكه اندازه جريان اتصال كوتاه متشكل از جمع جريانهاي اتصال كوتاه توسط شبكه اصلي و DG هاي موجود در ريزشبكه ميباشد، اين در حالي است كه در حالت ايزوله مقدار جريان اتصال كوتاه فقط توسط DG هاي موجود در ريزشبكه تغذيه ميشود. در نتيجه به علت تغييرات سطح جريان اتصال كوتاه و وابستگي اندازه اين جريان به توپولوژي شبكه، سيستم حفاظتي اضافه جريان، توانايي حفاظت از ريزشبكه را ندارد. به همين سبب در اين پاياننامه راهكاري براي حفاظت از ريزشبكه ارائه ميشود . الگوريتم بررسيشده در اين پاياننامه از طريق محاسبات حساسيت و تغييرات توان اكتيو بر پايه اندازهگيري فازور ولتاژ به صورت همزمان در يك زون حفاظتي، خطا را شناسايي كرده و فرمان تريپ را صادر ميكند. براي بررسي بيشتر اين الگوريتم از شبكه استاندارد CIGRE در برنامه ديگسايلنت استفاده شده و الگوريتم پيشنهادي با زبان برنامه نويسي پايتون در برنامه ديگسايلنت در قسمت DSL شبيهسازيشده است. مزيت اين الگوريتم براي حفاظت از ريزشبكه اين است كه، اولا توپولوژي شبكه بر كاركرد اين الگوريتم تاثير ندارد و ثانين توانايي تشخيص انواع خطا در دو مد كاري ريزشبكه را نيز داد. در پايان براي كاركرد بهتر اين الگوريتم به قسمت تصميمگيري آن يك شرط براي ولتاژ اضافه شده، كه باعث برطرف شدن تصميمگيري اشتباه توسط اين الگوريتم در زمان خطاهاي طولاني مدت ميشود

  The

   پايدارسازي گود و شيب ها در مناطق مختلف به دليل گسترش جمعيت ،رشد شهرسازي و راهسازي جهت تامين امنيت جاني و جلوگيري از خسارات مالي   امروزه مورد توجه بسياري از محققين قرار گرفته است . يكي از رايج ترين روشهاي پايدار سازي به دليل مزاياي بسيار ديوارهاي ميخ كوبي شده است. اخيرا برم بندي در ديوارهاي ميخ كوبي مورد پژوهش قرارگرفته و نتايج مثبت آن به وسيله مطالعات دوبعدي نشان داده شده است. باتوجه به هندسه هاي مختلف و مؤثر بر رفتار ديوار نظير گوشه ها و قوس ها با انجام مطالعات سه بعدي ميتوان رفتار ديوارهاي ميخ كوبي شده در اين شرايط را بهتر شناخت و منجر به ديد مفيد تري از عملكرد آنها شد . دراين پژوهش با استفاده از نرم افزار پلكسيس سه بعدي و دوبعدي اثرات ديوارهاي ميخ كوبي برم بندي شده را در حالت همزماني با گوشه در گود ها ، در يك تحليل عددي مورد بررسي قرار گرفته و نتايج مثبت همزمان ناشي از وجود گوشه و برم بندي وهمچنين تغييرات عرض آن بر روي نشست ها(از مقدار 9 سانتيمتر نشست به 1 سانتيمتر در عرض سه متر كاهش يافته)، تغيير مكان هاي افقي ديوار و پايداري بررسي   قرارگرفته و مدل رفتاري بررسي و ضعف مدل موركولمب نشان داده شد .

Approximatecomputing   are a promising technique for reducingpower consumption or improving circuit delay, with the help of which a suitable trade-offcan be achieved between power consumption, delay, and accuracy in circuitoutput. In this work, we propose approximate multiplication circuits withdifferent bit widths and the effect of using 8-bit multiplication in imageprocessing algorithms such as: Gaussian filter smoothing algorithm, ContrastStretching algorithm, edge detection algorithm with sobel-filter, andmultiplication of images algorithm. The proposed approximate base multiplier isa 4-bit multiplier that divides the circuit into two parts to reduce circuitdelay, the lower part is free of carry, and the upper part has a 2-bit carrychain independent of the lower part. To expand the circuit and produce 8-, 16-,and 32-bit multiplier circuits, we use a 4-bit base multiplier, and we useseven techniques for final accumulation and summation. The adder used for thefinal summation is an accurate adder and an approximate adder available withdifferent configurations to have accuracy at different levels. The results of approximate multiplier implementation show that theproposed 4-bit multiplier has a maximum of 11.55%, 11.75%, 7.99%, 45.64%,53.21%, 68.57%, 82.91% and 94.63% improvement in parameters Mean Error Distance(MED), Mean Normalized Error Distance (NMED), Mean Relative Error Distance(MRED), Power Consumption, Area, Delay, Power-Delay Product (PDP), and Energy-DelayProduct (EDP), respectively, compared to existing 4-bitmultiplication. In the proposed 4-bit multiplier, 72.81%, 74.35%, 83.33%, 95.46%and 99.24% improvement in parameters Power Consumption,Area,Delay, Power-Delay Product (PDP), and Energy-Delay Product (EDP), respectively, compared to accurate wallacetree multiplier. The results of using 8-bit multipliersin the mentioned image processing algorithms also show the acceptable qualityof the processed images.

پيشرفت فنّاوري­هاي ساخت تراشه­هاي سيليكوني و دستيابي به ابعاد نانومتري، امكان ساخت سيستم­هاي الكترونيكي بزرگ بر روي يك تراشه را فراهم نموده است. اين تراشه­هاي جديد با چالش­هاي جديدي نيز مواجه هستند و ممكن است در هر زماني در محيط كار دچار اشكال شوند. اين امر نياز به روش­هاي خودآزمايي دوره­اي در محيط كار را بيشتر كرده است. استفاده از روش­هاي خودآزمايي سخت­افزاري به علت ويژگي­هاي آزمون تصادفي نمي­تواند به تنهايي كافي باشد و براي رسيدن به سطح كيفيت مناسب براي آزمون پردازنده، بايستي از روش­هاي خودآزمايي نرم­افزاري نيز بهره برد. يكي از مهمترين مراحل در فرآيند آزمون يك تراشه، توليد بردارهاي آزمون كارا براي آزمون آن تراشه است. توليد بردارهاي آزمون با استفاد از روش­هاي قطعي توليد آزمون بسيار زمانبر است. علاوه بر اين محدوديت­هاي زماني و عملكردي در پردازنده­ها، باعث مي­شود كه توليد آزمون­هاي نرم­افزاري براي پردازنده با استفاده از روش­هاي قطعي توليد بردار آزمون، ناممكن و يا حداقل سخت و ناكارا باشد. استفاده از روش­هاي توليد آزمون مبتني بر شبيه­سازي به دليل غلبه بر اين محدوديت­ها، مي تواند يك جايگزين مناسب باشد. در روش­هاي مبتني بر شبيه­سازي، تعدادي الگوي آزمون به صورت كاملاً تصادفي و يا با استفاده از روش­هاي فرا ابتكاري توليد مي­شود. سپس اين بردارهاي آزمون بر اساس شاخص پوشش شكال، مقايسه شده و بهترين آنها انتخاب مي­شوند. در اين روش­ها، محاسبه­ي شاخص پوشش اشكال بردار آزمون زمانبر است. مي­توان به­جاي شاخص دقيق و زمانبر پوشش اشكال، از يك شاخص تقريبي و سريع براي ارزيابي و انتخاب بردارهاي آزمون استفاده نمود.   در اين راستا، در اين پايان­نامه يك شاخص تقريبي به نام APXD پيشنهاد شده است كه تقريبي مناسب از تعداد اشكال­هاي شناسايي شده توسط يك الگوي آزمون ارائه مي­كند. با تكيه­ بر اين شاخص، يك روش توليد آزمون مبتني بر شبيه­سازي به نام APXD_TG نيز در اين پايان­نامه پيشنهاد شده است و با استفاده از آن براي برخي از اجزاي يك پردازنده، آزمون نرم­افزاري توليد شده است. نتايج ارزيابي­هاي ما نشان مي­دهد كه شاخص APXD سرعت و دقت مناسب داشته و جايگزين مناسبي براي شاخص پوشش اشكال است. علاوه بر اين نتايج ارزيابي­ها نشان مي­دهند كه استفاده از شاخص APXD به جاي شاخص پوشش اشكال، ضمن حفظ كيفيت آزمون، زمان توليد آزمون را به نحو قابل توجهي كاهش مي­دهد. شاخص پيشنهادي بسيار سريع­تر از شاخص پوشش است. به طور ميانگين نسبت به روش موازي ?? برابر سريع­تر و نسبت به روش سريال 696.9 برابر سريع­تر است. لذا استفاده از آن در بخش ارزيابي بردارهاي آزمون كانديد، تسريع قابل توجهي در الگوريتم‌هاي توليد آزمون مبتني بر شبيه­سازي به وجود مي­آورد.