2021 29th Iranian Conference on Electrical Engineering (ICEE)最新文献_第9页

Fuzzy Fractional Order Sliding Mode Controller Design for a Wind Turbine with DFIG 带DFIG的风力机模糊分数阶滑模控制器设计

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544228

Mh. Aghaseyedabdollah, Y. Alaviyan, H. Azmi, A. Yazdizadeh

引用次数: 0

Joint Request Aggregation and Content Caching at the Edge via Named Data Networking 基于命名数据网络的边缘联合请求聚合和内容缓存

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544137

Parisa Bakhtou, S. Khorsandi

引用次数: 0

A Digital Method for Offset Cancellation of Fully Dynamic Latched Comparators 一种全动态锁存比较器偏移抵消的数字方法

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544133

A. Ahrar, M. Yavari

{"title":"A Digital Method for Offset Cancellation of Fully Dynamic Latched Comparators","authors":"A. Ahrar, M. Yavari","doi":"10.1109/ICEE52715.2021.9544133","DOIUrl":"https://doi.org/10.1109/ICEE52715.2021.9544133","url":null,"abstract":"In this paper, we have proposed a two-phase high precision digital offset cancellation method for dynamic latched comparators. The proposed method's first phase is weight balance control, and the second phase is named body bias control. The first phase reduces the offset of the comparator up to a few millivolts, and the second phase alleviates this amount to some decades of microvolts. The main reason for using the second phase is the weight balance calibration's sensitivity to the input pairs sizes and kickback noise. A retiming method is used to control the thermometer code DAC switching activities and minimize the glitches. The thermometer DAC structure is used for the body bias control method instead of R-2R DAC to ensure the body bias controller's monotonic signal. Circuitry simulations are done using Cadence with 180 nm standard CMOS technology under 1 V power supply. A strong-arm dynamic latched comparator is used for our calibration study. Before calibration, the input offset has three times of standard deviation equal to 19.56 millivolts. The weight balance control offset method has reduced this amount to almost 2.8 millivolts. Finally, the fully-calibrated comparator results have an offset equal to 363 microvolts. The calibration clock is set to be 33.3 MHz. Our offset cancellation prepares 53.9 times improvement in the input offset of the comparator using 389 microwatts.","PeriodicalId":254932,"journal":{"name":"2021 29th Iranian Conference on Electrical Engineering (ICEE)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126642133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Investigation of Li3P as Electrolyte and Lithium-ion conductor: An Ab- Initio Study Li3P作为电解质和锂离子导体的从头算研究

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544381

Keyvan Khosh Abady, Amin Niksirat, Negar Karpourazar, M. Pourfath

引用次数: 0

Multi WGAN-GP loss for pathological stain transformation using GAN 多WGAN-GP丢失用于GAN病理染色转化

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544310

Atefeh Ziaei Moghadam, H. Azarnoush, S. Seyyedsalehi

{"title":"Multi WGAN-GP loss for pathological stain transformation using GAN","authors":"Atefeh Ziaei Moghadam, H. Azarnoush, S. Seyyedsalehi","doi":"10.1109/ICEE52715.2021.9544310","DOIUrl":"https://doi.org/10.1109/ICEE52715.2021.9544310","url":null,"abstract":"In this paper, we proposed a new loss function to train the conditional generative adversarial network (CGAN). CGANs use a condition to generate images. Adding a class condition to the discriminator helps improve the training process of GANs and has been widely used for CGAN. Therefore, many loss functions have been proposed for the discriminator to add class conditions to it. Most of them have the problem of adjusting weights. This paper presents a simple yet new loss function that uses class labels, but no adjusting is required. This loss function is based on WGAN-GP loss, and the discriminator has outputs of the same order (the reason for no adjusting). More specifically, the discriminator has K (the number of classes) outputs, and each of them is used to compute the distance between fake and real samples of one class. Another loss to enable the discriminator to classify is also proposed by applying SoftMax to the outputs and adding cross-entropy to our first loss. The proposed loss functions are applied to a CGAN for image-to-image translation (here stain transformation for pathological images). The performances of proposed losses with some state-of-the-art losses are compared using Histogram Intersection Score between generated images and target images. The accuracy of a classifier is also computed to measure the effect of stain transformation. Our first loss performs almost similar to the loss that achieved the best results. (Abstract)","PeriodicalId":254932,"journal":{"name":"2021 29th Iranian Conference on Electrical Engineering (ICEE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113979073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-Agent Deployment Around a Source in the Plane Using Biased Extremum Seeking 基于有偏极值搜索的平面源周围多智能体部署

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544204

M. Ghadiri-Modarres, M. Mojiri, E. Fattahi

引用次数: 0

Noninvasive Diagnosis of the Type of Breast Tumor through Artificial Neural Networks 人工神经网络在乳腺肿瘤类型无创诊断中的应用

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544420

Pooya Tahmasebi, M. M. Dastjerdi, A. Fallah, S. Rashidi

{"title":"Noninvasive Diagnosis of the Type of Breast Tumor through Artificial Neural Networks","authors":"Pooya Tahmasebi, M. M. Dastjerdi, A. Fallah, S. Rashidi","doi":"10.1109/ICEE52715.2021.9544420","DOIUrl":"https://doi.org/10.1109/ICEE52715.2021.9544420","url":null,"abstract":"Different changes such as developing benign and malignant lesions in tissues lead to specific variations in their macroscopic and microscopic structure, which are associated with the alteration of their mechanical properties. In the present study, the mechanical parameters of different breast tissue lesions were noninvasively estimated with high precision based on the displacement data by using the powerful neural network method in order to detect the type of tumor in the breast tissue. The displacement data of various breast tissues, as well as the corresponding mechanical properties were acquired to develop and train the neural network models. For simulating breast tissue behavior and extracting the relevant displacement data to train the neural networks, the finite element modeling was applied using Abaqus software. Ogden and Yeoh hyperelastic models which are precise for expressing the hyperelastic behavior of soft tissues, specifically the breast, were used to create the finite element model for tumor-containing breast tissue. With the aim of obtaining a robust neural network model, the displacement data extracted from the finite element model and white noise summated to simulate laboratory conditions while deriving tissue data from finite element model. As indicated by the results, the trained neural network models represent high precision and efficiency in appraising the mechanical parameters of various breast tissues according to the displacement data, which promises its use for carefully diagnosing the type of breast lesion.","PeriodicalId":254932,"journal":{"name":"2021 29th Iranian Conference on Electrical Engineering (ICEE)","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Service Restoration in Electrical Distribution Networks Using a Bi-Stage Algorithm 基于双阶段算法的配电网有效业务恢复

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544452

Qasem Asadi, A. Amini, H. Falaghi, M. Ramezani

{"title":"Effective Service Restoration in Electrical Distribution Networks Using a Bi-Stage Algorithm","authors":"Qasem Asadi, A. Amini, H. Falaghi, M. Ramezani","doi":"10.1109/ICEE52715.2021.9544452","DOIUrl":"https://doi.org/10.1109/ICEE52715.2021.9544452","url":null,"abstract":"In Electrical Distribution System (EDS), usually following an outage, significant time is spent on fault locating, isolating the fault area, and energizing upstream and downstream loads. In addition to dissatisfaction in customers, electricity interruption even for a few seconds, can cause huge damages to advanced industries or critical customers. Currently, EDSs are equipped with Manually Switches (MS) and Remote Controlled Switches (RCS). The heuristic Service Restoration (SR) algorithm of this paper is a Bi-Stage algorithm that at the first stage quickly restore energy to some loads using RCSs and in the second stage, all types of switches (MSs and RCSs) are used to complete the SR process. Here, to solve the SR problem, minimizing Energy Not Supplied (ENS) is selected as the objective function. The proposed SR algorithm was applied on a sample network in different situations. In addition to using a Bi-Stage SR algorithm to minimize ENS and re-energizing customers in the shortest time after fault, taking into account the actual network conditions such as load profiles, network operation constraints, and providing switching sequence also increase the attractiveness of the proposed algorithm. Finally, it was proved that, using the proposed SR algorithm it is possible to achieve optimal and efficient solutions meeting today's EDSs needs.","PeriodicalId":254932,"journal":{"name":"2021 29th Iranian Conference on Electrical Engineering (ICEE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Blind Angle and Angular Range Detection in Planar and Limited-View Geometries for Photoacoustic Tomography 光声层析成像中平面和有限视场几何的盲角和角距离检测

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544114

Soheil Hakakzadeh, Z. Kavehvash

{"title":"Blind Angle and Angular Range Detection in Planar and Limited-View Geometries for Photoacoustic Tomography","authors":"Soheil Hakakzadeh, Z. Kavehvash","doi":"10.1109/ICEE52715.2021.9544114","DOIUrl":"https://doi.org/10.1109/ICEE52715.2021.9544114","url":null,"abstract":"In this paper, the relationship between the position of ultrasound transducers from the photoacoustic source and the reconstructed image is investigated. Our studies have shown that the distance of the transducer and its location from the photoacoustic source specifically affects and is closely related to the quality of the reconstructed image of the source. Also, we introduce a concept called blind angle for photoacoustic computed tomography (PACT) that have planar or circular limited-view geometry. Complete and accurate equations of this relationship are presented in this paper for all different 2D photoacoustic geometries. The main source of these equations is the spherically of the ultrasound wave produced by the photoacoustic source. The result of these equations is that depending on the location of the transducers with respect to the photoacoustic source, the characteristics of the reconstructed image and the quality of the reconstruction of each point of the photoacoustic source can be accurately obtained, and the reason for these dependencies is fully explained. The results show that the more transducers receive the ultrasound wave produced by the photoacoustic source at more different angles, the higher the quality of the reconstructed image would become.","PeriodicalId":254932,"journal":{"name":"2021 29th Iranian Conference on Electrical Engineering (ICEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122737629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Effect of the Number of Quantum-Dot Layers on the Performance of the 1.3 μm InAs/GaAs VCSELs 量子点层数对1.3 μm InAs/GaAs VCSELs性能的影响

2021 29th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2021-05-18 DOI: 10.1109/ICEE52715.2021.9544436

S. Alaei, M. Seifouri, S. Olyaee, Gholamreza Babaabbasi

引用次数: 1