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2nd IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2019

作者： Yeping, Fan Desheng, Yang Dong, Ma Dian, Jiao Xiaojin, Chi Mengxian, Chen State Grid Communication Industry Group CO. LTD Anhui Jiyuan Software CO. LTD Hefei China Hefei University of Technology School of Electric Engineering and Automation Hefei China State Grid Wenzhou Power Supply Company Wenzhou China

ISBN: (纸本)9781728130668

Face recognition technology is an important research field for deep learning. In order to overcome the shortcomings of traditional open-loop face cognition mode and deep neural network structure, and to imitate human cognition model with real-time evaluation of cognitive results and self-optimized regulation of feature space and classification criteria, drawing on the theory of closed-loop control theory, this paper explores a face intelligent cognition method with deep ensemble learning and feedback mechanism. Firstly, based on the DEEPID neural network, a multi-layer feature space of face images from the global to the local is established. Secondly, based on feature separability evaluation and variable precision rough set theory, a face cognition decision information system model with dynamic feature representation is established from the perspective of information theory, to compact the multilayer feature space. Thirdly, the ensemble random vector functional-link net is used to construct the classification criterion for the compacted multi-layer feature space. Finally, the entropy measure index of face recognition results is constructed to provide a quantitative basis for the self-optimization adjustment mechanism of face feature space and classification criteria. The experimental results show that the proposed method can effectively improve the recognition rate of face images compared with the existing methods. © 2019 IEEE.

关键词： Face recognition

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Measurement Data Acquisition System in Laboratory for Renewable Energy Sources

Measurement Data Acquisition System in Laboratory for Renewa...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Dario Došen Matej Žnidarec Damir Šljivac Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

This paper presents laboratory for renewable energy sources equipped with measurement data acquisition system. The system is developed by the Laboratory for Renewable Energy Sources (RESLAB) at the Faculty of Electrical engineering, Computer Science and Information technology Osijek, Croatia. Data acquisition system relies on specific hardware and software technologies incorporated into one system. It uses custom-based measuring hardware, strong communication protocols, automatic software processing mechanisms, web applications and server-side computation. Analysed system continuously measures electrical parameters acquired from photovoltaic (PV) technologies and meteorological parameters on the test site. Acquisition hardware consists of three main parts: data acquisition system of 5 PV modules made of different technologies; data acquisition system of 10 kWp PV system and data acquisition system of meteorological parameters. All measured data is stored in an extensive database. Stored data is organized and visualized on a dedicated web server. Measurement data acquisition system described in this paper represents unique, custom-developed and open-source data acquisition system used for long-term performance analysis of PVs and can be scaled and utilized for various research areas.

关键词： Temperature measurement Data acquisition Current measurement Hardware Microcontrollers Pollution measurement

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A grid-tied photovoltaic transformer-less inverter with reduced leakage current

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IOP Conference Series: Earth and Environmental Science 2021年第1期673卷

作者： S Ahmad S Mekhilef H Mokhlis Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Faculty of Engineering University of Malaya Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne VIC Australia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering Faculty of Engineering University of Malaya Malaysia

The importance of transformer-less inverters has been increased since these are highly efficient, less costly, reduced in weight compared to conventional inverters for PV systems connected to grid. This fact led researchers to propose various topologies of transformer-less inverters, which have been validated for only injection of real power towards grid. However, recently new regulation has been imposed by all international power regulatory agency is that these PV inverters integrated with grid should have the capability of handling flow of a certain quantity of reactive power. By considering this point, a noble topology of transformer-less inverter has been developed for grid integrated PV system. Unlike previous topologies during reactive power injection, the new topology does not face reverse recovery issues, which helps to enhance the efficiency of the proposed inverter by exploiting MOSFET switches. Moreover, leakage current becomes low by keeping the common mode (CM) voltage at input DC voltage's midpoint.

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

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Research on feedback Cognitive Method of Insulator Self-blast State Based on Multi-scale Convolutional Network

Research on feedback Cognitive Method of Insulator Self-blas...

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Hailong Gao Tianpeng Wang Ye Lu Yeping Fan Binbin Yang Dong Ma Weitao Li State Grid Xuzhou Electric Power Supply Company Xuzhou China Anhui Jiyuan Software CO. LTD State Grid Communication Industry Group CO. LTD Hefei China School of Electric Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

In view of the lack of the existing insulator self-blast state detection method and the scale defects of deep neural network structure, imitated the human cognitive model, and learn from the closed-loop control theory, this paper explores a feedback recognition method of insulator self-blast state with multi-scale convolutional neural network. Firstly, for the pre-processed insulator images, based on ResNet-18, branches with different network structure are added to improve the network ability to adapt to different resolutions. At the same time, the multi-scale information fusion module is added at the end of the network. Secondly, the multi-scale feature vector is sent to stochastic configuration networks (SCN) with universal approximation ability to establish the classification criterion of the self-blast state of insulator images with strong generalization ability. Finally, an imitation of human thinking patterns is employed that exhibits repeated deliberation and comparison. Consequently, based on generalized error and entropy theories, the performance index is defined to evaluate the uncertain results of the insulator self-blast states in real time. Then, the regulation mechanism is given to realize the self-optimizing of fixed feature space and the reconstruction of classification criteria, which renders the insulator self-blast states is re-recognized with feedback mechanism. Experimental results show that, compared with other open-loop and closed-loop algorithms, the proposed method enhances the generalization ability and improves the recognition accuracy of the model.

关键词： Insulators Feature extraction Training Convolution Cognition Image resolution Real-time systems

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation Computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era

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Scientific Reports 2025年第1期15卷 1-23页

作者： Malik, Kaleem Razzaq Sajid, Muhammad Almogren, Ahmad Malik, Tauqeer Safdar Khan, Ali Haider Altameem, Ayman Rehman, Ateeq Ur Hussen, Seada Department of Computer Science Air University Islamabad 44230 Pakistan Chair of Cyber Security Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia Department of Information and Communication Technology Bahauddin Zakariya University Punjab Multan 60800 Pakistan Department of Software Engineering Faculty of Computer Science Lahore Garrison University Punjab Lahore 54000 Pakistan College of Computer Science Beijing University of Technology Beijing 100124 China School of Computing Gachon University Seongnam-si 13120 South Korea Department of Electrical Power Adama Science and Technology University Adama 1888 Ethiopia

The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today’s digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data. Researchers have increasingly explored steganography as a reliable method for secure communication because it plays a crucial role in concealing and safeguarding sensitive information. This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. The proposed model achieved reconstruction accuracies of 96.2% using Xu Net and 95.7% with SR Net. By combining DCT with deep learning algorithms, the proposed approach overcomes the limitations of spatial domain methods, offering a more flexible and effective steganography solution. Furthermore, simulation results confirm that the proposed technique outperforms state-of-the-art methods across key performance metrics, inc

关键词： DCT Deep learning GAN Spatial domain Steganography

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Reliability Estimation of Approximate Circuits Based on Probabilistic Gate Model

Reliability Estimation of Approximate Circuits Based on Prob...

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Pacific Rim International Symposium on Dependable Computing

作者： Tao Wang Jianhui Jiang Zhen Wang School of Software Engineering Tongji University Shanghai CHINA School of Computer Science and Technology Shanghai University of Electric Power Shanghai CHINA

ISBN: (数字)9781728149615

ISBN: (纸本)9781728149622

The existing analytical methods for estimation of the reliability of approximate circuits are based on probability transfer module (PTM) method and Monte Carlo (MC) method. However, the PTM-based method is confined to small-scale approximate circuits and large circuits with weak signal correlation, and the MC method is time-consuming to obtain accurate results. This paper presents two methods for reliability estimation of gate-level approximate circuits based on probability gate model (PGM). The first is non-process correlation algorithm that does not consider the correlation among signals to obtain an approximate value of circuit reliability, and its time complexity keeps a linear relation with the number of gates. The second is the processing correlation algorithm which can estimate the correlation caused by fanout nodes of the approximate circuits, and it has the obvious advantage on accuracy, but its time complexity is exponential with the number of fanout nodes in the circuit. Experimental results demonstrate the validity and accuracy of the proposed methods.

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Performance assessment of mono and poly crystalline silicon photovoltaic arrays under Pannonian climate conditions

Performance assessment of mono and poly crystalline silicon ...

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Eurocon

作者： Matej Žnidarec Damir Šljivac Dario Došen Boris Dumniŋ Department of Power Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Software Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences Novi Sad Novi Sad Serbia

This paper conducted performance assessment of two photovoltaic arrays made of different silicon photovoltaic technologies installed at the test site of the Laboratory for Renewable Energy Sources at the Faculty of Electrical engineering, Computer Science and Information technology Osijek in Croatia. Each of the two photovoltaic arrays consists of 20 series-connected photovoltaic modules with total installed power of 5 kWp, which are then connected to the grid-tie inverter. Studied PV technologies are monocrystalline and polycrystalline silicon. Performance of the photovoltaic arrays is evaluated using 12 months of continuous measurements of the electrical output with assessment of the influence of European humid continental climate measured meteorological data on the performance, electricity production and efficiency of the investigated photovoltaic arrays. Monthly energy yield, performance ratio and capacity factor of each PV array is also calculated. Furthermore, empirical analysis of meteorological and electrical parameters is conducted and mathematical models of efficiency in relation to module temperature are determined.

关键词： Temperature measurement Meteorology Mathematical model Radiation effects Temperature dependence Silicon Photovoltaic systems

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