检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Zhang, Haochen Lin, Xingyu Peng, Sui Tang, Junjie Ponci, Ferdinanda Monti, Antonello Electrical and Computer Engineering Department University of California Los Angeles90095 United States State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China Grid Planning and Research Center Guangdong Power Grid Corporation CSG Guangzhou510080 China Institute for Automation of Complex Power Systems E.ON Energy Research Center RWTH Aachen University Aachen52074 Germany Digital Energy Fraunhofer FIT Aachen52072 Germany

Recently, a novel power flow model called weather dependent power flow (WDPF) has been proposed to incorporate conductor temperatures in power flow calculation. It is promising to adopt WDPF model in probabilistic analysis since it provides more accurate electrical and thermal states of a power system. However, calculation of the WDPF model is more computationally expensive than that of conventional power flow model, and hence repetitive calculation of the WDPF in probabilistic analysis is quite time-consuming. To overcome this challenge, a surrogate-model-based sequential algorithm is proposed for calculation of the WDPF model with high computational efficiency and good accuracy. Compared to the method that directly learns the whole WDPF model, the proposed method only learns the power flow sub-model whereas applies a linearization method to solve the thermal resistance sub-model. The construction of the proposed method considers the learning difficulty of the sub-model and the whole WDPF model, thus ensuring the high calculation accuracy. This study is the first to conduct a probabilistic power flow analysis based on the WDPF model. Numerical results in the IEEE 30-bus, 57-bus, and 118-bus systems demonstrate the importance of adopting the WDPF model in probabilistic analysis and the efficacy of the proposed method. © 2023, CC BY.

关键词： Linearization

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digital engineering school on the way to digital production 1

Digital engineering school on the way to digital production

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1st International Scientific Conference on Advances in Science, engineering and digital Education, ASEDU 2020

作者： Kovshov, E.E. Lesin, S.M. Kuvshinnikov, V.S. JSC "Research and Development Institute of Construction Technology - tomstroy" Scientific Engineering and Educational Laboratory of Digital Computer Systems and Automation Moscow Russia Moscow City University Laboratory of Research of Educational Policy of Department of the Strategic Growth Institute of System Projects Laboratory of Professional Development in Education Moscow Russia

Identifies approaches for improving and enhancing the quality of pre, basic, and additional school' engineering education according to key paradigm "Industry 4.0" based on the development and wide application of information and communication technology with opportunities in research areas of Industrial Informatics. As an example of implementation, innovative research and educational project of the digital engineering school is considered, including designed for the practical implementation of continuous engineering education "School-University-Enterprise". The main objectives of the project "digital engineering school" are formulated and disclosed, the approach to teaching programming in high-level languages using microcontrollers and microprocessor technology is highlighted, practical recommendations are given for its implementation in the structure of school education for both students and teachers. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： engineering education

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An Access Control Mechanism Based on Risk Prediction for the IoV 91

An Access Control Mechanism Based on Risk Prediction for the...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Liu, Yuanni Xiao, Man Zhou, Yanyan Zhang, Di Zhang, Jianhui Gacanin, Haris Pan, Jianli Chongqing University of Posts and Telecommunications Chongqing China Zhengzhou University Zhengzhou China National Digital Switching System Engineering Technological Rd Center China Nokia Bell Labs Antwerp Belgium University of Missouri-St.Louis Department of Mathematics and Computer Science MO United States

ISBN: (纸本)9781728152073

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent. © 2020 IEEE.

关键词： Generative adversarial networks

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing

arXiv

引用

arXiv 2022年

作者： Hao, Zhongkai Ying, Chengyang Dong, Yinpeng Su, Hang Song, Jian Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Department of Electronic Engineering Tsinghua University China RealAI China Peng Cheng Laboratory China Tsinghua University China Mobile Communications Group Co. Ltd. Joint Institute China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen China

Certified defenses such as randomized smoothing have shown promise towards building reliable machine learning systems against `p-norm bounded attacks. However, existing methods are insufficient or unable to provably defend against semantic transformations, especially those without closed-form expressions (such as defocus blur and pixelate), which are more common in practice and often unrestricted. To fill up this gap, we propose generalized randomized smoothing (GSmooth), a unified theoretical framework for certifying robustness against general semantic transformations via a novel dimension augmentation strategy. Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation. The surrogate model provides a powerful tool for studying the properties of semantic transformations and certifying robustness. Experimental results on several datasets demonstrate the effectiveness of our approach for robustness certification against multiple kinds of semantic transformations and corruptions, which is not achievable by the alternative baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Experience-driven PCG via reinforcement learning: A Super Mario Bros study

arXiv

引用

arXiv 2021年

作者： Shu, Tianye Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of Digital Games University of Malta Msida Malta

We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster's principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Keiki: Towards realistic danmaku generation via sequential GANs

arXiv

引用

arXiv 2021年

作者： Wang, Ziqi Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of Digital Games University of Malta Msida2080 Malta

Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus—the bullet hell shooting entity—explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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A proactive defense mechanism for mobile communication user data

引用

Science China(Information Sciences) 2018年第10期61卷 266-268页

作者： Caixia LIU Xinsheng JI Jiangxing WU Xiao QIN The National Digital Switching System Engineering & Technological R&D Center Department of Computer Science and Software Engineering Samuel Ginn College of EngineeringAuburn University

Dear editor,Several studies have recently reported on major vulnerabilities in the Signaling system No.7 (SS7)used in mobile networks [1, 2]. The reported vulnerabilities and security issues would lead to the illegal acquisition and tampering of mobile communication user data, known as cellphone user data. The cellphone user data contain important information such as identity identification, location identification, security parameter-set. Due to

关键词： A proactive defense mechanism for mobile communication user data MSISDN

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Specialty optical fibers for advanced sensing applications

引用

Opto-Electronic Science 2023年第2期2卷 1-26页

作者： Huanhuan Liu Dora Juan Juan Hu Qizhen Sun Lei Wei Kaiwei Li Changrui Liao Bozhe Li Cong Zhao Xinyong Dong Yuhan Tang Yihong Xiao Gerd Keiser Perry Ping Shum Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#21-01Connexis South TowerSingapore 138632Singapore School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Bionic Engineering of Ministry of Education Jilin UniversityChangchun 130022China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things Guangdong and Hong Kong Joint Research Centre for Optical Fibre SensorsShenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Guangdong Provincial Key Laboratory of Information Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Department of Electrical&Computer Engineering Boston UniversityBoston 02215USA Pengcheng Laboratory Shenzhen 518055China

Optical fiber technology has changed the world by enabling extraordinary growth in world-wide communications and *** rapid development and wide deployment of optical fiber sensors are driven by their excellent sensing performance with outstanding flexibility,functionality,and ***,the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing *** paper overviews recent developments in specialty optical fibers and their sensing *** specialty optical fibers are reviewed based on their innovations in special structures,special materials,and technologies to realize lab in/on a *** overview of sensing applications in various fields is *** prospects and emerging research areas of specialty optical fibers are also discussed.

关键词： specialty optical fibers photonic crystal fiber multifunctional multi-material fibers lab in/on fiber

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