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Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

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Journal of Modern Power systems and Clean Energy 2019年第3期7卷 433-448页

作者： Wei WEI Danman WU Qiuwei WU Miadreza SHAFIE-KHAH Joao PSCATALAO State Key Laboratory of Power System Department ofElectrical EngineeringTsinghua UniversityBeijingChina Department of Electrical Engineering Technical Universityof DenmarkCopenhagenDenmark School of Technology and Innovations University of VaasaVaasaFinland Institute for Systems and Computer Engineering Technologyand Science(INESC TEC)PortoPortugal Faculty of Engineering University of PortoPortoPortugal

The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment pro-tection. Integrating charging facilities, especially highpower chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced,especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is *** into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

关键词： Charging station Electric vehicle(EV) Interdependence Power flow Power distribution system Transportation system User equilibrium

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Generation of super-resolved optical needle and multifocal array using graphene oxide metalenses

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Opto-Electronic Advances 2021年第2期4卷 1-11页

作者： Hongtao Wang Chenglong Hao Han Lin Yongtian Wang Tian Lan Chengwei Qiu Baohua Jia Centre for Translational Atomaterials Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyP.O.Box 218Hawthorn VIC 3122Australia Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Engineering Product Development Pillar Singapore University of Technology and Design8 Somapah Road487372Singapore

Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial ***-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like *** this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material *** experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin *** proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.

关键词： femtosecond laser reduction graphene oxide metalens multifocal spots optical needle

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Double JPEG Compression Detection Based on Markov Model 18th

Double JPEG Compression Detection Based on Markov Model

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18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Narrowband-internet of things (NB-IoT) system for elderly healthcare services 5

Narrowband-internet of things (NB-IoT) system for elderly he...

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5th International Conference on engineering, Applied sciences and Technology, ICEAST 2019

作者： Manatarinat, Wiraphon Poomrittigul, Suvit Tantatsanawong, Panjai Electrical Engineering Department Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Software Engineering and Information System Department Faculty of Science and Technology Pathumwan Institute of Technology Bangkok Thailand Computer Technology Department Faculty of Science Silpakorn University Bangkok Thailand

ISBN: (纸本)9781728100678

3G and 4G wireless communication technology, which requires broad network resources and large amounts of available electricity, was designed for high-speed Internet usage. Most narrowband-Internet of Things (NB-IoT) devices do not require high-speed Internet because most IoTs work by sending commands to and receiving commands from a server only. NB-IoT is a low-power wide area (LPWA) technology that was developed to accommodate a broad variety of new IoT devices. A low-power wide area network (LPWAN) uses narrow frequencies that support the use of devices in the 900 MHz frequency range that are inexpensive and that can be used in both buildings and wide areas. NB-IoT devices can also be used to assist medical patients, the elderly, and disabled people that are susceptible to falling. Our newly developed monitoring system has the ability to immediately recognize a fall, and then automatically alert up to 4 contacts via the LINE application. If dispatch of a medical unit is required, the patient's location is automatically available without the need for voice contact by the patient. © 2019 IEEE.

关键词： Internet of things

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Phyllotaxis-inspired nanosieves with multiplexed orbital angular momentum

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eLight 2021年第1期1卷 46-56页

作者： Zhongwei Jin David Janoschka Junhong Deng Lin Ge Pascal Dreher Bettina Frank Guangwei Hu Jincheng Ni Yuanjie Yang Jing Li Changyuan Yu Dangyuan Lei Guixin Li Shumin Xiao Shengtao Mei Harald Giessen Frank Meyer zu Heringdorf Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China Faculty of Physics and Center for Nanointegration Duisburg-Essen(CENIDE) University of Duisburg-EssenLotharstrasse 1-2147057 DuisburgGermany Department of Materials Science and Engineering Shenzhen Institute for Quantum Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Beijing Qianjunyide Technology Co.Ltd Beijing 100031China. Physics Institute and Stuttgart Center of Photonics Engineering(SCoPE) University of Stuttgart70569 Stutt-gartGermany. School of Physics University of ElectronicScience and Technology of ChinaChengdu 611731China. Key Laboratory of Optoelectronic Materials Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China. Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong SARChina. Department of Materials Science and Engineering City University of Hong Kong83 Tat Chee AvenueKowloonHong Kong SARChina. State Key Laboratory On Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemShenzhen Graduate SchoolHarbin Institute of TechnologyShenzhen 518055China.

Nanophotonic platforms such as metasurfaces,achieving arbitrary phase profiles within ultrathin thickness,emerge as miniaturized,ultracompact and kaleidoscopic optical vortex ***,it is often required to segment or interleave independent sub-array metasurfaces to multiplex optical vortices in a single nano-device,which in turn affects the device’s compactness and channel ***,inspired by phyllotaxis patterns in pine cones and sunflowers,we theoretically prove and experimentally report that multiple optical vortices can be produced in a single compact phyllotaxis nanosieve,both in free space and on a chip,where one meta-atom may contribute to many vortices *** time-resolved dynamics of on-chip interference wavefronts between multiple plasmonic vortices was revealed by ultrafast time-resolved photoemission electron *** nature-inspired optical vortex generator would facilitate various vortex-related optical applications,including structured wavefront shaping,free-space and plasmonic vortices,and high-capacity information metaphotonics.

关键词： Phyllotaxis vortex nanosieve Free-space Plasmonic vortices Ultrafast time-resolved

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TBNet:Two-stream boundary-aware network for generic image manipulation localization

arXiv

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arXiv 2021年

作者： Gao, Zan Sun, Chao Cheng, Zhiyong Guan, Weili Liu, Anan Wang, Meng Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Faculty of Information Technology Monash University Clayton Campus Australia School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. Moreover, among the manipulation operations, splicing and copy-move are two frequently used methods, but as their characteristics are quite different, specific methods have been individually designed for detecting the operations of either splicing or copy-move, and it is very difficult to widely apply these methods in practice. To solve these issues, in this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In- The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization meth

关键词： Location

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Optoelectronic properties analysis of silicon light-emitting diode monolithically integrated in standard CMOS IC

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Chinese Physics B 2019年第10期28卷 115-120页

作者： Yanxu Chen Dongliang Xu Kaikai Xu Ning Zhang Siyang Liu Jianming Zhao Qian Luo State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaChengdu 610054China National ASIC System Engineering Research Center Southeast UniversityNanjing 210096China Department of Electrical Engineering University of South AfricaPretoria 0001South Africa Faculty of Electrical and Computer Engineering State University of Campinas-UNICAMPCampinas CEP 13083-852Brazil

Si p^+n junction diodes operating in the mode of avalanche breakdown are capable of emitting light in the visible range of 400-900 nm. In this study, to realize the switching speed in the GHz range, we present a transient model to shorten the carrier lifetime in the high electric field region by accumulating carriers in both p and n type regions. We also verify the optoelectronic characteristics by disclosing the related physical mechanisms behind the light emission phenomena. The emission of visible light by a monolithically integrated Si diode under the reverse bias is also discussed. The light is emitted as spatial sources by the defects located at the p-n junction of the reverse-biased diode. The influence of the defects on the electrical behavior is manifested as a current-dependent electroluminescence.

关键词： silicon light-emitting diode reverse bias electro-optic modulation

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Multigranular visual-semantic embedding for cloth-changing person re-identification

arXiv

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arXiv 2021年

作者： Gao, Zan Wei, Hongwei Guan, Weili Nie, Weizhi Liu, Meng Wang, Meng Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Faculty of Information Technology Monash University Clayton Campus Australia School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Person reidentification (ReID) is a very hot research topic in machine learning and computer vision, and many person ReID approaches have been proposed;however, most of these methods assume that the same person has the same clothes within a short time interval, and thus their visual appearance must be similar. However, in an actual surveillance environment, a given person has a great probability of changing clothes after a long time span, and they also often take different personal belongings with them. When the existing person ReID methods are applied in this type of case, almost all of them fail. To date, only a few works have focused on the cloth-changing person ReID task, but since it is very difficult to extract generalized and robust features for representing people with different clothes, their performances need to be improved. Moreover, visualsemantic information is often ignored. To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes. Specifically, to fully represent a person with clothing changes, a multigranular feature representation scheme (MGR) is employed to focus on the unchanged part of the human, and then a cloth desensitization network (CDN) is designed to improve the feature robustness of the approach for the person with different clothing, where different highlevel human attributes are fully utilized. Moreover, to further solve the issue of pose changes and occlusion under different camera perspectives, a partially semantically aligned network (PSA) is proposed to obtain the visual-semantic information that is used to align the human attributes. Most importantly, these three modules are jointly explored in a unified framework. Extensive experimental re

关键词： Semantics

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HDTCNet: A hybrid-dimensional convolutional network for multivariate time series classification

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Pattern Recognition 2025年 168卷

作者： Yongli Gu Xiang Yan Hanlin Qin Naveed Akhtar Shuai Yuan Honghao Fu Shuowen Yang Ajmal Mian School of Optoelectronic Engineering Xidian University Xi’an 710071 China Xi’an Key Laboratory of Infrared Technology and System Xi’an 710071 China School of Computing and Information Systems The University of Melbourne Parkville VIC 3052 Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore School of Computer Science and Software Engineering The University of Western Australia Crawley WA 6009 Australia

Currently, transformer-based models dominate time series classification due to their ability to capture global data dependencies. However, their large memory footprint and high computational demands make them less desirable for long multivariate time series modeling. At the same time, convolution-based models promise a desirable performance versus efficiency trade-off for the task. Nevertheless, the popularity of transformer models has impeded exploration of sophisticated convolution-based modeling for time series classification. In this work, we introduce a novel convolution-based model, called HDTCNet, that alternates between 1D and 2D convolutional modes to capture temporal pattern correlations between intra- and inter-sensor variations. We develop a Wavelet Time Convolution block to expand the effective receptive field of the underlying neural model with a small convolutional kernel that embeds a set of 1D convolution operators between a cascade of wavelet decomposition and reconstruction of the input time series. To efficiently represent the local features of multivariate time series, we devise a hierarchical difference convolution network to capture intra- and inter-sensor gradient variations. To refine the feature representation of sensor-level time series and achieve multi-view self-enhancement, we design a self-enhancing module. These innovations enable HDTCNet to achieve superior performance while maintaining efficiency as a purely convolutional model. Experimental results demonstrate that HDTCNet achieves 2.1% accuracy and 1% F1-score performance improvements over state-of-the-art methods on 25 UEA classification datasets and 3 anomaly detection datasets, respectively, while reducing parameter counts by two orders of magnitude. Our code will be available at https://***/Guuuli/HDTCNet .

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Development and performance analysis of machine learning methods for predicting depression among menopausal women

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Healthcare Analytics 2023年 3卷 100202-100202页

作者： Ali, Md. Mamun Algashamy, Hussein Ali A. Alzidi, Enas Ahmed, Kawsar Bui, Francis M. Patel, Shobhit K. Azam, Sami Abdulrazak, Lway Faisal Moni, Mohammad Ali Department of Software Engineering (SWE) Daffodil International University (DIU) Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh Medical laboratory techniques Al-Rafidain University College Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computer Engineering Marwadi University Gujarat Rajkot 360003 India College of Engineering IT and Environment Charles Darwin University Casuarina NT 0909 Australia Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya 46001 Iraq Artificial Intelligence & Digital Health School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Menopause is an obligatory phenomenon in a woman's life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features. © 2023 The Author(s)

关键词： Artificial intelligence Depression Machine learning Menopause Quality of life Random Forest

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