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SSRN 2023年

作者： Zu, Keke Zhang, Hu Lu, Jian Zhang, Lei Xu, Chen Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Shenzhen China University of Electronic Science and Technology of China Zhejiang Quzhou China

This work presents a novel module, namely multi-branch concat (MBC), toprocess the input tensor and obtain the multi-scale feature map. The proposedMBC module brings new degrees of freedom (DoF) for the design of attentionnetworks by allowing the type of transformation operators and the numberof branches to be flexibly adjusted. Two important transformation operators,multiplex and split, are considered in this work, both of which can representmulti-scale features at a more granular level and increase the range of receptivefields. By integrating the MBC and attention module, a multi-branch attention(MBA) module is consequently developed to capture the channel-wise interactionof feature maps for establishing the long-range channel dependency. By substituting the 3x3 convolutions in the bottleneck blocks of the ResNet with theproposed MBA, a novel block namely efficient multi-branch attention (EMBA)is obtained, which can be easily plugged into the state-of-the-art backbone CNNmodels. Furthermore, a new backbone network called EMBANet is establishedby stacking the EMBA blocks. The proposed EMBANet is extensively evaluatedon representative computer vision tasks including: classification, detection, andsegmentation. And it demonstrates consistently superior performance over thepopular backbones. © 2023, The Authors. All rights reserved.

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A 3-D whole-body human thermoregulatory model to simulate cold-induced vasodilation in the hands and feet

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Computers in Biology and Medicine 2024年 180卷 108935页

作者： Singh, Manpreet Reifman, Jaques Rubio, Jose E. Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center United States Army Medical Research and Development Command Fort Detrick MD United States The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc. Bethesda MD United States

The cold-induced vasodilation (CIVD) response of the human body to Arctic-like environments helps delay or prevent cold injuries to peripheral regions, such as the hands and feet. To more comprehensively predict the thermal responses of these body regions to cold stress, here we extended our previously developed and validated anatomically accurate three-dimensional whole-body thermoregulatory human model by incorporating a new phenomenological formulation of the CIVD mechanism. In this formulation, we modulated the cyclic vasodilation and vasoconstriction flow of warm blood from the body core to the peripheral regions solely by determining the heat-transfer exchange between the skin and the surrounding environment, and deactivated it when the core body temperature decreased to 36.5 °C. In total, we calibrated and validated the model using eight distinct studies involving 153 unique male subjects exposed to 10 diverse experimental conditions, including cold-air exposure of the whole body as well as air exposure and cold-water immersion of the hand or the foot. With CIVD incorporated, the model predictions generally yielded root mean square errors (RMSEs) of © 2024

关键词： Heat transfer

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An Aggregation Strategy of Flexible Industrial Load Based on K-Means Clustering

An Aggregation Strategy of Flexible Industrial Load Based on...

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China International Electrical and Energy Conference (CIEEC)

作者： Shuya Lei Wensi Zhang Yishen Wang Zhiqing Sun Mohan Liu Mingjian Cui State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Hangzhou Power Supply Company Hangzhou China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

In response to the “carbon peaking and carbon neutrality” goals, the energy structure in China has undergone a great transformation. Construction of the new power system has begun. The proportion of renewable generation has increased gradually. However, the operation and scheduling of the power system are influenced by the increasing renewable generation. This paper develops a multi-timescale flexible industrial load aggregation response method based on the K-means clustering analysis. First, the K-means method is used to cluster the daily load curves of different industries to obtain the typical electricity consumption behaviors. Second, the visual models of flexible industrial load are established based on the results of clustering analysis. Finally, the optimization model is constructed to generate the aggregation strategy in different timescales. The effectiveness of the developed method is verified via a load aggregator simulation case.

关键词： Industries Renewable energy sources Visualization Optimization models Electricity Production Power systems

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Exploring the potential of Distributed Ledger Technology in publication industry - A technological review

Exploring the potential of Distributed Ledger Technology in ...

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2021 Workshop on Technological Innovations in Education and Knowledge Dissemination, WTEK 2021

作者： Khurana, Parul Kumar, Gulshan Ganesan, Geetha School of Computer Applications Lovely Professional University Phagwara Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India CEO Advanced Computing Research Society Porur Chennai600116 India

Distributed ledger technology has shown enormous growth since 2015. More than 2000 articles, and proceedings paper have been published in Web of Science. These publications have the diversity from the leading field of Computer Science with 1626 publications in last 5 years, followed by the field of Engineering. Different organizations and funding agencies have also supported the research work done in this field with the approval of almost 1260 block chain based ideas. Countries like China, and USA together have published almost 922 publications in this field, followed by India and England. Different authors have tried to experiment blockchain in areas like IOT, Healthcare, Smart contracts and supply chain management. Among all IOT has received 304 publications in last 5 years. Various journals have also supported the publications of blockchain based applications with IEEE Access at the top with 169 publications, followed by its peers. Chinese academy of science has published 54 publications in this technology followed by CSIRO and Nanyang Technol University with 33 and 32 contributions. IOT has received 2905 citations followed by Healthcare which shows the real potential of this technology. Such results have given us the benchmark to explore the potential of this technology in the field of publication industry associated with different entities like authors, academicians publishing their work in different indexing databases like Scopus and Web of Science. This study presents the current evolution and scope of distributed ledger technology in the field of citations analysis and academic publishing. © 2021 CEUR-WS. All rights reserved.

关键词： Blockchain

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Modeling of Evaluation Index System for Coordinated Operation of Wind-solar-hydro Multi energy Complementary Systems at Multiple Time Scales

Modeling of Evaluation Index System for Coordinated Operatio...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Danni Shi Jie Li Bo Chai Siyan Liu Yishen Wang Xinghua Shi State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

The coordinated operation of multiple energy complementary power generation is gradually becoming the development direction of future power systems, and accurate analysis of their complementary characteristics is also particularly important. Although the current research on the characteristics of multi energy complementary power generation systems mainly focuses on calculating simple single evaluation indicators and has achieved fruitful results, there are problems such as a single evaluation scale and chaotic indicator classification. In response to this issue, this article constructs an indicator framework that comprehensively reflects the complementary characteristics of wind-solar-hydro. The entropy weight theory is used to scientifically assign and recombine different evaluation indicators to reveal the operational characteristics of wind-solar-hydro multi energy complementary power generation systems at different time scales. Based on this indicator system modeling, taking a hydroelectric power plant, photovoltaic power plant, and wind farm in a certain area in southwestern China as an example, the multi time scale characteristics of wind-solar-hydro multi energy complementary power generation were analyzed. The results indicate that the power output of the wind-solar-hydro complementary power generation system can exhibit different load tracking performance in different time scale scenarios. Increasing the time scale will cause a decrease in load tracking. Understanding this law can provide reference for the formulation of power system scheduling plans.

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Federated Learning Based Privacy-Preserving Renewable Energy Consumption for the Local Community

Federated Learning Based Privacy-Preserving Renewable Energy...

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International Conference on Power and Energy Technology (ICPET)

作者： Renjie Wei Yishen Wang Xiaohui Wang Ningyu An Xiao Liang Yuqiang Yang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Zhejiang Electric Power Co. Ltd. Zhejiang China

With the growing demand for sustainable energy, there is an increasing need for efficient management and distribution of renewable energy resources. However, collecting and sharing energy consumption data in a centralized manner poses a significant privacy risk for individuals. To address this issue, Federated Learning can be leveraged to train a predictive model on decentralized data sources while preserving data privacy. In this approach, each participant can train a local model using their own energy consumption data and share the model updates with a central server. The central server then aggregates the updates to generate a global model, which can be used to predict energy consumption patterns for the local community. The proposed approach can enable effective management of renewable energy resources while preserving the privacy of individual energy consumption data. It has the potential to provide a sustainable solution to the growing demand for renewable energy consumption in local communities.

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Deep Learning Super-Resolution-Based Channel Completion for Massive MISO Systems

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IEEE Signal Processing Letters 2025年 32卷 2254-2258页

作者： Zu, Keke He, Yuhan Chen, Hongyang Zheng, Yu Haardt, Martin Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Zhejiang China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Research Center for Graph Computing Zhejiang Lab Hangzhou China JD Intelligent Cities Research Beijing China Communications Research Lab Ilmenau University of Technology Ilmenau Germany

With the deployment of large-scale antenna arrays, the already limited time-frequency resources are becoming increasingly scarce. In this study, we propose a novel Laplacian Pyramid Channel Completion Network (LPCCNet) designed for channel completion, thereby reducing the demand for time-frequency resources in massive MIMO systems. Compared with existing network models, the proposed LPCCNet, by employing a progressive upsampling architecture, effectively mitigates aliasing effects, suppresses error propagation, and achieves a substantial reduction in computational complexity. The simulation results show that LPCCNet achieves a superior channel completion quality compared to existing methods, particularly in rapidly time-varying scenarios. © 1994-2012 IEEE.

关键词： Channel estimation Feature extraction Image reconstruction Interpolation Training Superresolution Filters Vectors Convolutional neural networks Time-frequency analysis

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Cloud-based Decentralized Smart Healthcare for Patient Monitoring on Deep Learning 2

Cloud-based Decentralized Smart Healthcare for Patient Monit...

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2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Arunachalam, Sripriya Shanthi, H.J. Sivagurunathan, G. Das, Shyamali Anand, D. Raj M, Thanga School of Computing Sciences Vels Institute of Science Technology & Advanced Studies - Vistas Computer Applications- Pg India Amet University Ecr Kanathur India St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Chennai600119 India Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Persistent Systems Limited Pune India

ISBN: (纸本)9781665456302

Over the past few years, there has been a meteoric surge in the quantity of digital information available online for instantaneous sharing, persistent archiving, and inquiring. It has expanded the possibilities for using digital data that is both decentralised and ad hoc in order to make decisions quickly. At present, e-Healthcare is among the most sought-after sectors for EHR and telemedicine communication. Securing electronic health records (EHR) has become a topic of intense interest in recent years, with previous works employing a wide range of methods to better ensure the confidentiality and security of EHR at a reasonable price. There are a number of serious problems with the current research, including computational complexity, increased process time, information leakage, vulnerability to various assaults, scalability difficulty, etc. Clinical data analysis presents several difficulties, but disease prediction is one of the most significant ones. The suggested study aims to apply deep learning (DL) classification algorithms for disease prediction. A technique that utilises cloud computing, fog computing, and IoMT more recently has been presented for diagnosing illness. Fast DL classification analysis is performed in the fog layer. When compared to the alternative proposed model Bi-CNN, the healthcare model's efficiency in the Bi-LSTM simulation yields significantly better results: 97.31% of accuracy, 97.58% of recall, 96.90% of precision, 94.90% of F1-measure, 97.25% of specificity, and 94.80% of G-mean. © 2023 IEEE.

关键词： Fog computing

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OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment

arXiv

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arXiv 2023年

作者： Alfasly, Saghir Al-Huda, Zaid Bello, Saifullah Elazab, Ahmed Lu, Jian Xu, Chen Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China School of Computing and Artificial Intelligent Southwest Jiaotong University China School of Biomedical Engineering Shenzhen University China

Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations. This paper tackles these two challenges to solve the rotation estimation of a parked bike with respect to its parking area. First, we leverage the power of 3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator (OSRE) by extending the object detection task to further regress the bike rotations in two axes. Since our model is purely trained on synthetic data, we adopt image smoothing techniques when deploying it on real-world images. The proposed OSRE is evaluated on synthetic and real-world data providing promising results. Our data and code are available at https://***/OSRE/. © 2023, CC BY.

关键词： Rotation

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TOCO: A Systolic Network for Efficient Transposed Convolutions with Output-Reuse Paths

TOCO: A Systolic Network for Efficient Transposed Convolutio...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Zhengzheng Ma Guojie Luo Center for Energy-Efficient Computing and Applications Peking University Advanced Institute of Information Technology Peking University

Transposed convolution is a learnable up-sampling operator widely-used in deep neural networks. It up-samples the input activations to generate useful information in applications like style transfer and super resolution. There exists a rising demand for accelerating transposed convolution layers since they occupy a large portion of computation in GAN-like networks.

关键词： Convolution Neural networks

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