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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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A Planning Model for Flexibility Retrofitting of Coal-Fired Power Plants

A Planning Model for Flexibility Retrofitting of Coal-Fired ...

引用

Power Systems and Electrical technology (PSET), IEEE International Conference on

作者： Xinjiang Chen Jianxiao Wang Zongxian Wang Michael R. Davidson Ruaridh MacDonald Jie Song Guannan He College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Department of Mechanical and Aerospace Engineering University of California San Diego San Diego CA USA MIT Energy Initiative Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

China’s distinctive resource endowment, characterized by abundant coal, limited oil, and scarce gas, shapes the long-term development focus on coal power. However, the requirement for stable combustion imposes limitations on the deep peak-shaving flexibility of coal power. While previous studies have explored the technical feasibility of deep peak shaving for coal power, there is a scarcity of research addressing the delicate balance between the costs of flexibility retrofitting and the benefits of promoting renewable energy integration in power system. To this end, in this paper, we develop an optimal planning technology for the flexibility retrofitting of coal plants. The proposed technology employs a capacity expansion and dispatch model which formulate a flexibility retrofitting plan for coal-fired power plants and a configuration scheme for battery energy storage. Incorporating the proposed model and real-world data from power grid in Yunnan, China, we assess the decarbonization potential of coal fleet flexibility. The experimental results demonstrate that coal fleet flexibility effectively reduces renewable energy curtailment by 3.0% and decreases carbon emissions by more than 6 megatons compared to the coal fleet without retrofitting. Furthermore, the flexibility retrofitting of coal-fired power plants is projected to yield a 12.5% total cost reduction and a 11.1% decrease in coal power generation.

关键词： Renewable energy sources Costs Coal Carbon dioxide Low carbon economy Planning Batteries Spatiotemporal phenomena Power generation Capacity planning

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An improving data compression capability in sensor node to support sensorml-compatible for internet-of-things

Journal of Network Intelligence

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Journal of Network Intelligence 2018年第2期3卷 74-90页

作者： Nguyen, Trong-The Dao, Thi-Kien Pan, Jeng-Shyang Horng, Mong-Fong Shieh, Chin-Shiuh Department of Information Technology Haiphong Private University No. 35 Danlap Rd. Hai-Phong Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Information Science and Engineering Fujian University of Technology Fujian China Department of Electronics Engineering National Kaohsiung University of Applied Sciences Taiwan

Limited bandwidth and lacked compatibility information of the sensor nodes in Wireless Sensor Network (WSN) are critical issues in implementing WSN applica-tions. This paper proposes a method of improvement to data compression capability to support SensorML interface for information exchange in the sensor nodes. The delivery of the packets with XML format of all nodes in WSN could cause the traffic load in-creases. This paper proposes a method of improvement of data compression capability for exchanging data in the sensorML for the Internet of Things (IoT). The delivery of XML formatted packets of all nodes in Wireless sensor networks (WSN) could cause the traffic load increases. The proposed method suggests data compression condensing the packets in the node and analyzes the relationship between the parameters and the performance. The settings of sliding window size and comparing length are factors to affect the performance of network traffic load significantly. The experimental results show that, with a format of 256 bytes and LZSS compression, the transmission performance is improved up to 48.8%, in comparison with the original approach. Besides, the proposed method can deliver up to 204% of information than the other works. © 2018, Taiwan Ubiquitous Information. All rights reserved.

关键词： Internet of things

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A Systematic Review for Transformer-based Long-term Series Forecasting

arXiv

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arXiv 2023年

作者： Su, Liyilei Zuo, Xumin Li, Rui Wang, Xin Zhao, Heng Huang, Bingding College of Applied Sciences Shenzhen University Shenzhen518060 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field. © 2023, CC BY.

关键词： Time series analysis

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

引用

arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Multiphysics-Coupled Stress analysis of Hydrogen Storage Filling Using Diverse Composite Layer Winding Patterns

Multiphysics-Coupled Stress Analysis of Hydrogen Storage Fil...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yi Chen Zuoqin Qian Jianxiao Wang Jie Song College of Engineering Peking University Beijing China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University Of Technology Wuhan China National Engineering Laboratory for Big Data Analysis and Applications Peking University Ordos Research Institute of Energy Beijing China College of Engineering Peking University Ordos Research Institute of Energy Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Hydrogen storage technology, as one of the core technologies in the hydrogen energy industry chain, is directly related to the efficient utilization and safe transportation of hydrogen energy. Currently, existing literature overlooks the impact of the wrapping patterns of composite layers, which can cause hydrogen storage containers to exhibit different stress characteristics. In this paper, a Type III hydrogen storage tank model is established to analyze the flow field during the filling process, obtaining the internal pressure load distribution. Subsequently, three different wrapping patterns are modeled to comparatively analyze the stress conditions of the hydrogen storage tank under these patterns. Case studies based on ANSYS reveal that when the wrapping angle tends more towards 90° or 0°, the stress conditions on the composite layers tend to be more extreme, while the stress conditions on the inner liner improve, resulting in a higher maximum stress. This trend gradually diminishes from the inner layers to the outer layers. Moreover, the stress distribution in each layer is related to the wrapping direction of that layer, i.e., the fiber direction, and the position of the layer within the stack, and is almost unaffected by other layers.

关键词： Analytical models Hydrogen storage Hydrogen Windings Transportation Filling Finite element analysis Wrapping Stress Load modeling

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PWG-IDS: An intrusion detection model for solving class imbalance in IIoT networks using generative adversarial networks

arXiv

引用

arXiv 2021年

作者： Zhang, Lei Jiang, Shuaimin Shen, Xiajiong Gupta, Brij B. Tian, Zhihong Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng47500 China Institute of Data and Knowledge Engineering Henan University Kaifeng47500 China School of Computer and Information Engineering Henan University Kaifeng47500 China National Institute of Technology Kurukshetra India Asia University Taichung Taiwan The Department of Cyberspace Insitute of Advanced Technology GuangZhou University GuangZhou510006 China

With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Intrusion detection

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Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv

引用

arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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The Cauchy problem for two dimensional generalized Kadomtsev-Petviashvili-I equation in anisotropic Sobolev spaces

arXiv

引用

arXiv 2017年

作者： Yan, Wei Li, Yongsheng Huang, Jianhua Duan, Jinqiao School of Mathematics and Information Science Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University Xinxiang Henan453007 China School of Mathematics South China University of Technology Guangzhou Guangdong510640 China College of Science National University of Defense Technology Changsha Hunan410073 China Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States

The goal of this paper is three-fold. Firstly, we prove that the Cauchy problem for generalized KP-I equation ut+ |Dx|αθxu + θ-1xθ2yu + 1/2 θx(u2) = 0, α ≥ 4 is locally well-posed in the anisotropic Sobolev spaces Hs1,s2(R2) with s1> -α-1/4 and s2≥ 0. Secondly, we prove that the problem is globally well-posed in Hs1,0(R2) with s1> -(α-1)(3α-4)/4(5α+3) if 4 ≤ α ≤ 5. Finally, we prove that the problem is globally well-posed in Hs1,0(R2) with s1> -α(3α-4)/4(5α+4) if α > 5. Our result improves the result of Saut and Tzvetkov (J. Math. Pures Appl. 79(2000), 307-338.) and Li and Xiao (J. Math. Pures Appl. 90(2008), 338-352.). Copyright © 2017, The Authors. All rights reserved.

关键词： Sobolev spaces

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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