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7th IEEE International Conference on Transportation information and Safety, ICTIS 2023

作者： Gong, Peizhu Liu, Jin Zhang, Xiliang Lin, Jianhua Wang, Junxiang Wu, Zhongdai Shanghai Maritime University College of Information Engineering Shanghai China National Engineering Research Center of Ship Transportation Control Systems Shanghai Ship and Shipping Research Institute Shanghai China National Engineering Research Center of Ship Transportation Control Systems Cosco Shipping Technology Co. Ltd Shanghai China State Key Laboratory of Maritime Technology and Safety Shanghai Ship and Shipping Research Institute Shanghai China

ISBN: (纸本)9798350308532

Synthetic aperture radar (SAR) imagery has been used as a promising data source for automatic ship detection with its capability of targeting objects in all-day and all-weather conditions. Due to the scarce labeled SAR images, previous research usually adopts optical data-based pre-training model to support SAR ship detectors and has made significant progress. Nonetheless, two fundamental issues persist: (i) SAR images suffer from serious intrinsic speckle noise with less texture features;(ii) Strong class-discriminative biases for imaging perspectives and geometry are introduced in the process of transferring pre-trained model. In this paper, we propose a multimodal contrastive pre-training framework (MCPF) to boost automatic ship detection with both intra- and cross-modal correlations between SAR and optical images. Specifically, MCPF implements multimodal contrastive learning strategy by introducing cross-modal contrastive module (CCM) and intra-modal contrastive module (ICM). CCM aims to pull the matched SAR-Optical pairs together while pushing those non-matched apart by measuring the similarity between latent representations. ICM maximizes agreement between differently augmented views of the same data example within each modality. To the best of our knowledge, MCPF is the first work that takes into account multimodal contrastive representation learning for automatic ship detection. Extensive experiments on benchmark datasets demonstrate that MCPF outperforms the existing methods and achieves the state-of-the-art performance. © 2023 IEEE.

关键词： Synthetic aperture radar

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Development of Cybernic Mirror System for Improvement of Hand Motor Functions for Patients with Hemiplegia*

Development of Cybernic Mirror System for Improvement of Han...

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IEEE/SICE International Symposium on System Integration

作者： S. Konishi H. Kawamoto A. Uehara Y. Sankai Department of Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering and Center for Cybernics Research University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

In the case of cybernics treatment, the wearer performs voluntary movements, in which the neuromuscular and central nervous systems work to produce the intended movement of the integrated wearable cyborg and the wearer’s body. In this way, the interactive Bio-Feedback (iBF) loop is established between the wearer and wearable cyborgs. Using wearable cyborg, motor function can be improvement. Cybernics treatment has been covered by medical insurance in Japan after a randomized controlled trial clinical evaluation. The cybernic mirror system is a wearable cyborg that performs intended bimanual symmetrical movements based on motor commands from the unaffected side. The cybernic mirror system has the potential to develop a new cybernics treatment specifically focused on improving finger functionality. The purpose of this study is to develop a cybernic mirror system for intention of bimanual symmetrical movements by transferring the hand motion of the unaffected hand to the affected hand and to confirm the feasibility of assisting finger motion by the developed system through basic experiments. The cybernic mirror system consisted of a hand motion assist unit and a hand motion control unit. The unaffected hand’s skeletal information was obtained by the hand motion control unit using the camera. The hand motion assist unit, using a tendon-driven structure, achieved motion assist for flexion and extension of the affected hand. At the basic experiments with three participants, the developed system performs within the acceptable delay time between both finger sides. In addition, the developed system enabled the performance of bimanual symmetrical movements for each of the five fingers individually. In conclusion, we confirmed the feasibility of assisting the finger motion by the developed system through basic experiments.

关键词： Hands Human-machine systems System integration Spasticity Motors Delays Paralysis Mirrors Synchronization Motion control

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How to Design More Empathetic Recommender systems in Social Media 43

How to Design More Empathetic Recommender Systems in Social ...

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43rd International Conference on information systems: Digitization for the Next Generation, ICIS 2022

作者： Bonenberger, Lukas Graf-Drasch, Valerie Lanzl, Julia Augsburg University of Applied Sciences FIM Research Center Branch Business & Information System Engineering of Fraunhofer FIT Germany University of Hohenheim FIM Research Center Branch Business & Information Systems Engineering of Fraunhofer FIT Germany

ISBN: (纸本)9781713893615

Social media's value proposition heavily relies on recommender systems suggesting products to buy, events to attend, or people to connect with. These systems currently prioritize user engagement and social media providers' profit generation over individual users' well-being. However, making these systems more "empathetic" would benefit social media providers and content creators as users would use social media more often, longer, and increasingly recommend it to other users. By way of a design science research approach, including twelve interviews with system designers, social media experts, psychologists, and users, we develop user-centric design knowledge on making recommender systems in social media more "empathetic." This design knowledge comprises a conceptual framework, four meta-requirements, and six design principles. It contributes to the research streams "digital responsibility" and "IS for resilience" and provides practical guidance in developing socially responsible recommender systems as next-generation social media services. © 2022 International Conference on information systems, ICIS 2022: "Digitization for the Next Generation". All Rights Reserved.

关键词： Recommender systems

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Modeling irrigation canal network flow for return flow on agricultural watershed using EPA-SWMM

Modeling irrigation canal network flow for return flow on ag...

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2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023

作者： Shin, Ji-Hyeon Nam, Won-Ho Irmak, Suat Sharma, Vivek Zhang, Xiang Department of Convergence of Information and Communication Engineering Hankyong National University Korea Republic of School of Social Safety and Systems Engineering Institute of Agricultural Environmental Science National Agricultural Water Research Center Hankyong National University Korea Republic of Pennsylvania State University Department of Agricultural and Biological Engineering University Park United States Department of Agricultural and Biological Engineering University of Florida Gainesville United States National Engineering Research Center of Geographic Information System School of Geography and Information Engineering China University of Geosciences Wuhan China

ISBN: (纸本)9781713885887

Agricultural water is mostly consumed for rice farming, and irrigation water supplied to paddy fields is consumed as evapotranspiration, infiltration, and drainage. The amount of irrigation water supplied to the water well that is returned to the river through infiltration or drainage without being consumed is called irrigation return flow. Irrigation return flow is defined as the sum of the quick return flow flowing into the river and the delayed return flow returning to the river through deep penetration. Irrigation return flow is an important component that constitutes the flow rate of rivers in agricultural basins, and plays a major role in basin water resource management such as water supply and demand planning, water intake facility design and irrigation organization operation, and rational water supply and demand management plans. According to the results of previous research studies in South Korea, the irrigation return flow rate shows a large difference in the range of about 32% to 86% by region depending on various factors such as weather conditions, crop cultivation methods, water supply conditions, and water management plans. Therefore, it is essential to study the estimation of the regression quantity reflecting the local water management system and topographical characteristics in order to establish a water supply plan for the basin and determine the amount of irrigation water used. In this study, we developed a model for estimating irrigation return flow by using the United States Environmental Protection Agency Storm Water Management Model (EPA-SWMM) that can simulate irrigation canal networks. This irrigation canal network modeling can be simulated reflecting the water management characteristics of each reservoir, and it is possible to analyze the water level-flow of the canal, water supply, drainage, and infiltration. This study proposes a modeling method for calculating irrigation return flow using the EPA-SWMM model that can simulate agricultural

关键词： Water supply

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Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing Control

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ACM Transactions on Evolutionary Learning and Optimization 2022年第1期2卷 1–32页

作者： Akimoto, Youhei Miyauchi, Yoshiki Maki, Atsuo Faculty of Engineering Information and Systems University of Tsukuba and RIKEN Center for Advanced Intelligence Project 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan Department of Naval Architecture and Ocean Engineering Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka565-0971 Japan

We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately. We analyze its convergence property on a strongly convex-concave problem and show its linear convergence toward the global min-max saddle point. Based on the convergence analysis, we develop a heuristic approach to adapt the learning rate. An implementation of the developed approach using the (1+1)-CMA-ES as the minimization oracle, namely, Adversarial-CMA-ES, is shown to outperform several existing approaches on test problems. Numerical evaluation confirms the tightness of the theoretical convergence rate bound as well as the efficiency of the learning rate adaptation mechanism. As an example of real-world problems, the suggested optimization method is applied to automatic berthing control problems under model uncertainties, showing its usefulness in obtaining solutions robust to uncertainty. © 2022 Association for Computing Machinery.

关键词： Optimization

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Improved YOLOv8 for Complex Environmental Fish Detection

Improved YOLOv8 for Complex Environmental Fish Detection

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Tonglin Cheng Juan Li Jintao Luo Zhihui Li Faculty of Information Technology Beijing University of Technology Beijing Engineering Research Center for loT Software and Systems

With the rapid development of computer vision and deep learning technologies, object detection plays a crucial role in various fields. Due to the complexity and diversity of underwater environments, underwater vision tasks face great challenges. In this paper, we improved the YOLOv8 detection model for the fish detection task in complex environments. Specifically, we proposed a multi-scale feature fusion method (MFANet) that combines multiscale weighted feature fusion with multiscale attention to efficiently capture multilevel features of fishes in complex environments. We designed a multiscale weighted feature fusion module that combines feature maps at different scales to enhance the model’s representation of the target. By introducing a multi-scale attention mechanism, the model is able to pay more attention to important feature regions. We conducted experiments on real underwater complex environment datasets. The results show that our proposed improved method significantly enhances the performance of the YOLOv8 model in fish detection tasks and lays a good foundation for subsequent underwater detection tasks.

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Day-Ahead Photovoltaic Output Prediction Basedon Similar Days and IAdam-GNN

Day-Ahead Photovoltaic Output Prediction Basedon Similar Day...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： He, Hongying Lee, Wei-Jen Hong, Runli Luo, Diansheng Liang, Wenju College of Electrical and Information Engineering Hunan University Changsha China University of Texas at Arlington Energy Systems Research Center ArlingtonTX United States State Grid Corporation of China Southwest Branch Planning Department Chengdu China

ISBN: (纸本)9798350352290

Aiming at higher accuracy of photovoltaic power prediction, a day-ahead prediction method based on similar days and improved Adam optimized graph neural network (IAdam-GNN) is proposed in this paper. The historical outputs and multiple meteorological information are fully utilized. In order to reduce the number of weather feature input parameters, the Maximum Relevance-Minimum Redundancy (mRMR) is employed to establish an optimal set of weather features which are strongly correlated with the photovoltaic power output with small redundancy. The similar days to be used to predict is obtained by an improved method by combining the grey correlation, cosine similarity and Euclidean distance. The output of the historical similar days and the meteorological information of the day to be predicted are used to construct a topology graph. Since graph neural network (GNN) has significant parallel processing ability on graph data, a GNN is designed to realize the full mining of the input characteristics and the correlation among the features by using the constructed graph as the input. To take advantage the Powerful time-frequency processing capability of wavelet neural network (WNN), an integrated WNN and GNN algorithm is employed for nodes and edges updating of the graph to obtain higher prediction accuracy. In order to enhance searching ability, the first moment estimation and second moment estimation of Adam are improved. The testing results show that the prediction accuracy of the proposed method is better than the Adam-BPNN, Adam-WNN, Adam-GRU, and Adam-GNN. Moreover, the computational burden is significantly reduced. © 2024 IEEE.

关键词： Photovoltaics

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Learning Based Routing Link Scheduling in Heterogeneous Wireless loT Networks 59

Learning Based Routing Link Scheduling in Heterogeneous Wire...

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2024 Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024

作者： Wang, Zhiyang Guo, Jianlin Parsons, Kieran Nagai, Yukimasa Sumi, Takenori Orlik, Philip CambridgeMA02139 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Information Technology R&d Center Mitsubishi Electric Corporation Kanagawa Kamakura2478501 Japan

ISBN: (纸本)9798350304053

With the advent of 5G and beyond communication technologies, the consumer Internet of Things (loT) devices are evolving from the current-generation to the next-generation. Next-generation loT devices can support multiple communication interfaces and perform more functions. Accordingly, loT network technologies must adapt to the emerging next-generation loT devices. Routing is an inevitable technology in multi-hop loT networks. However, as loT devices become more and more diverse, loT networks become more complex. As a result, the routing problem becomes more and more complicated for traditional protocols and mathematical optimization approaches to provide optimal solutions. Machine learning based routing techniques have been recently proposed and can outperform traditional routing methods in complex network environments. To that end, this paper presents a machine learning based routing link scheduling scheme for heterogeneous wireless loT networks. We formulate the routing link scheduling problem as a combinatorial optimization problem, which is then parameterized for application of machine learning algorithm and the parameterized problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is proposed to update the primal-dual problems. We evaluate the proposed HetGNN model under networks with randomly deployed heterogeneous nodes. Compared with a convolutional neural network (CNN) model and a homogeneous GNN (HomGNN) model, the proposed HetGNN model can improve network throughput, reduce link capacity violation and interference link violation. © 2024 IEEE.

关键词： Combinatorial optimization

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Contrast, Imitate, Adapt: Learning Robotic Skills from Raw Human Videos

arXiv

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arXiv 2024年

作者： Qian, Zhifeng You, Mingyu Zhou, Hongjun Xu, Xuanhui Fu, Hao Xue, Jinzhe He, Bin The College of Electronic and Information Engineering Frontiers Science Center for Intelligent Autonomous Systems Tongji University China

Learning robotic skills from raw human videos remains a non-trivial challenge. Previous works tackled this problem by leveraging behavior cloning or learning reward functions from videos. Despite their remarkable performances, they may introduce several issues, such as the necessity for robot actions, requirements for consistent viewpoints and similar layouts between human and robot videos, as well as low sample efficiency. To this end, our key insight is to learn task priors by contrasting videos and to learn action priors through imitating trajectories from videos, and to utilize the task priors to guide trajectories to adapt to novel scenarios. We propose a three-stage skill learning framework denoted as Contrast-Imitate-Adapt (CIA). An interaction-aware alignment transformer is proposed to learn task priors by temporally aligning video pairs. Then a trajectory generation model is used to learn action priors. To adapt to novel scenarios different from human videos, the Inversion-Interaction method is designed to initialize coarse trajectories and refine them by limited interaction. In addition, CIA introduces an optimization method based on semantic directions of trajectories for interaction security and sample efficiency. The alignment distances computed by IAAformer are used as the rewards. We evaluate CIA in six real-world everyday tasks, and empirically demonstrate that CIA significantly outperforms previous state-of-the-art works in terms of task success rate and generalization to diverse novel scenarios layouts and object instances. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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