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23rd International Conference on Computational Science, ICCS 2023

作者： Murakami, Sota Fujita, Kohei Ichimura, Tsuyoshi Hori, Takane Hori, Muneo Lalith, Maddegedara Ueda, Naonori Earthquake Research Institute and Department of Civil Engineering The University of Tokyo Tokyo Japan Center for Computational Science RIKEN Kobe Japan Japan Agency for Marine-Earth Science and Technology Yokohama Japan Center for Advanced Intelligence Project RIKEN Tokyo Japan

ISBN: (纸本)9783031360206

In this paper, we developed a 3D viscoelastic analysis solver with a data-driven method on GPUs for fast computation of highly detailed 3D crustal structure models. Here, the initial solution is obtained with high accuracy using a data-driven predictor based on previous time-step results, which reduces the number of multi-grid solver iterations and thus reduces the computation cost. To realize memory saving and high performance on GPUs, the previous time step results are compressed by multiplying a random matrix, and multiple Green’s functions are solved simultaneously to improve the memory-bound matrix-vector product kernel. The developed GPU-based solver attained an 8.6-fold speedup from the state-of-art multi-grid solver when measured on compute nodes of AI Bridging Cloud Infrastructure at National Institute of advanced Industrial Science and Technology. The fast analysis method enabled calculating 372 viscoelastic Green’s functions for a large-scale 3D crustal model of the Nankai Trough region with 4.2 × 10 9 degrees of freedom within 333 s per time step using 160 A100 GPUs, and such results were used to estimate coseismic slip distribution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finite element method

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Feedback Delay-Tolerant Proactive Caching Scheme Based on Federated Learning at the Wireless Edge

IEEE Networking Letters

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IEEE Networking Letters 2023年第1期5卷 26-30页

作者： Lin, Na Wang, Yamei Zhang, Enchao Yu, Keping Zhao, Liang Guizani, Mohsen Shenyang Aerospace University School of Computer Science Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan Riken Riken Center for Advanced Intelligence Project Tokyo103-0027 Japan Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Edge caching has emerged as a promising approach to meet explosive mobile data on 6G networks. One critical issue in edge caching is file popularity prediction. The federated learning (FL) based distributed algorithm is used to predict file popularity, which solves user privacy issues in centralized algorithms. However, due to the heterogeneity of user devices, the feedback time (i.e., the update and upload time of model parameters) is various for each user, which causes the feedback delay in the framework of FL-based edge caching. In this letter, we devise a feedback delay-tolerant proactive caching scheme (FLASH) based on FL in which each user utilizes hybrid filtering on stacked autoencoders to train the prediction model locally. Experimental results indicate that FLASH can effectively handle feedback delay and outperform counterpart algorithms in cache efficiency. © 2019 IEEE.

关键词： Forecasting

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Who Does Not Benefit from Fact-checking Websites?: A Psychological Characteristic Predicts the Selective Avoidance of Clicking Uncongenial Facts 23

Who Does Not Benefit from Fact-checking Websites?: A Psychol...

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2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

作者： Tanaka, Yuko Inuzuka, Miwa Arai, Hiromi Takahashi, Yoichi Kukita, Minao Inui, Kentaro Graduate School of Engineering Nagoya Institute of Technology Japan Department of Education Tokyo Gakugei University Japan Center for Advanced Intelligence Project RIKEN Japan Graduate School of Information Sciences Tohoku University Japan Graduate School of Informatics Nagoya University Japan

ISBN: (纸本)9781450394215

Fact-checking messages are shared or ignored subjectively. Users tend to seek like-minded information and ignore information that conflicts with their preexisting beliefs, leaving like-minded misinformation uncontrolled on the Internet. To understand the factors that distract fact-checking engagement, we investigated the psychological characteristics associated with users' selective avoidance of clicking uncongenial facts. In a pre-registered experiment, we measured participants' (N = 506) preexisting beliefs about COVID-19-related news stimuli. We then examined whether they clicked on fact-checking links to false news that they believed to be accurate. We proposed an index that divided participants into fact-avoidance and fact-exposure groups using a mathematical baseline. The results indicated that 43% of participants selectively avoided clicking on uncongenial facts, keeping 93% of their false beliefs intact. Reflexiveness is the psychological characteristic that predicts selective avoidance. We discuss susceptibility to click bias that prevents users from utilizing fact-checking websites and the implications for future design. © 2023 Owner/Author.

关键词： Websites

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Image Denoising of Fishing Nets Underwater with Range Selection Based on Convolutional Sparse Coding 4

Image Denoising of Fishing Nets Underwater with Range Select...

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4th International Symposium on Computer engineering and Intelligent Communications, ISCEIC 2023

作者： Wu, Sheng Liu, Chao Zhang, Honglei Duan, Enyue Jiang, Nian Harbin Engineering University Yantai Research Institute China Jiangsu Ocean University Jiangsu Key Laboratory of Function Control Technology for Advanced Materials Jiangsu China Marine Photoelectric Imaging Project Team Shandong Aerospace Electronic Technology Institute Shandong China

ISBN: (纸本)9798350306323

The underwater detection imaging technology mainly includes sonar imaging and underwater optical imaging. In order to overcome the disadvantages of low resolution of sonar imaging and short detection range of traditional optical imaging, optical gating technology is applied to underwater fishing net detection. This technology has higher resolution compared to sonar imaging, and its detection range has also been shown to be three times larger than that of conventional optical imaging. However, underwater range gating imaging can suffer from large random noise and low signal-to-noise ratio. In order to solve this problem, this paper proposes a convolutional sparse coding denoising neural network based on deep learning to improve image quality. This network consists of a local residual channel and a global convolutional sparse coding channel. Compared with DnCNN, ECNDNet and other methods, the average Peak signal-to-noise ratio of the proposed method is improved by 1.572.11dB, which can effectively remove random noise and reconstruct clear underwater fishing net range-gated images. © 2023 IEEE.

关键词： Signal to noise ratio

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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Random projections for Linear Programming: An Improved Retrieval Phase

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ACM Journal of Experimental Algorithmics 2023年第3期28卷 1-33页

作者： Liberti, Leo Manca, Benedetto Poirion, Pierre-Louis LIX CNRS Ecole Polytechnique Institut Polytechnique de Paris Palaiseau91128 France Dipartimento di Matematica e Informatica Università di Cagliari Via Ospedale 72 Cagliari09124 Italy RIKEN Center for Advanced Intelligence Project The University of Tokyo 113-8654 Engineering Bldg. 14 Hongo 7-3-1 Bunkyo-ku Tokyo Japan

One way to solve very large linear programs in standard form is to apply a random projection to the constraints, then solve the projected linear program [63]. This will yield a guaranteed bound on the optimal value, as well as a solution to the projected linear program. The process of constructing an approximate solution of the original linear program is called solution retrieval. We improve theoretical bounds on the approximation error of the retrieved solution obtained as in Reference [42] and propose an improved retrieval method based on alternating projections. We show empirical results illustrating the practical benefits of the new approach. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Linear programming

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Polyurethane-polysiloxane Copolymer Compatibilized SiR/TPU TPV with Comfortable Human Touch Toward Wearable Devices

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Chinese Journal of Polymer Science 2023年第2期41卷 258-266页

作者： Hong-Fang Zhang Qian Hao Hong-Chi Tian Peng-Jun Yao Xue-Ying Liu Bing Yu Nan-Ying Ning Ming Tian Li-Qun Zhang Key Laboratory of Carbon Fiber and Functional Polymers Ministry of EducationBeijing University of Chemical TechnologyBeijing10029China Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical TechnologyBeijing100029China Shandong Dawn Polymer Material Co. Ltd.Longkou265700China Zhejiang Collaborative Innovation Center for High Value Utilization of Byproducts from Ethylene Project Ningbo PolytechnicNingbo315800China

Because of its softness,wear resistance,biocompatibility,extremely comfortable human touch,thermoplastic vulcanizate(TPV)comprised of silicone rubber(SiR)and thermoplastic polyurethane(TPU)(SiR/TPU TPV)is especially suitable for wearable intelligent ***,developing good compatibilizer is still required to prepare high performance SiR/TPU *** this study,three kinds of polyurethane-polysiloxane copolymer(PU-co-PSi)with different contents of polysiloxane segment were selected,and their compatibilization effect on SiR/TPU blends was studied using nanomechanical mapping technique of *** results show that using PU-co-PSi with the highest content of polysiloxane segment(PU-co-HPSi),the interface thickness largely increases,and the average size of SiR dispersed phase largely decreases,indicating its good compatibilization *** using PU-co-HPSi as compatibilizer,SiR/TPU TPV with a fine SiR dispersed phase and good mechanical performance were successfully *** mechanism on the compatibilization effect of these PU-co-PSi on SiR/TPU TPVs was revealed.

关键词： Silicone rubber(SiR) Thermoplastic polyurethane(TPU) Compatibilization Thermoplastic vulcanizate(TPV)

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A Function Fitting System Based on Genetic Algorithm 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Sun, Qiuhong Wang, Jiaqi Zhou, Xiaokang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China The Faculty of Data Science Shiga University Hikone522-8522 Japan Department of Computer Science North China Electric Power University Baoding071003 China The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the development of science and technology, function fitting has penetrated into various fields of scientific research, scientific and technological innovation. For the function of fitting analysis of a given function image, there is no public software on the market that uses the idea of genetic algorithm to solve the problem of function fitting. In order to make up for the insufficiency of the existing software and seize the opportunity of function fitting in various industries, a function fitting system based on genetic algorithm was proposed. It is a low-threshold software with a wide range of applications. When designing the function of fitting and analyzing a given function image, the genetic algorithm is used. With the application of ray detection in Unity, the closest expression of function fitting is obtained. At the same time, the line graph of fitness reduction during genetic algorithm iteration is given. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Power Quality Mitigation at Petrochemical Plant Using Equipment Immunity Robustness Enhancement 9

Power Quality Mitigation at Petrochemical Plant Using Equipm...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Tahir, M Rahmat B. M. Bt Wan Nowali, Wan Umikalthum Halim, Salmey B. A. Rahman, A Karim B. A. Abdullah, Abdul Rahim Kamarudin, Khairul Anwar B Group Technical Solutions Project Delivery & Technology Petroliam Nasional Berhad Kuala Lumpur Malaysia Universiti Teknikal Malaysia Advanced Digital Signal Processing Laboratory Faculty of Electrical Engineering Melaka Malaysia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

This paper is presented power quality mitigation at petrochemical plant using equipment Immunity Robustness Enhancement. Electrical equipment immunity is critical element that designed to avoid unnecessary plant downtime due to power disturbance especially on power dip. This paper elaborates on the methodology and mitigation to address the power quality issues from IEC61000 series point of view, followed by discussion on power quality site survey at the utility system which supply power to petrochemical plant. A comprehensive dynamic power system study also conducted by simulating internal and external fault to identify the critical equipment that require immunity enhancement to meet IEC61000 series and plant standard requirement. Then, critical load ride through testing is also described to prove modification completion which complies with IEC61000 series and plant standard. © 2022 IEEE.

关键词： Power quality

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