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Green Routing Game: Pollution-Aware Mixed Fleet Logistics with Shared Charging Facilities

IEEE Transactions on Automatic Control 2025年

作者： Sasahara, Hampei Dan, Gyorgy Amin, Saurabh Sandberg, Henrik Institute of Science Tokyo Department of Systems and Control Engineering Tokyo152-8552 Japan KTH Royal Institute of Technology Division of Network and Systems Engineering StockholmSE-100 44 Sweden Massachusetts Institute of Technology Laboratory of Information and Decision Systems CambridgeMA02139 United States KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

Eco-friendly freight operations are crucial for decarbonizing the transportation sector. Systematic analysis of policy measures requires a principled modeling approach. While the commonly used model referred to as routing game considers the congestible nature of transportation facilities, exiting models fail to account for environmental factors. This paper aims at providing a mathematical framework to study strategic interaction between owners of mixed fleets comprising of both internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. This study introduces a "green"routing game with incomplete information that models strategic interaction among multiple logistic operators. These players face a pollution tax imposed on ICEVs and a potential delayed delivery cost due to EV charging requirements with uncertainty. In contrast to existing models, this novel model captures the players' trade-off between lengthier congestion delay at charging stations as the share of EV trucks increases and higher pollution costs with increased ICEVs usage, with uncertainty determined by a latent state. We first provide equilibrium characterization and present a condition for essential uniqueness. We show that this equilibrium can be computed in a distributed manner using a gradient projection method. We then introduce a public information system that broadcasts real-time information about the latent state. Importantly, we analyze value of information for providing a condition for the public information to be beneficial. Finally, we present numerical examples to illustrate settings where environmental taxation and information dissemination can improve social welfare. © 1963-2012 IEEE.

关键词： Trucks

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Multi-VM-Workloads Learning-based Forecasting Model for Resilient Server Management in Industry Clouds

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Procedia Computer science 2025年 260卷 923-929页

作者： Deepika Saxena Ashutosh Kumar Singh Division of Information Systems University of Aizu Aizuwakamatsu 965-0006 Fukushima Japan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal 462003 Madhya Pradesh India

Forecasting server contention in large-scale cloud data center networks is crucial for efficient cloud resource management. The existing research primarily focuses on single-task machine learning methods for predicting resource-constrained VM failures, which often results in lower accuracy and higher computational costs. To overcome these challenges, this paper introduces a novel approach aimed at enhancing resilient server management in industry clouds. The proposed approach develops Multi-tasks-Learning-based Long Short-Term Memory for multiple VMs workload forecasting model named MVL-LSTM to simultaneously learn resource requirements of all VM hosted on a common server. It facilitates a single-step forecasting of workload resource requirements for all VM segments hosted on a server within a cluster. Experimental results using real-world Google Cluster VM datasets demonstrate that MVL-LSTM significantly improves forecasting accuracy while achieving faster execution and higher energy efficiency compared to existing single-task machine learning algorithms. Specifically, MVL-LSTM model achieves a 15% reduction in processing time and a 50% decrease in energy consumption compared with LSTM-based approach.

关键词： Cloud computing VM failure prediction MVL-LSTM Server management

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Development of Efficient and Robust Linkage Pattern Mining for Multiple Sequential Data

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第1期52卷 223-232页

作者： Maeda, Kyosuke Yokota, Issei Okada, Yoshifurni Lee, Saerom Division of Science for Creative Emergence Kagawa University Graduate School 2-1 Saiwai-cho Kagawa Takamatsu760-8523 Japan Division of Science for Creative Emergence Kagawa University Graduate School 2-1 Saiwai-cho Kagawa Takamatsu760-8523 Japan College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Hokkaido Muroran050-8585 Japan Faculty of Engineering and Design Kagawa University 1-1 Saiwai-cho Kagawa Takamatsu760-8523 Japan

Linkage pattern mining is a method used to extract frequently occurring patterns from multiple sequential data without considering the similarity or correlation between frequent patterns. Therefore, it is expected to be a promising approach for disease prediction and voice data analysis. In the previous method, closed itemset mining was introduced in the linkage pattern-mining algorithm to ensure robustness against noise. Although this method can extract linkage patterns from noisy artificial dataseis, a reduction in computation time and stringent parameter settings are essential for its practical application to real data. In this study, we employed Episode Mining using Memory Anchor algorithm for frequent pattern mining to overcome the limitations of the previous method. The objective of this study is to develop a new robust linkage pattern-mining method that is more applicable to real data, A performance comparison between the previous and proposed methods using artificial datasets showed that the proposed method achieved a reduction in computation time while maintaining an extraction accuracy that is comparable to that of the previous method, particularly on noisy artificial datasets. © (2025), (International Association of Engineers). All rights reserved.

关键词： closed itemset EMMA interval graph linkage pattern sequential pattern mining

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Proposal of a novel peeling of nano-particle (PNP) process for localized material removal on a hard material surface by controllable magnetic fields 22

Proposal of a novel peeling of nano-particle (PNP) process f...

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22nd International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2022

作者： Permpatdechakul, Thitipat Khajornrungruang, Panart Suzuki, Keisuke Kutomi, Shotaro Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Avanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

This article proposes a novel Peeling of Nano-Particle (PNP) process to locally remove material on a hard material surface using controllable magnetic fields. Fe3O4 particles in the size range of 50-100 nm in aqueous solution (pH value 11 adjusted by KOH) were magnetically controlled to adhere and remove from a 4H-SiC surface by two electromagnetic coils, which were sandwiched between the substrate. The particle controlling phenomenon on the surface was experimentally observed and verified by our developed apparatus applying an evanescent field. According to the results, particles could be controlled to approach and remove from the substrate surface with 12mT of magnetic field at the selective area on the substrate surface to realise the proposed PNP process. © European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Magnetic fields

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Development of measurement system for subsurface scattering light of skin and analysis of its age-related changes

Development of measurement system for subsurface scattering ...

引用

Biomedical Imaging and Sensing Conference 2023

作者： Kikuchi, K. Katsuyama, M. Shibata, T. Hardeberg, J.Y. Yuasa, T. Aizu, Y. Shiseido Co. Ltd. MIRAI Technology Institute 1-2-11 Takashima Nishiku Yokohama220-0011 Japan Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Teknologivegen 22 Gjøvik2815 Norway Division of Production Systems Engineering Muroran Institute of Technology 27-1 Mizumoto Muroran Hokkaido050-8585 Japan

ISBN: (纸本)9781510663435

We have developed a system that separates and measures the optical properties of skin, i.e., the surface reflection, diffuse reflection, and sub-surface scattering components of the skin. This system includes two polarization filters that separate light from the skin into a surface reflection component image and a diffuse reflection component image. Furthermore, by using a projector as a light source and irradiating the skin using a high-frequency binary illumination pattern, the subsurface scattering component image alone can be separated and generated. Using the proposed system, we performed a survey of 154 Japanese women aged from their 20s to their 70s and analyzed age-related changes in the optical properties of their skin. The results revealed the following. First, the luminance value Y of the surface reflection from the cheek and its standard deviation within the analysis area increase with age. Second, the Y value of diffuse reflection from the skin decreases with age. Third, the amount of light in the sub-surface scattering components also decreases with age. The proposed system is expected to have a wide range of applications in the medical and cosmetic fields. © 2023 SPIE.

关键词： Surface scattering

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Anomaly Detection with LWE Encrypted Control

arXiv

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arXiv 2025年

作者： Alisic, Rijad Kim, Junsoo Sandberg, Henrik the Division of Decision and Control Systems KTH Royal Institute of Technology Sweden the Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea Republic of

Detecting attacks using encrypted signals is challenging since encryption hides its information content. We present a novel mechanism for anomaly detection over Learning with Errors (LWE) encrypted signals without using decryption, secure channels, nor complex communication schemes. Instead, the detector exploits the homomorphic property of LWE encryption to perform hypothesis tests on transformations of the encrypted samples. The specific transformations are determined by solutions to a hard lattice-based minimization problem. While the test’s sensitivity deteriorates with suboptimal solutions, similar to the exponential deterioration of the (related) test that breaks the cryptosystem, we show that the deterioration is polynomial for our test. This rate gap can be exploited to pick parameters that lead to somewhat weaker encryption but large gains in detection capability. Finally, we conclude the paper by presenting a numerical example that simulates anomaly detection, demonstrating the effectiveness of our method in identifying attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Cryptography

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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Hysteretic Optimality of Container Warming Control in Serverless Computing systems

IEEE Networking Letters

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IEEE Networking Letters 2021年第3期3卷 138-141页

作者： Chiang, Yi-Han Zhu, Chao Lin, Hai Ji, Yusheng Department of Electrical and Information Systems Osaka Prefecture University Osaka599-8531 Japan School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan

Keeping containers warm for prompt service responses and reducing warm containers for light-weight system management exhibit a fundamental tradeoff in serverless computing systems. In this letter, we investigate the problem of container warming control for serverless computing, and we formulate it as a Markov decision process (MDP). By observing that the value functions corresponding to the MDP are partially submodular, we show that the derived optimal policy is hysteretic and partially non-decreasing. Our numerical results show that the derived optimal policy exhibits a hysteretic structure, which can be realized via switching-up/-down thresholds in practice. © 2019 IEEE.

关键词： Hysteresis

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