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Thermal energy dependent transient permittivity of epsilon-near-zero material

science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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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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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Visualization of Judicial Precedents on Power Harassment Issues Using Relationship Chart

Visualization of Judicial Precedents on Power Harassment Iss...

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Thematic area Human Interface and the Management of information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Tanaka, Soichiro Kano, Toru Akakura, Takako Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan

ISBN: (纸本)9783031064234

Power harassment has become a problem in Japan recently. However, it has been found that reading judicial precedents is one of the ways to prevent it. In this paper, in order to promote the reading of judicial precedents, we visualized the relationships between people in judicial precedents, which are not easy for non-experts to read. We first extracted the words from the text of judicial precedents that represent people using morphological analysis. Next, we then extracted information about the relationship between the persons using dependency analysis. We then determined the direction of the extracted relationship. Finally, based on the automatically extracted relations between the persons, we visualized the structure of the case as relationship charts and fishbone diagrams. To confirm the usefulness of the proposed diagrams in reading judicial precedents, we conducted an evaluation experiment using both judicial precedents and diagrams on six Tokyo University of science students. The results suggest that diagrams make it easier to read judicial precedents. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Adaptive Discovering and Merging for Incremental Novel Class Discovery

arXiv

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arXiv 2024年

作者： Chen, Guangyao Peng, Peixi Huang, Yangru Geng, Mengyue Tian, Yonghong National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Peng Cheng Laboratory China

One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner. The central challenge is twofold: discovering and learning novel classes while mitigating the issue of catastrophic forgetting of established knowledge. To this end, we introduce a new paradigm called Adaptive Discovering and Merging (ADM) to discover novel categories adaptively in the incremental stage and integrate novel knowledge into the model without affecting the original knowledge. To discover novel classes adaptively, we decouple representation learning and novel class discovery, and use Triple Comparison (TC) and Probability Regularization (PR) to constrain the probability discrepancy and diversity for adaptive category assignment. To merge the learned novel knowledge adaptively, we propose a hybrid structure with base and novel branches named Adaptive Model Merging (AMM), which reduces the interference of the novel branch on the old classes to preserve the previous knowledge, and merges the novel branch to the base model without performance loss and parameter growth. Extensive experiments on several datasets show that ADM significantly outperforms existing class-incremental Novel Class Discovery (class-iNCD) approaches. Moreover, our AMM also benefits the class-incremental Learning (class-IL) task by alleviating the catastrophic forgetting problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Merging

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VR and Deep Learning Based Digital Twin System for Visual Inspection Training of Road Bridge

VR and Deep Learning Based Digital Twin System for Visual In...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Kyohei Wakabayashi Tetsuya Oda Takeharu Sato Toshiyuki Sato Masaharu Hirota Hideyuki Shimada Graduate School of Science and Engineering Okayama University of Science (OUS) Okayama Japan Department of Information and Computer Engineering Okayama University of Science (OUS) Okayama Japan Department of Biosphere-Geosphere Science Okayama University of Science (OUS) Okayama Japan Department of Information Science and Engineering Okayama University of Science (OUS) Okayama Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Road bridges constitute critical infrastructure that ensures safe and efficient transportation. However, they are susceptible to damage caused by aging and external factors. For example, cracking, corrosion of members, surface delamination and various other types of deterioration progress depending on the material and structural type. Beyond the degradation of structural functionality and durability, such damage heightens risks to vehicles and pedestrians. Consequently, effective inspection and maintenance strategies are imperative. In this paper, we propose a digital twin system based on virtual reality and deep learning to train inspection engineers for various bridge types and maintenance management. The proposed system enables bridge inspection training within a virtual environment using a three-dimensional bridge model and provides real-time feedback to the trainees. Furthermore, the effectiveness of the proposed system is evaluated through its application to a road bridge on National Route 2, a critical transportation artery in Japan with high traffic volumes.

关键词： Training Deep learning Bridges Visualization Structural panels Transportation Virtual environments Inspection Digital twins Surface cracks

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3D LiDAR-Based Lane Line Recognition and Obstacle Avoidance for Autonomous Mobile Robot - A Robust Approach without Image Sensors

3D LiDAR-Based Lane Line Recognition and Obstacle Avoidance ...

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SICE Annual Conference

作者： Takuya Yamakawa Koki Kubota Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester MI USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

The Intelligent Ground Vehicle Competition (IGVC) is an annual competition for autonomous mobile robots held in the U.S. One of the vital functions of success in this competition is the autonomous lane-following function, which requires stable lane line recognition while avoiding obstacles and staying within lanes. The most popular approach for lane line recognition employs image sensors such as cameras. However, this approach has inherent weaknesses due to changes in outdoor lighting conditions, which are caused by limitations in the dynamic range of the image sensor. To overcome the weaknesses of image sensor-based lane line recognition, we propose a new method that utilizes the reflection intensity of the point cloud acquired by 3D LiDAR. The combination of point clouds and intensity enables the detection of both lane lines and obstacles without the effect of lighting condition changes. In this paper, we propose a novel, safer, and more robust lane-following algorithm that uses only 3D LiDAR, without the need for an image sensor. Lane line detection is performed by extracting a group of points with reflection intensities within a certain range while estimating the direction of the lane line and using a reflection intensity threshold value. Detected lane lines and obstacles are represented as a one-dimensional evaluation cost function, and a local target destination is determined in the direction with the lowest cost. This enables robust control regardless of outdoor lighting conditions. The performance of lane-following, obstacle avoidance, and real-time operation were evaluated in a simulation environment.

关键词： Point cloud compression Image sensors Three-dimensional displays Laser radar Image recognition Velocity control Lighting Reflection Real-time systems Collision avoidance

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Gaze Analysis and Modeling of Cognitive Process During Debugging for Novice programmers’ Learning

Gaze Analysis and Modeling of Cognitive Process During Debug...

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Thematic area Human Interface and the Management of information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Yoshimori, Kohei Kano, Toru Akakura, Takako Graduate School of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan Faculty of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan

ISBN: (纸本)9783031064234

Recently, the demand for IT human resources has increased. Therefore, the expansion of programming education should be considered to strengthen the supply of it. This study aimed to propose debugging learning support for novice programmers using experts’ gaze behavior. First, we gave feedback to proficient users on their gazing behavior during program debugging and interviewed them about their cognitive processes. Based on the results, we developed a cognitive process model of expert debugging. However, most of the visualized information was based on gaze transitions, and it was difficult to see the characteristics of the cognitive process. This study helped to improve the previously proposed model and developed one that focuses more on the cognitive process during program debugging. Data from students with little programming experience were collected and compared with the cognitive process model of programming experts. This enabled the identification of the characteristics of the program debugging process of non-experts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： program debugging

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