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IEEE International Conference on Data Mining (ICDM)

作者： Hang Zhou Wentao Yu Sheng Wan Yongxin Tong Tianlong Gu Chen Gong School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Engineering Research Center of Trustworthy AI (Ministry of Education) Jinan University China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed “personalized Federated learning with Traffic Pattern Sharing” (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Training Data privacy Federated learning Urban areas Transportation Transforms Traffic control Data models Safety Servers

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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IEEE Conference on Wireless Communications and Networking

作者： Songhan Zhao Yusi Long Bo Gu Nguyen Cong Luong Bin Lyu Shimin Gong School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Faculty of Computer Science Phenikaa University Vietnam School of Communications and Information Engineering Nanjing University of Posts and Telecommunications China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode.

关键词： Radio frequency Trajectory planning Wireless networks Collaboration Switches Time-varying channels Throughput

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Exploring the Factors of Students’ Online Learning Based On Structural Equation Modeling

Exploring the Factors of Students’ Online Learning Based On...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Han Wan Hongzhen Luo Zihao Zhong Jianlei Yang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And these data make it possible for us to analyze students’ final academic performance. In this paper, we use structural equation modeling (SEM) to analyze the relationship between students’ learning factors. It is found that students’ lab scores (LS), exercise scores (LS) and participation in the discussion (PID) have a direct impact on their final programming scores (FPS). This paper also finds that students’ assignment submissions have an indirect influence on their final programming scores (FPS) but have a direct effect on lab scores (LS). In addition, students’ participation in the discussion (PID) has an indirect influence on assignment submissions (AS) and lab scores (LS). The research in this paper can provide instructional designers with references for instructional design.

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Single-Phase and Two-Phase Liquid Immersion Cooling of Data Center Power Supply Units for Heat Capture

Single-Phase and Two-Phase Liquid Immersion Cooling of Data ...

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Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Haoyun Qiu Pouya Kabirzadeh Vivek S. Garimella Arjit Bali Keerthivasan Gurusami Kang Joon Lee Andrew Stillwell Todd Salamon Nenad Miljkovic Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign Urbana IL Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL Optical Systems and Device Research Laboratory Nokia Bell Labs Murray Hill NJ

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Data center power supply units (PSU) have high power density due to the increasing power demand and their low profiles. Air cooling proves to be increasingly difficult in mitigating the hot spots, and it also consumes extra energy in fluid delivery, air conditioning, and rejection to the ambience. In this work, we redesign and modify the PSU cooling infrastructure and adopt quiescent immersion cooling. This design removes all active air-cooling elements and enables heat capture. The PSU used in the experiments is a 2 kW 80 Plus Platinum commercial unit (Bel PET2000-12-074RA). The cooling performance was characterized by tracking the steady-state case temperatures of the switching semiconductors. Flutec PP1 was used to study the cooling performance variations with respect to PSU load and capture temperature. Conventional fan-cooled PSU with heat sinks served as the benchmark. Experiments show that two-phase immersion cooling grants an overall enhancement of 4.4× over the original air-cooling approach at 40 °C heat capture. The results suggest that heat rejection from the dielectric fluid to the riding heatsink is the main bottleneck, and further reduction of the thermal resistance would allow heat capture at a temperature closer to the liquid boiling point. The PSU thermal management approach studied here can help lower data center power usage effectiveness (PUE) while increasing PSU power delivery density.

关键词： Power system measurements Cooling Density measurement Thermal resistance Thermomechanical processes Immersion cooling Switches Thermal management Resistance heating Data center power

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Catboost3D: A Novel CatBoost-based Approach for Efficient Classification of 3D Models 1

Catboost3D: A Novel CatBoost-based Approach for Efficient Cl...

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1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Bouksim, Mohcine Madani, Mohamed Amine Zakani, Fatima Rafii Arhid, Khadija Gadi, Taoufiq Aboulfatah, Mohamed National School of Applied Sciences Mohammed First University SmartICT Lab Oujda Morocco National School of Applied Sciences Mohammed First University Engineering Sciences Laboratory LSI Oujda Morocco SOVIA team National School of Applied Sciences University of Abdelmalek Essaâdi. Lab. of Applied Sciences Al Hoceima Morocco Higher School of Technology Cadi Ayyad University Lab. of Processes Signals Industrial Systems and Computer science Safi Morocco University of Hassan I. MISI Laboratory Faculty of Science and Technology Settat Morocco

ISBN: (纸本)9798350351736

The classification of 3D models consists in grouping similar objects in predefined classes. This task is challenging and requires results that align with human perception. This paper introduces a new classification method for 3D models, using the CatBoost classifier, a machine learning algorithm that has shown good results in various domains. Our approach addresses the challenge of accurately categorizing 3D models without needing a large database for training and using the 3D models directly without passing by intermediate representation. We trained the CatBoost classifier using the dihedral angle, shape index, and shape diameter function to create a model able to classify each query object to the correct class. We demonstrate, through extensive experimentation, that our method not only accurately classifies 3D models into their respective categories but also effectively handles the complexity and variability inherent in 3D data. © 2024 IEEE.

关键词： 3D modeling

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A Brain-inspired Fully Hardware Hopfield Neural Network based on Memristive Arrays

A Brain-inspired Fully Hardware Hopfield Neural Network base...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zilu Wang Xin Yao Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen China Research Institute of Trustworthy Autonomous Systems Shenzhen China School of Computer Science University of Birmingham Birmingham UK

In this work, we propose a fully hardware implementation of Hopfield neural network (HNN) based on memristive arrays, which adopts a bottom-up brain-inspired hardware framework. Memristors are used as key computing units to design cell modules in the low layer to play roles similar to neurons and synapses, so as to perform information integration, filtering, and plasticity of weights in a simple circuit structure and in-memory computing way. Cell modules are cascaded by the mode of encoding and mapping based on HNN in a structured regular circuit way to construct functional modules with braininspired parallel analog computing capacity in middle layers. Different functional modules perform information interaction according to the system's requirements and goals, thus realizing the overall system in the top layer. Our proposed HNN system is then used to solve combinatorial optimization problems. Different from other similar work, our system starts from a memristor-based brain-inspired framework and is implemented fully in hardware. The experimental results show that our work can not only improve the convergence speed, but also can be conveniently used to solve problems of different scales because of its good scalability. In addition, with hardware overhead and power consumption analysis, our system has been shown to be very hardware-friendly. Our work represents an advance towards a memristor-based hardware system with brain-inspired structure and high performance.

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Measurement of paracetamol concentration using a fiber laser system

Measurement of paracetamol concentration using a fiber laser...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Liliana Soares Rosa Ana Perez-Herrera Susana Novais António Ferreira Orlando Frazão Susana Silva Faculty of Engineering INESC TEC - Institute for Systems and Computer Engineering Technology and Science University of Porto Porto Portugal Department of Electrical Electronic and Communication Engineering Public Univ. of Navarra ISC - Institute of Smart Cities Public Univ. of Navarra Pamplona Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal LEPABE-Laboratory for Process Engineering Environment Biotechnology and Energy University of Porto Porto Portugal ALiCE - Associate Laboratory in Chemical Engineering University of Porto Porto Portugal

A linear fiber laser system for measurements of paracetamol concentration is experimentally demonstrated. The cavity is based on a fiber loop mirror and an FBG centered at 1567.8 nm. The sensing head corresponds to a refractometric sensor, whose which principle of operation is based on Fresnel reflection in the fiber tip (FBG side). The system works at detected variations of paracetamol concentrations with a sensitivity of $[(8.74\pm 0.34)\times 10^{-5}]\ \ \mu \mathrm{W}/(\mathrm{g}/\text{kg})$ and a resolution of 2.77 g/kg. The results prove that the fiber laser system could be an asset for processing industries, specifically for non-invasive and real-time measurements of concentration.

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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VISUAL GYROSCOPE: COMBINATION OF DEEP LEARNING FEATURES AND DIRECT ALIGNMENT FOR PANORAMIC STABILIZATION

arXiv

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arXiv 2024年

作者： Berenguel-Baeta, Bruno Andre, Antoine N. Caron, Guillaume Bermudez-Cameo, Jesus Guerrero, Jose J. Instituto de Investigacion en Ingenieria de Aragon Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain IRL Japan Universite de Picardie Jules Verne MIS laboratory Amiens France

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a 360◦ dual-fisheye camera mounted on different aerial vehicles. © 2024, CC BY.

关键词： Gyroscopes

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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