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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ryan Barron Maksim E. Eren Manish Bhattarai Ismael Boureima Cynthia Matuszek Boian S. Alexandrov Theoretical Division Los Alamos National Laboratory Los Alamos USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos USA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter $k$ to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific $k$ . The score is calculated for different trials over a range of $k$ , and the ideal $k$ is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good $k$ value, visiting a range of $k$ can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the $k$ search space for these grid-search ML algorithms by truncating the target $k$ values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal $k$ and the accuracy of the Binary Bleed in identifying the correct $k$ for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub.

关键词： Instruction sets Computational modeling Heuristic algorithms Power system stability Stability analysis Elbow Distributed computing Optimization Overfitting Software development management

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A 3D cluster-based channel model with time-space consistency (invited paper)

A 3D cluster-based channel model with time-space consistency...

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2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

作者： Huang, Ziwei Cheng, Xiang School of Electronics Engineering and Computer Science Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Beijing100871 China

ISBN: (纸本)9781665443852

This paper proposes a novel three-dimensional (3D) cluster-based sixth-generation (6G) model for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. In the proposed model, a novel method based on visibility region (VR) is developed, where the smooth and consistent birth and death of clusters in both the time domain and space domain are successfully captured. By using the developed method, the space-time non-stationarity and time-space consistency of channels can be modeled for the first time. Based on the proposed model, important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the smooth birth and death of clusters, time-space consistency, and space-time non-stationarity are modeled sufficiently. © 2021 IEEE.

关键词： Millimeter waves

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Fast Heterogeneous Multi-Problem Surrogates for Transfer Evolutionary Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Hao Xiong, Pu Gong, Maoguo Qin, A.K. Wu, Yue Xing, Lining Key Laboratory of Collaborative Intelligence Systems Ministry of Education School of Electronic Engineering Xidian University Xi’an China Department of Computing Technologies Swinburne University of Technology Hawthorn Victoria Australia School of Computer Science and Technology Xidian University Xi’an China

Transfer evolutionary multiobjective optimization leverages the relevant knowledge from other source problems (distinct but possibly related) to assist the optimization of the target problem of interest. Multi-problem surrogates stack multiple source surrogates to reduce the number of function evaluations of the target expensive problem. The current multi-problem surrogates only considers several source problems and the source and target problems are assumed to be homogeneous. In order to address the above issues, this paper proposes fast heterogeneous multi-problem surrogates for transfer evolutionary multiobjective optimization with a large number of surrogates. First, an iterative surrogate selection strategy is designed to select the highly relevant surrogates from the large-scale surrogate pool to avoid negative transfer. Second, heterogeneous multi-problem surrogates are established to align the features of the source and target models. Finally, an adaptive k-fold cross-validation method is proposed to obtain the predicted values of the target model with low computational costs. Experiments on the multiobjective optimization benchmark problems and multiobjective neural architecture search problems have demonstrated that the proposed method is able to avoid negative transfer in the large-scale scenarios and reduce the computational costs. IEEE

关键词： Multiobjective optimization

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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Risk-Averse Self-Scheduling of Storage in Decentralized Markets

Risk-Averse Self-Scheduling of Storage in Decentralized Mark...

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IEEE General Meeting Power& Energy Society

作者： Ogun Yurdakul Farhad Billimoria Energy Systems and Infrastructure Analysis Division Argonne National Laboratory Lemont IL USA Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Department of Engineering Science University of Oxford Oxford United Kingdom

Storage is expected to be a critical source of firming in low-carbon grids. A common concern raised from ex-post assessments is that storage resources can fail to respond to strong price signals during times of scarcity. While commonly attributed to forecast error or failures in operations, we posit that this behavior can be explained from the perspective of risk-averse scheduling. Using a stochastic self-scheduling framework and real-world data harvested from the Australian National Electricity Market, we demonstrate that risk-averse storage resources tend to have a myopic operational perspective, that is, they typically engage in near-term price arbitrage and chase only few extreme price spikes and troughs, thus remaining idle in several time periods with markedly high and low prices. This has important policy implications given the non-transparency of unit risk aversion and apparent risk in intervention decision-making.

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Bifurcation Analysis of Incompressible Fluid Flow by a High Order Mesh-Free Indicator with Padé Approximants 15th

Bifurcation Analysis of Incompressible Fluid Flow by a Hi...

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15th International Moroccan Congress of Mechanics, CMM 2022

作者： Rammane, M. Mesmoudi, S. Askour, O. Tri, A. Braikat, B. Damil, N. Laboratory of Materials Signals Systems and Physical Modeling Department of Physics Faculty of Sciences Agadir University Ibn Zohr BP 8106 Agadir80000 Morocco Hassan First University of Settat National School of applied Sciences LISA Laboratory Berrechid26100 Morocco Mathematics Computer Science and Communication Systems Laboratory National School of Applied Sciences of Safi Cadi Ayyad University BP 63 Safi Sidi Bouzid46000 Morocco Laboratory of Mechanics Faculty of Sciences Ain Chock Hassan II University of Casablanca Casablanca Morocco Km 7 Route d’El Jadida Casablanca Morocco Laboratory of Engineering and Materials Faculty of Sciences Ben M’sik Hassan II University of Casablanca B.P 7955 Sidi Othman Casablanca Morocco Central Casablanca Complex Systems and Interactions Research Center Green City Bouskoura27182 Morocco

ISBN: (纸本)9783031469725

In this work, a mesh-free indicator is proposed to detect the bifurcation points of incompressible viscous fluid flow in a 2D sudden expansion. This indicator is mainly based on the high order mesh-free algorithm (HO-MFA) to solve the nonlinear problems obtained from the linear stability analysis. The Padé approximants are then used to improve the validity range of the HO-MFA and to determinate the roots of the indicator. The combination of these mathematical tools allows us to determine the zero of the indicator corresponding to the critical point. The Lyapunov-Schmidt reduction is used to search all the bifurcated branches from the critical point. To show the accuracy of our model, comparisons of the obtained results with those of the literature are made. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linear stability analysis

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Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Traffic Signal Optimization by Integrating Reinforcement Lea...

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Smart World Congress (SWC), IEEE

作者： Vijayalakshmi K Kumarasamy Abhilasha Jairam Saroj Yu Liang Dalei Wu Michael P. Hunter Angshuman Guin Mina Sartipi Department of Computer Science and Engineering University of Tennessee at Chattanooga USA Applied Research for Mobility Systems Oak Ridge National Laboratory USA School of Civil and Environmental Engineering Georgia Institute of Technology USA

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

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Impact of the MMC level number to reduce THD rate and improve the power quality transmitted to the electrical grid 2

Impact of the MMC level number to reduce THD rate and improv...

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2nd IEEE International Conference on Signal, Control and Communication, SCC 2021

作者： Baazouzi, Mansour Bacha, Faouzi University of Carthage INSAT Laboratory of Computer Science for Industrial Systems Tunis Tunisia University of Tunis ENSIT High National School of Engineering of Tunis Tunis Tunisia

ISBN: (纸本)9781665402705

The High Voltage Direct Current (HVDC) power systems has an important role in transmitting high power among long distances with respect to the transportation of the AC high voltage (HVAC). The HVDC grid has a set of advantages such as robustness against disturbances and low power losses compared to the AC system and the connection between two AC power grids. Modular Multi-Level Converter (MMC) is more suitable kind of topology compared to other topologies used in HVDC applications. The increasing number of submodules (SM) presents a challenge for researchers in terms of ordering complexity. In this paper, two pulse width modulation techniques are compared in the quality of currents and voltages to the filter level, which presents one of the major problems of the MMC converter. The simulation was performed using MATLAB/Simulink software and the presented simulation results prove the performance of the proposed control systems. The output voltage waveforms and the total harmonic distortion (THD) rate of the proposed system are improved without the need for a filter. © 2021 IEEE.

关键词： HVDC power transmission

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Reliability Assessment of Solder Ball Joints Using Finite Element Analysis and Machine Learning techniques

Reliability Assessment of Solder Ball Joints Using Finite El...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： H. Reddad M. Zemzami N. El Hami N. Hmina N-Q. Nguyen Advanced Systems Engineering Laboratory ENSA Ibn Tofail University Kenitra Morocco ENSAM Mohamed V University Rabat Morocco Science and Engineering Laboratory ENSA University Ibn Tofail Kenitra Morocco Chaire Connected Innovation Computer Sciences and Digital Society Laboratory Universite de Technologie de Troyes Troyes Cedex France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper presents a comprehensive approach for reliability assessment and optimization of ball joints by combining finite element analysis (FEA) method with machine learning algorithms, in particular by exploiting the power of the XGBoost machine learning algorithm. FEA simulations were carried out using ANSYS to generate a dataset encompassing various solder joint geometrical parameters configurations. Key input parameters, including geometric dimensions, were identified by sensitivity analysis and used as features for training the XGBoost model. The trained model demonstrated solid performance. Feature importance analysis revealed critical factors influencing solder joint reliability and provided insights for optimization strategies. This research provides practical recommendations for optimizing solder joint and material design to improve reliability and performance in electronic packaging applications.

关键词： Training Support vector machines Machine learning algorithms Reliability engineering Fatigue Finite element analysis Reliability Copper Soldering Electronics packaging

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