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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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Estimation of Wrist Chattering Angle Based on Disturbance Observer 16

Estimation of Wrist Chattering Angle Based on Disturbance Ob...

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Zhang, Xinyu Sun, Zhenyu Shu, Bairui Yokoi, Hiroshi Yong, Xu Li, Guanglin Jing, Xiaobei The Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China The University of Electro-Communications Joint Doctoral Program for Sustainability Research Tokyo182-8585 Japan The University of Electro-Communications Department of Mechanical Engineering and Intelligent Systems Tokyo182-8585 Japan

ISBN: (纸本)9798350330755

Due to the physiological constraints and daily habits of hand movements, the wrist chattering effect (WCE) will inadvertently cause disturbance to the accuracy of fingers' pattern recognition, when healthy subjects or amputees undergoing mirror training control their fingers according to instructions. This paper proposes a method for estimating WCE angles using a disturbance observer. Based on the Denavit-Hartenberg (D-H) convention and inverse kinematics, the intervals of the WCE during finger flexion and extension are determined. The research objective is to eliminate disturbance terms containing redundant information referring to sliding mode anti-jittering methods, making subsequent machine learning models less susceptible to disturbances. © 2023 IEEE.

关键词： Inverse kinematics

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Classification of Multivariate Time Series Signals Using Self-Supervised Representation Learning for Condition Monitoring

Classification of Multivariate Time Series Signals Using Sel...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Koichi Sumiya Sumika Arima Doctoral Program in Policy and Planning Sciences University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

In the analysis of real-world data, two significant challenges often arise: high-dimensional signals and their temporal interactions. To address these issues and identify transitions in process conditions through end-to-end learning, we propose a self-supervised representation learning framework that conceptualizes signals as images. This straightforward approach classifies imaged signals as time-specific conditions by maximizing mutual information within a domain-specific feature space. We applied this methodology to two labeled open datasets and one unlabeled real-world process dataset, yielding promising results.

关键词： Representation learning Furnaces Time series analysis Self-supervised learning Glass Medical services Semiconductor device manufacture Turning Wool Mutual information

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Reconfigurable architecture for probabilistic neural network system

Reconfigurable architecture for probabilistic neural network...

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IEEE International Conference on Field-programmable Technology (FPT)

作者： R. Mizuno N. Aibe M. Yasunaga I. Yoshihara Doctoral Program Systems and Information Engineering University of Tsukuba Japan Institute of Information Sciences and Electronics University of Tsukuba Japan Faculty of Engineering Miyazaki University Japan

Reconfigurable architecture for the probabilistic neural network (PNN) is proposed and the PNN hardware system is developed using FPGAs. In the system, preprocessing circuits as well as the PNN can be reconfigured adapting each task. Potential performance of the PNN cannot be brought out until the total reconfigurability including the preprocessing is achieved. The developed system shows high recognition accuracy with high processing speed for some image recognition tasks.

关键词： Reconfigurable architectures Neural networks Neural network hardware Kernel Field programmable gate arrays Counting circuits Image recognition Neurons Registers systems engineering and theory

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Innoframe: A project and portfolio management multilayer framework to support innovation-driven SMEs 4

Innoframe: A project and portfolio management multilayer fra...

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4th International Conference on Quality engineering and Management, ICQEM 2020

作者： Tereso, A.P. Mishly, M.A. Production and Systems Department Centre ALGORITMI University of Minho Campus of Azurém Guimarães4804-533 Portugal Doctoral Program in Industrial and Systems Engineering University of Minho Campus of Azurém Guimarães4804-533 Portugal SchemaZone 18 Yonge Street TorontoONM5E 1Z8 Canada

ISBN: (纸本)9789895491100

Purpose - The purpose of this research was to discover what frameworks for project, portfolio, and innovation management there are and if necessary to propose a new framework useful for SMEs of the construction and building materials industry. Design/methodology/approach - The methodology used in this research was the case study, in which the Canadian construction and building materials industry was selected. The research was based on literature review, interviews, group discussion and survey. Findings - Although there are currently many frameworks available for the three management areas (project, portfolio and innovation), research has shown that a framework that helps companies to follow an approach that supports their business at these three levels is lacking. Originality/value - Innoframe is a new framework for project, portfolio, and innovation management that incorporates a pipeline and two phases as well as a performance management matrix, that can be used for project and portfolio management, to support innovation-driven SMEs. © 2020 Universidade do Minho. All rights reserved.

关键词： Framework Innovation Portfolio management Project management

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Recognition of sequential patterns by nonmonotonic neural networks

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systems and Computers in Japan 1999年第4期30卷 11-19页

作者： Morita, Masahiko Murakami, Satoshi Inst. of Info. Sci. and Electronics University of Tsukuba Tsukuba 305-8573 Japan Doctoral Program in Engineering University of Tsukuba Tsukuba 305-8573 Japan Dept. of Math. Eng. and Info. Phys. Faculty of Engineering University of Tokyo University of Tsukuba College of Engineering Systems University of Tsukuba Doctoral Program in Engineering

A neural network model that recognizes sequential patterns without expanding them into spatial patterns is presented. This model forms trajectory attractors in the state space of a fully recurrent network by a simple learning algorithm using nonmonotonic dynamics. When a sequential pattern is input after learning, the network state is attracted to the corresponding learned trajectory and the incomplete part of the input pattern is restored in the input part of the model;at the same time, the output part indicates which sequential pattern is being input. In addition, this model can recognize learned patterns correctly even if they are temporally extended or contracted.

关键词： Pattern recognition

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Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Data-Driven Approaches to Detecting Misdeliveries in Truck L...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hidaka, Ayumu Shin, Ryota Tsuchiya, Atsushi Nakabayashi, Norihiko Okada, Yukihiko Shida, Yohei University of Tsukuba Master's Program in Service Engineering Tsukuba Japan University of Tsukuba Doctoral Programs in Policy and Planning Sciences Tsukuba Japan Yamato Transport Co. Ltd. Minato Japan University of Tsukuba Yamato Transport Co.Ltd. Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan

ISBN: (纸本)9798350324457

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages. © 2023 IEEE.

关键词： anomaly detection GPS misdelivery trajectory method transport data truck logistics

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Setiawan, Aji Adi, Kusworo Widodo, Catur Edi Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Faculty of Engineering Darma Persada University Department of Information Technology Jakarta Indonesia Diponegoro University School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia

ISBN: (纸本)9798350339215

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%. © 2023 IEEE.

关键词： Support vector machines

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Adversarial Search Algorithms Performance in the Yote Game

Adversarial Search Algorithms Performance in the Yote Game

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis I. Torres Nicolas A. Barriga Doctoral Program in Engineering Systems Faculty of Engineeering Campus Curicó University of Talca Chile School of Videogames Development and VR Engineering Faculty of Engineering Campus Talca University of Talca Chile

ISBN: (纸本)9781665494090

The performance of several adversarial search algorithms is very dependent on the move generation and ordering procedure, as well as on the transposition table characteristics. In this work we compare Alpha-beta, MTD-f and NegaScout performance in the presence and absence of the following features: transposition table, Hash Move heuristic and move ordering using the evaluation function. We found that NegaScout did not yield major improvements over Alpha-Beta, which is likely due to the move ordering performed using a weak evaluation function. In most cases, MTD-f examines fewer nodes, and completes fixed depth searches in less time than Alpha-beta and NegaScout. This is consistent with the literature when using coarse grained evaluation functions. Finally, we found that enabling move ordering leads to fewer nodes examined than enabling the Hash Move heuristic, but, as sorting is time consuming, search is slower. Best results overall are found using MTD-f with all enhancements enabled. These results, in a previously unexplored game, are consistent with existing literature. This enables us to make predictions of algorithm and heuristics’ performance in novel games, based on an analysis of the games’ features, without having to implement all techniques.

关键词： Heuristic algorithms Games Prediction algorithms Standards Sorting

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Use of multicriteria decision aid methods in the context of sustainable innovations: bibliometrics, applications and trends

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Environment systems and Decisions 2021年第4期41卷 501-522页

作者： Delesposte, Jamile Eleutério Rangel, Luís Alberto Duncan Meiriño, Marcelo Jasmim Narcizo, Ramon Baptista Alencar Junior, André Armando Mendonça de Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Rio de Janeiro Brazil Volta Redonda School of Industrial Metallurgical Engineering Federal Fluminense University Volta Redonda Rio de Janeiro Brazil Institute of Science and Technology Federal Fluminense University Rio das Ostras Rio de Janeiro Brazil

Sustainable innovation has gained prominence in recent years, due to the demands imposed by global competition, social pressures and the needs of consumers and the environment. Decision-making processes about sustainable innovation are complex and often require auxiliary instruments to reduce uncertainties. Multicriteria decision aid (MCDA) methods can be a useful tool to make these kinds of decisions more assertive. This article presents the main discussions and theoretical approaches through bibliometrics on the application of MCDA methods in the context of sustainable innovations. We used the R package Bibliometrix library to organize the data of the publications and perform the processing for the generated analyzes. The bibliometric analysis provided an overview about the use of MCDA in sustainable innovations (MIS) and it presents citation analysis;identification of central authors through co-citation analysis;main topics, conceptual structure and thematic evolution of the literature by co-word analysis. As main results, we identified the main trends in MIS: there has been a substantial increase in publications on this theme, and the most explored focuses are “product development, production and distribution” and “environmental or social impact assessment” and there is an opportunity to further explore the focuses of “evaluation or selection of projects, suppliers or resources”, “product life cycle management or assessment” and “definition of decision criteria and standards for sustainable innovation performance”. In addition, several MCDA tools have been successfully used for studies of sustainable innovations, demonstrating that there is no preferable method to use for a given focus. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Bibliometric research Bibliometrix Innovation sustainable Multicriteria decision aid (MCDA)

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