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Symposium on Integrated Circuits and systems Design (SBCCI)

作者： Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Systems Graduate Program Automation and Control Group/GRACO Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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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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Determining the Significance of Two Consecutive Fiscal Years of Non-Conformance Cost Data Set in Project Execution of Smart Grid 23

Determining the Significance of Two Consecutive Fiscal Years...

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6th International Conference on Computers in Management and Business, ICCMB 2023

作者： Narvas, Miller F. Prasetyo, Yogi Tri Nadlifatin, Reny Ayuwati, Irene Dyah Persada, Satria Fadil School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines International Bachelor Program in Engineering Yuan Ze University 135 Yuan-Tung Rd. Chung-Li32003 Taiwan Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Information Technology Institut Teknologi Telkom Surabaya Surabaya Indonesia Entrepreneurship Department Binus Business School Undergraduate Program Bina Nusantara University Indonesia

ISBN: (纸本)9781450398046

Non-conformance cost is one of the key performance indicators in determining the success of executing a project. This study will substantiate the significance of two consecutive fiscal years of non-conformance cost data set in project execution of smart grid. A two-Sample T-Test was used to compare the means of two consecutive years' data sets. All the results of both fiscal years' data set were insignificant. Since it is insignificant, the best recommendation is to thoroughly review the lessons learned from the previous projects and implementation of preventive actions to mitigate or even prevent the occurrence/ reoccurrence of non-conformance and non-conformance costs. A continuous improvement in preventing failures will assure success and increase the profitability margin of the project. © 2023 ACM.

关键词： Smart power grids

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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Modular Safety-Critical Control of Legged Robots

arXiv

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arXiv 2023年

作者： Tosun, Berk Samur, Evren Graduate Program in Systems and Control Engineering Bogazici University Istanbul Turkey Department of Mechanical Engineering Bogazici University Istanbul Turkey

Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive signals. A novel deep-learning model utilizing an efficient transformer architecture is used for the terrain estimation. A quadratic program combines the terrain estimations with inverse dynamics and a novel exponential control barrier function constraint to filter and certify nominal control signals. The result is an optimal controller that acts as a filter. The filtered control signal allows safe locomotion of the robot. The resulting approach is generalizable, and could be transferred with low effort to any other legged system. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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Evaluating Standard Search Enhancements Performance in Zen Puzzle Garden

Evaluating Standard Search Enhancements Performance in Zen P...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Hans Schaa Jose A. del Solar-Zavala Nicolas A. Barriga Doctoral Program in Engineering Systems Universidad de Talca Curicò Chile Faculty of Engineering Universidad de Talca Talca Chile

Sokoban-type puzzles and their variants involve complex planning and decision-making challenges in wide and deep search spaces. Such scenarios are interesting for testing path-planning algorithms. This document uses A * and IDA * (Iterative Deepening A *) algorithms to solve Zen Puzzle Garden game boards; a Sokoban-type puzzle where you need rake the sandy area completely. The algorithms are evaluated in terms of speed and node expansion in moderately complex boards. Additionally, we implement three improvements: transposition tables, move ordering, and deadlock tables. The results show that IDA * with the three proposed improvements is on average faster than A *. Although its strength is its low memory usage, it can be seen that it is very competitive versus A * in terms of time usage. Transposition tables and deadlocks tables reduce by 500 times the number of nodes expanded by the IDA * without improvements. On the other hand, the ordering of nodes is not very effective for some boards, which causes a worse performance of the IDA * algorithm, giving rise to greater exploration in boards that do not lead to the solution. Studying these algorithms can be beneficial in the field of path-finding for Sokoban-type game domains or solver-based procedural content generators.

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Using Entropy for Modeling Difficulty in the Asteroid Escape Sliding Puzzle

Using Entropy for Modeling Difficulty in the Asteroid Escape...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Jose A. del Solar-Zavala Hans Schaa Nicolas A. Barriga Doctoral Program in Engineering Systems Universidad de Talca Curicó Chile Faculty of Engineering Universidad de Talca Talca Chile

Developing a standarized metric for difficulty that can be applied to a large number of puzzles is not trivial. This does not come as a surprise because of the subjective factors involved in defining difficulty. However, studies that address the design and application of metrics that can be applied to multiple games are still important, because they can still be used to increase the quality of content generated by automated agents. We take a look at applying information entropy concepts in the game Asteroid Escape and compare the results with other metrics to assess difficulty used in games of similar characteristics. This approach is largely based on future work proposal from a previous study done on The Witness puzzles that yielded positive results on using an automatic method to assess the difficulty of user rated puzzles. This study also includes a modification of BFS and A* capable of finding every shortest solution. From the implementation, it is possible to look at contradicting results with the literature, where basic metrics had a better result correlating with an official definition of difficulty from the game designers. The impact expected from this study is to propose an update to the the corpus of puzzle games where the uncertainty metric for difficulty has been applied. As having an accurate assessment of difficulty can open the doors to other studies, such as improving the quality of the output from procedural content generation methods in games or propose an automated method of difficulty adjustment based on this metric.

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Industry Voices on Software engineering Challenges in Dynamic systems of systems

Industry Voices on Software Engineering Challenges in Dynami...

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IEEE/ACM International Workshop on Software engineering for systems-of-systems (SESoS)

作者： Jana Heinrich Florian Balduf Martin Becker Rasmus Adler Frank Elberzhager Embedded Systems Engineering Fraunhofer IESE Kaiserslautern Germany Program Manager Automotive Systems Fraunhofer IESE Kaiserslautern Germany Architecture-Centric Engineering Fraunhofer IESE Kaiserslautern Germany

In order to achieve the required change of systems with regard to sustainable development goals, systems from different sectors such as energy, agriculture, mobility, and production must be transformed, foremost in a digital way. The resulting systems are called systems of systems (SoSs). Software and systems engineering can make contributions to the necessary transformation of these systems. SoSs that are characterized by special dynamics (e.g., more frequent context changes) are referred to as dynamic SoSs (dynaSoS). Currently, there are still many open questions and challenges in their design and realization. In order to get an overview of these, in this paper we present a survey that was executed within the context of the project “DynaSoS”. For this purpose, qualitative interviews were conducted with experts in software and SoS engineering from industry and academia. Based on the statements received, 36 challenges were grouped and prioritized in several workshops. This report provides an overview of ten central software engineering issues and research challenges in the realization of dynaSoS and aims to contribute to a better understanding of the application-oriented challenges facing industry.

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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Generative a bistant for digital twin simulations 1

Generative a bistant for digital twin simulations

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Boareto, Pedro Antonio De Freitas Rocha Loures, Eduardo Portela Santos, Eduardo Alves Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná 1155 Imaculada Conceição St. Curitiba Paraná80215-901 Brazil Federal University of Paraná 632 Prefeito Lothário Mei bner Av. Curitiba Paraná80210-170 Brazil

One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development proce bes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative A bistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests. © 2025 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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