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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Yi Liu, Haoming Hainan University School of International Communication and Arts Haikou China The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798331520861

In recent years, with the development of technologies such as signal processing, machine learning, and deep learning, speech emotion recognition has found wide applications in mental health, educational training, and social media analysis. However, current methods for speech emotion recognition mostly rely on data-driven models, and the lack of emotional speech datasets significantly hinders their progress. To address this issue, this paper proposes an emotional speech dataset consisting of 6,447 audio clips for emotion recognition and analysis. The dataset includes five emotional categories: happy, sad, surprised, fearful, and angry. In addition, four speech signal processing algorithms including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCC), and Continuous Wavelet Transform (CWT) are utilized for feature extraction, resulting in the creation of four emotional feature image datasets. Furthermore, eleven state-of-the-art (SOTA) classification models are selected to establish a benchmark. Experimental results show that most classification models achieve accurate recognition performance on the proposed emotional speech dataset. It is believed that the proposed dataset and benchmark can promote the research and development of emotion recognition and analysis. © 2024 IEEE.

关键词： Wavelet transforms

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Sub-optimal Perceptron Controller Using WOA Algorithm for Farming Watering Mobile Robot

Sub-optimal Perceptron Controller Using WOA Algorithm for Fa...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Oliveira, L.H. Bautista, J. Pinto-Lopera, Jesus Mendoza-Penaloza, Jose Munoz, Daniel M. University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Caquetá Colombia University of Brasilia Mechatronics Graduate Program Department of Mechanical Engineering Brasilia Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-The-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%. © 2024 IEEE.

关键词： MATLAB

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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A Reliability-Driven Design and Test (ReDDT) methodology for space nuclear power and propulsion systems

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Journal of Space Safety engineering 2025年第1期12卷 53-65页

作者： Thomas, Dr. L. Dale Rawlins, Samantha Raghu, Shreyas Lakshmipuram Aueron, Alexander Department of Industrial & Systems Engineering and Engineering Management The University of Alabama in Huntsville 301 Sparkman Drive Huntsville 35899 AL United States Department of Mechanical and Aerospace Engineering The University of Alabama in Huntsville 301 Sparkman Drive Huntsville 35899 AL United States

The development methodology employed in the United States’ Rover NERVA program is reviewed as the basis for the Reliability-Driven Design and Test (ReDDT) methodology described herein. Postulated as perhaps the first full programmatic implementation of probabilistic design, the NERVA foundational methodology appears to have never been adopted elsewhere following program cancelation. Nevertheless, much of the framework developed during NERVA remains applicable, and the ReDDT approach described in this work is based upon their approach. The ReDDT approach builds on the NERVA method by introducing a qualitative failure mode analysis to identify which tests are most useful and maximize program efficiency. The qualitative analysis is supported by uncertainty analysis, which is applied to identify the drivers of the system's technical uncertainty and thereby drivers of safety and reliability. The ReDDT approach is distinct from existing reliability-based methodologies including Reliability Based Design Optimization (RBDO) and Design for Reliability (DfR) in that ReDDT focuses on concurrent and synergistic system design, integration, and test planning. ReDDT has been developed for NASA's current Space Nuclear Propulsion effort, but it has broader applicability to nuclear power systems and complex aerospace system developments and upgrades in general. The ReDDT methodology has the goal of transforming the relationship of the design effort to the integration and test effort within a system development from one of test-fail-fix to model-test-evaluate. © 2025 International Association for the Advancement of Space Safety

关键词： Integration and test strategy Model based design Reliability informed design Uncertainty analysis

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Object Process Methodology to Turning Focused System Architecture Design for Manufacturing

Object Process Methodology to Turning Focused System Archite...

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IISE Annual Conference and Expo 2024

作者： Dey, Prithbey Raj Enke, David Buchely, Mario Department of Engineering Management and Systems Engineering Missouri University of Science and Technology RollaMO United States Department of Materials Science and Engineering Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9781713877851

Monitoring and predicting tool wear and surface roughness are considered crucial factors of automation and maximization of the manufacturing process like turning operations. However, nonlinearity and stochasticity in the formation and variation of tool wear contribute to considering turning as a complex system, making it difficult to design a precise prediction model to optimize machining parameters for maximum quality and production. Therefore, it is required to apply a systematic approach to identify the entities and relationships from a system design perspective and learn about the emergents like tool wear, surface roughness, force, etc., derived from the interaction of ‘form’ and ‘function’ during the turning operation. The representation of the instruments (machining parameters) of the process and their impacts on the operand (workpiece) could be worthwhile to explore for the sources of stochasticity. Object Process Methodology (OPM) has been established as an effective and integrated model that can incorporate the form, function, entities, and their relationship into a single model. Through the OPM, the objects, processes, and their relationships for the turning-focused complex system architecture are represented in this research. In consequence, nonlinear theoretical equations have been used to generate synthetic data using Monte Carlo Simulation (MCS) in the system. Finally, a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) is considered to design a predictive model for the emergent properties. This proposed design of the turning-focused complex system architecture could be useful for the development of system pipelines concerning automation, digital twin technology, and Industry 4.0. © IISE Annual Conference and Expo *** rights reserved.

关键词： Stochastic systems

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0906 | Experimental Investigation of Increased Bearing Capacity When Imparting Vibration to Loose Ground in Low Atmospheric Pressure 21

0906 | Experimental Investigation of Increased Bearing Capac...

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21st International and 12th Asia-Pacific Regional Conference of the International Society for Terrain-Vehicle systems, ISTVS 2024

作者： Watanabe, Tomohiro Iizuka, Kojiro Department of Engineering Mechanical Engineering Program Niigata University Niigata Prefecture Niigata City Japan Department of Science and Engineering Machinery and Control Systems Shibaura Institute of Technology Saitama Prefecture Saitama City Japan

ISBN: (纸本)9781942112570

Recently, legged robots have attracted considerable attention as highly mobile rovers for planetary exploration. However, the surfaces of celestial bodies such as Mars and the Moon are primarily loose, causing slippage due to surface deformation from the rover's leg movements. To mitigate this, a walking method aimed at preventing slippage was proposed. In this walking method, the bearing capacity is increased by imparting vibration from the rover's leg to the loose soil on the ground. This walking method improved the mobility performance of the legged rover. It is crucial to investigate the increase in bearing capacity by imparting vibration in a realistic space environment to validate the effectiveness of the proposed method. This study examines the change in bearing capacity due to vibration under low atmospheric pressure, a condition found on Mars and the Moon. This condition affects the characteristics of ground sand due to decreased air resistance. Our findings provide valuable insights into using vibration in planetary exploration to enhance the performance of legged rovers. The experimental results indicate that the bearing capacity under low atmospheric pressure is nearly identical to that under standard atmospheric pressure, suggesting that atmospheric pressure conditions have minimal impact on the bearing capacity when vibration is applied. © 2024 International Society for Terrain-Vehicle systems. All rights reserved.

关键词： Atmospheric pressure

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Design of a machine learning to classify health beverages preferences for elderly people: an empirical study during COVID-19 in Thailand

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International Journal of Industrial and systems engineering 2022年第3期42卷 319-337页

作者： Kengpol, Athakorn Klunngien, Jakkarin Advanced Industrial Engineering Management Systems Research Center Department of Industrial Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Thailand

This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO’s recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people’s preferences. Second, elderly people can be made aware of appropriate health beverages. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： COVID-19

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Developing a Green AI Platform for SMEs to Analyze Open-source Information with the Approach of Society 5.0

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SN Computer Science 2024年第7期5卷 923页

作者： Akarslan, Hüseyin Sağiroğlu, Şeref Management Information Systems Graduate School of Informatics Gazi University Türkiye Ankara Turkey Computer Engineering Department Engineering Faculty Gazi University Türkiye Ankara Turkey

management and information systems are considered two major disciplines that have to be combined to benefit from knowledge for rational decision-making which means strict procedures utilizing objective knowledge and logic for many years. Effective and efficient use of already invested organizational resources is a key subject to gain a competitive advantage by making decisions with the help of AI techniques which benefit from both internal and external knowledge. This paper introduces a new model to get benefits from open-source intelligence (OSINT) in the context of external knowledge with a green computing approach for solving global organizational challenges in the scope of Society 5.0. This research has a dual background, theoretical and practical. Problem defining and hypothesis formulation was carried out by literature review. Then, the application developed according to the proposed model was put into action in some organizations for testing, and feedback from experts who participated in the proof of concept (POC) activities was evaluated and discussed empirically. Finally, the findings of our research, the advantages of our model, and both academic and business future work were summarized. It has been practically determined that the model and application we have developed are largely successful in line with our hypotheses. However, the weaknesses and advantages of our study were also revealed. Our research is the first study that combines management Information systems and Open-Source Intelligence disciplines with the approach of Society 5.0 to support SMEs in analyzing open-source information to build better decision-making mechanisms. The developed application in this study can also be applied to solving other green distributed computing needs in different domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed computing External knowledge Green computing management information systems (MIS) Open-source intelligence (OSINT) Society 5.0

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An Ant-Lion Optimization Based Approach to Solve Phase Balancing Problem in Distribution Networks 59

An Ant-Lion Optimization Based Approach to Solve Phase Balan...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Yesilyurt, Gunnur Ceylan, Oguzhan Istanbul Technical University Department of Electrical Engineering Turkey Kadir Has University Management Information Systems Turkey

ISBN: (纸本)9798350379730

Phase unbalance is a significant issue for power distribution networks. It can lead to increased energy losses and voltage instability, undermining the electrical grid's reliability and efficiency. We propose an approach to minimize voltage unbalance through reactive power management from PV instal-lations and the optimization of charging/discharging of energy storage devices utilizing a control algorithm based on Ant-Lion Optimizer. We tested the approach on the IEEE 123-Bus Test System, incorporating PV generations by daily simulations. From the results, the combined operation of reactive power support from PVs and Storage Units with the help of the ALO algorithm offers a promising solution to the phase unbalance problem. © 2024 IEEE.

关键词： Power distribution networks

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Impact of Mentoring Relationships on Perceived Quality Of Life among engineering Graduate Students 67th

Impact of Mentoring Relationships on Perceived Quality Of Li...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： McCallum, Mariah Godwin, Ian Ma, Jiahui Barlow, Hailey Gamage, Dimantha Kottawa McCrory, Bernadette Industrial and Management Systems Engineering Department of Mechanical and Industrial Engineering Montana State University BozemanMT United States

Graduate students’ experiences bring on unprecedented challenges, influenced by physical, psychological, social, and environmental factors. Through a productive and authentic mentor-mentee relationship, it was hypothesized graduate students would be more satisfied with their graduate experience. An instrument was developed using the 26-item World Health Organization Quality of Life questionnaire consisting of four health domains (physical, psychological, social relationships, environmental) and additional questions on psychosocial help and the graduate student’s satisfaction with their advisors and overall graduate school experience. Overall, graduate students who were satisfied with their mentoring experience had a higher quality of life (QoL). Graduate students’ QoL was lower in social relationships (p=0.009) and psychological domains (p=0.000), similar to medical school students. Second-and third-year graduate students had lower QoL than those in the first or fourth+ year. Graduate students were satisfied with their overall mentoring experience, and better mentoring experience tended to have better QoL. © 2023 Human Factors and Ergonomics Society.

关键词： Mentoring Relationship Psychosocial Help World Health Organization Quality of Life

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