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Resilience indices from a family of recovery functions

Fundamental Research 2024年第1期4卷 13-20页

作者： Loon Ching Tang Lijuan Shen Department of Industrial Systems Engineering and Management National University of Singapore117576 Singapore ETH Zurich Future Resilient SystemsSingapore-ETH Centre138602 Singapore

Defining and measuring resilience using a unified framework has been a topic of intense *** article presents a perspective on how resilience could be quantitatively assessed through a set of *** starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of *** then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life *** particular,it is proposed that resilience should be evaluated according to different time periods,***,during and after a disruption has *** dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for *** numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October ***,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.

关键词： systems resilience Recovery function Recovery index Robustness index Reconfigurability

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A revenue-based decision-making approach for evaluating modular product release plans under resource constraints

A revenue-based decision-making approach for evaluating modu...

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作者： Adegbola, Adewole Allada, Venkat Engineering Management and Systems Engineering Department Missouri University of Science and Technology RollaMO65401 United States

This study introduces the module substitution concept to develop an approach for assessing different strategies for modular product release in a technology-receptive market. We consider a situation where product variants emerge from modules which have varying modular relationships, and each module is defined by specific attributes. The statistical program evaluation and review technique (statistical PERT) was then adopted to address the uncertainties associated with module attributes. A practical example involving the development of modules, product families and their product variants is used to demonstrate the applicability of the approach in which feasible strategies that satisfy the development resource constraint were identified. We then introduced a substitute module to yield a product variant and re-evaluated the strategies. The results obtained shows that the approach is instrumental in assessing various alternatives based on launch timings and revenue generation and can be adopted by managers in deciding on the appropriate product release plan. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Decision making

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A Study on Purchasing Behavior Analysis Method by Comparing Difference in Latent Class Distributions between Membership Stages

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Journal of Japan Industrial management Association 2022年第2期73卷 54-69页

作者： Yang, Tianxiang Yamashita, Haruka Goto, Masayuki Major of Industrial and Management Systems Engineering Graduate School of Creative Science and Engineering Waseda University Japan Department of Information and Communication Sciences Faculty of Science and Technology Sophia University Japan Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan

A marketing policy called the"Membership Stage System" is widely used in retail business. A membership stage provides benefits to customers such as shopping points when a customer's annual cumulative purchase amount exceeds a certain threshold and the customer's stage is raised a level. As a result, the company is not only able to promote the customer's willingness to purchase, but it can also obtain the purchasing history data, thereby enabling high-quality customer analysis. The most fundamental analysis is to infer the difference of purchasing characteristics between member stages and to construct different clustering models for each member stage. However, when the clustering models are learned independently for each membership stage, it is not possible to compare the obtained clusters between membership stages. In this study, we propose a new analytical method and its learning algorithm to analyze differences in cluster distribution between membership stages. Through demonstrating the proposed model applied to an actual data set of purchasing history data on a membership stage system, the effectiveness of our proposal is clarified. © 2022 Japan Industrial management Association. All rights reserved.

关键词： Sales

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Optimization of Stiffness and Damping Coefficient Using Genetic Algorithm in an Electric Bus with Air Suspension System 6th

Optimization of Stiffness and Damping Coefficient Using Gene...

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6th International Conference on Mechanical engineering, ICOME 2023

作者： Hendrowati, Wiwiek Sutantra, INyoman Yuniarto, M. Nur Solichin, Moch. Nurlita, Cony Mechanical Engineering Department Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl AriefRahmanHakim Surabaya60111 Indonesia Graduate of the Department of Mechanical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9789819778973

Riding comfort is the minimum vibration transferred by the road surface to a passenger, where it is characterized by maximum sprung mass acceleration. International standards determine riding comfort, specifically ISO 2631. This research utilizes a genetic algorithm to obtain optimal stiffness and damping coefficients for the vehicle suspension system. The system modeling is simulated using MATLAB software. Based on the genetic algorithm optimization performed, the optimal stiffness and damping coefficients obtained for the system are 203,800 N/s and 12,771 Ns/m for the front suspension stiffness and damping coefficient, respectively. The rear suspension stiffness and damping coefficient are 310,750 N/s and 10,702 Ns/m, respectively. With the optimized parameters, the dynamic responses of acceleration and displacement in vertical and pitch motions yield more comfortable results. The conclusions are drawn based on the root mean square (RMS) acceleration value for drivers who meet ISO 2631 comfort standards. The driver's system operates comfortably without complaints at low speeds, with only minor complaints at high rates. With the optimized suspension, the driver can sustain over 16 h of operation, whether subjected to sinusoidal road. The optimized parameters result in an improved transient vehicle response, characterized by reduced impact forces and quicker attainment of settling time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Damping

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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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Renewable wood-derived hierarchical porous,N-doped carbon sheet as a robust self-supporting cathodic electrode for zinc-air batteries

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Chinese Chemical Letters 2023年第1期34卷 133-138页

作者： Xiaohua Deng Zhu Jiang Yingying Chen Dai Dang Quanbing Liu Xiaoyang Wang Xu Yang School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhou 510006China Department of Chemical Systems Engineering Graduate School of EngineeringNagoya UniversityFuro-choChikusa-kuNagoya 464-8603Japan

Heteroatom doped porous carbon materials have emerged as essential cathode material for metal-air battery systems in the context of soaring demands for clean energy conversion and ***,a three-dimensional nitrogen-doped carbon self-supported electrode(TNCSE)is fabricated through thermal treatment and acid activation of raw *** resulting TNCSE retains the hierarchical porous architecture of parent raw lumber and holds substantial defect sites and doped N sites in the carbon *** as a cathode in the rechargeable zinc-air battery,the TNCSE exhibits a superior peak power density of 134.02 m W/cm^(2)and an energy density of 835.92 m Ah/g,significantly exceeding the ones reference commercial 20%Pt/C *** strikingly,a limited performance decay of 1.47%after an ultra long-period(500 h)cycle is also achieved on the *** work could offer a green and cost-save approach for rationally converting biomass into a robust self-supporting cathode material for a rechargeable zinc-air battery.

关键词： Carbon sheet Hierarchical porous structure Metal-free Zinc-air batteries Long-life stability

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Dynamic Sampling-Based Meta-Learning Using Multilingual Acoustic Data for Under-Resourced Speech Recognition

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IEEE Access 2024年 12卷 106070-106083页

作者： Hsieh, I-Ting Wu, Chung-Hsien Zhao, Zhe-Hong National Cheng Kung University Graduate Program of Multimedia Systems and Intelligent Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Under-resourced automatic speech recognition (ASR) has become an active field of research and has experienced significant progress during the past decade. However, the performance of under-resourced ASR trained by existing methods is still far inferior to high-resourced ASR for practical applications. In this paper, speech data from languages that share the most phonemes with the under-resourced language are selected as supplementary resources for meta-training based on the Model-Agnostic Meta-Learning (MAML) strategy. Besides supplementary language selection, this paper proposes a dynamic sampling method instead of the original random sampling method to select support and query sets for each task in MAML to improve meta-training performance. In this study, Taiwanese is selected as the under-resourced language, and the speech corpus of five languages, including Mandarin, English, Japanese, Cantonese, and Thai, are chosen as supplementary training data for acoustic model training. The proposed dynamic sampling approach uses phonemes, pronunciation, and speech recognition models as the basis to determine the proportion of each supplementary language to select helpful utterances for MAML. For evaluation, with the selected utterances from each supplementary language for meta-training, we obtained a Word Error Rate of 20.24% and a Syllable Error Rate of 8.35% for Taiwanese ASR, which were better than the baseline model (26.18% and 13.99%) using only the Taiwanese corpus and other methods. © 2013 IEEE.

关键词： Speech recognition

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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The defender’s perspective on automatic speaker verification: An overview

The defender’s perspective on automatic speaker verificatio...

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Wu, Haibin Kang, Jiawen Meng, Lingwei Meng, Helen Lee, Hung-Yi Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong

Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are several review papers that cover replay and synthetic speech, and adversarial attacks, there is a notable gap in a comprehensive review that addresses defense against adversarial attacks and the recently emerged partially fake speech. Thus, the aim of this paper is to provide a thorough and systematic overview of the defense methods used against these types of attacks. © 2023 CEUR-WS. All rights reserved.

关键词： Network security

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Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of Technology Approach 24

Predicting Factors Influencing the Actual Use of E-Learning ...

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4th International Conference on Internet and E-Business, ICIEB 2024

作者： Benito, Omar Paolo Prasetyo, Yogi Tri Roque, Ralph Andre C. Flores, Fernan Patrick Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Industrial Engineering and Management Yuan Ze University Taiwan School of Industrial Engineering and Engineering Management Mapua University Philippines School of Graduate Studies Mapua University Philippines Department of Information Systems Sepuluh Nopember Institute of Technology Indonesia University of Surabaya Indonesia

ISBN: (纸本)9798400709739

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions. A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms. This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics;the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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