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Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?

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International Journal of industrial and Systems engineering 2023年第4期43卷 555-581页

作者： Vaghefzadeh, Mohammad Hossein Karimi, Behrooz Ahmadi, Abbas Industrial Engineering and Management Systems Faculty Amirkabir University of Technology Tehran Iran Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

The present research applies two different modelling approaches to evaluate the historical demand for a special mobile value-added service (VAS) that is offered and delivered to airline customers. The first method is deterministic and includes non-stationary time series models that cover both mean and variance fluctuation, as well as seasonality effect, in the dataset. The second method is a metaheuristic approach in the form of artificial neural network time series analysis (ANN-TSA). These methods are used to evaluate the power of each category and to choose the best model based on appropriate criteria. The results show that non-stationary time series models outperform ANN-TSA, as indicated by the smaller number of errors in the simulation of the demand dataset. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Neural networks

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Transformer-based lithium battery fault diagnosis research for electric vehicles

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9329-9341页

作者： Huang, Jui-Chan Shu, Ming-Hung Lin, Hsiang-Tsen Day, Jen-Der Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle's lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a dataset and retired battery group to validate the model's performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles. © 2024 - IOS Press. All rights reserved.

关键词： Failure analysis

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An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification

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Mathematical Biosciences and engineering 2024年第2期21卷 1959-1978页

作者： Huang, Mei-Ling Huang, Zong-Bin Department of Industrial Engineering & Management National Chin-Yi University of Technology Taichung Taiwan

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Image classification

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A cluster impurity-based hybrid resampling for imbalanced classification problems

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Applied Intelligence 2024年第20期54卷 9671-9684页

作者： Park, You-Jin Cheng, Ke-Yong Department of Industrial Engineering and Management National Taipei University of Technology Taipei10608 Taiwan

As one of the supervised learning techniques, classification plays a crucial role in categorizing and predicting the observations across a wide range of machine learning applications such as software defect detection, fraud detection in financial sector, fault and defect detection in manufacturing industry, medical diagnosis, etc. However, most classification algorithms have been developed with the assumption that the class distribution is balanced although unequal class distributions are quite common in many practical cases. When a class imbalance problem exists, in general, the classifier tends to become biased towards the majority class and thus the minority class instances are often misclassified to the majority class. Along with the class imbalance problem, the class overlap is also known as one of the sources that makes the learning task become difficult or sometimes deteriorates the classification performance, especially, when class imbalance problem also exists. Thus, in this research, we propose a cluster impurity-based hybrid resampling method including the partially balanced strategy to improve the classification performance of class imbalanced data with considering intra-cluster class imbalance and inter-cluster overlap problems. Specifically, several clustering methods are employed for identifying the groups (i.e., clusters) of all the instances and the cluster impurity of each instance is computed for measuring the degree of cluster overlap. Then, based on the cluster impurity, the instances are generated and eliminated recursively. To demonstrate the effectiveness of the proposed method, comprehensive experiments are conducted on forty imbalanced datasets and two non-parametric hypothesis tests are employed to show the statistical difference in classification performances between the proposed method and other traditional resampling methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supervised learning

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Thermodynamic Performance Analysis of Geothermal Power Plant Based on Organic Rankine Cycle (ORC) Using Mixture of Pure Working Fluids

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Energy engineering 2024年第8期121卷 2023-2038页

作者： Abdul Sattar Laghari Mohammad Waqas Chandio Laveet Kumar Mamdouh El Haj Assad Department of Mechanical Engineering Mehran University of Engineering and TechnologyJamshoro76062Pakistan Department of Mechanical and Industrial Engineering College of EngineeringQatar UniversityDoha00000Qatar Sustainable&Renewable Energy Engineering Department University of SharjahSharjah00000United Arab Emirates

The selection of working fluid significantly impacts the geothermal ORC’s *** a mixture as a working fluid is a strategy to improve the output of geothermal *** the current study,modelling and thermodynamic analysis of ORC,using geothermal as a heat source,is carried out at fixed operating *** model is simulated in the engineering Equation Solver(EES).An environment-friendly mixture of fluids,i.e.,R245fa/R600a,with a suitable mole fraction,is used as the operating *** mixture provided the most convenient results compared to the pure working fluid under fixed operating *** impact of varying the evaporator pressure on the performance parameters,including energy efficiency,exergy efficiency and net power output is *** system provided the optimal performance once the evaporator pressure reached the maximum *** efficiencies:Energy and Exergy,and Net Power output of the system are 16.62%,64.08%and 2199 kW for the basic cycle and 20.72%,67.76%and 2326 kW respectively for the regenerative cycle.

关键词： Organic rankine cycle internal heat exchanger moderate-temperature geothermal source mixture of the fluid exergy

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Developing an explainable hybrid deep learning model in digital transformation: an empirical study

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Journal of Intelligent Manufacturing 2024年第4期35卷 1793-1810页

作者： Chiu, Ming-Chuan Chiang, Yu-Hsiang Chiu, Jing-Er Department of Industrial Engineering and Engineering Management National Tsing-Hua University Hsinchu Taiwan Department of Industrial Engineering and Management National Yunlin University of Science and Technology No.123 University Road Section 3 Yunlin Douliou64002 Taiwan

Automated inspection is an important component of digital transformation. However, most deep learning models that have been widely applied in automated inspection cannot objectively explain the results. Their resulting outcome, known as low interpretability, creates difficulties in finding the root cause of errors and improving the accuracy of the model. This research proposes an integrative method that combines a deep learning object detection model, a clustering algorithm, and a similarity algorithm to achieve an explainable automated detection process. An electronic embroidery case study demonstrates the explainable method, which can quickly be debugged to enhance accuracy. The results show an accuracy during testing of 97.58% with inspection time reduced by 25.93%. This proposed method resolves several challenges involved with automated inspection and digital transformation. Academically, the automated detection deep learning model proposed in this study has high accuracy along with good interpretability and debugability. In practice, this process can speed up the inspection process while saving human effort. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Object detection

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The improved strategy of BOA algorithm and its application in multi-threshold image segmentation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10471-10492页

作者： Wang, Lai-Wang Hung, Chen-Chih Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. © 2024 - IOS Press. All rights reserved.

关键词： Image segmentation

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Comparative Analysis of Telecommunications Infrastructure Resilience in BRICS Nations: An Integrated MCDM Approach

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IEEE Access 2024年 12卷 35081-35096页

作者： Wang, Chia-Nan Syu, Shao-Dong Nhieu, Nhat-Luong National Kaohsiung University of Science and Technology Department of Industrial Engineering and Management Kaohsiung807618 Taiwan

This study conducts a detailed evaluation of telecommunications infrastructure across the BRICS nations (Brazil, Russia, India, China, and South Africa) using an integrated Multiple Criteria Decision-Making (MCDM) approach. The primary objectives are to assess the resilience and performance of telecommunications infrastructure in these countries, identifying strengths and weaknesses through a comprehensive analytical lens. Employing a novel combination of distance-based CRITIC and modified OCRA methods within the MCDM framework, the study aims to provide a nuanced analysis of various indicators, offering a holistic view of the telecommunications landscape in these emerging economies. The research focuses on comparing and contrasting the telecommunications sectors of the BRICS nations, culminating in a ranking system where India appears as the top performer. This innovative methodological approach provides valuable insights for policymakers, contributing to strategic development and enhancement of telecommunications infrastructure in these countries. The study also outlines its limitations and recommends future research directions, including broadening the range of indicators and conducting longitudinal studies. © 2013 IEEE.

关键词： Decision making

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A Mathematical Model-Based Integrated Decision-Making Approach for Lithium Battery Manufacturers Evaluation

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IEEE Access 2024年 12卷 40037-40048页

作者： Wang, Chia-Nan Imperial, Kristofer Neal Castro Nhieu, Nhat-Luong Trieu, Nguyen Dang Minh National Kaohsiung University of Science and Technology Department of Industrial Engineering and Management Kaohsiung80778 Taiwan

Today, the transportation industry contributes significantly to greenhouse gas emissions-roughly 23% of worldwide emissions. Battery electric vehicles (BEVs) are a viable technical option since they have the ability to drastically cut emissions (e.g., up to 70% compared to gasoline automobiles). Lithium-ion batteries, the fundamental component of BEVs, are essential to the efficiency and performance of the vehicle. Nevertheless, it might be difficult to make the best decision given the wide range of battery producers. In order to close this gap, eleven of the top producers of lithium batteries (e.g., Tesla, Ford and Toyota) were assessed for their 2019-2021 performance. We evaluate battery performance using both the Ordinal Priority Approach (OPA) and Malmquist productivity index (MPI). According to the results, Ford, BMW, and Tesla had the greatest average MPI efficiency. Conversely, Toyota, Hyundai, and Mercedes-Benz secured the highest positions among lithium-battery manufacturers in the OPA rankings. Through the use of these methodologies, we aim to provide comparative rankings that will eventually help promote sustainable mobility by giving decision-makers, investors, consumers and other stakeholders an overview for well-informed battery selections. © 2013 IEEE.

关键词： Decision making

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An integrated framework of Industry 3.5 and an empirical study of simulation-based automated material handling system for semiconductor manufacturing

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International Journal of Logistics Research and Applications 2024年第2期27卷 309-325页

作者： Chien, Chen-Fu Hong, Tzu-Yen Department of Industrial Engineering & Engineering Management National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering & Management National Taipei University of Technology Taipei Taiwan

Industry 4.0 empowered by the advanced technologies has driven digital transformation of global manufacturing networks for smart production. However, most of the industries may not be ready for direct migration. To fill up the gaps, this study aims to develop an integrated framework of Industry 3.5 as a phased migration towards Industry 4.0 while employing methodologies and technologies to show systematic approach for the existing manufacturing ecosystem. Moreover, an empirical study of automated material handling system (AMHS) for semiconductor manufacturing is conducted as validation, in which congestion of AMHS can be reduced by integrating machine learning, dispatching strategies, and simulation modelling. The empirical study has shown the practical viability of the proposed integrated framework of Industry 3.5 under the existing system and manufacturing environment. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Automated Material Handling System (AMHS) Industry 3.5 Industry 4.0 manufacturing transformation semiconductor manufacturing

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