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Enhancing the effectiveness of output projection in wafer fabrication using an Industry 4.0 and XAI approach

International Journal of Advanced Manufacturing Technology 2024年第1-2期134卷 113-125页

作者： Chen, Tin-Chih Toly Wang, Yu-Cheng Lin, Chi-Wei Department of Industrial Engineering and Management National Yang Ming Chiao Tung University University Road Hsinchu1001 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung City Taiwan Department of Industrial Engineering and Systems Management Feng Chia University Taichung Taiwan

Industry 4.0 and deep learning methods have been widely used for output projection in wafer fabrication. Although such applications appeared to be effective, they were difficult to understand and/or improve. Therefore, in this study, an Industry 4.0 and explainable artificial intelligence (XAI) approach is proposed to enhance the interpretability of a deep learning application for output projection. First, a deep neural network (DNN) is constructed to estimate the output times of jobs based on real-time job, machine, and factory information collected using Industry 4.0 technologies. Then, the DNN is approximated by a random forest (RF) to generate interpretable decision rules. SHAP analysis is also performed to generate local explanations. The proposed methodology has been applied to a real case from a wafer fab. Through the synergy of Industry 4.0 and deep learning, the proposed methodology achieved a satisfactory output projection performance. The MAE was only about two wafer lots. Additionally, user communication was also facilitated since the output projection mechanism using deep learning has been explained using XAI techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Industry 4.0

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Evaluation of Process and Economic Feasibility of Implementing a Topping Cycle Cogeneration

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Energy engineering 2023年第2期120卷 345-365页

作者： Unique Karki Bhaskaran Gopalakrishnan Department of Industrial&Management Systems Engineering West Virginia UniversityMorgantown26506USA

Industrial applications that require steam for their end-use generally utilize steam boilers that are typically oversized,citing operations ***,gas turbine-based power plants corroborate a gas turbine system that may eventually relieve the usable exhaust into the *** study explores the economic and technical feasibility of a topping cycle combined heat and power(CHP)*** does so by leveraging a partially loaded boiler or gas turbine by increasing its unused load to generate steam and heat for subsequent *** this end,a decision support tool(COGENTEC)was developed,which emulates a given facility’s boiler or gas-turbine system,and its operational parameters with the application of steam *** tool provides necessary insights into the most appropriate parameters that enable a CHP system to be technically and economically *** on input variables such as boiler-rated capacity,steam pressure,steam temperature,and existing boiler load,among others,COGENTEC designs a topping cycle CHP system to inform a user whether this system is feasible in their facility or *** applicable,the tool assists the user to realize the point of break-even(fuel cost incurred and cost savings)at the desired steam flow *** also conducts sensitivity analyses between energy usage,cost savings,and payback on the investment of the operating parameters to understand the relationship between relevant *** utilizing parameters from a pulp and paper manufacturing facility,the research determines that the fuel cost,electricity cost,and steam flow rate are the most important parameters for the feasibility of the system with a desirable payback on the investment.

关键词： Cogeneration topping cycle CHP industrial decarbonization energy efficiency

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Improving lung cancer detection via MobileNetV2 and stacked-GRU with explainable AI

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1189-1196页

作者： Bagheri Tofighi, Abolfazl Ahmadi, Abbas Mosadegh, Hadi Department of Industrial Engineering and Management Systems Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

Accurate and timely detection of lung cancer is crucial for effective treatment planning. This study introduces MobileNetV2-SGRU, a novel transfer learning-based predictor for lung cancer classification. It utilizes MobileNetV2 to automatically extract meaningful features from lung CT images. These extracted features processed through stacked gated recurrent unit layers, enabling the model to capture the sequential and temporal information present in the images. Additionally, the model incorporates Grad-CAM, an explainable artificial intelligence technique, to enhance interpretability and transparency of the model. An evaluation of MobileNetV2-SGRU on the IQ-OTH/NCCD dataset demonstrated its outstanding performance in lung cancer classification. The model achieves impressive accuracy, precision, recall, and F1-score of 96.83%, 96.78%, 96.83%, and 96.78%, respectively, demonstrating its superior predictive capabilities compared to existing methods. Furthermore, the results indicate reduced computation time and enhanced accuracy in diagnosing lung cancer. With advanced performance metrics and the incorporation of Grad-CAM visualization for interpretability, the model offers valuable insights for clinicians in validating and understanding its predictions, ultimately contributing to more effective treatment planning. © Bharati Vidyapeeth's Institute of Computer Applications and management 2024.

关键词： Explainable AI Grad-CAM Lung cancer Medical imaging MobileNetV2-SGRU Transfer learning

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Deep learning for Bitcoin price direction prediction:models and trading strategies empirically compared

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Financial Innovation 2024年第1期10卷 598-623页

作者： Oluwadamilare Omole David Enke Laboratory for Investment and Financial Engineering Department of Engineering Management and Systems EngineeringMissouri University of Science and Technology203 Engineering Management600 W.14th StreetRollaMO65409-0370USA Laboratory for Investment and Financial Engineering Department of Engineering Management and Systems EngineeringMissouri University of Science and Technology221 Engineering Management600 W.14th StreetRollaMO65409-0370USA

This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these *** study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain ***-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature *** indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through *** long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction *** study provides evidence of the potential profitability of predictive models in Bitcoin trading.

关键词： Backtesting Bitcoin Cryptocurrency Deep learning Feature selection On-chain data

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

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

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 141-156页

作者： Luigi D’Alfonso Francesco Giannini Giuseppe Franzè Giuseppe Fedele Francesco Pupo Giancarlo Fortino IEEE the Department of Computer Engineering ModelingElectronics and SystemsUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy the Department of Mechanical Engineering Energy Engineering and ManagementUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the a

关键词： Distributed model predictive control distributed reinforcement learning routing decisions urban road networks

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Optimizing life cycle sustainability based on municipal solid waste streams and treatment potentials

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Environment systems and Decisions 2024年第4期44卷 887-905页

作者： Aleisa, Esra Al-Jarallah, Rawa Industrial and Management Systems Engineering Department College of Engineering and Petroleum Kuwait University Safat 13060 Kuwait Civil Engineering Department College of Engineering and Petroleum Kuwait University Safat 13060 Kuwait

This study applies multicriteria mathematical modeling to optimize municipal solid waste (MSW) management across a three bottom-line (BL) framework: environmental, social and economic. The interrelationships and the ripple secondary impacts among the three BLs are examined systematically using an augmented simplex lattice mixture (ASLM) method. Detailed waste and waste treatments, including pyrolysis (PY), anaerobic digestion (AD), animal feed (AF), composting (CP), recycling (RE), incineration (INC) and landfilling (LF), are constructed based on waste stream fractions and treatment allowable limits. The environmental BL is assessed using life cycle assessment (LCA). The economic BL is determined by calculating the per ton capital recovery with return, and the social bottom line is assessed using the analytic hierarchy process (AHP). The three bottom lines are optimized through a mathematical model using CPLEX solver. The results indicate that CP abates 973 kgCO₂eq/t compared to 61.8 kgCO₂eq/t from AD and 28.3 kgCO₂eq/t from AF. CP generates $23.5/t despite its low social desirability. Plastic waste PY credits ethylene by 364 kg/t, however, it costs $226.7/t despite the subtraction of credited energy and recovered byproducts. Metal RE carbon and water footprints are −236 kgCO₂eq/t and 268 m³/t, respectively. AF is the second-best economic scenario after metal RE as it generates up to $122.6/t in profit. AF production scores are second highest within the social BL after plastic PY;however, the supporting legislation sub-indicator is low. The ASLM support policy that assigns 66% to the environmental BL and 16.7% to the economic and social BL to achieve carbon neutrality within the MSW sector. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Economic assessment Kuwait Life cycle assessment (LCA) Mathematical modeling Municipal solid waste (MSW) Sensitivity Social assessment

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SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation

SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-A...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Minda Zong, Licheng Wang, Hongru Zhou, Jingyan Li, Jingjing Gao, Yichen Wong, Kam-Fai Li, Yu King, Irwin Department of Computer Science and Engineering Hong Kong Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761681

Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented approaches face challenges in addressing diverse queries and documents, particularly for medical knowledge queries, resulting in sub-optimal performance. To address these limitations, we propose a novel plug- and-play LLM-based retrieval method called Self-Rewarding Tree Search (SeRTS) based on Monte Carlo Tree Search (MCTS) and a self-rewarding paradigm. By combining the reasoning capabilities of LLMs with the effectiveness of tree search, SeRTS boosts the zero-shot performance of retrieving high-quality and informative results for RAG. We further enhance retrieval performance by fine-tuning LLMs with Proximal Policy Optimization (PPO) objectives using the trajectories collected by SeRTS as feedback. Controlled experiments using the BioASQ-QA dataset with GPT-3.5-Turbo and LLama2-7b demonstrate that our method significantly improves the performance of the BM25 retriever and surpasses the strong baseline of self-reflection in both efficiency and scalability. Moreover, SeRTS generates higher-quality feedback for PPO training than self-reflection. Our proposed method effectively adapts LLMs to document retrieval tasks, enhancing their ability to retrieve highly relevant documents for RAG in the context of medical knowledge queries. This work presents a significant step forward in leveraging LLMs for accurate and comprehensive biomedical question answering. © 2024 Association for Computational Linguistics.

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A Study on Customer Utility Based on Differences in Stage Configuration and Ticket Sales Methods

A Study on Customer Utility Based on Differences in Stage Co...

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2024 IEEE International Conference on Industrial engineering and engineering management, IEEM 2024

作者： Mototaka, Katsuki Hasuike, Takashi Department of Industrial and Management Systems Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350386097

Stage configuration and ticket sales methods are crucial elements that influence the customer experience of live entertainment. This study uses social simulations considering both stage configuration and ticket sales methods for live entertainment and assesses their impact on customer utility. Five stage configurations are examined (rectangular, convex, two-stage, three-stage, and circular configurations), with each seat’s value defined and calculated as the seat utility value. Based on these values, simulations are conducted for three ticket sales methods (uniform pricing, multi-seat pricing, and auction sales). The results show that the circular stage configuration is the optimal layout for maximizing customer utility across all three sales methods, while the frequently used rectangular stage configuration has the lowest provided value. Additionally, the combination of the convex stage configuration with ticket sales considering agents’ winning history and the two-stage configuration with auction sales are both effective and suitable. © 2024 IEEE.

关键词： Sales

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Tube-based coalitional model predictive control for tracking 63

Tube-based coalitional model predictive control for tracking

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sanchez-Amores, A. Ferramosca, A. Maestre, J.M. Camacho, E.F. University of Seville Department of Systems and Automation Engineering Spain University of Bergamo Information and Production Engineering Department of Management Italy

ISBN: (纸本)9798350316339

This work explores a coalitional control approach for input-coupled multi-agent systems to robustly track changing setpoints. In this partially cooperative framework, agents can share a public portion of their input with neighboring agents while keeping the remainder private. Negotiations over the bounds defining the public and private input sets can be triggered on demand. Additionally, coupling disturbances are addressed using a tube-based approach, guiding the system towards desired target points. The effectiveness of the proposed approach is demonstrated through its application to a simulated eight-input coupled tank benchmark. © 2024 IEEE.

关键词： Multi agent systems

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