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作者： DeBerry, W. T AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT Air Force Research Laboratory Plans and Programs Directorate Wright-Patterson Air Force Base OH Air Force Institute of Technology Plans and Programs Directorate Wright-Patterson Air Force Base OH

This research presents the Wargaming Commodity Course of Action Automated Analysis Method (WCCAAM), a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the Military Decision Making Process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP's Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on the nodes and edges, where the commodities represent units, and the nodes represent blue bases and red threats, and then programmatically processes the MDMP steps to output the recommended COA. To demonstrate WCCAAM effectiveness, a wargame scenario compares COA outcomes within the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) and statistical analysis. The AFSIM results demonstrate a 71 objective completion improvement with the WCCAAM COA versus a human-generated COA. Statistical analysis reveals that over a 300 run test matrix, WCCAAM produces the optimal, minimal risk COA.

关键词： Air force Statistical analysis United states Algorithms engineering Simulations Air force research laboratories Commerce Commodities Governments Military research Information science Materials Military operations Operations research Standards Statistics

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Scenario Assessment of Neodymium and Cobalt Recycling in ASEAN Countries Based on a Dynamic Material Flow Analysis

SSRN

引用

SSRN 2023年

作者： Rustandi, Rendi Ahmad Morimoto, Shinichirou Borrirukwisitsak, Siriporn Khwamsawat, Kannika Chaipurimas, Supaluk Leewattananukul, Surachai Khaodhiar, Sutha Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Ibaraki Tsukuba305-8573 Japan Department of Energy and Environment National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8569 Japan Department of Environmental Science Faculty of Science Silpakorn University Nakhon Pathom73000 Thailand Center of Excellence on Hazardous Substance Management Chulalongkorn University Bangkok10330 Thailand

Tackling global greenhouse gas (GHG) emissions warrants a radical shift to low-carbon technologies, including batteries and permanent magnets. This shift will increase demand for critical raw materials (CRMs). This study offers dynamic material flow analysis (MFA) to address uncertainties associated with the sustainable supply of neodymium (Nd) and cobalt (Co): CRMs used in NdFeB magnets and lithium-ion batteries in ASEAN. Annual maximum and minimum scenario estimates were conducted for demand and waste of CRMs and CO2 emissions of recycled versus virgin materials. Based on the results, in 2050, 3,209 ton/y of Nd will be discarded, of which 25.3% will be accumulated as hibernating stock. Moreover, 33.79% of Co will be kept as hibernating stock. Recycling NdFeB magnets and batteries could reduce CO2 emissions up to 1,536 kton CO2-Eq/y and 313.5 kton CO2-Eq/y, respectively, by 2050. The proposed dynamic MFA is a useful foundation for the creation of effective global standards. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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ENHANCING SPEAKING STYLES IN CONVERSATIONAL TEXT-TO-SPEECH SYNTHESIS WITH GRAPH-BASED MULTI-MODAL CONTEXT MODELING

arXiv

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arXiv 2021年

作者： Li, Jingbei Meng, Yi Li, Chenyi Wu, Zhiyong Meng, Helen Weng, Chao Su, Dan Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong AI Lab Tencent Shenzhen China

Comparing with traditional text-to-speech (TTS) systems, conversational TTS systems are required to synthesize speeches with proper speaking style confirming to the conversational context. However, state-of-the-art context modeling methods in conversational TTS only model the textual information in context with a recurrent neural network (RNN). Such methods have limited ability in modeling the inter-speaker influence in conversations, and also neglect the speaking styles and the intra-speaker inertia inside each speaker. Inspired by DialogueGCN and its superiority in modeling such conversational influences than RNN based approaches, we propose a graph-based multi-modal context modeling method and adopt it to conversational TTS to enhance the speaking styles of synthesized speeches. Both the textual and speaking style information in the context are extracted and processed by DialogueGCN to model the inter- and intra-speaker influence in conversations. The outputs of DialogueGCN are then summarized by attention mechanism, and converted to the enhanced speaking style for current utterance. An English conversation corpus is collected and annotated for our research and released to public. Experiment results on this corpus demonstrate the effectiveness of our proposed approach, which outperforms the state-of-the-art context modeling method in conversational TTS in both MOS and ABX preference rate. © 2021, CC BY.

关键词： Recurrent neural networks

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Creep behavior of carbon fiber reinforced plastic sheet anchor embedded in concrete with epoxy resin

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Zairyo/Journal of the Society of Materials Science, Japan 2021年第4期70卷 293-299页

作者： Yamashita, Ryo Hiroi, Yukio Arazoe, Masaki Yamamoto, Takashi Miyagawa, Toyoaki Engineering Dept. PC Division IHI Construction Service Co. Ltd Koto-ku Tokyo135-0016 Japan Research & Development Dept. IHI Infrastructure Systems Co. Ltd Minato-ku Tokyo108-0023 Japan Infrastructural Use Dept. Composite Division Nippon Steel Chemical & Material Co. Ltd. Chuo-ku Tokyo103-0027 Japan Dept. of Civil and Earth Resource Engineering Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto615-8540 Japan Infra-System Management Research Unit Kyoto University Nishikyo-ku Kyoto615-8540 Japan

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A Novel Approach for Unsupervised Sensor Error Classification

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Procedia CIRP 2024年 130卷 206-213页

作者： Samiha Durnagöz Tobias Eberhardt Felix Fuchs Peter Reimann Thomas Ackermann Maik Fiedler Mathias Mayer Marco F. Huber Alexander Verl Graduate School of Excellence advanced Manufacturing Engineering GSaME University of Stuttgart Nobelstraße 12 70569 Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Allmandring 35 70569 Stuttgart Germany AUDI AG NSU-Straße 1 74172 Neckarsulm Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 70569 Stuttgart Germany Institute for Control Engineering of Machine Tools and Manufacturing Units ISW University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 72293 Glatten Germany Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 70569 Stuttgart Germany

The precise detection and classification of sensor errors is crucial for the optimal operation of smart manufacturing systems. Sensor errors, which can manifest in various forms, such as drifts or constant values, significantly challenge data integrity. This work introduces a novel approach that combines unsupervised learning and system simulations to detect and classify relevant sensor errors. Our approach avoids extensive manual labeling and uses only error-free simulated time series data to create initial embeddings of sensor data. This establishes a baseline for normal sensor behavior. To facilitate a precise identification of error types through a classifier, simulated sensor errors are introduced to create context-aware embeddings. We further analyze and compare conventional to advanced embedding methods such as TS2Vec. We apply our approach to a synthetic dataset obtained from a vacuum handling system and accurately identify and classify relevant sensor error types. Our work contributes to advancing operational efficiency and reliability in automated manufacturing processes within the Industry 4.0 framework by improving the detection capabilities for sensor errors.

关键词： Unsupervised learning Sensor errors TS2Vec System simulation

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IIOT Within The Architecture Of The Manufacturing Company 20

IIOT Within The Architecture Of The Manufacturing Company

引用

Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service

作者： Anastasia Levina Sofia Kalyazina Alena Ershova Peter Cornelis Schuur Graduate school of Business and Management Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia Industrial Engineering and Business Information Systems University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781450388900

The introduction of the Internet of Things (IoT) in industry can increase the level of production management, increase the speed of managerial decision-making, and provide higher quality products. The article discusses the use of the Industrial Internet of Things (IIoT) in the architecture of a manufacturing enterprise. The technical solutions currently used and the main effects of the introduction of the Industrial Internet of Things are analyzed.

关键词： Manufacturing Internet of Things Industrial Internet of Things ERP

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Cloud Application For Sheet Materials Cutting Optimization 20

Cloud Application For Sheet Materials Cutting Optimization

引用

Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service

作者： Igor Ilin Aleksander Kubarskii Peter Cornelis Schuur Aleksander Lepekhin Alissa Dubgorn Graduate school of Business and Management Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia Industrial Engineering and Business Information Systems University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781450388900

The purpose of this study is to provide a working SaaS application that can be used for sheet materials cutting optimization using heuristics. For the research, literature and open sources in the internet were analyzed. The list of the most common and well-known applications for cutting optimization was formed. The cloud and on premises applications were briefly compared and cloud solutions advantages were highlighted. Few algorithms were chosen and implemented using JavaScript on server-side and deployed, using Amazon Web Services. However, there is a great room for improvement for methods used and implementation of algorithm for automatic cutting method choosing.

关键词： Cloud computing Cutting optimization SaaS

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Determining Factors Affecting Perceived Quality among Shoe Manufacturing Workers towards Shoe Quality: A Structural Equation Modeling Approach

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Chuenyindee, Thanatorn Torres, Robimarl B. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines School of Graduate Studies Mapúa University Manila 1002 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia

The shoe industry has been continuously growing in recent years. The goal of this study was to identify factors affecting perceived shoe quality among shoe manufacturing workers. A sample of 350 shoe manufacturing workers participated in answering a survey that was distributed using a purposive sampling approach. Structural Equation Modeling (SEM) indicated that quality training was the most significant factor on perceived shoe quality, indicating that quality training should be prioritized and included in company programs to sustain quality products. Interestingly, perceived tooling/machinery conditions, teamwork and cooperation, the operator’s technical skills, and the operator’s quality mindset, also had significant effects on perceived shoe quality. This study is one of the first studies to explore perceived shoe quality among shoe manufacturing workers. The results may convey information for future research on perceived product quality, particularly for the shoe industry. Finally, our framework can be utilized for the enhancement of perceived product quality in shoe manufacturing industries worldwide. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： perceived quality shoe quality Structural Equation Modeling

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Photocatalytic degradation of aqueous Methyl Orange using nitrogen-doped TiO_2 photocatalyst prepared by novel method of ultraviolet-assisted thermal synthesis

引用

Journal of Environmental Sciences 2018年第4期30卷 81-93页

作者： Mohsen Nasirian Mehrab Mehrvar Graduate Programs in Environmental Applied Science and Management Ryerson University Department of Chemical Engineering Ryerson University

A nitrogen-doped titanium dioxide composite photocatalyst（N–TiO2） with heterojunction structures is synthesized by three different approaches： a novel UV-assisted thermal synthesis, annealing, and microwave technique. Photocatalytic activities of synthesized photocatalysts are evaluated by the degradation of Methyl Orange under ultraviolet light types A（UV-A）, B（UV-B）, and C（UV-C）, visible light, and direct sunlight irradiation. Results show that by using N–TiO_2 photocatalyst prepared by the UV-assisted thermal synthesis and annealing, the degradation increases by 16.5% and 20.4%, respectively, compared to that by bare TiO2. The best results are obtained at a nitrogen to TiO2 mass ratio of 0.15（N：TiO2）. The enhancement of the photocatalytic activity observed in the visible range is mainly attributed to the increasing separation rate of photogenerated charge carriers. The novel UV-assisted thermal synthesis has produced encouraging results as a preparation method for the nitrogen-doped TiO2 photocatalyst; thus, further studies are recommended for process optimization, immobilization, and scale-up to evaluate its applicability in wastewater treatment.

关键词： Ultraviolet-assisted thermal synthesis N–TiO2 Photocatalysis Advanced oxidation processes

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