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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： ZhiCong Hong Ting Qu Yongheng Zhang Kai Zhang Mingxing Li George Q. Huang School of Management Jinan University Guangzhou China Base of Science and Technology for GBA Smart Logistics Guangdong International Cooperation Zhuhai China The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Batch processing of small-batch customer orders can effectively reduce operating costs. Manufacturers need third-party logistics (3PLs) to provide services in the production process to quickly deliver finished products to customers. However, the two have very different batch processing of cus-tomer orders: manufacturers merge customer orders with identical demands, while 3PLs merge customer orders in prox-imate regions. Consequently, when both manufacturers and 3PLs are involved in decision-making, the efficiency and economy of production-logistics operations are severely compromised under batch processing. Therefore, the key issue ad-dressed in this paper is how to achieve production-delivery synchronization under batch processing mode, as well as synchronized control in a highly dynamic environment. To ad-dress this issue, a digital twin-based production-delivery synchronized decision framework is proposed, centered on the concept of “cloud-edge-terminal”. A synchronization control mechanism based on digital twin is designed. A collaborative optimization-based synchronized mathematical model and optimization algorithm are developed. Finally, a comparison between two synchronization modes, demonstrates that the proposed solution effectively achieves optimal production-logistics operational states and reduces total costs.

关键词： Costs Heuristic algorithms Batch production systems Interference Real-time systems Mathematical models Digital twins Synchronization Optimization Logistics

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DISENTANGLING CONTENT AND FINE-GRAINED PROSODY INFORMATION VIA HYBRID ASR BOTTLENECK FEATURES FOR VOICE CONVERSION

arXiv

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arXiv 2022年

作者： Zhao, Xintao Liu, Feng Song, Changhe Wu, Zhiyong Kang, Shiyin Tuo, Deyi Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Huya Inc Guangzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong

Non-parallel data voice conversion (VC) have achieved considerable breakthroughs recently through introducing bottleneck features (BNFs) extracted by the automatic speech recognition(ASR) model. However, selection of BNFs have a significant impact on VC result. For example, when extracting BNFs from ASR trained with Cross Entropy loss (CE-BNFs) and feeding into neural network to train a VC system, the timbre similarity of converted speech is significantly degraded. If BNFs are extracted from ASR trained using Connectionist Temporal Classification loss (CTC-BNFs), the naturalness of the converted speech may decrease. This phenomenon is caused by the difference of information contained in BNFs. In this paper, we proposed an any-to-one VC method using hybrid bottleneck features extracted from CTC-BNFs and CE-BNFs to complement each other advantages. Gradient reversal layer and instance normalization were used to extract prosody information from CE-BNFs and content information from CTC-BNFs. Auto-regressive decoder and Hifi-GAN vocoder were used to generate high-quality waveform. Experimental results show that our proposed method achieves higher similarity, naturalness, quality than baseline method and reveals the differences between the information contained in CE-BNFs and CTC-BNFs as well as the influence they have on the converted speech. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Dramatic reduction in thermal conductivity by high-density dislocations in SrTiO3

arXiv

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arXiv 2025年

作者： Ding, Jinxue Zhang, Jiawen Dong, Jinfeng Higuchi, Kimitaka Nakamura, Atsutomo Lu, Wenjun Sun, Bo Fang, Xufei Institute for Applied Materials Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Institute of Materials and Systems for Sustainability Nagoya University Aichi Nagoya464-8601 Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka560−8531 Japan Tsinghua SIGS and Guangdong Provincial Key Laboratory of Thermal Management Engineering & Materials Tsinghua University Shenzhen518055 China

Decreasing thermal conductivity is important for designing efficient thermoelectric devices. Traditional engineering strategies have focused on point defects and interface design. Recently, dislocations as line defects have emerged as a new tool for regulating thermal conductivity. In ceramics-based thermoelectric materials, the key challenge lies in achieving sufficiently high-density dislocations to effectively scatter phonons, as the typical dislocation density in ceramics after bulk deformation is constrained to ~1012 m−2. In this work, we adopted the mechanical imprinting method and achieved a dislocation density of ~1015 m−2 in single-crystal SrTiO3, which is known for its room-temperature plasticity and acts as a promising material for thermoelectric applications. Using the time-domain thermoreflectance (TDTR) method, we measured a ~50% reduction in the thermal conductivity over a broad temperature range (80−400 K) with the engineered high-density dislocations. These results suggest that tuning dislocations could offer a new path to minimizing thermal conductivity for engineering thermoelectric materials. © 2025, CC BY.

关键词： Single crystals

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Multi-Faceted Ergonomics Assessment in Medical Device Manufacturing: A Case Study

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2021年第1期10卷 247 - 250页

作者： Bukola Saliu Bernadette McCrory Industrial and Management Systems Engineering Department of Mechanical and Industrial Engineering Montana State University Bozeman MT

Background: According to the National Safety Council and the Bureau of Labor Statistics there were over 4,000 preventable injury related deaths. The Occupational Health and Safety Administration emphasizes that safety cultures should consist of shared beliefs, practices, and attitudes that exist at an establishment. The purpose of this industry-sponsored research project was to assist a medical manufacturing facility with safety program OSHA compliance. Method: Job Safety Analysis (JSA), also ca lled job hazard analysis, and facility inspections were used to identify hazards focused on the worker, task, tools, and the work environment. Results: Areas with high hazard scores according to the JSA were a ssigned to supervisors and the research team to provide immediate process and environment changes. Conclusion: JSA is a great tool to identify safety hazards on a job site though job functions. This will prevent injuries and a llow even small companies to remain compliant with federal regulations. Creating an atmosphere from top to bottom with a culture of safety responsibility andownership can promote a safe and productive environment.

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Detecting defects of railway tracks by using computer vision methodology

Detecting defects of railway tracks by using computer vision...

引用

IISE Annual Conference and Expo 2021

作者： Ravishankar, Premkumar Tokgoz, Berna Eren Zhang, Xulong Zhang, Jing Hwang, Seokyon Department of Industrial and Systems Engineering Lamar University BeaumontTX United States Department of Computer Science Lamar University BeaumontTX United States Construction Management Program Lamar University BeaumontTX United States

ISBN: (纸本)9781713838470

Maintaining railway tracks in healthy conditions is critical to ensuring the safe operation of railroad transportation. According to the Federal Railroad Administration (FRA), nearly 23% of train accidents that occurred between 2015 to 2020 were due to the defects of the tracks. In an effort to prevent such accidents, railway companies regularly inspect tracks to detect and fix defects of track systems. This research proposes an innovative method for conducting railroad track inspection to enhance the maintenance operation. The proposed approach aims to overcome the existing inspection practices based on human observation that is costly, time-consuming, and error prone. To this end, the proposed method utilizes computer vision methodologies to achieve high-level automation and accuracy of defect detection through the analysis of digital images. The outcome of this study is envisioned to help railway companies perform predictive maintenance more effectively and, thereby, reduce the risk of train accidents and increase the resiliency of their assets. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Railroad tracks

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Leverage from Blockchain in Commodity Exchange: Asset-Backed Token with Ethereum Blockchain Network and Smart Contract 4th

Leverage from Blockchain in Commodity Exchange: Asset-Backed...

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4th International Conference on Smart Trends for Computing and Communications, SmartCom 2020

作者： Richard Heryadi, Yaya Lukas Trisetyarso, Agung Information Systems Department School of Information Systems Bina Nusantara University Jakarta11840 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara Univesity Jakarta11840 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (纸本)9789811552236

Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange the token across the network participants. This utility from the token drives many blockchain enthusiasts to create decentralized applications. ERC-20 token could be used to collect project funds, decision voting, or even can be used to provide investment platforms for people that want to hedge the volatility of the currency with buying an asset that is back to back with the crypto-token. This research shows the main characteristics of asset-backed token and its implementation on a complete end-to-end "digitalization assets system" (assets buy and assets redemption). The result shows that asset-backed token needs support from government and other legalizing parties to verify the legitimacy of the asset that backing the token itself. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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3-D work simulation oriented to digital transformation for shipyard: Applying to sub-assembly process

3-D work simulation oriented to digital transformation for s...

引用

SNAME Maritime Convention 2020, SMC 2020

作者： Shinoda, Takeshi Tanaka, Takashi Kishigami, Kento Fujisawa, Shunsuke Department of Marine Engineering Systems Faculty of Engineering Kyushu University Japan Department of Urban and Environmental Engineering Graduate School of Engineering Kyushu University Japan

Most processes in many shipyards involve a large amount of work in progress at every work stage. Efficient construction, such as shortening work periods on production systems and reducing stocks of work in progress, therefore, is essential in improving the earnings of a shipyard. Digital transformation (DX) can be considered to ensure these improvements. In this research, as a first step of applying DX, we investigate a more efficient construction system based on tact time control management to standardize units in the sub-assembly process and a conveyor line production system using a three-dimensional work simulation model. Copyright © 2020 Society of Naval Architects and Marine Engineers (SNAME).

关键词： Shipyards

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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A Dispatching Method for Integrated Energy System Based on Dynamic Time-interval of Model Predictive Control

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Journal of Modern Power systems and Clean Energy 2020年第5期8卷 841-852页

作者： Xun Dou Jun Wang Zhen Wang Lijuan Li Linquan Bai Shuhui Ren Min Gao IEEE College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjingChina Department of Systems Engineering and Engineering Management University of North Carolina at CharlotteCharlotteUSA Falcon Computing Solutions 10880 Wilshire Blvd Suite 1132Los AngelesCA 90024USA

In integrated energy systems(IESs),traditional fixed time-interval dispatching scheme is unable to adapt to the need of dynamic properties of the transient network,demand response characteristics,dispatching time scales in energy subsystems and renewable power *** scheme may easily result in uneconomic source-grid-load-storage operations in *** this paper,we propose a dispatching method for IES based on dynamic time-interval of model predictive control(MPC).We firstly build models for energy sub-systems and multi-energy loads in the power-gas-heat ***,we develop an innovative optimization method leveraging trajectory deviation control,energy control,and cost control frameworks in MPC to handle the requirements and constraints over the timeinterval of ***,a dynamic programming algorithm is introduced to efficiently solve the proposed *** and simulation results prove the effectiveness of the method.

关键词： Dispatching scheme dynamic time-interval integrated energy system model predictive control

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Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing systems

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IFAC-PapersOnLine 2022年第2期55卷 548-553页

作者： Nicolas Nebelung Mario D.S. de Oliveira Santos Sofia T. Helena Athon F.C.S. de Moura Leite Matheus B. Canciglieri Anderson L. Szejka Control and Automation Engineering Department Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Junior Scientific Initiation Program at the Nossa Senhora de Medianeira School and Pontifical Catholic University of Paraná Curitiba – Paraná Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

关键词： Industry 4.0 Smart System Artificial Intelligence Machine Learning Ontology Machine Failure Forecast

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