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2023 7th International Conference on Mechanical, Aeronautical and Automotive engineering, ICMAA 2023

作者： Shiau, Y.R. Fang, J. Huang, Y.C. Department of Industrial Engineering and Systems Management Feng Chia University Taichung407 Taiwan Department of Aerospace and Systems Engineering Feng Chia University Taichung407 Taiwan Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Taichung407 Taiwan

Several types of multirotor unmanned aerial vehicles (UAVs) are widely used in various industrial activities. UAVs with electric ducted fans (EDFs) have been developed to mitigate the risks associated with rotary blades, which are installed on the exterior structure of conventional UAVs and exposed to the ambient environment. Particularly, vertical EDFs have become a focus in the field of UAV design because they require less space for the flow channel compared with horizontal EDFs. In this study, a vertical EDF UAV model was developed. Structural and flow field analysis was performed in Ansys software to determine the optimal design configuration. The results confirmed that the designed model facilitated smooth air flow with favorable inlet and outlet velocity. The vehicle structure required minor adjustment to reduce the frequency of occurrence of vortices. Overall, this study made two research contributions. First, an EDF UAV model was developed to mitigate the risk of rotary blades being exposed to the ambient environment. Second, a vertical EDF system was proposed to reduce the flow channel space and maintain the outlet velocity, thereby improving the takeoff, landing, and thrust performance of UAVs. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Channel flow

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QoS-Aware Decentralized Trustworthy Forensic Evidence management Framework Using the Internet of Vehicle Things (IoVT) 3rd

QoS-Aware Decentralized Trustworthy Forensic Evidence Manage...

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3rd International Conference on Advanced Computing and Applications, ICACA 2024

作者： Das, Puja Singh, Moutushi Roy, Deepsubhra Guha Department of Computer Science and Business Systems KPRIET Uthupalayam641407 India Department of Information Technology Institute of Engineering and Management IEM Centre of Excellence for InnovAI University of Engineering and Management Kolkata India Institute of Engineering and Management IEM Centre of Excellence for Cloud Computing and IoT University of Engineering and Management Kolkata700091 India

ISBN: (纸本)9789819747986

Vehicles are becoming physical computer systems that connect with other cars and collect data from numerous pieces of equipment. These improvements contribute to developing smart and connected automobiles that will supply valuable data to owners, producers, insurance firms, and repair network operators for various uses. In post-accident circumstances, especially for auto vehicles (self-driving cars), using transportation data might help identify the guilty party. Verification, accessibility, secrecy, and dependability are all privacy needs for a vehicular ad hoc network. These criteria are linked to critical attacks that make operating a network difficult. However, to access different types of information in automobiles, we proposed a permissioned blockchain framework to organize the acquired transportation information among the numerous parts concerned. We incorporate the public key exchange protocol on the vehicle into the projected blockchain-based forensic system to ensure participation identification and anonymity. The proposed system offers a deep learning-based predictive incident modeling with blockchain for the incident data stored in the blockchain. Moreover, several methods have been combined in forensic management with blockchain to establish an auto-evaluation scheme. As a result, we extensively evaluated, analyzed, and discussed the works and contributions of skilled researchers to uncover new avenues in an ongoing and prospective study. The main goal of this research might be to explore the future potential for smart and connected automobiles using blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicular ad hoc networks

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An Optimization Model of a Retailer and a Manufacturer in a Green Supply Chain 3rd

An Optimization Model of a Retailer and a Manufacturer in a ...

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3rd International Conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian Conference of management Science and Applications, ACMSA 2023

作者： Sakurai, Wataru Nakade, Koichi Nagoya Institute of Technology Systems Management and Engineering Program Gokiso-cho Showa-ku Nagoya-shi Aichi466-8555 Japan

ISBN: (纸本)9789819701933

In recent years, the development of a green supply chain model that takes sustainability into consideration has become an urgent issue in corporate management and policy operation. For example, in the fashion industry, the proper circulation of not only used items purchased by consumers, but also unsold items generated in retail stores is one of the most important issues. In this study, we examine a supply chain model for the appropriate circulation of unsold new products from retail outlets. Specifically, we considered a supply chain model in which a retailer’s inventory is divided into two states under stochastic demand fluctuations: new and old items, and the unsold old items are collected and reproduced by a manufacturer. By formulating this model as a Markov decision process, the optimal decisions regarding the retailer’s ordering policy and the retail price and the manufacturer’s wholesale price are obtained. Optimal investment and appropriate institutional design to reduce CO2 emissions generated in a supply chain are also considered. Specifically, we examine the decision of green investment in manufacturing technology to reduce CO2 emissions when a manufacturer produce items. And we examine the design of a carbon tax system to control CO2 emissions. Sensitivity analysis on the carbon tax system shows that raising the carbon tax rate increases the optimal retail price, the optimal wholesale price, and the optimal green investment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Investments

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engineering Graduate Students’ Attitudes Towards Social Justice 67th

Engineering Graduate Students’ Attitudes Towards Social Jus...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： McCallum, Mariah Barlow, Hailey Ma, Jiahui Godwin, Ian McCrory, Bernadette Industrial and Management Systems Engineering Department of Mechanical and Industrial Engineering Montana State University BozemanMT United States

In post-secondary education engineers are not formally introduced to socially just engineering. This education focuses on technical expertise, often overlooking the social impacts of engineering outputs. Using essay prompts graduate school applicants’ views were assessed on social justice. Odds ratios and Chi-Square analyses were used to associate views on social justice and self-perceived programmatic competencies. While applicants were aware of social justice issues, their main reason for applying was to further career goals. Nearly forty percent of essays suggested that engineers are responsible for optimizing systems and mentioned the department’s inclusive design competencies 33 times. Even though most applicants reported interest in management systems, roughly 31.1% of applications advocated for systems thinking to be the primary social justice mechanism. When prompted, 96.2% of respondents reported that engineers should have a professional and ethical responsibility. Increasing exposure to socially just engineering is critical in producing holistically minded engineers. © 2023 Human Factors and Ergonomics Society.

关键词： higher education social responsibility socially just engineering

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An improved real time detection of data poisoning attacks in deep learning vision systems

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Discover Artificial Intelligence 2022年第1期2卷 1-17页

作者： Raghavan, Vijay Mazzuchi, Thomas Sarkani, Shahram Department of Engineering Management and Systems Engineering The George Washington University 2121 I St NW WashingtonDC20052 United States

The practice of using deep learning methods in safety critical vision systems such as autonomous driving has come a long way. As vision systems supported by deep learning methods become ubiquitous, the possible security threats faced by these systems have come into greater focus. As it is with any artificial intelligence system, these deep neural vision networks are first trained on a data set of interest, once they start performing well, they are deployed to a real-world environment. In the training stage, deep learning systems are susceptible to data poisoning attacks. While deep neural networks have proved to be versatile in solving a host of challenges. These systems have complex data ecosystems especially in computer vision. In practice, the security threats when training these systems are often ignored while deploying these models in the real world. However, these threats pose significant risks to the overall reliability of the system. In this paper, we present the fundamentals of data poisoning attacks when training deep learning vision systems and discuss countermeasures against these types of attacks. In addition, we simulate the risk posed by a real-world data poisoning attack on a deep learning vision system and present a novel algorithm MOVCE—Model verification with Convolutional Neural Network and Word Embeddings which provides an effective countermeasure for maintaining the reliability of the system. The countermeasure described in this paper can be used on a wide variety of use cases where the risks posed by poisoning the training data are similar. © The Author(s) 2022.

关键词： Learning systems

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System of System Architecture for Cybersecurity of Power systems Operations 11

System of System Architecture for Cybersecurity of Power Sys...

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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Ur Rahman, Ehab Ashiku, Lirim Dagli, Cihan Bo, Rui Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO United States Missouri Universtity of Science and Technology Engineering Management and Systems Engineering RollaMO United States

ISBN: (纸本)9798350362541

Smart grids are at risk of cyber-attacks due to more connected devices being introduced. Different kinds of attacks may incur different consequences in the operation of smart grids. Attackers have identified different vulnerable points in smart grid operations and have developed malicious stealthy attacks to disrupt the operation of physical systems. In this paper we have formulated the power system cyber security problem as a five-dimensional optimization problem. A general framework is developed using the system of system explorer tool providing a defensive solution to the vulnerable points of a smart grid prone to malicious attacks. An optimal meta-architecture composed of defensive system constituents is achieved using Genetic algorithm (GA) and fuzzy inference system providing a solution to ensure cybersecurity of power system operations. © 2024 IEEE.

关键词： Cyber attacks

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Advanced Liveness Detection for Facial Recognition: Combating Fake Faces with Reflectivity Analysis 10

Advanced Liveness Detection for Facial Recognition: Combatin...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Xinpeng Wang, Wen Singapore Management University School of Engineering Singapore Xi'an Jiaotong-Liverpool University Department of Intelligence Systems China

ISBN: (纸本)9798331520861

Facial recognition technology has become integral to daily life, facilitating processes such as online payments, smart door locks, and mobile phone unlocking, while enhancing security. However, its accuracy can be compromised by variations in illumination and action scale, making it vulnerable to high-fidelity fake face attacks. This project addresses these issues by identifying the differing reflectivity of human skin, video images, and fake face headgear under light. Utilizing spatio-temporal domain characteristics of facial key points, a novel face action liveness detection algorithm was developed. This algorithm integrates motion trajectory, historical data, and skin color information to improve liveness detection. The project processes facial video to extract reflective layers at key points, using this data to train a model capable of distinguishing real faces from fake ones, thereby improving the robustness of facial recognition systems. © 2024 IEEE.

关键词： Face recognition

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Public Healthcare Informatics for COVID-19 from Social Media Data 46

Public Healthcare Informatics for COVID-19 from Social Media...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Kumar, Vishwa V. Sahoo, Avimanyu Kumar, Ritika Loyd, Nicholas Industrial & Systems Engineering and Engineering Management United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering United States North Alabama Medical Center FlorenceAL United States

ISBN: (纸本)9798350371499

The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X"(Twitter), to understand public sentiment and needs regarding healthcare supply chain challenges, such as the availability of testing kits, oxygen supplies, and hospital beds, during the pandemic. By identifying pertinent opinionated keyphrases and employing advanced natural language processing (NLP) techniques, we sifted through over 3.9 million tweets to collect health informatics. The content of these tweets is analyzed to investigate the word trends and sentiments. The extracted sentiments were then organized into ten distinct categories using the K-means clustering method that pointed out top-ten healthcare supply needs. The findings underscore the value of social media as a tool for gathering timely and actionable insights on healthcare supply chain logistics, offering critical data for effective response strategies during health crises. © 2024 IEEE.

关键词： COVID-19

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Cooperative Donation programs in Supply Chains With Non-Governmental Organizations (NGOs)

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IEEE Transactions on engineering management 2024年 71卷 574-585页

作者： Pahlevani, Mahsa Choi, Tsan-Ming Heydari, Jafar Xu, Xiaoyan University of Tehran School of Industrial Engineering College of Engineering Tehran*** Iran Dalhousie University Department of Industrial Engineering HalifaxNSB3H 4R2 Canada National Taiwan University Department and Graduate Institute of Business Administration College of Management Taipei10617 Taiwan The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

Today, corporate social responsibility (CSR) is known as a critical element of supply chain (SC) operations. In this article, we analytically study an SC coordination problem with social efforts for charity. The proposed SC includes one retailer and one manufacturer in which the manufacturer gives donations to a nongovernmental organization (NGO) in two forms, namely a lump sum amount plus a per-sold-item donation amount. While CSR efforts have been considered as an abstract concept in prior studies, this article contributes to the literature by specifically exploring an SC with an NGO in the form of two types of donations as CSR activities. The problem is first modeled under a decentralized decision-making setting in which each member focuses on its own profit. The second model is developed under the centralized decision-making structure in which the whole SC profit under the centralized scenario is improved. To achieve Pareto-improving channel coordination, a cooperative donation program using a combined 'wholesale price and cost-sharing' contract is proposed. Our analytically proven results demonstrate that the proposed SC coordination contract can achieve Pareto improvement. Moreover, we prove that the NGO receives more donations under the proposed contract. Therefore, the proposed contract is beneficial to both the SC members and the NGO. In the extended model, we investigate the case under the endogenous 'low/high pricing regimes' and reveal that the main findings remain robust. Managerial insights and implications are discussed. © 1988-2012 IEEE.

关键词： Supply chains

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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Computer Modeling in engineering & Sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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