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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Kotaro Yamada Yoshiyuki Karuno Hitoshi Iima Mechanodesign Program Graduate School of Science and Technology Kyoto Institute of Technology Kyoto Japan Faculty of Mechanical Engineering Kyoto Institute of Technology Kyoto Japan Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In this paper, we consider a scheduling issue for parcel delivery and pickup services by a truck-drone last-mile delivery system. We are given a single carrier truck and multiple identical drones to serve a finite set of customers. The single carrier truck plays the role of a mobile depot for the drones, and it visits several stops in a given order of them. The given order of stops constitutes a fixed truck route, and the carrier truck is allowed to launch (resp., to retrieve) a drone to (resp., from) a customer at a stop. For each sortie of a drone, the launching stop and the retrieving stop must be the same. The load capacity of a drone is limited to one, and for a sortie of a drone at a stop, there are three options: (i) only deliver a parcel to a customer, (ii) only pick up a parcel from a customer, and (iii) deliver a parcel to a customer, and then pick up a parcel from a customer. The scheduling problem asks to find an assignment of customers to drones with sortie options and a choice of a truck stop for each sortie in the assignment. The objective is to minimize the total duration of the carrier truck over all the stops, which is equivalent to minimizing the makespan. In this paper, we first propose an extended integer program for the case with both delivery and pickup services from an existing one for the case with delivery service only. We also conduct numerical experiments to demonstrate the solution quality of the proposed integer program by utilizing an integer programming solver, and report the results.

关键词： Integer programming Heuristic algorithms programming Batteries Drones Logistics

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Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes

arXiv

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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way RaleighNC27607 United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way RaleighNC27607 United States

industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods. © 2024, CC BY.

关键词： Failure modes

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Capacity Planning of Access Point Deployment for Indoor Positioning System

Capacity Planning of Access Point Deployment for Indoor Posi...

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2021 International Conference on Power, Energy and Innovations, ICPEI 2021

作者： Thitinaruemit, Aphirak Sitjongsatapom, Suchada Prongnuch, Sethakarn Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Industrial Technology Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9781665402163

This paper present the network design and capacity planning of access point (AP) deployment for indoor positioning system. The estimation of WiFi distance is described briefly how to measure the signal strength of AP. Co-channel interference is a key for network design of access point installation for network capacity. The objective function for network capacity planning of AP deployment is to optimize the number of access point and location for indoor positioning system concerning with the minimized co-channel interference. Simulations are tested to install AP for lecture hall in university. Capacity planning design of wireless AP deployment is proposed for indoor position systems in terms of the received signal strength indications (RSSI). We assume the situation is supported for the crowded conference. The requirement of the results of wireless AP positioning in crowded conference rooms are placing the wireless AP in forms of overlap the signal in order to get the strong signal. Experimental results have been tested their received signal of WiFi APs with the 2.4GHz frequency using the receiver installed in laptops to check the signal. The empirical results shown that the strength of RSSI will be received a signal of -44 dBm at a distance of approximately 20 meters. © 2021 IEEE.

关键词： Signal receivers

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Path Planning Method for Mobile Robots in Pedestrian-Friendly Environments Using Drone Bird’s-Eye View

Path Planning Method for Mobile Robots in Pedestrian-Friendl...

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IEEE/SICE International Symposium on System Integration

作者： Linghao Ye Daisuke Chugo Satoshi Muramatsu Sho Yokota Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan School of Information Science and Technology Tokai University Hiratsuka Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The objective of this paper is to develop a robot system that can transmit the video taken by the drone to the computer in real time, use YOLO to recognize the image of pedestrians, and enable the ground robot to plan the path to avoid pedestrians. External sensors, such as laser range finders (LRF) and CCD cameras, are often used on mobile robots. As we all know, these sensors are susceptible to occlusion. Therefore, even if the mobile robot avoids the pedestrian in front of it, it is likely to collide with other pedestrians that cannot be detected due to occlusion. In this study, we used a drone to shoot videos at high altitudes, transferred the videos to a computer, used YOLO on the computer to perform image recognition of pedestrians around the robot, and passed the recognition results to ROS, which enabled it to plan a path to avoid pedestrians around it. As a result, showed that the videos shot at high altitudes could effectively identify pedestrians around the robot after being recognized by YOLO.

关键词： YOLO Pedestrians Image recognition Robot kinematics System integration Real-time systems Sensors Mobile robots Collision avoidance Drones

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Mapping the research in household electrical power services customer satisfaction

Mapping the research in household electrical power services ...

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2016 industrial and systems engineering Research Conference, ISERC 2016

作者： de Lima, Edson Pinheiro Abrão, Ana Glória Souza, Maria Eduarda Letti Catapan, Angela Gouvea da Costa, Sergio E. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Brazil Federal University of Technology Parana Brazil

In essence companies develop their business models for attending expectations of a selected group of customers that define their served markets. For public services or companies there are some particularities for defining their stakeholders that could create singularities in terms of setting clients' expectations. In some cases, these companies have a license to serve a region that could be create some sort of monopoly, and this is the case of Brazilian electricity distributors. The aim of this paper is to study customer satisfaction in household electrical power distribution services;mapping for this purpose the research that is being conducted over the last decades. Mapping the literature contributes for framing the evolution of technical and scientific publications in terms authorships, specialized journals outlets, research topics and other factors that contribute to the full understanding of this research theme. This paper presents a systematic literature review by developing a research protocol and process based on the Cochrane method. Portfolio analysis provides social networks and bibliometrics information that result in a research agenda for the studies in customer satisfaction for residential power services. In this sense, the paper contribution is mapping research trends in the studied research theme. © 2016 Proceedings of the 2016 industrial and systems engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Customer satisfaction

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Blockchain: A Review from the Perspective of Operations Researchers

Blockchain: A Review from the Perspective of Operations Rese...

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Simulation Winter Conference

作者： Hong Wan Kejun Li Yining Huang Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Operations Research Graduate Program North Carolina State University Raleigh NC USA

ISBN: (纸本)9781665476621

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.

关键词： Analytical models Operations research Tutorials Data science Data models Blockchains Cryptocurrency

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Towards a Machine Learning Failure Prediction System Applied to a Smart Manufacturing Process 17th

Towards a Machine Learning Failure Prediction System Applied...

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17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020

作者： da Rocha, Tainá Canciglieri, Arthur Beltrame Szejka, Anderson Luis dos Santos Coelho, Leandro Canciglieri Junior, Osiris Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Polytechnic Center Curitiba Brazil

ISBN: (纸本)9783030628062

At a time when the competitive market is operating rapidly, manufacturing industries need to stay connected, have interchangeability and interoperability in their factories, ensuring that there is heterogeneous communication between sectors, people, machines and the client, challenging the manufacturing industry to discover new ways to bring new products or improve their manufacturing process. Precisely because of the need to adjust to these new market demands, factories pursue complex and quick decision-making systems. This work aims to propose applications of Machine Learning techniques to develop a decision-making platform applied to a manufacturing line reducing scrap. This goal will be achieved through a literature review in the fields of Artificial Intelligence (AI) and Machine Learning to identify core concepts for the development of a failure prediction system. This research has demonstrated the problems and challenges faced by manufacturing daily, and how, through the application of AI techniques, it is possible to contribute to assist in these problems by improving quality, performance, scrap rates and rework, through connectivity and integration of data and processes. This paper contributes to evaluate the performance of machine learning ensembles applied in a real smart manufacturing scenario of failure prediction. © 2020, IFIP International Federation for Information Processing.

关键词： Interoperability

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

引用

arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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Design and analysis of component for non-rotor unmanned aerial vehicle 6

Design and analysis of component for non-rotor unmanned aeri...

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6th International Conference on Precision Machinery and Manufacturing Technology, ICPMMT 2021

作者： Shiau, Y.R. Fang, J. Chen, Y.L. 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

Unmanned aerial vehicles (UAVs) are widely applied in various industries and fields. Because they use a variety of rotor types, from single rotors to multi-rotors, UAVs offer a wide array of functions. However, continuously rotating rotors can be dangerous if they accidentally encounter foreign objects or bare hands. Therefore, non-rotor UAVs are the focus of discussion and modification in the present study. Non-rotor UAVs do not contain visible rotor mechanical components. Compared with the development of popularized multirotor UAVs, that of non-rotor UAVs is challenging in terms of structure and flight control. To address this challenge, wind tunnel structure models were developed in this study for different levels of aerodynamic force, and computer-aided engineering was used to conduct structural and flow field analyses to determine the key elements that reinforce non-rotor UAV structures. The research results revealed that the thickness and fillet design at the joint of the non-rotor UAV aerodynamic wind tunnel system were crucial factors influencing the system's performance. Motor rotors can be embedded inside support structures to reduce airflow turbulence in the wind tunnel. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Wind tunnels

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