Journal of Computing and Information Science in Engineering最新文献_第2页

Impact of Task Constraint on Agent Team Size of Self-Organizing Systems Measured by Effective Entropy 用有效熵衡量任务约束对自组织系统代理团队规模的影响

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-17 DOI: 10.1115/1.4065343

Hao Ji, Yan Jin

{"title":"Impact of Task Constraint on Agent Team Size of Self-Organizing Systems Measured by Effective Entropy","authors":"Hao Ji, Yan Jin","doi":"10.1115/1.4065343","DOIUrl":"https://doi.org/10.1115/1.4065343","url":null,"abstract":"\u0000 Self-organizing systems can perform complex tasks in unpredictable situations with adaptability. Previous work has introduced a multiagent reinforcement learning based model as a design approach to solving the rule generation problem with complex tasks. A deep multiagent reinforcement learning algorithm was devised to train self-organizing agents for knowledge acquisition of the task field and social rules. The results showed that there is an optimal number of agents that achieve good learning stability and system performance. However, finding such a number is nontrivial due to the dynamic task constraints and unavailability of agent knowledge before training. Although extensive training can eventually reveal the optimal number, it requires training simulations of all agent numbers under consideration, which can be computationally expensive and time-consuming. Thus, there remains the issue of how to predict such an optimal team size for self-organizing systems with minimal training experiments. In this paper, we proposed a measurement of the complexity of the self-organizing system called effective entropy, which considers the task constraints. A systematic approach, including several key concepts and steps, is proposed to calculate the effective entropy for given task environments, which is then illustrated and tested in a box-pushing case study. The results show that our proposed method and complexity measurement can accurately predict the optimal number of agents in self-organizing systems, and training simulations can be reduced by a factor of 10.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing robotic grasping detection accuracy with the R2CNN algorithm and force-closure 利用 R2CNN 算法和力闭合提高机器人抓取检测精度

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-15 DOI: 10.1115/1.4065311

Hsien-I Lin, M. Shodiq, Hong-Qi Chu

{"title":"Enhancing robotic grasping detection accuracy with the R2CNN algorithm and force-closure","authors":"Hsien-I Lin, M. Shodiq, Hong-Qi Chu","doi":"10.1115/1.4065311","DOIUrl":"https://doi.org/10.1115/1.4065311","url":null,"abstract":"\u0000 This study aims to use an improved rotational region convolutional neural network (R2CNN) algorithm to detect the grasping bounding box for the robotic arm that reaches supermarket goods. This algorithm can calculate the final predicted grasping bounding box without any additional architecture, which greatly improves the speed of grasp inferences. In this study, we added the force-closure condition, so that the final grasping bounding box could achieve grasping stability in a physical sense. We experimentally demonstrated that the deep model treated object detection and grasping detection are the same tasks. We used transfer learning to improve the prediction accuracy of the grasping bounding box. In particular, the ResNet-101 network weights, which were originally used in object detection, were used to continue training with the Cornell dataset. In terms of grasping detection, we used the trained model weights that were originally used in object detection as the features of the to-be-grasped objects and fed them to the network for continuous training. For 2,828 test images, this method achieved nearly 98% accuracy and a speed of 14–17 frames per second.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model using Probabilistic Learning with Partial Observability and Incomplete dataset 利用部分可观测性和不完整数据集的概率学习更新不确定喷嘴模型的非线性随机动力学特性

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-15 DOI: 10.1115/1.4065312

E. Capiez-Lernout, O. Ezvan, Christian Soize

{"title":"Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model using Probabilistic Learning with Partial Observability and Incomplete dataset","authors":"E. Capiez-Lernout, O. Ezvan, Christian Soize","doi":"10.1115/1.4065312","DOIUrl":"https://doi.org/10.1115/1.4065312","url":null,"abstract":"\u0000 This paper introduces a methodology for updating the nonlinear stochastic dynamics of a nozzle with uncertain computational model. The approach focuses on a high-dimensional nonlinear computational model constrained by a small target dataset. Challenges include the large number of degrees-of-freedom, geometric nonlinearities, material uncertainties, stochastic external loads, under-observability, and high computational costs. A detailed dynamic analysis of the nozzle is presented. An updated statistical surrogate model relating the observations of interest to the control parameters is constructed. Despite small training and target datasets, and partial observability, the study successfully applies Probabilistic Learning on Manifolds (PLoM) to address these challenges. PLoM captures geometric nonlinear effects and uncertainty propagation, improving conditional mean statistics compared to training data. The conditional confidence region demonstrates the ability of the methodology to accurately represent both observed and unobserved output variables, contributing to advancements in modeling complex systems.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Network Interference Approach to Analyzing Change Propagation in Requirements 分析需求变化传播的网络干扰方法

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-05 DOI: 10.1115/1.4065273

Phyo Htet Hein, Elisabeth Kames, Cheng Chen, Beshoy Morkos

{"title":"A Network Interference Approach to Analyzing Change Propagation in Requirements","authors":"Phyo Htet Hein, Elisabeth Kames, Cheng Chen, Beshoy Morkos","doi":"10.1115/1.4065273","DOIUrl":"https://doi.org/10.1115/1.4065273","url":null,"abstract":"\u0000 Requirements are frequently revised due to iterative nature of the design process. If not properly managed, these changes may result in financial and time losses due to undesired propagating effect. Currently, predictive models to assist designers in making well informed decisions prior to change implementation do not exist. Current modeling methods for managing requirements do not offer formal reasoning necessary to manage requirement change and its propagation. The ability to predict change during the design process may lead to valuable insights in designing artifacts more efficiently by minimizing unanticipated changes due to mismanaged requirement changes. Two research questions (RQs) are addressed in this paper: (1) How do complex network metrics of requirements, considering both node and edge interference, influence the predictability of requirement change propagation across different case studies? (2) How does the performance of the complex network metrics approach compare to the Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool, developed from our prior study, in accurately predicting requirement change propagation? Requirement changes are simulated by applying the node interference and the edge interference methods. It is found that complex network metrics can be used to predict requirement change propagation. Based on the studied data, the performance ranking of metrics is characterized by edge interference across the changes. The results reveal that the R-ARCPP tool ranks higher than comparatively performing complex network metrics.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Spatiotemporal Heterogeneity of Customer Preferences with Small-scale Aggregated Data: A Spatial Panel Modeling Approach 用小规模聚合数据模拟客户偏好的时空异质性：空间面板建模方法

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-01 DOI: 10.1115/1.4065211

Yuyang Chen, Youyi Bi, Jian Xie, Zhenghui Sha, Mingxian Wang, Yan Fu, Wei Chen

{"title":"Modeling Spatiotemporal Heterogeneity of Customer Preferences with Small-scale Aggregated Data: A Spatial Panel Modeling Approach","authors":"Yuyang Chen, Youyi Bi, Jian Xie, Zhenghui Sha, Mingxian Wang, Yan Fu, Wei Chen","doi":"10.1115/1.4065211","DOIUrl":"https://doi.org/10.1115/1.4065211","url":null,"abstract":"\u0000 Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for understanding the trend of customer preferences. However, existing choice models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. Learning-based spatiotemporal data modeling methods usually require large-scale datasets for model training, which are not applicable to small aggregated data, such as the sale records of a product in several regions and years. To fill this research gap, we propose a spatial panel modeling approach to investigate the spatiotemporal heterogeneity of customer preferences. Product and regional attributes varying in time are included as model inputs to support the demand forecasting in engineering design. With a case study using the dataset of small SUV in China’s automotive market, we demonstrate that the spatial panel modeling approach outperforms other statistical spatiotemporal data models and non-parametric regression method in goodness of fit and prediction accuracy. Our results show that the increases of price and fuel consumption of small SUVs tend to have negative impact on their sales in all provinces. We illustrate a potential design application of the proposed approach in a portfolio optimization of two vehicles from the same producer. While the spatial panel modeling approach exists in econometrics, applying this approach to support engineering decisions by considering spatiotemporal heterogeneity and introducing engineering attributes in demand forecasting is the contribution of this work.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140761600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Integrated Detection-Prognostics Methodology for Components with Intermittent Faults 间歇性故障部件的综合检测诊断方法

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-04-01 DOI: 10.1115/1.4065212

Michael Ibrahim, Heraldo Rozas, N. Gebraeel

{"title":"An Integrated Detection-Prognostics Methodology for Components with Intermittent Faults","authors":"Michael Ibrahim, Heraldo Rozas, N. Gebraeel","doi":"10.1115/1.4065212","DOIUrl":"https://doi.org/10.1115/1.4065212","url":null,"abstract":"\u0000 Some industrial components, such as valves, relay switches, and motors occasionally experience intermittent faults (IFs) that usually disappear without any repair or intervention. This phenomenon occurs at a relatively low frequency even in components that are in an “as good as new” state. However, an increase in the frequency of IFs often indicates the onset of degradation. We develop an integrated detection-prognostics model for components that exhibit IFs and whose degradation data is high-dimensional. We discuss the use of Dynamic Time Warping (DTW) and a Variational Autoencoder (VAE) to perform feature engineering on the data. We then propose a Hidden Markov Model (HMM) based monitoring strategy composed of two parts: (1) a detection model that tracks and flags changes in the intermittent fault frequency (IFF), and (2) a prognostic model that leverages how the transition probabilities of the HMM evolve with progressive degradation to compute the remaining life distribution (RLD) of the component. We examine the performance of our modeling framework using high-dimensional data generated from a vehicular electrical system testbed designed to accelerate the degradation of a vehicle starter motor.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140784946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Analysis of CNN Architectures for Automated Knee Segmentation in Medical Imaging: a Performance Evaluation 用于医学影像中膝关节自动分割的 CNN 架构比较分析：性能评估

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-01-08 DOI: 10.1115/1.4064450

Anna Ghidotti, A. Vitali, D. Regazzoni, Miri Weiss Cohen, C. Rizzi

{"title":"Comparative Analysis of CNN Architectures for Automated Knee Segmentation in Medical Imaging: a Performance Evaluation","authors":"Anna Ghidotti, A. Vitali, D. Regazzoni, Miri Weiss Cohen, C. Rizzi","doi":"10.1115/1.4064450","DOIUrl":"https://doi.org/10.1115/1.4064450","url":null,"abstract":"\u0000 Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, that is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumor, to segment knee bones from Magnetic Resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as Dice Similarity Coefficient and Hausdorff Distance. In the training phase, various combinations of hyper-parameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice Similarity Coefficients of 0.94 and Hausdorff Distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics 科学计算中的物理引导、物理信息和物理编码神经网络与运算器》（Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing）：流体与固体力学

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-01-08 DOI: 10.1115/1.4064449

S. A. Faroughi, Nikhil M. Pawar, Célio Fernandes, Maziar Raissi, Subasish Das, Nima K. Kalantari, S. K. Mahjour

{"title":"Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics","authors":"S. A. Faroughi, Nikhil M. Pawar, Célio Fernandes, Maziar Raissi, Subasish Das, Nima K. Kalantari, S. K. Mahjour","doi":"10.1115/1.4064449","DOIUrl":"https://doi.org/10.1115/1.4064449","url":null,"abstract":"\u0000 Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multi-physics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multi-physics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Layered Security Guidance for Data Asset Management in Additive Manufacturing. 增材制造数据资产管理分层安全指南》。

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-01-01 DOI: 10.1115/1.4064128

Fahad Ali Milaat, Joshua Lubell

{"title":"Layered Security Guidance for Data Asset Management in Additive Manufacturing.","authors":"Fahad Ali Milaat, Joshua Lubell","doi":"10.1115/1.4064128","DOIUrl":"10.1115/1.4064128","url":null,"abstract":"<p><p>Manufacturing industries are increasingly adopting additive manufacturing (AM) technologies to produce functional parts in critical systems. However, the inherent complexity of both AM designs and AM processes render them attractive targets for cyber-attacks. Risk-based Information Technology (IT) and Operational Technology (OT) security guidance standards are useful resources for AM security practitioners, but the guidelines they provide are insufficient without additional AM-specific revisions. Therefore, a structured layering approach is needed to efficiently integrate these revisions with preexisting IT and OT security guidance standards. To implement such an approach, this paper proposes leveraging the National Institute of Standards and Technology's Cybersecurity Framework (CSF) to develop layered, risk-based guidance for fulfilling specific security outcomes. It begins with an in-depth literature review that reveals the importance of AM data and asset management to risk-based security. Next, this paper adopts the CSF asset identification and management security outcomes as an example for providing AM-specific guidance and identifies the AM geometry and process definitions to aid manufacturers in mapping data flows and documenting processes. Finally, this paper uses the Open Security Controls Assessment Language to integrate the AM-specific guidance together with existing IT and OT security guidance in a rigorous and traceable manner. This paper's contribution is to show how a risk-based layered approach enables the authoring, publishing, and management of AM-specific security guidance that is currently lacking. The authors believe implementation of the layered approach would result in value-added, non-redundant security guidance for AM that is consistent with the preexisting guidance.</p>","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10895930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Approach to Line Clipping Against a Rectangular Window 一种针对矩形窗口的线裁剪新方法

IF 3.1 3区工程技术

Journal of Computing and Information Science in Engineering Pub Date : 2024-01-01 DOI: 10.1115/1.4062634

Hongfeng Yu, Y. He, W. J. Zhang

引用次数: 0