Computers in Industry最新文献_第4页

Domain ontology to integrate building-integrated photovoltaic, battery energy storage, and building energy flexibility information for explicable operation and maintenance

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2025-01-23 DOI: 10.1016/j.compind.2025.104250

Xiaoyue Yi , Llewellyn Tang , Reynold Cheng , Mengtian Yin , Yu Zheng

{"title":"Domain ontology to integrate building-integrated photovoltaic, battery energy storage, and building energy flexibility information for explicable operation and maintenance","authors":"Xiaoyue Yi , Llewellyn Tang , Reynold Cheng , Mengtian Yin , Yu Zheng","doi":"10.1016/j.compind.2025.104250","DOIUrl":"10.1016/j.compind.2025.104250","url":null,"abstract":"<div><div>Building-integrated photovoltaics (BIPV) incorporated with battery energy storage (BES) and building energy flexibility (BEF) system is nowadays increasingly prevalent. During the operation and maintenance (O&M) of BIPV, BES, and BEF, various knowledge is contained and generated. This highlights information interaction among systems and the demand for incorporating diverse domain knowledge. However, these systems remain relatively isolated during O&M and suffer from inadequate machine-readable knowledge representation. In the era of semantic web technology, ontology-based methods are promising to integrate heterogeneous information. This study developed a domain ontology named “BIPV-BES-BEF” to integrate BIPV, BES, and BEF O&M information by enriching ontology semantics through relevant standards and leveraging existing ontology resources. In the process ontology construction, classes associated with BIPV, BES, and BEF were initially identified from relevant ontologies based on concepts in authorized codes. The classes with high cosine similarity within these recognized classes were subsequently integrated. Concepts and rules concerning the O&M of BIPV, BES, and BEF from relevant standards were then incorporated to the ontology and semantic web rules. The resulting ontology consists of a total of 2595 axioms and 649 classes, encompassing comprehensive concepts related to BIPV, BES, and BEF components, system specifics, assessment criteria, as well as O&M elements. The built ontology was assessed to be coherent and capable of reasoning through the built knowledge. This study contributes to an ontology purposing BIPV, BES, and BEF O&M, highlighting the potential of ontology-based approaches in BIPV, BES, and BEF data integration and knowledge inference.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104250"},"PeriodicalIF":8.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acoustic signal-based wear monitoring for belt grinding tools with pyramid-structured abrasives using BO-KELM 基于声信号的金字塔结构磨料带磨具磨损监测

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2025-01-03 DOI: 10.1016/j.compind.2024.104235

Yingjie Liu , Wenxi Wang , Xiaoyu Zhao , Shudong Zhao , Lai Zou , Chao Wang

引用次数: 0

Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology 利用物联网边缘计算和区块链技术在智慧城市中进行基于预测分析的可持续废物管理

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2025-01-03 DOI: 10.1016/j.compind.2024.104234

C. Anna Palagan , S. Sebastin Antony Joe , S.J. Jereesha Mary , E. Edwin Jijo

{"title":"Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology","authors":"C. Anna Palagan , S. Sebastin Antony Joe , S.J. Jereesha Mary , E. Edwin Jijo","doi":"10.1016/j.compind.2024.104234","DOIUrl":"10.1016/j.compind.2024.104234","url":null,"abstract":"<div><div>Effective waste management has become the key challenge in developing smart cities with the increase in population. Traditional waste management systems are often inefficient, which leads to unnecessary trips, high operational costs, difficulties in tracking waste, and the inefficient use of resources. The proposed work aims to integrate real-time predictive analysis-based waste collection and disposal processes using federated learning with blockchain, overcoming the challenges specified. Initially, IoT sensors were installed in waste bins across different sites to monitor the depth of waste accumulated. Local edge gateways preprocess the collected data, which the random forest model analyzes to determine the bin status. The aggregated data is sent to a global model that predicts overall waste generation trends. Furthermore, the processed data is securely recorded on a blockchain network combined with smart contracts, accessed through a decentralized application called D-App, which gives real-time updates for scheduling waste collection, performs efficient communication with users and stakeholders to access real-time data to monitor bin status, and track waste collection trucks. As a result, the model predicts bin status with 99.25 % accuracy using an RF algorithm and blockchain helped achieve a user trust level by 95 %. Thus, the proposed work reduces operational expenses, optimizes waste collection routes, makes better decisions, and provides a scalable solution for sustainable waste management.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104234"},"PeriodicalIF":8.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human Digital Twins: A systematic literature review and concept disambiguation for industry 5.0 人类数字孪生：工业5.0的系统文献回顾和概念消歧

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2025-01-02 DOI: 10.1016/j.compind.2024.104230

Ben Gaffinet , Jana Al Haj Ali , Yannick Naudet , Hervé Panetto

{"title":"Human Digital Twins: A systematic literature review and concept disambiguation for industry 5.0","authors":"Ben Gaffinet , Jana Al Haj Ali , Yannick Naudet , Hervé Panetto","doi":"10.1016/j.compind.2024.104230","DOIUrl":"10.1016/j.compind.2024.104230","url":null,"abstract":"<div><div>Human Digital Twins (HDTs) are an emerging concept with the potential to create human-centric systems for Industry 5.0. The concept has rapidly spread to new application domains, most notably Healthcare, leading to diverging conceptual interpretations. This Systematic Literature Review analyses the conceptual understanding of HDTs across all application domains to clarify the conceptual foundation. Our review reveals a consensus that an HDT’s twinned entity is a human individual. However, there is little agreement on the data flows between the individual and their HDT. We address this shortcoming by proposing three categories based on the level of data integration: Human Digital Models, Human Digital Shadows, and Human Digital Twins. Finally, we synthesise our findings in a domain-agnostic general definition for HDT. We highlight an edge case where the twinned entity is a human individual alongside a strongly coupled technical system, and name it augmented Human Digital Twin (aHDT). The definition and categorisation scheme provide the needed conceptual clarity for inter-disciplinary collaboration to address open challenges. Notable challenges are sensing human data, reliable data transfers and modelling, especially behavioural modelling. Additional ethical issues concerning security, privacy and consent are central to successful HDT adoption. We call for cross-disciplinary efforts to establish a standardised framework and ethical guidelines to enable future developments.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"166 ","pages":"Article 104230"},"PeriodicalIF":8.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An approach for adaptive filtering with reinforcement learning for multi-sensor fusion in condition monitoring of gearboxes 用于变速箱状态监测的多传感器融合强化学习自适应滤波方法

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-27 DOI: 10.1016/j.compind.2024.104214

Shahis Hashim, Sitesh Kumar Mishra, Piyush Shakya

{"title":"An approach for adaptive filtering with reinforcement learning for multi-sensor fusion in condition monitoring of gearboxes","authors":"Shahis Hashim, Sitesh Kumar Mishra, Piyush Shakya","doi":"10.1016/j.compind.2024.104214","DOIUrl":"10.1016/j.compind.2024.104214","url":null,"abstract":"<div><div>Condition monitoring of gearboxes is integral to maintaining floor safety, system stability, and inventory management. Capturing vibration response using sensors and subsequent response analysis is the standard procedure for gearbox fault detection. However, the sensors are susceptible to non-constant reliability due to the convolution of vibration responses from multiple sources, background noise interference, and transfer-path effect. The problem is multi-fold when ideal sensor attachment locations are unavailable due to spatial constraints of industrial floors. The response component reflective of the fault information must be enhanced for adequate fault severity estimations. The present study addresses this hurdle by proposing a multi-sensor framework with available sensor attachment locations for gearbox condition monitoring. Adaptive filtering is done in the framework with parameters optimised to enhance fault information. A proximal policy optimisation agent is trained with a reinforcement learning environment for parameter refinement. Further, fault severity estimation is achieved by a weighted fusion of spectral features reflective of the side-band excitation effect caused by gear fault. The proposed method is applied to datasets acquired from an in-house seeded fault test bed. The proposed method underscores superior performance compared to conventional single-sensor-based fault severity analysis and alternate fusion approaches.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104214"},"PeriodicalIF":8.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production 用于预测印刷电路板生产周期的瓦瑟斯坦分布稳健学习法

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-19 DOI: 10.1016/j.compind.2024.104213

Feng Liu , Yingjie Lu , Debiao Li , Raymond Chiong

{"title":"Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production","authors":"Feng Liu , Yingjie Lu , Debiao Li , Raymond Chiong","doi":"10.1016/j.compind.2024.104213","DOIUrl":"10.1016/j.compind.2024.104213","url":null,"abstract":"<div><div>This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of the PCB production cycle time difficult. In addition, practical production situations with significant disturbances in feature data make traditional prediction models inaccurate, especially when there is new data. Therefore, we establishe a WDRL model, derive a tight upper bound for the expected loss function, and reformulate a tractable equivalent model based on the bound. To demonstrate the effectiveness of this method, we collected data related to surface mounted technology (SMT) production lines from a leading global display manufacturer for our computational experiments. In addition, we also designed experiments with perturbations in the training and testing datasets to verify the WDRL model’s ability to handle perturbations. This proposed method has also been compared with other machine learning methods, such as the support vector regression combined with symbiotic organism search, decision tree, and kernel extreme learning machine, among others. Experimental results indicate that the WDRL model can make robust predictions of PCB cycle time, which helps to effectively plan production capacity in uncertain situations and avoid overproduction or underproduction. Finally, we implement the WDRL model for the actual SMT production to predict the production cycle time and set it as the target for production. We observed a 98–103 % achievement rate in the last 20 months since the implementation in September 2022.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104213"},"PeriodicalIF":8.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BRepQL: Query language for searching topological elements in B-rep models BRepQL：用于搜索 B-rep 模型拓扑元素的查询语言

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-18 DOI: 10.1016/j.compind.2024.104207

Seungeun Lim , Changmo Yeo , Byung Chul Kim , Kyung Cheol Bae , Duhwan Mun

{"title":"BRepQL: Query language for searching topological elements in B-rep models","authors":"Seungeun Lim , Changmo Yeo , Byung Chul Kim , Kyung Cheol Bae , Duhwan Mun","doi":"10.1016/j.compind.2024.104207","DOIUrl":"10.1016/j.compind.2024.104207","url":null,"abstract":"<div><div>Topological elements form the basis for tasks such as geometric calculations, feature analysis, and direct modeling in 3D CAD systems. Handling these elements is also essential in various automated systems. This study proposes a method to search for topological elements within a boundary representation (B-rep) model by employing topological queries. To address complex scenarios that are difficult to handle using a single query, a topological query procedure that sequentially executes a predefined set of topological queries is used. To verify the effectiveness of the proposed method, experiments were conducted on Test Cases 1, 2, and 3, confirming the successful search of all target topological elements. Furthermore, tests on modified Snap-fit hook A and Bridge B models demonstrated that the same queries remained effective, provided the topological relationships and geometric constraints expressed in the query were preserved. In addition, a search time comparison showed that the proposed method reduced search time by over 90 % compared to manual processes. Finally, in an experiment involving participants with varying levels of programming proficiency, the results indicated that, for a developer with high programming skills, writing topological queries reduced the time required to search for a single topological element by more than 95 % compared to writing the program code.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104207"},"PeriodicalIF":8.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Handheld Augmented Reality Interaction Techniques for Developing AR Instructions using AR Authoring Tools 使用 AR 创作工具开发 AR 说明的手持增强现实交互技术比较研究

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-17 DOI: 10.1016/j.compind.2024.104205

Kay Hönemann , Björn Konopka , Michael Prilla , Manuel Wiesche

{"title":"A Comparative Study of Handheld Augmented Reality Interaction Techniques for Developing AR Instructions using AR Authoring Tools","authors":"Kay Hönemann , Björn Konopka , Michael Prilla , Manuel Wiesche","doi":"10.1016/j.compind.2024.104205","DOIUrl":"10.1016/j.compind.2024.104205","url":null,"abstract":"<div><div>Augmented Reality (AR) instructions offer companies tremendous savings potential. However, developing these AR instructions has traditionally been challenging due to the need for programming skills and spatial knowledge. To address this complexity, industry and academia are working to simplify AR development. A crucial aspect of this process is the accurate positioning of AR content within the physical environment, which requires effective AR interaction techniques that enable full 3D manipulation of AR elements. In this study, we conducted an experimental comparison of three different AR interaction techniques with 55 participants to empirically assess their performance, workload, and user satisfaction across tasks related to AR instruction development. Our findings contribute to the design of future AR instructions and AR authoring tools, emphasizing the importance of evaluating AR interaction techniques that can be utilized by users without programming experience tailored to the specific needs of the intended application domain.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104205"},"PeriodicalIF":8.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering data spaces: A classification of design options 发现数据空间：设计方案分类

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-15 DOI: 10.1016/j.compind.2024.104212

Anna Gieß , Thorsten Schoormann , Frederik Möller , Inan Gür

引用次数: 0

Enhanced detection of unknown defect patterns on wafer bin maps based on an open-set recognition approach 基于开放集识别方法的晶圆分区图未知缺陷模式强化检测

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-14 DOI: 10.1016/j.compind.2024.104208

Jin-Su Shin , Min-Joo Kim , Beom-Seok Kim , Dong-Hee Lee

{"title":"Enhanced detection of unknown defect patterns on wafer bin maps based on an open-set recognition approach","authors":"Jin-Su Shin , Min-Joo Kim , Beom-Seok Kim , Dong-Hee Lee","doi":"10.1016/j.compind.2024.104208","DOIUrl":"10.1016/j.compind.2024.104208","url":null,"abstract":"<div><div>It is crucial to detect and classify defect patterns on wafers in semiconductor-manufacturing processes for wafer-quality management and prompt analysis of defect causes. In recent years, continuous technological innovation and advancements in semiconductor-industry processes have led to an increase in unknown defect patterns, which must be detected and classified. However, detection of unknown defect patterns is difficult due to complex reasons, such as training on non-existent defect classes, closed datasets owing to industrial security, and labeling large volumes of manufacturing data. Owing to these challenges, methods for detecting unknown defect patterns in an actual semiconductor-manufacturing environment primarily rely on qualitative indicators, such as intuition and experience of engineers. To overcome these problems, this study proposes a methodology based on open-set recognition to accurately detect unknown defect patterns. This methodology begins with two preprocessing steps: constrained mean filtering (C-mean filtering); and Radon transform to diminish noise and efficiently extract features from wafer-bin maps. This study then develops an entropy-estimation one-class support vector machine (EEOC-SVM), which accounts for the uncertainty in the one-class SVM classification results. EEOC-SVM computes entropy-uncertainty scores based on the distance between decision boundaries and samples and then reclassifies uncertain samples using a weighted sum of uncertainties for each class. This method can effectively detect unknown defect patterns. The proposed method achieves a detection performance of over 98 % for various defect classes based on experiments conducted with new defect patterns occurring in actual semiconductor-manufacturing environments. These results confirm that the proposed method is an effective tool for detecting and addressing unknown defect patterns.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104208"},"PeriodicalIF":8.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0