Integrated Computer-Aided Engineering最新文献

{"title":"A parametric and feature-based CAD dataset to support human-computer interaction for advanced 3D shape learning","authors":"Rubin Fan, Fazhi He, Yuxin Liu, Yupeng Song, Linkun Fan, Xiaohu Yan","doi":"10.3233/ica-240744","DOIUrl":"https://doi.org/10.3233/ica-240744","url":null,"abstract":"3D shape learning is an important research topic in computer vision, in which the datasets play a critical role. However, most of the existing 3D datasets use voxels, point clouds, mesh, and B-rep, which are not parametric and feature-based. Thus they can not support the generation of real-world engineering computer-aided design (CAD) models with complicated shape features. Furthermore, they are based on 3D geometry results without human-computer interaction (HCI) history. This work is the first to provide a full parametric and feature-based CAD dataset with a selection mechanism to support HCI in 3D learning. First, unlike existing datasets, mainly composed of simple features (typical sketch and extrude), we devise complicated engineering features, such as fillet, chamfer, mirror, pocket, groove, and revolve. Second, different from the monotonous combination of features, we invent a select mechanism to mimic how human focuses on and selects a particular topological entity. The proposed mechanism establishes the relationships among complicated engineering features, which fully express the design intention and design knowledge of human CAD engineers. Therefore, it can process advanced 3D features for real-world engineering shapes. The experiments show that the proposed dataset outperforms existing CAD datasets in both reconstruction and generation tasks. In quantitative experiment, the proposed dataset demonstrates better prediction accuracy than other parametric datasets. Furthermore, CAD models generated from the proposed dataset comply with semantics of the human CAD engineers and can be edited and redesigned via mainstream industrial CAD software.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-level simulator for Network-on-Chip","authors":"Paulo Cesar Donizeti Paris, Emerson Carlos Pedrino","doi":"10.3233/ica-240743","DOIUrl":"https://doi.org/10.3233/ica-240743","url":null,"abstract":"This study presents a high-level simulator for Network-on-Chip (NoC), designed for many-core architectures, and integrated with the PlatEMO platform. The motivation for developing this tool arose from the need to evaluate the behavior of application mapping algorithms and the routing, both aspectsare essential in the implementation and design of NoC architectures. This analysis underscored the importance of having effective NoC simulators as tools that allow for studying and comparing various network technologies while ensuring a controlled simulation environment. During this investigation and evaluation, some simulators, such as Noxim, NoCTweak, and NoCmap, among others, offered configurable parameters for application traffic, options to synthetically define topology and packet traffic patterns. Additionally, they include mapping options that optimize latency and energy consumption, routing algorithms, technological settings such as the CMOS process, and measurement options for evaluating performance metrics such as throughput and power usage. However, while these simulators meet detailed technical demands, they are mostly restricted to analyzing the low-level elements of the architecture, thus hindering quick and easy under- standing for non-specialists. This insight underscored the challenge in developing a tool that balances detailed analysis with a comprehensive learning perspective, considering the specific restrictions of each simulator analyzed. Experiments demonstrated the proposed simulator efficacy in handling algorithms like GA, PSO, and SA variant, and, surprisingly, revealed limitations of the XY algorithm in Mesh topologies, indicating the need for further investigation to confirm these findings. Future work will expand the simulator functionalities, incorporating a broader range of algorithms and performance metrics, to establish it as an indispensable tool for research and development in NoCs.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient surface defect detection in industrial screen printing with minimized labeling effort","authors":"Paul Josef Krassnig, Matthias Haselmann, Michael Kremnitzer, Dieter Paul Gruber","doi":"10.3233/ica-240742","DOIUrl":"https://doi.org/10.3233/ica-240742","url":null,"abstract":"As part of the evolving Industry 4.0 landscape, machine learning-based visual inspection plays a key role in enhancing production efficiency. Screen printing, a versatile and cost-effective manufacturing technique, is widely applied in industries like electronics, textiles, and automotive. However,the production of complex multilayered designs is error-prone, resulting in a variety of defect appearances and classes. These defects can be characterized as small in relation to large sample areas and weakly pronounced. Sufficient defect visualization and robust defect detection methods are essential to address these challenges, especially considering the permitted design variability. In this work, we present a novel automatic visual inspection system for surface defect detection on decorated foil plates. Customized optical modalities, integrated into a sequential inspection procedure, enable defect visualization of production-related defect classes. The introduced patch-wise defect detection methods, designed to leverage less labeled data, prove effective for industrial defect detection, meeting the given process requirements. In this context, we propose an industry-applicable and scalable data preprocessing workflow that minimizes the overall labeling effort while maintaining high detection performance, as known in supervised settings. Moreover, the presented methods, not relying on any labeled defective training data, outperformed a state-of-the-art unsupervised anomaly detection method in terms of defect detection performance and inference speed.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Battery parameter identification for unmanned aerial vehicles with hybrid power system","authors":"Zhuoyao He, David Martín Gómez, Pablo Flores Peña, Arturo de la Escalera Hueso, Xingcai Lu, José María Armingol Moreno","doi":"10.3233/ica-240741","DOIUrl":"https://doi.org/10.3233/ica-240741","url":null,"abstract":"Unmanned aerial vehicles (UAVs) nowadays are getting soaring importance in many aspects like agricultural and military fields. A hybrid power system is a promising solution toward high energy density and power density demands for UAVs as it integrates power sources like internal combustion engine (ICE), fuel cell (FC) and lowcapacity lithium-polymer (LIPO) batteries. For robust energy management, accurate state-of-charge (SOC) estimation is indispensable, which necessitates open circuit voltage (OCV) determination and parameter identification of battery. The presented research demonstrates the feasibility of carrying out incremental OCV test and even dynamic stress test (DST) by making use of the hybrid powered UAV system itself. Based on battery relaxation terminal voltage as well as current wave excitation, novel methods for OCV determination and parameter identification are proposed. Results of SOC estimation against DST through adaptive unscented Kalman filter (AUKF) algorithm show that parameters and OCV identified with longer relaxation time don’t yield better SOC estimation accuracy. Besides, it also discloses that OCV played the vital role in affecting SOC estimation accuracy. A detailed analysis is presented showing that mean discharging rate and current wave amplitude are the major factors which affect the quality of OCV identified related to SOC estimation accuracy.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of deep learning techniques in TV programs classification: A comparative analysis","authors":"Federico Candela, Angelo Giordano, Carmen Francesca Zagaria, Francesco Carlo Morabito","doi":"10.3233/ica-240740","DOIUrl":"https://doi.org/10.3233/ica-240740","url":null,"abstract":"<h4><span>Abstract</span></h4><p>In the application areas of streaming, social networks, and video-sharing platforms such as YouTube and Facebook, along with traditional television systems, programs’ classification stands as a pivotal effort in multimedia content management. Despite recent advancements, it remains a scientific challenge for researchers. This paper proposes a novel approach for television monitoring systems and the classification of extended video content. In particular, it presents two distinct techniques for program classification. The first one leverages a framework integrating Structural Similarity Index Measurement and Convolutional Neural Network, which pipelines on stacked frames to classify program initiation, conclusion, and contents. Noteworthy, this versatile method can be seamlessly adapted across various systems. The second analyzed framework implies directly processing optical flow. Building upon a shot-boundary detection technique, it incorporates background subtraction to adaptively discern frame alterations. These alterations are subsequently categorized through the integration of a Transformers network, showcasing a potential advancement in program classification methodology. A comprehensive overview of the promising experimental results yielded by the two techniques is reported. The first technique achieved an accuracy of 95%, while the second one surpassed it with an even higher accuracy of 87% on multiclass classification. These results underscore the effectiveness and reliability of the proposed frameworks, and pave the way for a more efficient and precise content management in the ever-evolving landscape of multimedia platforms and streaming services.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Railway alignment optimization in regions with densely-distributed obstacles based on semantic topological maps","authors":"Xinjie Wan, Hao Pu, Paul Schonfeld, Taoran Song, Wei Li, Lihui Peng","doi":"10.3233/ica-240739","DOIUrl":"https://doi.org/10.3233/ica-240739","url":null,"abstract":"<h4><span>Abstract</span></h4><p>Railway alignment development in a study area with densely-distributed obstacles, in which regions favorable for alignments are isolated (termed an isolated island effect, i.e., IIE), is a computation-intensive and time-consuming task. To enhance search efficiency and solution quality, an environmental suitability analysis is conducted to identify alignment-favorable regions (AFRs), focusing the subsequent alignment search on these areas. Firstly, a density-based clustering algorithm (DBSCAN) and a specific criterion are customized to distinguish AFR distribution patterns: continuously-distributed AFRs, obstructed effects, and IIEs. Secondly, a study area characterized by IIEs is represented with a semantic topological map (STM), integrating between-island and within-island paths. Specifically, between-island paths are derived through a multi-directional scanning strategy, while within-island paths are optimized using a Floyd-Warshall algorithm. To this end, the intricate alignment optimization problem is simplified into a shortest path problem, tackled with conventional shortest path algorithms (of which Dijkstra’s algorithm is adopted in this work). Lastly, the proposed method is applied to a real case in a mountainous region with karst landforms. Numerical results indicate its superior performance in both construction costs and environmental suitability compared to human designers and a prior alignment optimization method.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A weakly supervised active learning framework for non-intrusive load monitoring","authors":"Giulia Tanoni, Tamara Sobot, Emanuele Principi, Vladimir Stankovic, Lina Stankovic, Stefano Squartini","doi":"10.3233/ica-240738","DOIUrl":"https://doi.org/10.3233/ica-240738","url":null,"abstract":"Energy efficiency is at a critical point now with rising energy prices and decarbonisation of the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a software-based technique to monitor individual appliances inside a building from a single aggregate meter reading and recent approaches are based on supervised deep learning. Such approaches are affected by practical constraints related to labelled data collection, particularly when a pre-trained model is deployed in an unknown target environment and needs to be adapted to the new data domain. In this case, transfer learning is usually adopted and the end-user is directly involved in the labelling process. Unlike previous literature, we propose a combined weakly supervised and active learning approach to reduce the quantity of data to be labelled and the end user effort in providing the labels. We demonstrate the efficacy of our method comparing it to a transfer learning approach based on weak supervision. Our method reduces the quantity of weakly annotated data required by up to 82.6–98.5% in four target domains while improving the appliance classification performance.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of thrust bearing’s performance in Mixed Lubrication regime","authors":"Konstantinos P. Katsaros, Pantelis G. Nikolakopoulos","doi":"10.3233/ica-240737","DOIUrl":"https://doi.org/10.3233/ica-240737","url":null,"abstract":"A hydrodynamic thrust bearing could be forced to operate in mixed lubrication regime under various circumstances. At this state, the tribological characteristics of the bearing could be affected significantly and the developed phenomena would have a severe impact on the performance of the mechanism. Until recently, researchers were modeling the hydrodynamic lubrication problem of the thrust bearings either with analytical or with numerical solutions. The analytical solutions are very simple and do not provide enough accuracy in describing the actual problem. To add to that, following only computational methodologies, can lead to time consuming and complex algorithms that need to be repeated every time the operating conditions change, in order to draw safe conclusions. Recent technological advances, especially on the field of computer science, have provided tools that enhance and accelerate the modeling of thrust bearings’ operation. The aim of this study is to examine the application of Artificial Neural Networks as Machine Learning models, that are trained to predict the coefficient of friction for lubricated pad thrust bearings in mixed lubrication regime. The hydrodynamic analysis of the thrust bearing is performed by solving the Average 2-D Reynolds equation numerically. In order to describe the roughness of the profiles, both the flow factors suggested by N. Patir and H.S. Cheng (1978) and the model of J.A. Greenwood and J. H. Tripp (1970) are taken into consideration. Three lubricants, the SAE 0W30, the SAE 10W40 and the SAE 10W60, are tested and compared for a variety of operating velocities and applied coatings. The numerical analysis results are used as training datasets for the machine learning algorithms. Four different ML methods are applied in this investigation: Artificial Neural Networks (ANNs), Multi- Variable Quadratic Polynomial Regression, Quadratic SVM and Regression Trees. The coefficient of determination, R2 is calculated and used to determine the most accurate ML method for the current study. The results showed that ANNs provide very good accuracy in the prediction of friction coefficient compared to the rest of the ML models discussed.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-label classification with imbalanced classes by fuzzy deep neural networks","authors":"Federico Succetti, Antonello Rosato, Massimo Panella","doi":"10.3233/ica-240736","DOIUrl":"https://doi.org/10.3233/ica-240736","url":null,"abstract":"Multi-label classification is an advantageous technique for managing uncertainty in classification problems where each data instance is associated with several labels simultaneously. Such situations are frequent in real-world scenarios, where decisions rely on imprecise or noisy data and adaptableclassification methods are preferred. However, the problem of class imbalance represents a common characteristic of several multi-label datasets, in which the distribution of samples and their corresponding labels is non-uniform across the data space. In this paper, we propose a multi-label classification approach utilizing fuzzy logic in order to deal with the class imbalance problem. To eliminate the need for an expert to determine the logical rules of inference, deep neural networks are adopted, which have proven to be exceptionally effective for such problems. By combining both fuzzy inference systems and deep neural networks, the strengths and weaknesses of each approach can be mitigated. As a further development, a symbolic representation of time series is put in place to reduce data dimensionality and speed up the training procedure. This allows for more flexibility in model application, in particular with respect to time constraints arising from the causality of observed time series. Tests carried out on a multi-label classification dataset related to the current and voltage profiles of several household appliances show that the proposed model outperforms four baseline models for time series classification.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140587756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode","authors":"J. Grosset, A.-J. Fougères, M. Djoko-Kouam, J.-M. Bonnin","doi":"10.3233/ica-240735","DOIUrl":"https://doi.org/10.3233/ica-240735","url":null,"abstract":"The smart factory leads to a strong digitalization of industrial processes and continuous communication between the systems integrated into the production, storage, and supply chains. One of the research areas in Industry 4.0 is the possibility of using autonomous and/or intelligent industrial vehicles. The optimization of the management of the tasks allocated to these vehicles with adaptive behaviours, as well as the increase in vehicle-to-everything communications (V2X) make it possible to develop collective and adaptive intelligence for these vehicles, often grouped in fleets. Task allocation and scheduling are often managed centrally. The requirements for flexibility, robustness, and scalability lead to the consideration of decentralized mechanisms to react to unexpected situations. However, before being definitively adopted, decentralization must first be modelled and then simulated. Thus, we use a multi-agent simulation to test the proposed dynamic task (re)allocation process. A set of problematic situations for the circulation of autonomous industrial vehicles in areas such as smart warehouses (obstacles, breakdowns, etc.) has been identified. These problematic situations could disrupt or harm the successful completion of the process of dynamic (re)allocation of tasks. We have therefore defined scenarios involving them in order to demonstrate through simulation that the process remains reliable. The simulation of new problematic situations also allows us to extend the potential of this process, which we discuss at the end of the article.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}