Integrated Computer-Aided Engineering最新文献

{"title":"A self-adaptive multi-objective feature selection approach for classification problems","authors":"Yu Xue, Hao Zhu, Ferrante Neri","doi":"10.3233/ica-210664","DOIUrl":"https://doi.org/10.3233/ica-210664","url":null,"abstract":"In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve classification accuracy, both of which are commonly treated as the two objectives in FS problems. Many meta-heuristic algorithms have been applied to solve the FS problems and they perform satisfactorily when the problem is relatively simple. However, once the dimensionality of the datasets grows, their performance drops dramatically. This paper proposes a self-adaptive multi-objective genetic algorithm (SaMOGA) for FS, which is designed to maintain a high performance even when the dimensionality of the datasets grows. The main concept of SaMOGA lies in the dynamic selection of five different crossover operators in different evolution process by applying a self-adaptive mechanism. Meanwhile, a search stagnation detection mechanism is also proposed to prevent premature convergence. In the experiments, we compare SaMOGA with five multi-objective FS algorithms on sixteen datasets. According to the experimental results, SaMOGA yields a set of well converged and well distributed solutions on most data sets, indicating that SaMOGA can guarantee classification performance while removing many features, and the advantage over its counterparts is more obvious when the dimensionality of datasets grows.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"15 1","pages":"3-21"},"PeriodicalIF":6.5,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86031287","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":"Conditional StyleGAN modelling and analysis for a machining digital twin","authors":"E. Zotov, Ashutosh Tiwari, V. Kadirkamanathan","doi":"10.3233/ICA-210662","DOIUrl":"https://doi.org/10.3233/ICA-210662","url":null,"abstract":"Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"75 1","pages":"399-415"},"PeriodicalIF":6.5,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83839839","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 methodology using classification for traffic prediction: Featuring the impact of COVID-19","authors":"S. Liapis, Konstantinos Christantonis, Victor Chazan Pantzalis, Anastassios Manos, D. Filippidou, Christos Tjortjis","doi":"10.3233/ICA-210663","DOIUrl":"https://doi.org/10.3233/ICA-210663","url":null,"abstract":"This paper presents a novel methodology using classification for day-ahead traffic prediction. It addresses the research question whether traffic state can be forecasted based on meteorological conditions, seasonality, and time intervals, as well as COVID-19 related restrictions. We propose reliable models utilizing smaller data partitions. Apart from feature selection, we incorporate new features related to movement restrictions due to COVID-19, forming a novel data model. Our methodology explores the desired training subset. Results showed that various models can be developed, with varying levels of success. The best outcome was achieved when factoring in all relevant features and training on a proposed subset. Accuracy improved significantly compared to previously published work.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"31 1","pages":"417-435"},"PeriodicalIF":6.5,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87681958","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":"Geo-AI to aid disaster response by memory-augmented deep reservoir computing","authors":"Konstantinos Demertzis, L. Iliadis, E. Pimenidis","doi":"10.3233/ICA-210657","DOIUrl":"https://doi.org/10.3233/ICA-210657","url":null,"abstract":"It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a very large area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"27 1","pages":"383-398"},"PeriodicalIF":6.5,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89578878","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 Must-read Journal for Engineering","authors":"A. D. L. Escalera","doi":"10.3233/ICA-210658","DOIUrl":"https://doi.org/10.3233/ICA-210658","url":null,"abstract":"","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"21 1","pages":"219-220"},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86018562","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":"Back-propagation of the Mahalanobis istance through a deep triplet learning model for person Re-Identification","authors":"M. J. Gómez-Silva, A. D. L. Escalera, J. M. Armingol","doi":"10.3233/ICA-210651","DOIUrl":"https://doi.org/10.3233/ICA-210651","url":null,"abstract":"The automatization of the Re-Identification of an individual across different video-surveillance cameras poses a significant challenge due to the presence of a vast number of potential candidates with a similar appearance. This task requires the learning of discriminative features from person images and a distance metric to properly compare them and decide whether they belong to the same person or not. Nevertheless, the fact of acquiring images of the same person from different, distant and non-overlapping views produces changes in illumination, perspective, background, resolution and scale between the person’s representations, resulting in appearance variations that hamper his/her re-identification. This article focuses the feature learning on automatically finding discriminative descriptors able to reflect the dissimilarities mainly due to the changes in actual people appearance, independently from the variations introduced by the acquisition point. With that purpose, such variations have been implicitly embedded by the Mahalanobis distance. This article presents a learning algorithm to jointly model features and the Mahalanobis distance through a Deep Neural Re-Identification model. The Mahalanobis distance learning has been implemented as a novel neural layer, forming part of a Triplet Learning model that has been evaluated over PRID2011 dataset, providing satisfactory results.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"75 1","pages":"277-294"},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74690035","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":"Both Interdisciplinary and Interesting","authors":"H. Adeli","doi":"10.3233/ICA-210648","DOIUrl":"https://doi.org/10.3233/ICA-210648","url":null,"abstract":"Sometimes all it takes to realize the significance of a thing is simply to understand its name. This is certainly the case for the journal Integrated ComputerAided Engineering (ICAE), which has now been benefitting interdisciplinary researchers for 30 years. The name ICAE starts with “integrated,” which speaks to the very interdisciplinary nature of the journal. ICAE is about research projects, not simple research papers. To publish in ICAE is to present research in a larger context, making ICAE articles of interest to researchers and enthusiasts in several technology fields at once. The second part of ICAE’s name is “computer,” and some elaboration is necessary here. ICAE papers are not about computer architecture or about computer networks; instead, ICAE is about using computer architectures and networks in optimal ways to solve technical problems. This comes to the third part of the four-part name: “aided”. Computing is used to aid technologists in their research and development challenges. Computing is a tool, and ICAE authors are expected to actually use their tools properly. If it’s a nail, you’d better use a hammer, but if it’s a screw, you’d better use a screwdriver. If a paper is submitted to ICAE, it had better not simply grab the latest convolutional neural network (CNN) design and apply it to a known data set and present its 2% reduction in error rate as a finding. That is simply a verification that CNN designs continue to incrementally improve, and as such is nothing more than a verification that CNNs continue to be useful tools for the technical community. Reaching back to the earlier analogy, that is tantamount to showing that the latest hammer will pound a nail with 2% more efficiency. Nice to know, but not interesting. Perhaps this is why the third part of the name of ICAE is so important. When one must use a tool to aid in a task, it should be an interesting task. For me, this is why ICAE is one of my favorite journals. It is both interdisciplinary and interesting. Incremental articles are not ICAE articles. Hojjat Adeli, the Founder and Editor-in-Chief of ICAE, makes this clear in the reviewer form for ICAE, which specifically asks “If you are aware of the authors’ other recent publications please explain how the current submission is different from their previous publication. Please point out the duplication, if any, and provide specific suggestions to minimize any duplication.” In other words, any duplication is grounds for constructive, but also restrictive, feedback to the authors. It is computer-aided, not computer-using, research. This brings us to the fourth part of the name, “engineering.” Engineers are applied researchers. They build devices, they test what they build, they create useful and reproducible outputs. We need only consider the next part of the reviewer feedback to see this need for building, testing, and utility: “Please comment whether examples presented in the paper are appropriate and justified consideri","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"2 1","pages":"115-116"},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88784686","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-stage deep learning perception system for mobile robots","authors":"E. Macias-Garcia, Deysy Galeana Pérez, Jesus Medrano-Hermosillo, E. Bayro-Corrochano","doi":"10.3233/ICA-200640","DOIUrl":"https://doi.org/10.3233/ICA-200640","url":null,"abstract":"This paper presents a novel multi-stage perception system for collision avoidance in mobile robots. In the here considered scenario, a mobile robot stands in a workspace with a set of potential targets to reach or interact with. When a human partner appears gesturing to the target, the robot must plan a collision-free trajectory to reach the goal. To solve this problem, a full-perception system composed of consecutive convolutional neural networks in parallel and processing stages is proposed for generating a collision-free trajectory according to the desired goal. This system is evaluated at each step in real environments and through several performance tests, proving to be a robust and fast system suitable for real-time applications.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"12 1","pages":"191-205"},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87986117","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":"Research Articles","authors":"Matt Carter","doi":"10.1016/b978-0-12-815377-2.00010-x","DOIUrl":"https://doi.org/10.1016/b978-0-12-815377-2.00010-x","url":null,"abstract":"","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"23 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77918501","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-behaviors coordination controller design with enzymatic numerical P systems for robots","authors":"Xueyuan Wang, Gexiang Zhang, Xiantai Gou, Prithwineel Paul, Ferrante Neri, Haina Rong, Qiang Yang, Hua Zhang","doi":"10.3233/ica-200627","DOIUrl":"https://doi.org/10.3233/ica-200627","url":null,"abstract":"Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"13 1","pages":"119-140"},"PeriodicalIF":6.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81656553","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}