Engineering Applications of Artificial Intelligence最新文献

{"title":"A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites","authors":"","doi":"10.1016/j.engappai.2024.109214","DOIUrl":"10.1016/j.engappai.2024.109214","url":null,"abstract":"<div><p>Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122155","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":"Data and knowledge-driven dual surrogate-assisted multi-objective rough fuzzy clustering algorithm for image segmentation","authors":"","doi":"10.1016/j.engappai.2024.109229","DOIUrl":"10.1016/j.engappai.2024.109229","url":null,"abstract":"<div><p>Most multi-objective clustering algorithms (MOCAs) do not fully utilize the spatial and edge information of an image in image segmentation areas. Moreover, the objective evaluations are generally expensive for MOCAs, because the computation cost is related to the number of image pixels. Introducing approximate predictions of surrogate model to replace extensive objective evaluations can improve segmentation efficiency of MOCAs. However, accurately fitting objective functions using only a single surrogate is challenging. To resolve the above-mentioned issues, a data and knowledge-driven dual surrogate-assisted multi-objective rough fuzzy clustering algorithm (DK-DSMRFC) is proposed. First, an edge information-guided local neighborhood weighted filtering strategy is designed to obtain the spatial information with rich image details. Second, three complementary clustering objective functions are constructed to recognize complex clustering structures, which focus on rough fuzzy intra-class compactness with multi-level image information, dual centroids-based inter-class separation, and neighborhood consistency, respectively. To efficiently optimize these objective functions, we construct a data and knowledge-driven dual-surrogate assisted evolutionary framework, in which the radial basis function is used as a principal surrogate model to predict objective functions, and the Kriging model is adopted as an assistant surrogate to provide uncertainty information of predictions. Furthermore, a knowledge-induced multi-perspective infill sampling criterion is designed to promote exploration and exploitation. Finally, a rough fuzzy clustering validity index with spatial constraints and neighborhood consistency is constructed to select the optimal individual. The performance of evolutionary framework is verified on benchmark functions. Experiments on images from four datasets confirm the effectiveness and robustness of the DK-DSMRFC. <em>Keywords</em>: Image segmentation, Rough fuzzy clustering, Surrogate assisted multi-objective optimization, Data and knowledge-driven optimization.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122156","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":"Robust semi-supervised learning with reciprocal weighted mixing distribution alignment","authors":"","doi":"10.1016/j.engappai.2024.109185","DOIUrl":"10.1016/j.engappai.2024.109185","url":null,"abstract":"<div><p>Recent semi-supervised learning(SSL) methods have achieved great success owing to the impressive performances brought by the combination of pseudo-labeling and consistency regularization. These methods often use pre-defined constant thresholds or dynamical thresholds to select unlabeled samples that contribute to training. However, many correct/incorrect pseudo-labels may be ignored/selected. Especially in distribution mismatched scenario, threshold-adjusted strategy is often complex and ineffective. To alleviate this issue, we develop a simple yet powerful framework whose idea is to abandon this strategy and utilize distribution alignment to adjust the predictions generated from a biased model softly. Specifically, first, we create two classifiers to predict pseudo-label(i.e., the sample belongs to a specific category) and complementary pseudo-label(i.e., the sample does not belong to a specific category), respectively. Second, by maintaining the distributions of pseudo-labels, complementary pseudo-labels and their reverse versions from past iterations, we enforce a reciprocal weighted mixing according to the predicted category weights. Third, a reciprocal distribution alignment is applied to the mixed distributions to adjust the predicted distributions. Finally, we propose Implication Alignment Loss , which keeps consistency between the predictions of the same implications but from different versions. We empirically demonstrate the effectiveness of our proposed method in comparison with state-of-the-art benchmarks. Especially, our method achieves a 1.18% error rate reduction over the latest state-of-the-art method MutexMatch on CIFAR-10 with 2 labels per class and exhibits robustness in the scenario of mismatched distribution.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122158","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":"Semi-supervised learning for gas insulated switchgear partial discharge pattern recognition in the case of limited labeled data","authors":"","doi":"10.1016/j.engappai.2024.109193","DOIUrl":"10.1016/j.engappai.2024.109193","url":null,"abstract":"<div><p>Semi-supervised learning has better and more efficient performance than supervised algorithms in the case of limited labeled data. Existing methods for diagnosing partial discharge (PD) insulation defects in gas-insulated switchgear (GIS) equipment can only be effective if there is sufficient labeled data. However, in the actual working conditions of GIS equipment, insulation defect data is very scarce, where labeled data is more expensive to obtain and most of the data is unlabeled. In the case of limited PD labeled data, it is still a serious challenge to achieve higher classification accuracy of GIS PD pattern recognition. Therefore, we propose a semi-supervised self-training algorithm based on density peaks of local neighbor Information. Firstly, an improved density peak clustering algorithm based on local neighbor information is proposed, which no longer depends on the truncation distance, and considers the local information to better reflect the local density. Secondly, using local neighbor Information of labeled data, the criterion of confidence of unlabeled data is improved. Then, the PD unlabeled data with pseudo-labels are used to build a strong classifier for GIS PD pattern recognition. The experimental results show that the proposed algorithm has higher classification accuracy than other semi-supervised algorithms. When the proportion of labeled data is 10 %, the recognition accuracy can reach 65.98 %, which is the highest in the comparison algorithm. The proposed algorithm provides a feasible solution for GIS PD pattern recognition in the case of limited labeled data.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122157","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":"Combining optical flow and Swin Transformer for Space-Time video super-resolution","authors":"","doi":"10.1016/j.engappai.2024.109227","DOIUrl":"10.1016/j.engappai.2024.109227","url":null,"abstract":"<div><p>Space–time video super-resolution is a task that aims to interpolate low frame rate, low resolution videos to high frame rate, high resolution ones. While existing Transformer-based methods have achieved results comparable to convolutional neural networks-based methods, the computational cost of Transformer limits its performance with constrained computational resources. Moreover, Swin Transformer may fail to fully exploit the spatio-temporal information of video frames due to the limitation of window size, impeding its effectiveness in handling large motions. To address these limitations, we propose an end-to-end space–time video super-resolution architecture based on optical flow alignment and Swin Transformer. The alignment module is introduced to extract spatio-temporal information from adjacent frames without significantly increasing the computational burden. Additionally, we design a residual convolution layer to enhance the translational invariance of the features extracted by Swin Transformer and introduces additional nonlinear transformations. Experimental results demonstrate that our proposed method achieves superior performance on various benchmark datasets compared to state-of-the-art methods. In terms of Peak Signal-to-Noise Ratio, our method outperforms the state-of-the-art methods by at least 0.15 dB on Vimeo-Medium dataset.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117479","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":"Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm","authors":"","doi":"10.1016/j.engappai.2024.109220","DOIUrl":"10.1016/j.engappai.2024.109220","url":null,"abstract":"<div><p>In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117478","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":"Integrating multimodal contrastive learning with prototypical domain alignment for unsupervised domain adaptation of time series","authors":"","doi":"10.1016/j.engappai.2024.109205","DOIUrl":"10.1016/j.engappai.2024.109205","url":null,"abstract":"<div><p>Unsupervised domain adaptation (UDA) addresses the challenge of transferring knowledge from a labeled source domain to an unlabeled target domain. This task is particularly critical for time series data, characterized by unique temporal dynamics. However, existing methods often fail to capture these temporal dependencies, leading to domain discrepancies and loss of semantic information. In this study, we propose a novel framework for the unsupervised domain adaptation of time series (UDATS) that integrates Multimodal Contrastive Adaptation (MCA) and Prototypical Domain Alignment (PDA). MCA leverages image encoding techniques and prompt learning to capture complex temporal patterns while preserving semantic information. PDA constructs multimodal prototypes, combining visual and textual features to align target domain samples accurately. Our framework demonstrates superior performance across various application domains, including human activity recognition, mortality prediction, and fault detection. Experiments show our method effectively addresses domain discrepancies while preserving essential semantic content, outperforming state-of-the-art models.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096199","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":"Complex Pythagorean neutrosophic normal interval-valued set with an aggregation operators using score values","authors":"","doi":"10.1016/j.engappai.2024.109169","DOIUrl":"10.1016/j.engappai.2024.109169","url":null,"abstract":"<div><p>The complex Pythagorean neutrosophic normal interval-valued set approach solves the multiple-attribute decision-making problem. We introduce the new concepts such as complex Pythagorean neutrosophic normal interval-valued weighted averaging, complex Pythagorean neutrosophic normal interval-valued weighted geometric, complex generalized Pythagorean neutrosophic normal interval-valued weighted averaging and complex generalized Pythagorean neutrosophic normal interval-valued weighted geometric operator. We demonstrate that complex Pythagorean neutrosophic normal interval-valued set satisfy algebraic structures such as associative, idempotent, bounded, commutative and monotonic properties. Additionally, we develop algorithm and flowchart that solve problems using these operators. Examples of using enhanced score values and accuracy values in real-world environments are provided in this paper. Artificial intelligence refers to the simulation or approximation of human intelligence in machines. Its goals include computer enhanced learning, reasoning and perception. Artificial intelligence is being used today across different industries, from finance to healthcare. Agricultural robots have been described as being highly dependent on computer and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: the controller’s sophistication, the software efficiency, the maximum moment of inertia, and the manufacturer’s reliability. The best alternative can be determined by comparing expert opinions to the criteria. Therefore, the parameter <span><math><mo>∇</mo></math></span> has a very significant impact on the results of the model. This comparison aims to prove that the models under consideration are valid and valuable by comparing them with the available and proposed models. In conclusion, the value of <span><math><mo>∇</mo></math></span> significantly impacts the model performance. Based on the comparison and sensitivity analysis, we conclude that the proposed aggregation operation is superior and more reliable than the existing one. The criteria were compared to the most appropriate options based on expert assessments.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096200","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 new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine","authors":"","doi":"10.1016/j.engappai.2024.109218","DOIUrl":"10.1016/j.engappai.2024.109218","url":null,"abstract":"<div><p>The existing chiller fault diagnosis approaches often ignore the problem of data imbalance of chiller, which leads to low accuracy in diagnosing minority class fault samples. To conquer this issue, this paper proposes an improved generative adversarial network (IGAN) with an enhanced deep extreme learning machine (EDELM) method. Firstly, to better learn the latent structure of chiller fault data, the multi-head attention (MHA) mechanism is integrated into the traditional generative adversarial network (GAN) method to generate new samples that are more in line with the distribution of minority class fault samples for the purpose of obtaining a rebalanced dataset. Secondly, to fully handle the nonlinear features hidden in the massive chiller data, the deep extreme learning machine (DELM) basic classifier is trained on the rebalanced dataset. To enhance more attention to the misclassified samples, the adaptive boosting (AdaBoost) ensemble strategy is employed to train multiple DELM basic classifiers by updating the sample weights following the classification results through the iterative rounds. The voting weight of the current DELM basic classifier is given according to its fault diagnosis accuracy. Finally, multiple DELM basic classifiers are ensembled according to their voting weights to obtain the final ensemble classifier. The pattern of the snapshot sample is determined through the weighted voting strategy. Detailed experimental results based on the research project 1043 (RP-1043) conducted by the American society of heating, refrigeration, and air conditioning engineers (ASHRAE) confirm the effectiveness of the proposed IGAN-EDELM approach under imbalanced data environments.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096196","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":"Learning automatic navigation control skills for miniature helical robots from human demonstrations","authors":"","doi":"10.1016/j.engappai.2024.109187","DOIUrl":"10.1016/j.engappai.2024.109187","url":null,"abstract":"<div><p>Magnetic micro-robotic technology holds immense potential for revolutionizing minimally invasive procedures, particularly in the realm of interventional medicine. The ability to effectively and precisely control micro-scale, magnetically actuated robots in real-world scenarios is important. Mastering this capability promises to elevate the precision and efficacy of medical interventions, thereby enhancing patient outcomes. Numerous methods were proposed in previous work and achieved significant progress. However, these efforts primarily focused on model-based strategies and control improvements, with little attention given to the manipulation of the surgeon. This paper presents an exploration of an imitation learning approach, leveraging extensive manual operation experiments, to replicate the task of robotic navigation in simulated vascular environments. The control strategies are directly acquired from experimental observations and encapsulated within a high-dimensional neural network, specifically a tailored variant of the Residual Network (ResNet). The robustness and effectiveness of our proposed methodology are validated through comprehensive experimentation. In automatic navigation trials, the average error spanned from 2.29 mm to 3.32 mm, leading to a mean trajectory deviation of approximately 2.92 mm. The average error rate is 31% lower than that observed in traditional model-based Proportional Integral Derivative (PID) controller (approximately 3.81 mm). In addition, the maximum error (4.87 mm) is 83% of that of the traditional method (5.85 mm). Our findings emphasize the viability and benefits of learning-based techniques in micro-robot control, paving the way for innovative control strategies in interventional surgeries applications.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095609","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}