International Journal of Intelligent Systems最新文献

{"title":"Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index","authors":"Ji-Wan Ham, Siheon Jeong, Min-Gwan Kim, Joon-Young Park, Ki‐Yong Oh","doi":"10.1155/2023/8212790","DOIUrl":"https://doi.org/10.1155/2023/8212790","url":null,"abstract":"This paper proposes a novel and practical crack-detection method for infrastructure. The proposed method exhibits three key components. First, a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) is proposed to accurately estimate crack candidates comprising linear and curvilinear features. Second, the proposed neural network is trained using only public image-sets. The main principle of this approach is that cracks have unique and distinct features, and therefore, public image-sets provide sufficient information to estimate crack candidates for a neural network. Third, a line similarity index (LSI), which is calculated using the Hough transform and coordinate transformation with principal component analysis, is incorporated to eliminate non-crack candidates from crack candidates based on two key characteristics: the variation in crack features with respect to the representative line and the number of crack features that crossed the representative line. Addressing these two crack-related characteristics improves accuracy and robustness by effectively eliminating non-crack features. Field tests performed inside a building and in an underground power tunnel demonstrated the effectiveness of the proposed method. The MSML Mask DCNN outperformed other neural networks, accurately recognizing local crack candidates characterized by linear and curvilinear features even though only public image-sets were used for training. The proposed LSI also effectively eliminated non-crack candidates estimated by the MSML Mask DCNN. The proposed method is practical for real-world applications, where several non-crack objects and noises are typically present.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64800137","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 Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms","authors":"Ming He, Sheng Hu","doi":"10.1155/2023/2959503","DOIUrl":"https://doi.org/10.1155/2023/2959503","url":null,"abstract":"As recommendation systems heavily depend on user data, these systems are susceptible to potential privacy breaches. To mitigate this issue, differential privacy (DP) protection techniques have been developed to offer robust privacy safeguards. Nevertheless, a majority of the extant DP-based recommendation algorithms tend to introduce excessive noise, consequently impairing the quality of recommendations. In response, this study presents a novel DP-preserving recommendation algorithm that integrates matrix factorization (MF) and a genetic algorithm (GA). Initially, the MF problem is transformed into two interrelated optimization problems, namely, the user-hidden factor and the item-hidden factor. Subsequently, GA is employed to address these optimization issues. An enhancement index mechanism is incorporated into the individual selection of GA, while the variation process of GA is devised based on identifying crucial hidden factors. Utilizing the enhancement index mechanism aids in minimizing the algorithm’s perturbation level, thereby achieving an optimal balance between privacy protection and algorithm utility. Experimental analyses, encompassing recommendation accuracy, efficiency, and parameter variation effects, are conducted on Last.fm and Flixster datasets. The findings corroborate that the proposed system outperforms existing alternatives under stringent privacy constraints, thereby attesting to its efficacy.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"3 1","pages":"1-14"},"PeriodicalIF":7.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64792533","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 Lightweight and Multisource Information Fusion Method for Real-Time Monitoring of Lump Coal on Mining Conveyor Belts","authors":"Ligang Wu, L. Zhang, Le Chen, Jianhua Shi, Jiafu Wan","doi":"10.1155/2023/5327122","DOIUrl":"https://doi.org/10.1155/2023/5327122","url":null,"abstract":"Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real-time monitoring of lump coal, the method Ghost-ECA-Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross-channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating-point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real-time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real-time monitoring of lump coal is realized.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-11"},"PeriodicalIF":7.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64796863","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":"Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel-Level Accuracy","authors":"Zihao Wu, Yunchao Tang, Bo Hong, Bingqiang Liang, Yuping Liu","doi":"10.1155/2023/9940881","DOIUrl":"https://doi.org/10.1155/2023/9940881","url":null,"abstract":"In dam engineering, the presence of cracks and crack width are important indicators for diagnosing the health of dams. The accurate measurement of cracks facilitates the safe use of dams. The manual detection of such defects is unsatisfactory in terms of cost, safety, accuracy, and the reliability of evaluation. The introduction of deep learning for crack detection can overcome these issues. However, the current deep learning algorithms possess a large volume of model parameters, high hardware requirements, and difficulty toward embedding in mobile devices such as drones. Therefore, we propose a lightweight MobileNetV2_DeepLabV3 image segmentation network. Furthermore, to prevent interference by noise, light, shadow, and other factors for long-length targets when segmenting, the atrous spatial pyramid pooling (ASPP) module parameters in the DeepLabV3+ network structure were modified, and a multifeature fusion structure was used instead of the parallel structure in ASPP, allowing the network to obtain richer crack features. We collected the images of dam cracks from different environments, established segmentation datasets, and obtained segmentation models through network training. Experiments show that the improved MobileNetV2_DeepLabV3 algorithm exhibited a higher crack segmentation accuracy than the original MobileNetV2_DeepLabV3 algorithm; the average intersection rate attained 83.23%; and the crack detail segmentation was highly accurate. Compared with other semantic segmentation networks, its training time was at least doubled, and the total parameters were reduced by more than 2 to 7 times. After extracting cracks through the semantic segmentation, we proposed to use the method of inscribed circle of crack outline to calculate the maximum width of the detected crack image and to convert it into the actual width of the crack. The maximum relative error rate was 11.22%. The results demonstrated the potential of innovative deep learning methods for dam crack detection.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-16"},"PeriodicalIF":7.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64806613","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":"GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module","authors":"Niccolò Ferrari, Michele Fraccaroli, E. Lamma","doi":"10.1155/2023/7773481","DOIUrl":"https://doi.org/10.1155/2023/7773481","url":null,"abstract":"Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task that is usually achieved using a basic comparison between generated image and the original one, implementing some blob analysis or image-editing algorithms in the postprocessing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a generative adversarial network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using preprocessing algorithms, formerly developed with blob analysis and image-editing procedures. To test our model, we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-18"},"PeriodicalIF":7.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64799375","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":"Two-Stage Focus Measurement Network with Joint Boundary Refinement for Multifocus Image Fusion","authors":"Hao Zhai, Xin Pan, You Yang, Jinyuan Jiang, Qing Li","doi":"10.1155/2023/4155948","DOIUrl":"https://doi.org/10.1155/2023/4155948","url":null,"abstract":"Focus measurement, one of the key tasks in multifocus image fusion (MFIF) frameworks, identifies the clearer parts of multifocus images pairs. Most of the existing methods aim to achieve disposable pixel-level focus measurement. However, the lack of sufficient accuracy often gives rise to misjudgments in the results. To this end, a novel two-stage focus measurement with joint boundary refinement network is proposed for MFIF. In this work, we adopt a coarse-to-fine strategy to gradually achieve block-level and pixel-level focus measurement for producing more fine-grained focus probability maps, instead of directly predicting at the pixel level. In addition, the joint boundary refinement optimizes the performance on the focused/defocused boundary component (FDB) during the focus measurement. To improve feature extraction capability, both CNN and transformer are employed to, respectively, encode local patterns and capture long-range dependencies. Then, the features from two input branches are legitimately aggregated by modeling the spatial complementary relationship in each pair of multifocus images. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in both subjective perception and objective assessment.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-16"},"PeriodicalIF":7.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64794216","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":"Driving in the Rain: A Survey toward Visibility Estimation through Windshields","authors":"J. Morden, Fabio Caraffini, Ioannis Kypraios, A. Al-Bayatti, Richard Smith","doi":"10.1155/2023/9939174","DOIUrl":"https://doi.org/10.1155/2023/9939174","url":null,"abstract":"Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-26"},"PeriodicalIF":7.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64806473","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":"Distributed Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Actuator Failures","authors":"Jianhui Wang, Chen Wang, Kairui Chen, Zitao Chen","doi":"10.1155/2023/8818233","DOIUrl":"https://doi.org/10.1155/2023/8818233","url":null,"abstract":"A fixed-time event-triggered consensus control method is proposed for uncertain nonlinear multiagent systems with actuator failures. Since actuator failures, external disturbances and control gains are time-varying and completely unknown, the effects of these system constraints on the system are completely unknown, which makes the implementation of fixed-time tracking control challenging. To deal with these system constraints, radial basis function neural networks (RBFNNs) are applied to approximate the uncertain dynamics, and a boundary estimation method is presented to achieve adaptive compensation for them. Furthermore, considering that the implementation of this boundary estimation method requires a large number of communication resources, an event triggering mechanism is designed to reduce the update frequency of the controller. It is theoretically confirmed that using the proposed control scheme, all the followers can track the leader with sufficient accuracy in a predetermined time, and all the closed-loop signals are bounded. Finally, the simulation experiments verify the theoretical results.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-16"},"PeriodicalIF":7.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64800866","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 Fuzzy Control Strategy to Synchronize Fractional-Order Nonlinear Systems Including Input Saturation","authors":"Zahra Rasooli Berardehi, Chongqi Zhang, Mostafa Taheri, Majid Roohi, M. Khooban","doi":"10.1155/2023/1550256","DOIUrl":"https://doi.org/10.1155/2023/1550256","url":null,"abstract":"One of the most important engineering problems, with numerous uses in the applied sciences, is the synchronization of chaos dynamical systems. This paper introduces a dynamic-free T-S fuzzy sliding mode control (TSFSMC) method for synchronizing the different chaotic fractional-order (FO) systems, when there is input saturation. Using a new definition of fractional calculus and the fractional version of the Lyapunov stability theorem and linear matrix inequality concept, a Takagi–Sugeno fuzzy sliding mode controller is driven to suppress and synchronize the undesired behavior of the FO chaotic systems without any unpleasant chattering phenomenon. Finally, an example of synchronization of complex power grid systems is provided to illustrate the theoretical result of the paper in real-world applications.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-18"},"PeriodicalIF":7.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64790941","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":"SentMask: A Sentence-Aware Mask Attention-Guided Two-Stage Text Summarization Component","authors":"Rui Zhang, Nan Zhang, Jianjun Yu","doi":"10.1155/2023/1267336","DOIUrl":"https://doi.org/10.1155/2023/1267336","url":null,"abstract":"The text summarization task aims to generate succinct sentences that summarise what an article tries to express. Based on pretrained language models, combining extractive and abstractive summarization approaches has been widely adopted in text summarization tasks. It has been proven to be effective in many existing pieces of research using extract-then-abstract algorithms. However, this method suffers from semantic information loss throughout the extraction process, resulting in incomprehensive sentences being generated during the abstract phase. Besides, current research on text summarization emphasizes only word-level comprehension while paying little attention to understanding the level of the sentence. To tackle this problem, in this paper, we propose the SentMask component. Taking into account that the semantics of sentences that are filtered out during the extraction process is also worth considering, the paper designs a sentence-aware mask attention mechanism in the process of generating a text summary. By applying the extractive approach, the paper first selects the most essential sentences to construct the initial summary phrases. This information leads the model to modify the weights of the attention mechanism, which provides supervision for the generative model to ensure that it focuses on the sentences that convey important semantics while not ignoring others. The final summary is constructed based on the key information provided. The experimental results demonstrate that our model achieves higher ROUGE and BLEU scores compared to other baseline models on two benchmark datasets.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-12"},"PeriodicalIF":7.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64789757","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}