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Adaptive autonomous navigation system for coal mine inspection robots: overcoming intersection challenges

INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2024年第6期51卷 922-935页

作者： Wang, Hongwei Li, Chao Liang, Wei Wang, Di Yao, Linhu Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan Peoples R China Taiyuan Univ Technol Coal Mine Intelligent Equipment Res Ctr Shanxi Pro Taiyuan Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan Peoples R China Taiyuan Univ Technol Taiyuan Peoples R China

PurposeIn response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) ***/methodology/approachLeveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot's adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this *** experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.Originality/valueThis paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

关键词： Mine robots Intersection environments Adaptive navigation clustering algorithm Geometric model Motion control

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A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN

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SENSORS 2024年第8期24卷 2611页

作者： Guo, Yihan Zocca, Simone Dabove, Paolo Dovis, Fabio Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Politecn Torino Dept Environm Land & Infrastruct Engn I-10129 Turin Italy

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

关键词： multipath non-line-of-sight pseudorange bias clustering algorithm

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Digital twin-enabled hybrid deep evolutionary framework for smart building sustainable infrastructure management

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2024年 65卷

作者： Xu, Yunbo Zhang, Jiachao Qin, Heyu Zhou, Hao Yang, Zikai Henan Univ Engn Coll Civil Engn Zhengzhou 451191 Peoples R China

Combining services has become a major challenge in the use of service-based technology in smart building. The composition of services has numerous problems, specifically the quality of service (QoS), becoming increasingly difficult as dynamic environments change. The other concern is selecting services with a higher level of reliability based on trust value. A number of metaheuristic algorithms are employed to reduce the NP-hard complexity of service composition. Thus, the study adopts a hybrid gray wolf with cuckoo search optimization (HGWCSO) algorithm to achieve its objectives. The trust challenge is additionally addressed by using a trust-driven clustering algorithm using deep convolutional neural network (CNN) and k-means. The synergy between k-means and CNNs can result in improved clustering accuracy and better representation of underlying patterns in the data. According to the outcomes, the suggested algorithm is more effective in small-scale problems than the gray wolf optimization and particle swarm optimization algorithms. As the search space is clustered and reduced, response times are enhanced;it is also easier to select trusted services. The outcomes on the large-scale digital twin of smart building indicate that the suggested approach outperforms the average outcomes of similar studies in terms of computational time.

关键词： clustering algorithm Smart building service composition Convolutional neural network Digital twin Cloud computing

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A novel patent technology characterization method based on heterogeneous network message passing algorithm and patent classification system

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Chang, Zhi-Xing Guo, Wei Wang, Lei Fu, Zhong-Lin Ma, Jian Zhang, Guan-Wei Wang, Zi-Liang Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin Peoples R China Tianjin Renai Coll Tianjin Peoples R China

Patents are widely recognized as important data for generating innovation. Accurate and rational characterizing patent technology content is a prerequisite for applying innovation generation algorithms. Existing research widely employs classification codes that annotate the technology content of patents to represent patents. However, the oversight of technology similarity information within patent classification systems results in deficiencies in the accuracy and effectiveness of their representations. To fill this research gap, we analyze the hierarchical structure of patent classification systems to extract the technology similarity information embedded within them. Then, we propose a novel patent technology characterization method based on the heterogeneous network message passing algorithm, which integrates the technology similarity information in the classification code co-occurrence information and the patent classification system to obtain a more accurate patent characterization. Subsequently, several evaluation experiments were conducted to compare our method with typical existing methods. The results demonstrate that our method outperforms these methods in accuracy and effectiveness. Finally, we conducted a case study to validate the reliability and practicality of our approach. In summary, our method exhibited superior performance, thereby providing robust support for innovation generation methods based on patent characterization, with high application value and extension prospects.

关键词： Heterogeneous network Message-passing algorithm Technology topic clustering algorithm Patent characterization

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Cluster-CAM: Cluster-weighted visual interpretation of CNNs' decision in classification

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NEURAL NETWORKS 2024年 178卷 106473页

作者： Feng, Zhenpeng Ji, Hongbing Dakovic, Milos Cui, Xiyang Zhu, Mingzhe Stankovic, Ljubisa Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Kunshan Innovat Inst Xian Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision, has drawn increasing attention. Gradient -based CAMs are efficient, while the performance is heavily affected by gradient vanishing and exploding. In contrast, gradient -free CAMs can avoid computing gradients to produce more understandable results. However, they are quite time-consuming because hundreds of forward interference per image are required. In this paper, we proposed Cluster -CAM, an effective and efficient gradient -free CNN interpretation algorithm. Cluster -CAM can significantly reduce the times of forward propagation by splitting the feature maps into clusters. Furthermore, we propose an artful strategy to forge a cognition -base map and cognition -scissors from clustered feature maps. The final salience heatmap will be produced by merging the above cognition maps. Qualitative results conspicuously show that Cluster -CAM can produce heatmaps where the highlighted regions match the human's cognition more precisely than existing CAMs. The quantitative evaluation further demonstrates the superiority of Cluster -CAM in both effectiveness and efficiency.

关键词： Explainable artificial intelligence Class activation mapping clustering algorithm Image classification

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Liu, Tao Cheng, Shuli Du, Anyu Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China

Currently, unsupervised person re-identification (Re-ID) typically directly computes the similarity matrix between samples and then applies clustering algorithms to generate pseudo labels for model training, achieving significant progress to some extent. However, distance matrices calculated from a single perspective cannot effectively handle complex and variable sample noise environments, leading to pseudo labels that are not comprehensive or accurate enough, with considerable noise that can misguide model optimization. To address this issue, this paper proposes a Multi-view Similarity Aggregation and Multi-level Gap Optimization (MSAMGO) to improve the accuracy of pseudo labels before and after clustering. Specifically, Multi-view Similarity Aggregation (MSA) involves integrating similarity matrices calculated from multiple perspectives to generate reliable pseudo labels for clustering, greatly reducing noise interference from the source. Additionally, the Multi-level Gap Optimization (MGO) is introduced to leverage the positive and negative centroids of similarity between cluster-level samples and the correlation between instance-level samples. Together, they ensure the overall quality of pseudo-labels, reduce noise interference, and collectively promote model optimization. Experiments were conducted on several datasets, demonstrating that our method surpasses the majority of existing unsupervised person re-identification to a large extent and exhibits excellent generalization.

关键词： clustering algorithm Similarity matrix Unsupervised person Re-ID Pseudo label purification

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Contrastive learning via swapped cluster assignments for bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第1期35卷 015023-015023页

作者： Wang, Kai Liu, Chun Chen, Hongtian Xiu, Xianchao Shanghai Univ Inst Artificial Intelligence Sch Future Technol Shanghai 200444 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Univ Inst Artificial Intelligence Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

In industrial scenarios, machinery and equipment operate in environments with many uncontrollable factors. These factors have a significant impact on vibration signals. For instance, differences in rotational speeds can make the collected data inconsistent. As a result, the tags collected from industrial environments are untrustworthy. It is impossible to know how many types of vibration signals are included in the dataset. In this paper, we propose a self-supervised pre-training method for bearing fault diagnosis called contrastive learning via swapped cluster assignments (CLSCA). Technically, CLSCA is based on momentum contrast (MoCo) and uses an unsupervised clustering algorithm to replace the classification of positive and negative samples in MoCo. By autonomously extracting representations from unlabelled datasets and then generating pseudo-labels using a clustering algorithm, CLSCA is able to learn in a self-supervised manner. When the value of the clustering center is in the appropriate range, the classification accuracy of CLSCA is improved by about 4% compared to MoCo.

关键词： intelligent fault diagnosis contrastive learning data augmentation clustering algorithm dataset availability

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Geological modeling of coalbed methane reservoirs in the tectonically deformed coal seam group in the Dahebian block,western Guizhou,China

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Frontiers of Earth Science 2024年第1期18卷 44-67页

作者： Yong SHU Shuxun SANG Xiaozhi ZHOU Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process(Ministry of Education) China University of Mining and TechnologyXuzhou 221116China Carbon Neutrality Institute China University of Mining and TechnologyXuzhou 221008China School of Resources and Geosciences China University of Mining and TechnologyXuzhou 221116China Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization China University of Mining and TechnologyXuzhou 221008China

The widely spread Carboniferous-Permian coal seam group in southern China has great potential for coalbed methane resources,but the extensively developed tectonically deformed coal seriously restricts its *** the Dahebian block in western Guizhou as the study area,the geological model of coalbed methane reservoirs in the tectonically deformed coal seam group was established,and the spatial distribution pattern of model parameters was clarified by clustering algorithms and factor *** facies model suggests that the main coal body structures in Nos.1,4,and 7 coal seams are cataclastic coal and granulated coal,whereas the No.11 coal seam is dominated by granulated coal,which has larger thicknesses and spreads more *** in situ permeability of primary undeformed coal,cataclastic coal,granulated coal,and mylonitized coal reservoirs are 0.333 mD,0.931 mD,0.146 mD,and 0.099 mD,respectively,according to the production performance analysis *** property model constructed by facies-controlled modeling reveals that Nos.1,4,and 7 coal seams have a wider high-permeability area,but the gas content is lower;the high-permeability area in the No.11 coal seam is more limited,but the gas content is *** results of the self-organizing map neural network and K-means clustering indicate that the geological model can be divided into 6 clusters,the model parameter characteristics of the 6 clusters are summarized by data analysis in combination with 6 factors extracted by factor analysis,and the application of data analysis results in multi-layer coalbed methane co-development is *** study provides ideas for the geological modeling in the tectonically deformed coal seam group and its data analysis.

关键词： geological modeling tectonically deformed coal coal seam group clustering algorithm Dahebian block,western Guizhou

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Resource Allocation for Joint Communication and Positioning in mmWave Ad Hoc Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2187-2201页

作者： Luo, Xueni Lu, Xiaofeng Yin, Benquan Yang, Kun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Joint communication and positioning will be a critical driver in future wireless networks for emerging application areas. Supporting mobility, Ad-hoc networks can freely and dynamically self-organize an arbitrary and temporary network topology without any pre-existing infrastructure. Combined with millimeter-wave (mmWave), Ad-hoc networks can construct communication links with less time and higher directivity due to directional antennas and building blockage. The wide spectrum of mmWave could provide a high-oriented channel for positioning, which is significant for multi-user conditions. In this article, we concentrate on high-efficiency algorithms to allocate spectrum and power to different services and achieve a performance tradeoff between the communication and positioning process. Besides, the severe interference between users would degrade the actual system performance. To address these challenges, this article proposes an optimal clustering algorithm based on the mmWave Line of Sight (LoS) probability to form two different sub-nets for communication and positioning services, respectively. Then, the available spectrum resources are divided into two parts for the above sub-nets under the Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) technique, which could design sub-bands independently. Finally, we proposed an optimal algorithm to allocate sub-bands and power to improve the performance of the communication sub-net while guaranteeing the positioning performance in the corresponding sub-net. Numeric simulation results demonstrate that the proposed resource allocation algorithm could achieve better performance both in the communication and position process.

关键词： Millimeter wave communication Ad hoc networks Resource management OFDM Interference clustering algorithms Linear antenna arrays Joint communication and positioning millimeter-wave clustering algorithm resource allocation Ad hoc network

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AWeb Application Fingerprint Recognition Method Based on Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 887-906页

作者： Yanmei Shi Wei Yu Yanxia Zhao Yungang Jia School of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin300382China College of International Business Zhejiang Yuexiu UniversityShaoxing312030China School of Public Administration Zhejiang Gongshang UniversityHangzhou310018China Tianjin Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Tianjin300100China

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and *** fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web *** response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering *** method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality *** core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated *** transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint *** experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad *** novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

关键词： Web application fingerprint recognition unsupervised learning clustering algorithm feature extraction automated testing network security

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