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Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

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Computers, Materials & Continua 2024年第10期81卷 933-949页

作者： Shuang Kang Yinchao He Wenwen Li Sen Liu School of Mechanical and Control Engineering Baicheng Normal UniversityBaicheng137000China School of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu ***,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the *** algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and ***,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu ***,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is *** with the adaptive iterative threshold algorithm,the threshold selection process is further *** results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive *** average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.

关键词： Morphological enhancement improved Otsu algorithm infrared image grayscale inversion adaptive iterative thresholding

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An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

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Computers, Materials & Continua 2024年第6期79卷 4413-4431页

作者： Yu Zuo Wenwen Li School of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China School of Mechanical and Control Engineering Baicheng Normal UniversityBaicheng137000China

In cornfields,factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed *** addition,remote areas such as farmland are usually constrained by limited computational resources and limited collected ***,it becomes necessary to lighten the model to better adapt to complex cornfield scene,and make full use of the limited data *** this paper,we propose an improved image segmentation algorithm based on ***,the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability;secondly,the pyramid pooling module is introduced to enhance the network’s ability of acquiring contextual information as well as the ability of dealing with the small target loss problem;and lastly,Finally,to further enhance the segmentation capability of the model,the squeeze and excitation mechanism is introduced in the expansion *** used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved *** improved model has a total parameter of 3.79 M and miou can achieve 87.9%.The fps on a single 3050 ti video card is about *** experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy.

关键词： Semantic segmentation deep learning UNet pyramid pooling module

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Semantic Comprehension Method for Chinese Sentences Based on Minimal Semantic Structures and Its Application

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Chinese Journal of Electronics 2023年第3期32卷 613-624页

作者： WEN Hao HE Qianru School of Information and Control Engineering Xi'an University of Architecture Technology

The importance of small Chinese sentences is no less than that of sentences, which is an inherent feature of Chinese itself. According to this characteristic, this paper proposes a sentence semantic understanding method for Chinese scientific and technological abstracts based on the minimum semantic structure. Firstly,a conceptual model was established for identifying the minimum semantic structure of a sentence based on a corpus of verbs, relative words, prepositions and markers based on Language technology Planform(LTP) ***, the model was used to extract the minimum semantic structure of abstract sentence. Finally, three experiments were carried out, namely, the classification of the abstract sentences, knowledge graph generation and automatic semantic inference discovery. Our study confirmed the practical value of the small Chinese *** experimental results show that the effect of using small sentences to understand the semantics of Chinese text is better than that of the full stop sentence, and the minimum semantic structure can be used as the basic unit of the Chinese sentence semantic comprehension. This method is conducive in the automatic understanding of the basic semantics of sentences in unstructured Chinese science and technology text sentences.

关键词： Semantics Knowledge graphs Computer applications Big Data Feature extraction Grammar Task analysis

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Process Industries Soft sensor modeling based on Semi-supervised Growing Echo States Gaussian Process

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IEEE Sensors Journal 2025年第11期25卷 20695-20707页

作者： Li, Wenyi Dai, Wei Li, Biao Nan, Jing China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Suzhou University School of Mechanical and Information Engineering Suzhou234000 China

The echo state Gaussian process (ESGP) is an efficient method for modeling dynamical systems and has been successfully employed in soft sensor modeling within the process industry. However, the ESGP operates as a supervised learner, necessitating manual configuration of the reservoir. In practical process industries, many industrial samples lack labels, resulting in incomplete data. To address these challenges, this paper introduces a semi-supervised growing ESGP (SS-GESGP). Firstly, we propose a growing ESGP (GESGP) for incremental automatic reservoir construction. Subsequently, to tackle soft sensing modeling with incomplete data, we integrate a state resampling technique into the GESGP learning process. This technique resamples the echo state, incorporating both labeled and unlabeled spatiotemporal information. By leveraging the resampled states for Gaussian process, we convert semi-supervised problems into supervised learning scenarios. Finally, the SS-GESGP is evaluated using both time-series benchmarks and real-world process industry data. Experimental results demonstrate that the proposed method exhibits superior soft sensing performance compared to state-of-the-art approaches. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Traffic Light Recognition Based on Improved YOLOv5l

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Journal of Shanghai Jiaotong University (Science) 2024年 1-15页

作者： Dong, Ruyi Shi, Cong College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132022 China

Accurate recognition of traffic lights is essential for ensuring the safety of passengers and pedestrians, especially in the context of self-driving car technology. However, traffic lights present challenges due to their small size and limited recognition accuracy. This paper proposes an enhanced version of the YOLOv5l algorithm specifically designed for traffic light recognition. First, the K-means++ clustering algorithm is employed to generate the prior frame. Second, the SiLU activation function in the basic convolution module is replaced with the adaptive Meta-ACONC activation function, significantly improving the model’s detection accuracy. Third, the coordinate attention mechanism is integrated into the trunk feature extraction network to incorporate coordinate information into the channel, thereby enhancing the network’s sensitivity to small target positions and mitigating the ambiguity caused by increased network depth. Finally, the network’s detection scale is improved by removing the original 20 × 20 large target detection head, leading to an improved accuracy and speed for detecting small targets. The proposed approach is evaluated on self-created traffic light datasets, and compared with the original YOLOv5l model;the improved YOLOv5l model achieves a 7.1% increase in mAP@0.5, reaching 83.3%, effectively meeting the requirements for traffic light detection and recognition. © Shanghai Jiao Tong University 2024.

关键词： K-means clustering

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Influencer identification of dynamical networks based on an information entropy dimension reduction method

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Chinese Physics B 2024年第4期33卷 375-384页

作者：段东立纪思源袁紫薇 School of Information and Control Engineering Xi'an University of Architecture and TechnologyXi'an 710311China School of Mechanical Engineering Northwestern Polytechnical UniversityXi'an 710072China

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and *** methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network ***, these algorithms do not consider network state *** applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network *** using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by *** applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the *** neural *** observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

关键词： dynamical networks network influencer low-dimensional dynamics network disintegration

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Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

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Chinese Physics B 2024年第5期33卷 173-180页

作者：段东立毕菲菲李思凡吴成星吕长春蔡志强 School of Information and Control Engineering Xi'an University of Architecture and TechnologyXi'an 710311China School of Mechanical Engineering Northwestern Polytechnical UniversityXi'an 710072China

Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called ***,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete *** capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest *** we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the *** work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.

关键词： complex networks tipping points dimension reduction k-core

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An Improved YOLO for Road and Vehicle Target Detection Model

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第2期11卷 197-216页

作者： Yu, Qinghe Liu, Huaiqin Wu, Qu School of Information and Control Engineering Qingdao University of Technology Qingdao China

The yolo series is the prevalent algorithm for target identification at now. Nevertheless, due to the high real-time, mixed target parity, and obscured target features of vehicle target recognition, missed detection and incorrect detection are common. It enhances the yolo algorithm in order to enhance the network performance of this method while identifying vehicle targets. To properly portray the improvement impact, the yolov4 method is used as the improvement baseline. First, the structure of the DarkNet backbone network is modified, and a more efficient backbone network, FBR-DarkNet, is presented to enhance the effect of feature extraction. In order to better detect obstructed cars, a thin feature layer for focused detection of tiny objects is added to the Neck module to increase the recognition impact. The attention mechanism module CBAM is included to increase the model's precision and speed of convergence. The lightweight network replaces the MISH function with the H-SWISH function, and the improved algorithm improves by 4.76 percentage points over the original network on the BDD100K data set, with the mAP metrics improving by 8 points, 8 points, and 7 points, respectively, for the car, truck, and bus categories. Compared to other newer and better algorithms, it nevertheless maintains a pretty decent performance. It satisfies the criteria for real-time detection and significantly improves the detection accuracy. © 2023 River Publishers.

关键词： Convolutional neural networks

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AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

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China Communications 2023年第4期20卷 356-367页

作者： Yunyun Li Yanjing Sun Qingyan Ren Song Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ***,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node *** propose an adaptive AUV-assisted data collection strategy for ocean currents to address these ***,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy *** AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target ***,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for *** simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

关键词： underwater sensor networks data collection ocean currents value of information energy consumption

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Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1643-1655页

作者： Meng Zhou Zihao Wang Jing Wang Zhengcai Cao the School of Electrical and Control Engineering North China University of TechnologyBeijing 100144China IEEE the College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian ***, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

关键词： Dynamic obstacle avoidance multi-robot collaborative hunting obstacle-avoiding Voronoi cells task allocation

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