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Rapid Detection of Rice Disease Based on FCM-kM and Faster R-CNN Fusion

IEEE ACCESS 2019年 7卷 143190-143206页

作者： Zhou, Guoxiong Zhang, Wenzhuo Chen, Aibin He, Mingfang Ma, Xueshuo Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha 410004 Hunan Peoples R China

In this paper, a method for detecting rapid rice disease based on FCM-kM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the k-means clustering algorithm (FCM-kM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-kM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

关键词： Chaos theory faster R-CNN firefly algorithm Otsu threshold segmentation k-means clustering algorithm rice disease detection weighted multistage median filter

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Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Zhiyong, Lv Liu, Tongfei Wang, Robert Yu Benediktsson, Jon Atli Saha, Sudipan Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Univ Iceland Fac Elect & Comp Engn IS-107 Reykjavik Iceland Tech Univ Munich TUM Munich AI Future Lab AI4EO Artificial Intelligenc D-82024 Taufkirchen Germany

Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.

关键词： Terrain factors Remote sensing Training Shape Tuning clustering algorithms Standards k-means clustering algorithm land-cover change detection (LCCD) landslide inventory mapping (LIM) very-high-spatial-resolution (VHR) images

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Fracture behavior and acoustic emission characteristics of layered sandstone with a bedding-parallel crack

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2024年 131卷

作者： Liu, kewei Zou, Liansong Guo, Tengfei Guo, Can Yang, Jiacai Zhang, Yi Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China

Bedding planes and cracks are widely found in natural rock mass, exerting a substantial effect on the stability and security of layered rock structures. Cracked straight-through Brazilian disc (CSTBD) tests were conducted to analyze the fracture mechanics of layered sandstone. Meanwhile, acoustic emission (AE) monitoring system was applied to reveal AE characteristics. Test results indicate that bedding planes substantially influence the strength and crack extension behaviors. Tensile failure along bedding planes corresponds to the lowest strength, and the highest strength is observed when failure appears perpendicular to the beddings. Three failure modes are recognized: bedding planes failure (theta = 0 degrees), mixed failure (theta = 15 degrees, 30 degrees, 45 degrees, 60 degrees, and 75 degrees), and matrix failure (theta = 90 degrees). The b-value can be considered as a predecessor index of rock engineering instability, and fracture will occur when b-value achieves the minimum value. Comparing the proportions of tensile-shear crack obtained from four different classification methods, it is found that the average frequency (AF) and rise angle (RA) distribution acquired through the k-means clustering algorithm provides optimal results for identifying the tensile and shear crack types. Furthermore, the study reveals that the absolute value of mode I fracture toughness declines first and then increases with the theta growth, while mode II fracture toughness exhibits the opposite trend. Finally, the mixed-mode fracture mechanism of layered sandstone was analyzed, and results showed that the generalized maximum tangential stress (GMTS) criterion could produce better projections for fracture toughness and crack initiation orientations.

关键词： CSTBD Layered sandstone AE characteristics k-means clustering algorithm Fracture characteristics

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Adaptive multilabel image segmentation model with prior constraints based on geometric structure characteristics

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JOURNAL OF ELECTRONIC IMAGING 2022年第6期31卷 063031-063031页

作者： Tian, Yuzhu Pang, Liping Pang, Yanbo Zhang, Hongwei Dalian Univ Technol Sch Math Sci Dalian Peoples R China Key Lab Computat Math & Data Intelligence Liaonin Dalian Peoples R China Univ Tokyo Ctr Spatial Informat Sci Tokyo Japan

Image segmentation is an essential digital image processing problem closely related to computer vision. The limitations of the total variational regular term for the continuous Potts model make the transition of segmentation results over-smooth. In this paper, we propose an improved model with prior conditions to realize the improvement of image segmentation results quality. To extract the structural features of the images, we utilize the simple and practical k-mean clustering (kM) algorithm to set the corresponding volume structure as a prior condition, which is also crucial for the initial label selection of the proposed model. Then, we choose two convex relaxation methods to solve the original nonconvex variational problem. Using these methods, we verify that increasing the constraint of volume structure can maintain slender structures in the original image and achieve a good balance between image segmentation quality and computation. Consequently, we use the unified, convergent primal-dual (PD) algorithm to solve the minimization problem in the proposed model. Extensive experimental comparisons between our method and the pure kM method, the graph-cut method, and the corresponding Potts model without volumetric structure are provided. The segmentation results illustrate that our model performs better in terms of both visualization and evaluation metrics. (c) 2022 SPIE and IS&T

关键词： image segmentation k-means clustering algorithm total variation regularization volume constraint

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Performance enhanced probabilistically shaped PAM4 signal based on nonlinear differential coding for IM/DD system

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OPTICAL FIBER TECHNOLOGY 2022年 71卷

作者： Liu, Tianyi Ma, Jie Lu, Jia Liu, Jianfei Zeng, Xiangye Wang, Yang Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China

In this paper, the probabilistically shaped pulse amplitude modulation (PS-PAM) signal is proposed in intensity modulation and direct detection (IM/DD) system. For the traditional shaping scheme with constant composition distribution matcher (CCDM), there are several problems that high-precision algorithm for long block coding, the loss of information rate for short block coding. In addition, it can increase nonlinear interference. In order to reduce computational complexity and alleviate the influence of nonlinear interference on the system, the nonlinear differential coding (NLDC) scheme is used to realize PS, which can be better integrated with the costeffective optical access network. This paper evaluates seven shaping spectrums obtained by different NLDC coding coefficients. As the coding coefficient increases, the performance of the system gradually improves. Moreover, this paper proposed the machine learning includes neural network (NN) and improved k-means clustering algorithm at the receiver. The results show that the PS-PAM4 signal is significantly better than the ordinary PAM4 signal in improving the channel bandwidth tolerance and signal transmission rate. Under the same data rate, using NLDC can save 5.8 GHz bandwidth for the system. For the bandwidth-limited system, the PS-PAM4 signal can obtain 19Gbit/s bit rate gain compared with the ordinary PAM4 signal. Compared with the typical shaping scheme using CCDM, this scheme can achieve 2.2 GHz band tolerance gain and 5Gbit/s rate gain in nonlinear fiber channel.

关键词： Intensity modulation and direct detection Probabilistic shaping Nonlinear differential coding Neural network k-means clustering algorithm

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A New Porosity Evaluation Method Based on a Statistical Methodology for Granular Material: A Case Study in Construction Sand

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7379页

作者： Wang, Binghui Xin, Shuanglong Jin, Dandan Zhang, Lei Wu, Jianjun Guo, Huiyi Jiangsu Univ Sci & Technol Fac Architecture & Civil Engn Zhenjiang 212100 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

关键词： porosity observed porosity target particles digital image k-means clustering algorithm

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On the Deployment Problem of Multiple Drones in Millimeter Wave Systems

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JOURNAL OF NETWORk AND SYSTEMS MANAGEMENT 2024年第4期32卷 71-71页

作者： Shakhatreh, Hazim Al-Dagamseh, Wa'ed Albasheer, Samah Bani-Hani, khaled Hayajneh, khaled Yarmouk Univ Hijjawi Fac Engn Technol Dept Telecommun Engn Irbid 21163 Jordan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

Millimeter wave (mm-Wave) frequency bands have paved the way for wide drone applications to support future wireless networks. This research studies the integration of drone-assisted wireless networks with 5G mm-Wave communications. Two scenarios are considered: the single and multiple drone scenarios. In the first scenario, we aim to find an effective 3D positioning of a single drone that minimizes the overall transmit power required to serve the users. Therefore, we utilize the particle swarm optimization (PSO) algorithm to solve the optimization problem. In the second scenario, a drone's low transmission power may be inefficient in utilizing a single drone to cover a set of users. To that end, we aim to minimize the number of drones and find efficient 3D placements for the drones required to serve the users. Minimizing the number of drones will reduce operational costs during drones' wireless network planning and operations. Moreover, it helps operators utilize unused drones to extend wireless coverage. Therefore, we propose the clustering algorithm to solve this optimization problem. Finally, the results of simulations are used to verify the efficacy of the suggested algorithms in our scenarios.

关键词： Drones Millimeter wave communications 5G and beyond wireless applications PSO algorithm k-means clustering algorithm

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VkECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and k-means algorithm

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SENSORS 2023年第2期23卷 573-573页

作者： Gou, Pingzhang Guo, Baoyong Guo, Miao Mao, Shun Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VkECE-3D, based on 3D-Voronoi partitioning and the k-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the k-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VkECE-3D improves network coverage and greatly lengthens the network's lifetime.

关键词： heterogeneous wireless sensor networks highly destructive polynomial mutation strategy 3D-Voronoi partitioning k-means clustering algorithm polling working mechanism

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A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics

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BUILDINGS 2024年第9期14卷 2646页

作者： Chen, Ping Tang, Hao Shandong Jianzhu Univ Sch Architecture & Urban Planning SAU Jinan 250101 Peoples R China

The adaptive fa & ccedil;ades serve as the interface between the indoor and outdoor energy of the building. Adaptive fa & ccedil;ade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive fa & ccedil;ade optimization design. This paper aims to reveal the potential functional relationship between building fa & ccedil;ade performance characteristics and adaptive fa & ccedil;ade design. It proposes an adaptive fa & ccedil;ade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a k-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive fa & ccedil;ade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson's correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive fa & ccedil;ades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive fa & ccedil;ade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 kWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive fa & ccedil;ade optimization design, which helps to achieve multiple performance objectives for buildings.

关键词： adaptive fa & ccedil ades building envelope performance daylighting performance quality of view solar energy utilization k-means clustering algorithm multi-objective optimization

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Shipwreck Target Recognition in Side-Scan Sonar Images by Improved YOLOv3 Model Based on Transfer Learning

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IEEE ACCESS 2020年 8卷 173450-173460页

作者： Tang Yulin Jin, Shaohua Bian, Gang Zhang, Yonghou Dalian Naval Acad Dept Hydrog & Cartog Dalian 116018 Peoples R China

When used to recognize side-scan sonar images of shipwreck targets, the Faster R-CNN model is time-consuming and has low efficiency and a high missed detection rate for small targets. Considering that existing datasets of side-scan sonar images of shipwreck targets are small, we propose a YOLOv3 model that can automatically recognize side-scan sonar images of shipwreck targets based on transfer learning. Based on the Darknet-53 network, we froze part of the convolutional layer of the YOLOv3 model trained on COCO dataset images, and conducted transfer learning. Multi-scale training of shallow feature fusion was done based on multi-scale feature fusion with Feature Pyramid Networks (FPN) support, and the proportion of recognized shallow features of shipwreck targets increased. Meanwhile, the parameters and sizes of target anchor boxes were reset using k-means clustering, which allowed us to improve the speed of target recognition and the precision of smaller target recognition and positioning. Lastly, the binary classification cross entropy function was used to improve the loss function of the YOLOv3 algorithm. Experimental results show that under the same recognition target, the average precision (AP) value of the YOLOv3 model based on transfer learning reached 89.49%, which is an improvement of 0.31% and 1.77%, respectively, compared with the Faster R-CNN model and the traditional YOLOv3 model. Moreover, the YOLOv3 model based on transfer learning had the highest harmonic mean (F1), reaching 90.71%, which is 3.96% and 1.63% higher, respectively, than the harmonic means of the Faster R-CNN and the traditional YOLOv3 model. Lastly, the traditional YOLOv3 model takes an average 0.17s to identify a target. In contrast, the Faster R-CNN model takes an average of 2.8s to identify a target. Hence, our transfer learning YOLOv3 model greatly improves detection efficiency, meets the needs of real-time target recognition, and ultimately has better overall performance than exis

关键词： Target recognition Feature extraction Training Sonar detection Object detection Predictive models Side-scan sonar shipwreck target YOLOv3~model transfer learning shallow feature fusion k-means clustering algorithm binary classification cross entropy

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