Hongpeng Tian

Zhengzhou University

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Zuowei Zhang

Northwestern Polytechnical University

Zhunga Liu

Northwestern Polytechnical University

Miao Ma

Shaanxi Normal University

Arnaud Martin

Institut de Recherche en Informatique et Systèmes Aléatoires

Kuang Zhou

Cell Technology (China)

Zongfang Ma

Xi'an University of Architecture and Technology

Zhen Zhang

East China Normal University

Zhen Zhang

Southwest University

Hao Chong-yang

North China Electric Power University

Ling-Zhi Yan

Shaanxi Polytechnic Institute

Cooperative Institutions

Northwestern Polytechnical University

Xi'an University of Science and Technology

Shaanxi Normal University

Institut de Recherche en Informatique et Systèmes Aléatoires

Université de Rennes

Ministry of Education of the People's Republic of China

Centre National de la Recherche Scientifique

Zhengzhou University

Chinese Academy of Sciences

University of Chinese Academy of Sciences

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Stability Control of Flight Attitude Angle for Four Rotor Aircraft

Hongpeng Tian Weibo

In the four-rotor aircraft control system, the stability control of the attitude angle is the key to the stable flight of the four-rotor aircraft. Maintaining the stability of the attitude angle can ensure its safety, have better flight experience and accomplish specific tasks. Nonlinear, multivariable, and susceptible to external disturbances make attitude angle control difficult. Hence, a differential prior PID control algorithm is proposed to stabilize the attitude angle of a quadrotor. The algorithm improves the stability of the attitude angle and the anti-interference ability of the system under high frequency interference and frequent lifting. In order to study the performance of this algorithm, the dynamic model of the four rotor aircraft was established, simulated and compared with the conventional PID control algorithm. The simulation results show that the differential PID controller has a faster response speed, less overshoot and stronger anti-interference ability than the conventional PID controller in high frequency interference and frequent lifting environment, which effectively improves the stability control of the attitude range of the quadrotor.

Overshoot (microwave communication)

10.1109/icsess.2018.8663726

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Citations (5)

Image denoising using grey relational analysis in spatial domain

Visual Communications and Image Processing (2005)

Miao Ma Hongpeng Tian Hao Chong-yang

The search for efficient image denoising methods still is an open question. In this paper, we develop a universal algorithm based on grey relational analysis for similar binary image denoising. After taking the differences in spatial distribution between noise and edge into consideration, we select two referential sequences to represent inner off/on pixels, and a group of comparative sequences to stand for the pixels to be processed. Then, by analyzing the grey relational coefficients of the two kinds of sequences, we distinguish edge pixels from non-edge pixels, and reset the non-edge pixels to binary, thereby noise reduced and edges kept. The interest of the method lies in the fact that, without any precedent knowledge, it not only can reduce speckle, salt & pepper and gaussian noise at one time but also provide a trade-off between edge reservation and noise reduction via grey relational threshold. Experimental results show that the method obviously outperforms the three conventional spatial filters: median filter, wiener filter and mean filter. Possible applications include the processing before the recognition of printed or handwritten character, vehicle license plate, and the optimization of scanned binary trademark, engineering drawing and extracted binary watermark.

Grey relational analysis

Salt-and-pepper noise

Non-Local Means

Gaussian Noise

10.1117/12.631421

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Citations (8)

Enhancing Road Pothole Detection Algorithm of YOLOv5

2022 7th International Conference on Image, Vision and Computing (ICIVC) (2023)

Hongpeng Tian Miao Zhang

To address the issues of low accuracy and difficult deployment in road pothole detection, an improved algorithm for road pothole detection based on YOLOv5s is proposed in this paper. The algorithm incorporates a GhostNet backbone feature extraction network to reduce model complexity. Additionally, GSConv (Ghost Shuffle Convolution) is introduced in the Neck module to ensure model accuracy while reducing model weight. Moreover, a weighted bidirectional feature pyramid network (BiFPN) is employed to capture more feature information and enhance the information representation capability of feature maps, thereby increasing the detection accuracy of the network.Experimental results demonstrate that the improved model achieves a 30% reduction in algorithm complexity and a 33.3% decrease in computational workload. The mAP0.5:0.95 is improved by 4%, and the precision is increased by 5.3%. The improved algorithm achieves a balance between lightweight design and accuracy. Overall, the proposed algorithm achieves a significant reduction in complexity while maintaining high accuracy. This improvement contributes to the effective detection of road potholes, addressing the challenges of accuracy and deployment in practical applications.

Pothole (geology)

Pyramid (geometry)

Feature (linguistics)

Convolution (computer science)

10.1109/icivc58118.2023.10270164

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A survey of evidential clustering: Definitions, methods, and applications

Information Fusion (2024)

Zuowei Zhang Yiru Zhang Hongpeng Tian Arnaud Martin Zhunga Liu

10.1016/j.inffus.2024.102736

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Citations (1)

Learning a Credal Classifier With Optimized and Adaptive Multiestimation for Missing Data Imputation

IEEE Transactions on Systems Man and Cybernetics Systems (2021)

Zuowei Zhang Hongpeng Tian Ling-Zhi Yan Arnaud Martin Kuang Zhou

The classification analysis of missing data is still a challenging task since the training patterns may be insufficient and incomplete in many fields. To train a high-performance classifier and pursue high accuracy, we learn a credal classifier based on an optimized and adaptive multiestimation (OAME) method for missing data imputation on training and test sets. In OAME, some incomplete training patterns are estimated as multiple versions by a global optimization method thereby expanding the training set. On the other hand, the test pattern is adaptively estimated as one or multiple versions depending on the neighbors. For the test pattern with multiple versions, the corresponding outputs with different discounting factors (weights), represented by the basic belief assignments (BBAs), are fused for final credal classification based on evidence theory. The discounting factor contains two aspects: the importance and reliability factors that are used, respectively, to quantify the importance of the edited version itself and to represent the reliability of the classification result of the version. The effectiveness of OAME is widely validated on several real datasets and critically compared to other related methods.

Imputation (statistics)

Training set

10.1109/tsmc.2021.3090210

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Citations (15)

Multiple adaptive over-sampling for imbalanced data evidential classification

Engineering Applications of Artificial Intelligence (2024)

Zhen Zhang Hongpeng Tian Jin-shuai Jin

Adaptive sampling

10.1016/j.engappai.2024.108532

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Citations (3)

Fast SAR image segmentation method based on grey Tsallis entropy

Jisuanji yingyong yanjiu (2009)

Hongpeng Tian

Aiming at the speckle noise in SAR image and slow segmentation speed,the paper suggested a fast SAR image segmentation method based on grey theory and Tsallis entropy.In the method,after deduced an approximation image and a gradient image respectively from the origin image via wavelet transform,constructed their approximation-gradient cooccurrence matrix.On the basis of the matrix,designed a 2D grey Tsallis entropy model to locate the best threshold value via grey theory and Tsallis entropy.Additionally,introduced particle swarm optimization(PSO) to speed up the segmentation procedure.Some experimental results indicate that the new algorithm not only shortens the segmenting time obviously,but also ignores the disturbance of inherent speckle in SAR image and illustrates some flexibility in segmenting different objects.

Tsallis entropy

Source

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Citations (1)

Mixed-Type Imputation for Missing Data Credal Classification via Quality Matrices

IEEE Transactions on Systems Man and Cybernetics Systems (2024)

Zuowei Zhang Zhunga Liu Hongpeng Tian Arnaud Martin

Imputation (statistics)

10.1109/tsmc.2024.3389464

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Citations (3)

3D object reconstruction based on attention point cloud NeRF

Hongpeng Tian Rui Yang

Among the 3D target reconstruction methods, the neural radiation field is a micro-renderable process proposed on the basis of classical body rendering, which realizes the effective combination of neural field and graphics components and has shown its potential in the field of new viewpoint generation. Although the neural radiation field can generate high-quality reconstruction results, it requires rendering the scene under each viewpoint, leading to slow and time-consuming modeling. By contrast, the 3D point cloud, which is generated using the attention mechanism, presents both the spatial geometry of the object and preserves the color-texture features of the points around the object. Combining the neural radiation field with the attention point cloud avoids the rendering of missed points, resulting in improved reconstruction speed. Furthermore, the surface texture information of the object is preserved in the ray-tracing-based rendering pipeline. According to experimental results, the neural radiation field based on the attention point cloud method maintains the visual consistency of geometric contours with the original rendering method, thus achieving fast 3D target reconstruction. Quantified results based on SSIM, PSNR, and LPIPS further confirm the feasibility of this algorithmic model.

Light Field

3D rendering

10.1117/12.3012492

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Citations (0)

New method to quality evaluation for image fusion using gray relational analysis

Optical Engineering (2005)

Miao Ma Hongpeng Tian Hao Chong-yang

Aiming at the performance evaluation of image fusion, this paper discusses the existing criteria and the guidelines for choosing performance parameters, and then a new method is proposed to index the fusion algorithms on their fused images. In the method, each of the fused images is used to extract a comparative sequence. Meanwhile, a reference sequence is constructed by combining all the fused images involved. Then an overall performance index is obtained from the gray relational degrees between the two kinds of sequences. The interest of the method lies in the fact that it does not require a ground-truth or reference image in forming the reference sequence and is inexpensive in processing time and storage memory. Experimental results show that the method is effective, and its potential application includes intellichoice and intellectual control.

Sequence (biology)

Gray (unit)

10.1117/1.2011088

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Citations (16)