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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Lin Fan Ruiling Yao Zhongmin Wang School of Computer Science and Technology Xi'an University of Posts and Telecommunications China and Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time.

关键词： Hilbert-Huang Transform Hypertension Principal Component analysis Pulse Diagnosis

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Research on feature mining algorithm and disease diagnosis of pulse signal based on piezoelectric sensor

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Informatics in Medicine Unlocked 2021年 26卷

作者： Lin, Fan Zhang, Jincheng Wang, Zhongmin Zhang, Xiaokang Yao, Ruiling Li, Yan School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 Shaanxi China

The human pulse contains various information reflecting the internal environment of the human body. However, the classical method of pulse diagnosis in traditional Chinese medicine (TCM) has the disadvantages of relying too much on the doctor's experience and the diagnosis result is too subjective. Based on the principle of TCM pulse diagnosis, the use of photoelectric sensors to collect the pulse signals of multiple healthy people and patients with chronic diseases, and organize the detailed pulse information into a data set and analyze it with algorithms, is a solution to overcome this problem through modern technology. However, this method is still difficult to understand the patient's physiological condition in detail, and it is also difficult to explain the internal connection between abnormal pulse conditions and their physiological conditions. In the experiment, after denoising, smoothing, and eliminating the baseline drift of the subjects' pulse data, we designed two algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. The specific feature values obtained are converted into a multi-dimensional array and trained in a support vector machine (SVM) classifier. The classification accuracy is higher than the basic temporal features. Experimental results show that it is feasible to use specific feature mining algorithms for disease detection. Through analysis, this paper found the pathological characteristics reflected in the two-dimensional pulse image, discovered the internal connection between the pulse waveform characteristics of the human body and the disease, and tried to describe it through algorithms, trying to establish a method for detecting specific diseases using photoelectric signals. © 2021 The Authors

关键词： Pathological feature mining Physiological signal feature Pulse diagnosis Pulse wave analysis

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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A High Parallel Accelerator Design Based on the Sparsity of Convolutional Neural network

SSRN

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SSRN 2022年

作者： Pan, Xiao Ying Mu, Yuanzhen Jia, Ningxin Gao, Xuan Rong Feng, Tong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shannxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Shannxi Xi’an710121 China

Designing a domain-specific accelerator is an effective way to improve the performance of inference network on resource-constrained embedded devices. However, there are a large number of invalid memory accesses and calculations in the convolutional neural network. Based on this, this paper proposes an accelerator that adopts a flexible neural network structure that adjusts the processing flow of the network, reduces the size of the feature map sent into the activation function, and greatly reduces the inherent invalid calculation and memory access of the network. The accelerator uses loop expansion and loop reconstruction to solve the read-write problem in convolution operation, realize instruction-level parallelism and data-level parallelism. We determine the design parameters of parallel computing by modelling the constrained single-objective optimization problem to find the optimal cyclic expansion factor. Evaluate the performance, resource utilization, and energy consumption of accelerators in different neural networks. Compared with the general processor CPU and GPU, the running time of the accelerator is shortened by 6.89s and 1.16s, and the power consumption is only 7.47% of CPU and 3% of GPU. Compared with the state-of-the-art accelerator, the performance is increased by 2.8x and the power consumption is reduced by approximately 6W. © 2022, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

An Adaptive Model Update Object Tracking Algorithm based on ...

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International Conference on networking and network Applications (NaNA)

作者： Sugang Ma Lei Zhang Lei Pu Xiaobao Yang Zhiqiang Hou Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Information and Navigation Air Force Engineering University Xi'an shaanxi China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation.

关键词： Adaptation models Visualization Correlation Target tracking Feature extraction Object tracking Strain

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Real-Time Semantic Segmentation Algorithm Based on Tversky Loss Function and Mixed Pooling

Real-Time Semantic Segmentation Algorithm Based on Tversky L...

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International Conference on networking and network Applications (NaNA)

作者： Peng Xu Ziyi Zhao Sugang Ma Xi'an Science and Technology Museum Xi'an Shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

In recent years, semantic segmentation methods based on deep learning have made remarkable developments. Despite achieving high segmentation accuracy, the performance of real-time segmentation methods cannot satisfy real-world applications. In order to achieve a balance between segmentation accuracy and speed, a real-time semantic segmentation algorithm based on Tversky loss function and mixed pooling is proposed in this paper. A Short-Term Dense Concatenate network (STDC network) is used to construct the encoder, and a mixed pooling module is used for the final part of the encoder, which uses strip pooling and average pooling to enhance the feature representation while expanding the receptive field. Additionally, a Tversky-based loss function is used for the detail guidance module of the backbone network in the encoder, and a joint loss function is used to supervise the whole network's training. We achieved 76.9% mIoU at 110.7 FPS on the Cityscapes dataset, a 2.4% improvement in accuracy over the benchmark algorithm STDCSeg, and 72.2% mIoU at 177.6 FPS on the Camvid dataset, satisfied the requirements of the real-time segmentation task.

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PVGAN: A Pathological Voice Generation Model Incorporating a Progressive Nesting Strategy

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Journal of Voice 2023年

作者： Pan, Xiaoying Feng, Tong Zhang, Nijuan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China

The voice generation task is to solve the problem of limited samples in the voice dataset using computer technology. By increasing the number of samples, the accuracy of voice disorder diagnosis can be improved, which has a wide range of application value in medical diagnosis and other fields. At present, there are insufficient models for detailed features such as pitch, timbre, and different frequency components in pathological voice data. Therefore, this paper proposes a PVGAN network for learning different frequency information of audio to generate pathological voice data. The proposed network captures the multi-scale features and different periodic patterns of audio signals by designing multiscale perceptual residual blocks and periodic discriminators. At the same time, a progressive nesting strategy was proposed to combine the generator and the discriminator to improve the learning ability of different resolution information. In addition, a latent mapping network is designed to fuse the latent vector with the condition information to generate sound features related to specific diseases or pathological states. The loss function is optimized to further improve the model performance. On the Saarbruecken Voice database(SVD), the average values of each index of the data generated after training with different pathological types as conditional information are similar to the original data. Finally, the generated data were used to expand the SVD dataset, and the accuracy of the two classification experiments was improved to a certain extent. © 2023 The Voice Foundation

关键词： Deep learning Generative adversarial networks Voice analysis Voice generation

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Image Manipulation Detection Using the Attention Mechanism and Faster R-CNN

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第4期50卷 1261页

作者： Tan, Kang Li, Linna Huang, Qiongdan School of Control Science and Engineering Xi’an University of Post and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Post and Telecommunications Xi’an710121 China School of Communication and Information Engineering Xi’an University of Post and Telecommunications Xi’an710121 China

With the advancement of image authoring software, the traces of image tampering operation have become increasingly difficult to detect. To enhance the performance of manipulation detection, we propose a novel end-to-end image tampering detection method using the attention mechanism and two-stream Faster R-CNN. In our model, we eliminate steganalysis rich model (SRM) filter, which suppresses image content, and employ a constrained convolution layer to adaptively extract image tamper features during model training. These features are then inputted into the backbone network in parallel with the RGB image. To emphasize image tamper features and restrict irrelevant features, we introduce the Convolutional Block Attention Module (CBAM) into the model, enabling better delivery of tamper-related information. Then, the output of backbone network is fed into the Region of Interest (RoI), where we replace max pooling operation with bilinear interpolation. This modification allows the model to retain more tamper information. After that, we use bilinear pooling for feature fusion of the two streams, and the fused results perform tamper classification operations. The RGB stream through Region Proposal network (RPN) to achieve tamper localization. We evaluate our model on three publicly available standard image tampering datasets and demonstrate through experiments that our approach significantly improves the precision of manipulation detection and localization. © 2023, International Association of Engineers. All rights reserved.

关键词： Convolution

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A new runoff calculation model based on multi-source radar remote sensing technology 6

A new runoff calculation model based on multi-source radar r...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhao, Jianhui Wang, Ning Wu, Lin Min, Lin College of Computer and Information Engineering Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation of water depth, river width and flow velocity is complex. Radar remote sensing can observe target all-time and all-weather, and has the ability to penetrate clouds, rain and fog. The common Radar remote sensing runoff calculation model is depth-runoff relationship model, which is constructed by fitting water depth and in-situ runoff. The river depth is derived from Radar Altimetry (RA) data. But this model has only one input parameter, the accuracy of this model inverted runoff is not high. In view of the above problems, this paper proposed the Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM). Firstly, river width and relative water depth were derived from Synthetic Aperture Radar (SAR) images and RA data. Secondly, based on the Manning's equation, the flow velocity were described from river width and relative water depth. Thirdly, river width relative water depth, and in-situ runoff were fitted to construct MRRS-RCM. Experiments show the accuracy of MRRS-RCM estimated runoff is better than depth-runoff relationship model, the RRMSE values of MRRS-RCM reach 13%. © 2019 IEEE.

关键词： Synthetic aperture radar

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Siamese network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update

Siamese Network Visual Tracking Algorithm Based on GCT Atten...

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Natural Language processing (ICNLP), International Conference on

作者： Sugang Ma Siwei Sun Lei Pu Xiaobao Yang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China School of Operational Support Rocket Force Engineering University Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%; On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements.

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