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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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A Recurrent Gaussian Process Regression Model with Composite Kernel for Industrial Process Quality Prediction

A Recurrent Gaussian Process Regression Model with Composite...

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International Conference on networking and network Applications (NaNA)

作者： Yanping Chen Cong Gao Weihui Yang Zhongmin Wang Zhong Yu Hong Xia School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods.

关键词： Ground penetrating radar Kernel Predictive models Gaussian processes data models Biological neural networks Training

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A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and Power Model with Decision Strategies

arXiv

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arXiv 2022年

作者： Sun, Jiaze Deng, Jiahui Li, Yang Han, Nan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China School of Information Science and Technology Northwest University Xi’an710127 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of economic cost, primary energy consumption, and pollutant emissions. The mathematical expression of the CCHP system is proposed, and a multi-objective optimization model with constraints is established. According to different usage requirements, two decision-making strategies are designed, which can adapt to different scenarios. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) algorithm is proposed to optimize the CCHP model for the first time. The algorithm provides the optimal energy scheduling scheme by optimizing the production capacity of different capacity equipment. The simulation is conducted in three application scenarios: hotels, offices, and residential buildings. The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%. Concurrently, the Wilcoxon signed-rank test shows that BCS-GDE is significantly better than OMOPSO, NSGA-II, and SPEA2 with greater than 95% confidence. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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LMVD: A Large-Scale Multimodal Vlog dataset for Depression Detection in the Wild

arXiv

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arXiv 2024年

作者： He, Lang Chen, Kai Zhao, Junnan Wang, Yimeng Pei, Ercheng Chen, Haifeng Jiang, Jiewei Zhang, Shiqing Zhang, Jie Wang, Zhongmin He, Tao Tiwari, Prayag The School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China The Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China The College of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710016 China The Institute of Intelligent Information Processing Taizhou University Zhejiang Taizhou318000 China The School of Education Shenzhen University Shenzhen518060 China The School of Information Technology Halmstad University Halmstad Sweden

Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects’ privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://***/helang818/LMVD/. © 2024, CC BY-NC-ND.

关键词： Deep learning

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Robust MIMO Channel Estimation from Incomplete and Corrupted Measurements

Robust MIMO Channel Estimation from Incomplete and Corrupted...

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International Conference on Information Fusion

作者： Fuxi Wen Zhongmin Wang Chen Liang State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

Location-aware communication is one of the enabling techniques for future 5G networks. It requires accurate temporal and spatial channel estimation from multidimensional data. Most of the existing channel estimation techniques assume that the measurements are complete and noise is Gaussian. While these approaches are brittle to corrupted or outlying measurements, which are ubiquitous in real applications. To address these issues, we develop a l_p-norm minimization based iteratively reweighted higher-order singular value decomposition algorithm. It is robust to Gaussian as well as the impulsive noise even when the measurement data is incomplete. Compared with the state-of-the-art techniques, accurate estimation results are achieved for the proposed approach.

关键词： Tensile stress Robustness Channel estimation Minimization MIMO communication Noise measurement Estimation

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A novel method for ECG signal classification via one-dimensional convolutional neural network

A novel method for ECG signal classification via one-dimensi...

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作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Chen, Qinghui Tang, Shaojie Tang, Jinshan Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Department of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the s

关键词： Electrocardiograms

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Indicator & crowding Distance-Based Evolutionary Algorithm for Combined Heat and Power Economic Emission Dispatch

arXiv

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arXiv 2020年

作者： Sun, Jiaze Deng, Jiahui Li, Yang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Heat and power have become the most indispensable resources. However, the traditional ways of generating power and heat are inefficient and cause high pollution;a CHP (Combined Heat and Power) unit can solve these problems well. In recent years, more attention has been paid to energy conservation and environmental protection, and Combined Heat and Power Economic Emission Dispatch (CHPEED) has become an important multi-objective optimization problem. In this paper, an Indicator & crowding Distance-based Evolutionary Algorithm (IDBEA) is put forward for handling this non-convex and non-linear problem. With consideration of the valve-point effects and power transmission loss, IDBEA is tested on three standard test systems with different types, including four units, five units and seven units. In the experiment, IDBEA is compared with several evolutionary algorithms, the simulation results demonstrate that IDBEA has strong stability and superiority, while the solutions show better convergence and diversity than several typical algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Cogeneration plants

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Social cognitive optimization with tent map for combined heat and power economic dispatch

arXiv

引用

arXiv 2018年

作者： Sun, Jiaze Li, Yang School of Computer Science and Technology Xi 'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi 'an University of Posts and Telecommunications Xi 'an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Combined Heat and Power Economic Dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel Social Cognitive Optimization algorithm with Tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, Adaptive Constraints Relaxing (ACR) rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by carrying out two typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Functions

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