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2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Wu, Juntao Jing, Hefang Wang, Weihong Guo, Xiaoxiao Guo, Xinxia Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

As one of the main irrigation canals in Ningxia, West Main Canal plays a very important role in the irrigation of the land nearby the canal, such as farms, meadows, and tree farms. Because of the long-term use of the Yellow River water for irrigation, the soil salinization in this area become more serious. In this paper, the problem of soil salinization along the West Main Canal in Ningxia is taken as the research object. 82 sample points in the studied area are chosen and a total number of 164 soil samples are measured for the eight main salt ions and pH value. Correlation analysis and principal component analysis are employed to study the distribution of the soil salinity ions and pH value. In addition, two dimensional interpolation is applied to obtain the spatial distribution of the total saltiness. It can be found that Cl-, SO42-, Ca2+, Na+ and HCO3- are the main salt ions and the main soluble salts are NaCl, KCl, CaSO4, Na2SO4 and CaCl2 in the soil of the studied area. The spatial distribution of the total soil salt is mainly in the patterns of north high and south low, far high and near low, up high and down low. © Published under licence by IOP Publishing Ltd.

关键词： Sodium chloride

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An Improved Lmmse Channel Estimation Algorithm for Mimo-Ofdm System

SSRN

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Wei, Mingzhe Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology 999077 Hong Kong The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

This paper studies channel estimation techniques to recover the received signal more accurately and quickly. The classical Least Squares (LS) and Linear Minimum Mean-squared Error (LMMSE) estimation have the problems of poor estimation performance and high complexity. An Fast-LMMSE channel estimation algorithm is proposed to balance the performance and complexity. In order to apply it to the actual system, the corresponding pilot is also designed. Simulation results are provided and the comparison shows the performance gains achieved by the proposed technique over LS and MMSE estimation in SISO and MIMO communication systems. © 2022, The Authors. All rights reserved.

关键词： Channel estimation

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Experimental Study on Flow Characteristics in a Compound Open Channel with Flexible Vegetation

Experimental Study on Flow Characteristics in a Compound Ope...

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2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Guo, Xiaoxiao Jing, Hefang Wang, Weihong Wu, Juntao Zhang, Qiaoling Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

In order to study the effect of flexible vegetation for the flow in open channels with flood plains, a series of experiments were carried out in a laboratory flume with a two-stage floodplain. Four cases, i.e., dense parallel arrangement, normal parallel arrangement, sparse parallel arrangement and no vegetation condition, were designed with fixed water depth and discharge. By using three dimensional (3D) laser Doppler velocimeter (LDV) and other instruments, under the condition of the four cases,3D flow velocities, water level and turbulence intensity were measured, The experimental results show that, compared with the non-vegetation situation, the arrangement of grasses on the floodplain will increase the water surface gradient, hydraulic gradient and turbulence intensity to a certain extent. © Published under licence by IOP Publishing Ltd.

关键词： Vegetation

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on intelligent Computing and Signal processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Image enhancement

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Cortical reorganization pattern of stroke patients under upper limb multi-joint linkage task 12

Cortical reorganization pattern of stroke patients under upp...

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12th International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2022

作者： Chu, Qitong Huo, Congcong Zhang, Tengyu Zhang, Xuemin Xu, Gongcheng Cheng, Shengcui Li, Huiyuan Xie, Ping Guo, Xin School of Artificial Intelligence and Data Science Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology The Ministry of Education Hebei University of Technology Tianjin300130 China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Key Laboratory of Neuro-functional Information and Rehabilitation Engineering The Ministry of Civil Affairs Beijing100176 China Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China School of Electrical Engineering Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Hebei Qinhuangdao066004 China Qinhuangdao Research Institute of National Research Center for Rehabilitation Technical Aids Qinhuangdao066004 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Motor dysfunction in stroke patients is caused by the brain damage, which greatly affects the life quality of the survivors. It is essential to explore the cortical reorganization pattern of stroke patients for revealing the mechanism of neural remodeling and guiding rehabilitation training. A total of 9 stroke patients and 12 age matched healthy subjects were recruited. Changes of oxyhemoglobin concentration (ΔHbO2) of bilateral prefrontal and motor cortex were measured in resting state and upper limb multi-j oint linkage motor state using functional near-infrared spectroscopy (fNIRS) neuroimaging technique. Cortical activation and functional connectivity were analyzed to describe the brain functional status associated with specific motor task. Results showed that compared with healthy subjects, significantly greater cortical activation was observed in stroke patients in responding to the multi-joint motor of the affected limb. Besides, inter-hemisphere connectivity of bilateral homotopic MC and PFC were significantly lower than healthy controls, however, the connectivity between PFC and MC were significantly higher than healthy controls. The results indicate the brain functional reorganization of stroke patients and offer new insights into the neural basis for cortical plasticity for stroke rehabilitation. To perform complex motor tasks, more brain resources was expended and more brain regions were involved in stroke patients. © 2022 IEEE.

关键词： Brain

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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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Investigation of open channel flow with unsubmerged rigid vegetation by the lattice Boltzmann method

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Journal of Hydrodynamics 2020年第4期32卷 771-783页

作者： He-fang Jing Yin-juan Cai Wei-hong Wang Yakun Guo Chun-guang Li Yu-chuan Bai State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300350China Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan 750021China School of Civil Engineering North Minzu UniversityYinchuan 750021China Faculty of Engineering and Informatics University of BradfordBradfordUK

The aquatic vegetation can significantly affect the flow structure,the sediment transport,the bed scour and the water quality in rivers,lakes,reservoirs and open *** this study,the lattice Boltzmann method(LBM)is applied in the two-dimensional numerical simulation of the flow structure in a flume with rigid vegetation.A multi-relaxation time model is applied to improve the stability of the numerical scheme for flows with a high Reynolds *** vegetation induced drag force is added in the lattice Boltzmann equation model in order to improve the simulation accuracy and an algorithm of the multi-relaxation time is *** simulations are performed for a wide range of flow and vegetation conditions and are validated by comparing with the laboratory *** of the simulated and experimentally measured flow Helds shows that the numerical simulation can satisfactorily reproduce the laboratory experiments,indicating that the proposed lattice Boltzmann model enjoys a high accuracy for simulating the flow-vegetation interaction in open channels.

关键词： Lattice Boltzmann method(LBM) multi-relaxation time model aquatic vegetation drag force open channel flow

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Adaptive learning controller design for uncertain nonlinear active suspension system with actuator input delay and time-varying state constraints

Adaptive learning controller design for uncertain nonlinear ...

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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper investigates the adaptive tracking control problem for active suspension system(ASS) with time-varying constraints via the neural network(NN) *** nonlinear terms in the spring and damper coefficients,input time delay of the actuator and the suspension performances are also considered *** NNs are used to approximate the unknown function caused by the uncertain car-body *** is known to all that ensuring the vertical displacement and speed states in the constraint regions can guarantee the stability and safety of the ***,by introducing the symmetric barrier lyapunov function from the time-varying constraints,an adaptive controller based on the NNs and nonlinear backstepping technique is developed to guarantee the states within *** the stability of the whole system,including the semi-global ultimately uniformly boundedness of the all signals of the ASSs,is analyzed by the Lyapunov stability ***,simulation results are provided to verify the feasibility of the adaptive learning controller.

关键词： Active suspension systems adaptive control input delay neural networks

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