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EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

Science China(information Sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of Artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

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Fractional-Order Sliding Mode Finite Time control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill

Fractional-Order Sliding Mode Finite Time Control for the Sp...

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第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

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Fast Super-Resolution DOA Estimation for Unknown Number of Radiation Sources Based on Complex Convolutional Neural Networks

Fast Super-Resolution DOA Estimation for Unknown Number of R...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Hui Ma Xianpeng Liu Hongwei Liu Junxiang Yang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China ElectromechanicalDynamic Control Laboratory Xian Institute of Electromechanical Information Technology Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

To address the high computational complexity of traditional Direction of Arrival (DOA) estimation techniques and the limitations of previous neural networks in handling fixed signal sources, a new signal processing model was developed. This model employs sparse representation methods and deep learning through complex convolutional neural networks(CCNN). Focused research on the array’s performance under unknown radiation sources using the complex convolutional neural network algorithm for super-resolution DOA estimation was conducted in a PyTorch environment. The results indicate that the complex convolutional model has a lower DOA computation complexity and higher precision in handling unknown radiation sources. After incorporating sparse representation with sampled data through the CCNN model for DOA estimation, an improvement in estimation accuracy and a reduction in computational complexity were observed compared to methods relying solely on sparse representation. Additionally, this model can effectively detect incoming waves from unknown radiation sources, unlike previous neural networks.

关键词： Direction-of-arrival estimation Sparse approximation Computational modeling Superresolution Estimation Signal processing algorithms Data models Convolutional neural networks Computational complexity Signal to noise ratio

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Prediction and influencing factors analysis of stored grain temperature and intergranular relative humidity

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Food and Bioproducts processing 2025年 153卷 67-76页

作者： Yifei Qin Yuan Zhang Shanshan Duan Yunhao Cao Xintao Jia Quanen Chen School of Mechanical and Electrical Engineering Henan University of Technology Zhengzhou 450001China Key Laboratory of Grain Information Processing and Control Henan Key Laboratory of Grain Photoelectric Detection and Control School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Beijing Guomao Dongfu Engineering Technology Co. Ltd. Beijing 100037 China

To address the issues of insufficient accuracy and poor interpretability in the prediction of temperature and intergranular relative humidity (RH) for bulk-stored grain, this study developed an optimized prediction model based on the extreme gradient boosting (XGBoost) algorithm. The model incorporated the interaction of meteorological factors with the temperature and RH within the bulk grain. Hyperparameters were optimized using the grid search method, and Shapley Additive exPlanations (SHAP) was employed to quantify the contribution and interaction mechanism of each input variable, thereby enhancing model interpretability. Seven meteorological variables and the surface RH of the bulk grain were used to predict the surface temperature. Similarly, meteorological data and surface temperature were used to predict the surface RH, and the model performance was validated using an experimental silo dataset. For benchmarking purposes, several commonly used models, including Linear Regression (LR), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Regression (SVR) were also tested. The results indicated that the XGBoost model outperformed the baseline models in predictive accuracy. The RMSE, R², MAE, and MAPE results for average surface temperature prediction were 1.59, 0.88, 0.79, 3.56 %, respectively, while those for average surface RH were 0.32, 0.95, 0.16, 0.28 %. SHAP analysis revealed that the influence of meteorological variables on grain surface temperature and intergranular RH was complex, with air temperature, air pressure, and air RH identified as the most significant factors. The proposed model demonstrated both high predictive accuracy and strong interpretability, offering a scientific basis for the monitoring and control of grain storage conditions.

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Improving energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

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2022 International Conference on Networks, Communications and information Technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improving the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the Time-series block data is constructed according to the data shape of the Periodic block;then the Time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the Time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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Experimental demonstration of phase-sensitive multimode continuous variable quantum key distribution with improved secure key rate

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Photonics Research 2023年第11期11卷 1861-1869页

作者： ZIKANG SU JINTAO WANG DAJIAN CAI XIAOJIE GUO DAWEI WANG ZHAOHUI LI Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics TechnologyJinan UniversityGuangzhou 510632China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China

We develop and experimentally demonstrate a phase-sensitive continuous variable quantum key distribution system with improved secure key *** is achieved using multimode coherent states with phase-conjugated subcarrier modulation and phase-sensitive *** local oscillator for phase-sensitive detection is regenerated from a polarization-multiplexed carrier wave via optical injection *** proposed scheme has a higher classical information capacity at a given number of received photons and exhibits a higher secure key rate when applying the security analysis of the GG02 *** results confirm the higher secret key rate and better excess noise tolerance of the new scheme compared to the typical implementation of GG02.

关键词： phase distribution quantum

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Chimera-inspired dynamics: When higher-order interactions are expressed differently

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Physical Review E 2024年第4期110卷 044204页

作者： Xinrui Ji Xiang Li Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems and The State key laboratory of Intelligent Autonomous Systems Department of Control Science and Engineering College of Electronics and Information Engineering

The exploration of chimera-inspired dynamics in nonlocally coupled networks of Kuramoto oscillators with higher-order interactions is still in its nascent stages. Concurrently, the investigation of collective phenomena in higher-order interaction networks is gaining attraction. Here, we observe that hypergraph networks tend to synchronize through lower-order interactions, whereas simplicial complex networks exhibit a preference for higher-order interactions. This observation suggests that higher-order representations manifest substantial differences in chimera-inspired synchronization regions. Moreover, we introduce an explicit expression for identifying the chimera state. With a comprehensive basin stability analysis and the interplay of pairwise and higher-order interaction strengths, the emergence of the chimera state is inherent in high-order interaction networks. Our findings contribute to the understanding of chimera-inspired dynamics in higher-order interaction networks.

关键词： Chimera states Continuous phase transition Coupled oscillators Synchronization Synchronization transition

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