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IAENG International Journal of computer Science 2025年第6期52卷 2015-2021页

作者： Zheng, Shuailong Tao, Huawei Yu, Hang Xi, Ji Zhu, Chunhua Wang, Xuemei Key Laboratory for Grain Information Processing and Control Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou450001 China Computer Information Engineering Changzhou Institute of Technology Jiangsu213000 China

The limited number of labeled samples can significantly affect the accuracy of speech lie detection systems. However, semi-supervised algorithms have been shown to effectively improve detection performance under such constraints. To address this challenge, this paper proposes a semi-supervised speech lie detection algorithm that utilizes multiple features and adaptive thresholds. First, through comparative analysis, this paper identifies 312-dimensional acoustic features and 768-dimensional self-supervised features as the key elements of lie detection. Then, these acoustic features are processed using a bidirectional long short-term memory network (Bi-LSTM), which obtains comprehensive information by analyzing acoustic statistical features in both forward and backward directions. Subsequently, this paper develops a semi-supervised feature fusion module (BSFM) to extract deeper insights from basic features and achieves adaptive feature fusion through targeted training. In addition, based on a detailed analysis of the distribution characteristics of lie detection samples, this paper proposes an improved Freematch algorithm to better utilize unlabeled data. Experimental results show that the proposed algorithm outperforms most existing methods on the self-built H-Wolf dataset and the Columbia/SRI/Colorado Corpus (CSC). © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Semi-supervised learning

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Depth map cavity repair algorithm based on RGB image 8

Depth map cavity repair algorithm based on RGB image

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Sheng, Yiyang Fu, Haohai Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China School of Computer Science and Technology Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350302455

The 3D reconstruction process often requires high-precision depth images. However, based on the limitations of the current depth sensor's own equipment and the influence of shootingobjects, environment and other factors, the obtained depth map often has cavities and noise. Aiming at the problem that the traditional depth map cavity repair algorithm can not take into account the cavity filling and object edge preservation, and can noteffectively use the RGB image information obtained synchronously, a cavity repair algorithm based on RGB image is proposed. The algorithm divides the cavities in the depth map into edge parts and non-edge parts by obtaining the edge information in the RGB image, and then uses different methods to repair the two parts of the cavities:For non-edge cavities, because the neighborhood depth value changes continuously, the neighborhood depth value is directly used for repair;for the cavity at the edge, due to the discontinuous change of the neighborhood depth value, the depthvalue corresponding to the pointwith the largest distance centerweight in the neighborhood is selected to repair the cavity by using the pixel points corresponding to the cavity in the RGB image and its neighborhood. The experimental results show that the algorithm makes full use of the RGB image information, which can accurately fill the depth map cavitys and effectively avoid the edge blur of the object. © 2023 IEEE.

关键词： Repair

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Dynamic Event-Triggered Consensus control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 649-660页

作者： Meilin Li Yue Long Tieshan Li Hongjing Liang C.L.Philip Chen the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China IEEE the Yangtze Delta Region Institute University of Electronic Science and Technology of ChinaHuzhou 313000 the Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100089China the School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first ***,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale *** is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event *** addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable ***,the results are extended to regional consensus of the MASs with the bounded control *** simulations show the effectiveness of the proposed approach.

关键词： Constrained input designable minimum inter-event time directed communication topology dynamic event-triggered mechanism MASs consensus control

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Emotion detection on phone calls during emergency using ensemble model with hyper parameter tuning

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International Journal of Information technology (Singapore) 2023年第2期15卷 745-757页

作者： Selvan, A. Kalai Nimmi, K. Janet, B. Sivakumaran, N. C-DAC Thiruvananthapuram India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India

The paper’s main focus is on speech-based emotion detection in Malayalam phone call records, including emergency calls [(emergency response support system (ERSS)] and elicited *** emotions taken into consideration are fear, anger, sad, disgust, panic, anxious are among the emotions considered, while all other emotions are labeled as others. When reporting an emergency, callers commonly exhibit emotional stress, which results in speech output discrepancies. In such uncontrolled situations, determining caller qualities is exceedingly challenging due to poor speech quality and background noise. Fear, anger, and sadness are the three basic emotions expressed during an emergency. In order to classify the emotions, this paper used a bagging and boosting ensemble model. Using the Malayalam speech emotion dataset (MSED), the proposed support vector machine ensembles with Bagging with radial basis function kernel function were found to have an accuracy of 93.01%. Elicited and emergency speech from emergency calls are used to train support vector machine ensembles. For comparison of the performance of the model, we have used RAVDESS and Emo-DB. The accuracy obtained were 81.40% while using the RAVDESS dataset and 93.01% while using Emo-DB dataset which is high when compared to existing machine learning and ensemble model. The performance of boosting models was comparatively lesser than that of the Bagging model. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Emergency response support system (ERSS) Ensemble model Mel frequency cepstral co-efficient (MFCC) Speech emotion detection (SED) Support vector machine (SVM) ZeroCrossing rate (ZCR)

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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An Ecological and Economic Model of Carbon Neutrality 15th

An Ecological and Economic Model of Carbon Neutrality

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Moiseev, Nikita Olenev, Nicholas Zasukhina, Elena Teymurazov, Kirill Medennikov, Anton Moscow Institute of Physics and Technology Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031791185

The paper considers a mathematical model that describes the interactions between the economic system and the ecological system through waste emissions, resource consumption, and the restoration of restorable resources. It is assumed that the state levies a tax on waste emissions, thereby controlling emissions and the consumption of resources by the economy. The model also describes the impact of economic and environmental factors on demographics. The state should strive for economic growth and avoid environmental disasters, and the model can be useful for achieving these goals. Besides, a software implementation of the model was constructed. Some technical details of the software implementation were also considered. Numerical experiment with one of the possible sets of parameters’ values was carried out, which displayed one of the possible situations of stable development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Carbon capture and utilization

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On the Observation of Glucose–Insulin Models

On the Observation of Glucose–Insulin Models

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94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2024

作者： Röbenack, Klaus Gerbet, Daniel Faculty of Electrical and Computer Engineering Institute of Control Theory TUD Dresden University of Technology Dresden Germany

The glucose–insulin subsystem has been modeled in different ways as a low-dimensional dynamical system. These models consist of various parameters, which are fitted such that the model coincides with specific experiments. For individuals with the Diabetes Mellitus syndrome model-based controllers for insulin injection have been proposed. These controllers, however, require the knowledge of the model parameters as well as the full systems state, which must be estimated as well. In addition, the system is disturbed by, in general, unknown carbohydrate intake. From a control-theoretic point of view, the state estimation using only the measurable glucose concentration is an observer problem. Due to the nonlinearity of the system, its observability is more difficult to test. There may also be some points in the state space, where the system is not locally observable for some parameters tuples. Those issues are important for a robust observer design. In this contribution, several observers are discussed for the widely used Bergman minimal model. These include unknown-input observers that make use of the special systems structure as well as observers based on the observability canonical form with different embedding dimensions. The canonical form is computed by algebraic methods. © 2024 The Author(s).

关键词： Observability

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Optimizing Electrocardiogram Signal Augmentation for Realistic Synthetic Data in Deep Learning Model 27

Optimizing Electrocardiogram Signal Augmentation for Realist...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Safdar, Muhammad Farhan Palka, Piotr Faresi, Ahmed Al Nowak, Robert Marek Institute of Computer Science Warsaw University of Technology Poland Institute of Control and Computation Engineering Warsaw University of Technology Poland United Arab Emirates University College of Information Technology United Arab Emirates

ISBN: (纸本)9788362065486

Electrocardiograms (ECG) are non-invasive signals and have proven useful in assessing the heart condition. Given the necessity for extensive datasets in ECG classification using deep learning (DL) models, there is a critical imperative to devise data augmentation methods capable of generating synthetic but realistic dataset suitable for training DL model. In this study, we propose a novel approach for augmenting ECG signals, aiming to produce realistic signals while optimizing memory usage and resource requirements. Building upon our previous work in ECG signal augmentation, we revisit the methodology to address limitations observed in the generation of synthetic signals. The existing method segmented ECG signals into fixed-length segments and combined them, occasionally resulting in unrealistic heart cycles within the signals in extreme condition. To address this issue, our proposed technique incorporates R peak detection, signal segmentation, and reordering based on the R-peaks information. We evaluated the proposed method using three benchmark datasets, including PTB-XL, Chapman-Shaoxin from PhysioNet, and the dataset from China Physiological Signal Challenge 2018 (CPSC-2018), for classifying atrial fibrillation from normal samples. Our approach achieved an accuracy of 0.83, sensitivity of 0.86, specificity of 0.80, F1-score of 0.83, and precision of 0.80. These results underscore the effectiveness and efficiency of our method in augmenting ECG signals for various applications in healthcare and biomedical research. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of technology (DSPES PUT).

关键词： Convolutional neural networks

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