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检索条件"机构=School of Control and Computer Engineering"

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GES Model :Combining Pearson Correlation CoefficientAnalysis with Multilayer Perceptron

GES Model :Combining Pearson Correlation CoefficientAnalysis...

引用

2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022

作者： Sui, Chunyu Huang, Sirui Li, Xinrui Song, Yinghang Zan, Yunpeng School of Computer Science and Technology Shandong University Jinan China School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9781450399548

With the development of technological progress, mining on asteroids is becoming a reality[1][25]. This paper focuses on how to distribute asteroid mineral resources in a reasonable way to ensure global equity. To distribute asteroid resources fairly, 7 primary indicators and 20 secondary indicators are introduced to build a mathematical model to evaluate global equity and the weights are given by Analytic Hierarchy Process (AHP). Then Global Equity Score(GES) Model based on 12 primary indicators and 40 secondary indicators is built and TOPSIS method is applied to rank all countries. A t-distribution probability density function is applied to simulate the rate of asteroid mining. The Backward Algorithm is applied to quantitatively measure the impact of changing indicators on global equity. Then Pearson correlation coefficient analysis is conducted for each indicator, and t-test is performed lastly. The results demonstrate that asteroid mining promotes global equity that poor countries can be allocated slightly more mineral resources, and a schedule of the implementation of each measure is given. To gain more insight, sensitivity analysis is conducted and the results demonstrate that scores vary less than . It can be concluded that our GES model have great potential as its robustness, accuracy and strengths. © 2022 ACM.

关键词： Sensitivity analysis

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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph

Exploiting Machine Learning and LSTM for Human Activity Reco...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Matthew Oyeleye Tianhua Chen Pan Su Grigoris Antoniou Department of Computer Science School of Computing and Engineering University of Huddersfield UK School of Control and Computer Engineering North China Electric Power University China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more.

关键词： Heart rate Machine learning Feature extraction Recording Human activity recognition Biosensors Biomedical monitoring

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Adaptive Sampling of Algal Blooms Using an Autonomous Underw...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Fonseca, Joana Rocha, Alexandre Aguiar, Miguel Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures StockholmSE-100 44 Sweden

ISBN: (纸本)9798350335446

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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2023 International Conference on Automation, control and Electronics engineering, CACEE 2023

作者： Monko, Gloriana Joseph Kimura, Masaomi Shibaura Institute of Technology Graduate School Department of Functional Control Systems Tokyo Japan Shibaura Institute of Technology Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350309201

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities;it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications. © 2023 IEEE.

关键词： Parameter estimation

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zhao, Ximei Zhang, Hanyu Shao, Lijun Huang, Lili Li, Jiao School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality. © 2023 IEEE.

关键词： Feedback

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Multimodal Sentiment Recognition Based on Sentiment Fusion Transformer 5

Multimodal Sentiment Recognition Based on Sentiment Fusion T...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Guan, Xinyu Wang, Yuan College of Intelligent Equipment Shandong University of Science and Technology Tai'an China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350310603

Multimodal emotion recognition (MER), which relies on its role in processing and analysing comments posted on social media and identifying the corresponding target emotion states, has a very important position in education, social media and especially in the field of HCI. In order to solve the problem of emotion recognition for video, text and other forms of data in social media, we propose a Transformer with feature. The Transformer is able to take into account the combination of different feature relations and extract global keys, so it is introduced for long-range sequential context extraction, and the residual connections are placed after the Transformer to prevent information collapse. In addition, this paper uses a multi-headed attention mechanism to combine the extracted different feature laws, and finally the fused feature vectors are fed into the classifier to classify the sentiment and output it to achieve multimodal sentiment recognition in a multimodal context. © 2023 IEEE.

关键词： Emotion Recognition

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Research on Multilevel Degradater Model under Unsupervised Learning 6

Research on Multilevel Degradater Model under Unsupervised L...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Lv, Jieyi Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an China

ISBN: (纸本)9798350305388

In most super-resolution experiments, because the data set in the real scene cannot meet the quantity requirements, it is necessary to downsample the high-resolution images using the degradation method to obtain the same proportion of low-resolution images. However, because the same degradation method is used to process images, the processing process of low-resolution images is relatively simple, which is easy to cause model solidification in the subsequent training process. After the training is completed, the generalization ability will be generally low due to the single underlying degradation method. This paper uses the SRMD method to build an unsupervised multilevel degradation, and takes advantage of the adduction network generated in unsupervised learning. After extracting image features, it adopts anisotropic Gaussian fuzzy kernel, bi-cubic subsampling and additive Gaussian noise to compose the degradation training data set. Compared with the traditional bi-cubic PNSR, the performance is improved by 5%. The SSIM value is increased by 3%, and the running speed is increased by 7% on average. © 2023 IEEE.

关键词： Gaussian distribution

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Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China 3

Study on Landslide Susceptibility Evaluation Based on Improv...

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Zhao, Li Duan, Gonghao Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan China

ISBN: (纸本)9798350337952

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20"Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy. © 2023 IEEE.

关键词： Hazards

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Multimodal Feature Fusion and Emotion Recognition Based on Variational Autoencoder 5

Multimodal Feature Fusion and Emotion Recognition Based on V...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Wang, Yuan Guan, Xinyu School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligent Equipment Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350310603

Multimodal emotion recognition is one of the mainstream frontier directions in the field of AI, and has potential significant application value and wide application in related application scenarios involving intelligent science and human-computer interaction. Since the connection of semantic information features between contexts is a factor that needs to be focused on in multimodal emotion recognition (ER), although the CNN model can predict the information contained in the time series, it does not fully consider. The special relationship between semantic information has certain limitations. The CMN uses the semantic information that GRU can store in the memory unit. However, the practical application of this model is only to dynamically monitor the emotional changes of both parties over a period of time. There are deficiencies in the dialogue process involved. The recognition model based on variational autoencoder and multi-modal feature fusion constructed in this paper extracts rich contextual semantic information through Bi-LSTM, and uses the attention mechanism to analyze the multi-modal features extracted by multiple speakers. Features are fused to obtain important features, which effectively solves the problems mentioned above. The MFF-VAE recognition model proposed in this article effectively combines the extended feature extraction of horizontal multiple participants with the complementarity and connection of vertical semantic information, providing the next level of research direction for further improving recognition accuracy in the field of multimodal emotion recognition. © 2023 IEEE.

关键词： Emotion Recognition

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