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International Conference on Awareness Science and Technology (iCAST)

作者： Peng Mei Fuquan Zhang Lin Xu Hongyong Leng Lei Chen Guo Liu School of Software Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Innovative Information Industry Research Institute Fujian Normal University Fuzhou China

Overcome context-independent probabilities based reasoning, with decomposition of categories and predicates into features as non-stable predicates. With distinction between generative classifier and discriminative classifier, we purpose to use some discriminative classifiers such as dual-form perceptron and kernelized support vector machine to improve to result of reasoning process. With capability of dual-form perceptron and kernelized support vector machine, finding linear or non-linear decision boundary for similarity-like supporting predicate for reasoning process.

关键词： Cognition Classification algorithms Support vector machines Logistics Kernel Training Training data

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A polynomial chaos approach to robust H∞ static output-feedback control with bounded truncation error

arXiv

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arXiv 2021年

作者： Wan, Yiming Shen, Dongying E. Lucia, Sergio Findeisen, Rolf Braatz, Richard D. Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States TU Dortmund University Dortmund44227 Germany Otto-von-Guericke University Magdeburg Magdeburg39106 Germany

This article considers the H∞ static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system which characterizes stochastic state uncertainty propagation. A closed-loop polynomial chaos transformation is proposed to derive the closed-loop expanded system. The approach explicitly accounts for the closed-loop dynamics and preserves the L2-induced gain, which results in smaller transformation errors compared to existing polynomial chaos transformations. The effect of using finite-degree polynomial chaos expansions is first captured by a norm-bounded linear differential inclusion, and then addressed by formulating a robust polynomial chaos based control synthesis problem. This proposed approach avoids the use of high-degree polynomial chaos expansions to alleviate the destabilizing effect of truncation errors, which significantly reduces computational complexity. In addition, some analysis is given for the condition under which the robustly stabilized expanded system implies the robust stability of the original system. A numerical example illustrates the effectiveness of the proposed approach. © 2021, CC BY.

关键词： Feedback control

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A unified optimization method for real-time trajectory generation of mobile robots with kinodynamic constraints in dynamic environment

A unified optimization method for real-time trajectory gener...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Wenhao Luo Jun Peng Weirong Liu Jing Wang Wentao Yu Department of Automation Key Laboratory of System Control And Information Processing Shanghai China School of Information Science and Engineering Hunan Engineering Laboratory for Advanced Controland Intelligent Automation Changsha China Computer Engineering Program Bethune-Cookman University Daytona Beach FL USA

This paper presents an efficient analytic method for a mobile robot to determine a collision-free trajectory with unified optimization. The robot kinodynamic constraints and the geometric constraints due to obstacles are addressed by considering a set of constrained inequalities from parameterized trajectories model. Two optimal performance matrices are employed to assess optimization problems. Particularly, both the constraints and performance functions are incorporated by similar forms in terms of trajectory parameters, which thereby can be considered in a uniform way. A parameter space of adjustable trajectory parameters is constructed to solve the unified optimization problem with constraints and results in more flexible and better optimal performance. The proposed approach is complete and enhances methods of deterministic real-time planning by overcoming typical drawbacks as intermediate configuration, curvature discontinuities, vertical singularities and incomplete optimization. Simulation results verify the validity and superiority of the proposed method.

关键词： Trajectory Robot kinematics Collision avoidance Mobile robots Acceleration Optimization

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Chinese Lexical Based Sentiment Analysis Framework in Meteorology

Chinese Lexical Based Sentiment Analysis Framework in Meteor...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yinan Li Fuquan Zhang Yifan Zhu Sifan Zhang Yu Mao Zhendong Niu School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Evaluating the performance of meteorological service not only relies on physical measurements but also requires public feedback. As a significant part of the evaluation, assessing the sentiment of these feedbacks gives an insightful view which can help to establish reliable decision support in policy making and emergency responding. However, climate and weather are always accompanying phenomena in the evolutionary process of human beings, making the climate and weather-related words themselves contain a large number of extend meanings in people's daily expression. These polysemy phenomena have led to a decline in the performance of traditional general sentiment calculations in the vertical field of meteorology. As far as we know, there is no Chinese sentiment calculation tool in the meteorological field, due to the different expressions between different languages. Facing with the above problems and issues, this paper proposes and establishes a sentiment analysis based on lexicon-based framework of Chinese text in the meteorological field. The framework fuses two major sentiment assessment method consists of two approaches, namely machine learning-based and knowledge based one. First, word embedding similarity and word co-occurrence methods are utilized to construct the domain sentiment dictionary which includes 8,796 negative words and 4,751 positive words. Then, by using the extend sentiment dictionary to select theme words from original text as feature, machine learning models are introduced to assess the sentiment tendency. Experiment showed that the performance of the proposed framework was improved up to 85.79% in terms of overall accuracy compared with traditional methods.

关键词： Dictionaries Meteorology Sentiment analysis Feature extraction Machine learning Vocabulary Semantics

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Ultrasound image Denoising with Multi-shape Patches Aggregation Based Non-local Means

Ultrasound Image Denoising with Multi-shape Patches Aggregat...

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International Conference on intelligent Computation and Bio-Medical Instrumentation (ICBMI)

作者： Wanjun Chen Mingyue Ding Yalin Miao Lei Luo Department of Information Science Xi'an University of Technology Xi'an Shaanxi China College of Life Science and Technology lmage processing and Intelligent Control key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China

The Non-Local Means (NLM) filter uses the redundancy of information in the image to remove noise, this scheme gives some of the best results among other powerful methods such as wavelet based approaches or diffusion techniques. Though simple to implement and efficient in practice, the classical NLM suffers from ringing artifacts around edges when using square patches, due to an abrupt lack of redundancy of the image. This paper presents an extended NLM based on Multi-Shape Patches Aggregation (NLM-MSPA) to overcome this problem, and uses it to remove medical ultrasound images corrupted by multiplicative speckle noise. We have incorporated a preprocessing step to make the speckle noise much closer to the real additive white Gaussian noise, hence more amenable to a denoising algorithm such as NLM-MSPA. Results on real images and artificially speckled images show that the proposed scheme outperforms several classical methods chosen for comparison in its ability to reduce speckle and preserve edge details.

关键词： Speckle Ultrasonic imaging Noise reduction Shape Biomedical imaging Signal to noise ratio

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Locating and Sizing Charging Station in Multi-Period to Promote Electric Vehicles Adoption in Urban Areas

SSRN

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SSRN 2023年

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei The School of Management South-central Minzu University Wuhan430074 China The School of Engineering RMIT University MelbourneVIC3001 Australia The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

The layout of public fast charging stations often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multiperiod locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multiperiod change formula is proposed to reflect the interaction between public fast charging stations and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast charging stations to improve the charging service quality and promote the adoption of EVs. © 2023, The Authors. All rights reserved.

关键词： Location

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Capacity Analysis of Finite Scatterer MIMO Wireless Channels

Capacity Analysis of Finite Scatterer MIMO Wireless Channels

引用

IEEE International Conference on Communications

作者： Shangbin Wu Cheng-Xiang Wang Bo Ai Yejun He Joint Research Institute for Signal and Image Processing Heriot-Watt University Edinburgh UK State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China College of Information Engineering Shenzhen University Shenzhen Guangdong China

This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas but also the finite angular resolution of antenna arrays. The probability mass function (PMF) of rank is studied and derived based on the Stirling number of the second kind. As a result, the closed-form expression of the capacity of a FS MIMO channel model is obtained. Furthermore, asymptotic analyses such as the large-scale system analysis, infinite scatterer analysis, and the multiplexing gain analysis are investigated.

关键词： MIMO Antenna arrays Channel models Directive antennas Channel capacity Receivers Multiplexing

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Data Augmentation for Deep Learning Based a Utomaticmo Dulation Recognition

SSRN

引用

SSRN 2023年

作者： Ma, Hongbin Feng, Zhixi Yang, Shuyuan Wang, Min Wu, Tao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an710071 China Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xidian University China Institute School of Artificial Intelligence Xidian University TaiBai road 2# Shannxi Province Xi’an China

Data augmentation, as a strategy to expand training datasets, can be utilized to improve the accuracy of Deep Learning(DL)-based models to a certain extent. However, existing data augmentation methods for the DL-based Automatic Modulation Recognition (AMR) task lack a theoretical basis, and the availability of these methods to different types of AMR models has not been evaluated. In this paper, we first model the transformation process from an information source to a modulation signal and analyze the parameters that influence the difference between modulation signals for the theoretical basis of data augmentation in the AMR field. Then we propose nine signal augmentation methods to generate argumentation signals by modifying the influence parameters in the amplitude domain, the time domain, and the frequency domain. Finally, we conduct a series of experiments on the public RML2016.10a and RML2016.10b datasets to evaluate the effectiveness of the proposed methods andanalyze the availability of these methods to Convolutional Neural Network (CNN)-based AMR models, and hybrid AMR models based on CNN and Recurrent Neural Network. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Detecting Interest-Factor Influenced Abnormal Evaluation of Teaching via Multimodal Embedding and Priori Knowledge Based Neural Network

Detecting Interest-Factor Influenced Abnormal Evaluation of ...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yu Mao Yifan Zhu Sifan Zhang Dexiu Zhang Fuquan Zhang Xiaozhong Fan School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Science Minnan Normal University Zhangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Student evaluations of teaching (SET) helps to quantitatively measure the teaching performance of classes. However, concerns arouse when the result of SET and students' final grades are highly positively correlated, which means the students may make the evaluation based on the final grades they take. For the purpose of avoiding retaliation and mutual reward rating, some methods based on statistics or rules are used to detect the presence of anomaly evaluation. Whereas, the problem is still challenging since the characteristics of this abnormal evaluation are implicit and multimodal. Besides, additional information such as the domain of subject, instructors' teaching style, and the reputation spread by students also requires corresponding prior knowledge to supplement the abnormal evaluation detection model. Therefore, in this paper, we proposed a multimodal embedding and prior knowledge based neural network to detect potential so called Interest Factor Influenced Abnormal Evaluation (IFAE). The method proposed in this paper uses SDNE and PV-DM to embed the evaluation network between students and teachers and the comment text of students. Taking into account the continuity of mutual evaluation between students and teachers during the teaching cycles and the different emphasis of students' comment texts on teachers, the features of students and teachers are comprehensively constructed. Then, the attention mechanism is used in the comment text to perform final prediction jointly with above features. The experiment result shows that our proposed model outperforms other state-of-art models which based on single type of features on F1 score by 9.25%.

关键词： Education Feature extraction Neural networks Data mining Knowledge based systems Computer science Quality control

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Design of Flight Attitude control Model for the Mini-quadrotor

Design of Flight Attitude Control Model for the Mini-quadrot...

引用

IEEE International Conference on Mechatronics and Automation

作者： Ma Zhongli Li Huixin Li Qianqian Gu Yanming Li Zuoyong College of Automation Harbin Engineering University Heilongjiang 150001 China School of Astronautics Harbin Institute of Technology Heilongjiang 150001 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 China

ISBN: (纸本)9781509067602

Micro-quadrotor has recently been used in different areas even military field. However, controlling mini-quadrotor to flight attitude in the air has not been perfectly achieved due to mini-quad rotor's small size and light weight. The purpose of this paper is to introduce the flight attitude control system of mini-quadrotor, including design and simulation of calculations and controllers. By using distributed multi-sensors, the attitude and horizontal position information of mini-quadrotor is obtained. The simulation of the attitude controller is applied with the MATLAB Simulink library. In conclusion, the designed attitude controller can make mini-quadrotor have smooth flight and move in all directions and enable the mini-quadrotor to properly flight.

关键词： Mini-quadrotor Flight attitude control system Calculation controller

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