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OCC Model-Based Emotion Extraction from Online Reviews

OCC Model-Based Emotion Extraction from Online Reviews

引用

11th IEEE International Conference on Intelligence and Security Informatics (IEEE ISI)

作者： Huangfu, Luwen Mao, Wenji Zeng, Daniel Wang, Lei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467362139;9781467362146

Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. To facilitate the automatic extraction of emotions from textual data, in this paper, we propose an emotion model based approach to emotion extraction from online reviews. Informed by the widely used OCC emotion model, we employ a statistical method to extract emotion words with their dimension values from texts, and implement OCC model to obtain emotions based on the emotion-dimension dictionary. We conduct an empirical study using security-related news reviews. The experimental results demonstrate the effectiveness of our proposed approach.

关键词： OCC emotion model emotional dimensions opinion mining

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An FES-assisted Training Strategy Combined with Impedance control for a Lower Limb Rehabilitation Robot

An FES-assisted Training Strategy Combined with Impedance Co...

引用

IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Chen, Yixiong Hu, Jin Wang, Weiqun Peng, Liang Peng, Long Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China

ISBN: (纸本)9781479927449

In order to investigate the feasibility of integrating functional electrical stimulation (FES) with robot-based rehabilitation training, this paper proposes an FES-assisted training strategy combined with impedance control for our self-made exoskeleton lower limb rehabilitation robot. This control strategy is carried out in a leg press task. Through impedance control, an active compliance of the robot is established, and the patient's voluntary effort to accomplish the task is inspired. During the training process, the patient's related muscles are applied with FES which provides an extra assistance to the patient. The intensity of the FES is properly chosen aiming to induce a desired active torque which is proportional to the voluntary effort of the patient. This kind of enhancement serves as a positive feedback which reminds the patient of the correct attempt to fulfill the desired motion. FES control is conducted by a combination of neural network-based feedforward controller and a PD feedback controller. The feasibility of this control strategy has been verified in Matlab.

关键词： Robots

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Predicting User Participation in Social Networking Sites

Predicting User Participation in Social Networking Sites

引用

11th IEEE International Conference on Intelligence and Security Informatics (IEEE ISI)

作者： Kong, Qingchao Mao, Wenji Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467362139;9781467362146

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, ***. The experimental results show the effectiveness of our approach.

关键词： user participation behavior modeling and prediction social networking sites

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Combined Use of sEMG and Accelerometer in Hand Motion Classification Considering Forearm Rotation

Combined Use of sEMG and Accelerometer in Hand Motion Classi...

引用

35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Peng, Liang Hou, Zengguang Chen, Yixiong Wang, Weiqun Tong, Lina Li, Pengfeng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781457702167

Hand motion classification using surface electromyography (sEMG) has been widely studied for its applications in upper-limb prosthesis and human-machine interface etc. Pattern-recognition based control methods have many advantages, and the reported classification accuracy can meet the requirements of practical applications. However, the pattern instability of sEMG in actual use limited their real implementations, and limb position variations may be one of the potential factors. In this paper, we give a pilot study of the reverse effect of forearm rotations on hand motion classification, and the results show that the forearm rotations can substantially degrade the classifier's performance: the average intra-position error is only 2.4%, but the average interposition classification error is as high as 44.0%. To solve this problem, we use an extra accelerometer to estimate the forearm rotation angles, and the best combination of sEMG data and accelerometer outputs can reduce the average classification error to 3.3%.

关键词： accelerometers biomechanics electromyography medical signal processing signal classification

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Adaptive Optimal control for the Uncertain Driving Habit Problem in Adaptive Cruise control System

Adaptive Optimal Control for the Uncertain Driving Habit Pro...

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Zhao Dongbin Xia Zhongpu Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781479903801

In this paper, a novel adaptive optimal control approach based on Q-function is proposed to address the problem as the driving habits change among drivers and over time in the adaptive cruise control system. The proposed approach, adopting the special structure Q-function of the linear discrete-time system, uses policy iteration method to derive the optimal control policy online. It repeats between policy evaluation where the polynomial neural network is employed to approximate the cost function of the system and policy improvement where the control policy is updated based on the converged neural network, until the optimal controller is achieved. Simulation is conducted and results show the effectiveness for uncertain driving habit problem in the adaptive cruise control system.

关键词： adaptive cruise control driving habit Q-function adaptive optimal control policy iteration

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Multi-class Classification via Discriminative Multiple Subspace Learning

Multi-class Classification via Discriminative Multiple Subsp...

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International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

作者： Tang, Tang Qiao, Hong Zheng, Suiwu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst O Beijing Peoples R China

ISBN: (纸本)9781479925650

Subspace learning has long been a fundamental yet important problem of modeling data distributions. In this paper, we propose to learn multiple linear subspaces in a supervised way for multi-class classification. To this end, a discriminative term redefining decision margin in terms of reconstruction error is incorporated into the model. The term enjoys similar properties of hinge loss function to the benefit of classification and leads to a training process seeking the balance between unsupervised learning and supervised learning. In the experiments on written digits dataset, our algorithm outperforms other methods proposed recently in both accuracy and computation efficiency.

关键词： subspace learning generative model discriminative model

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EMG-based Estimation of Knee Joint Angle under Functional Electrical Stimulation Using an Artificial Neural Network

EMG-based Estimation of Knee Joint Angle under Functional El...

引用

32nd Chinese control Conference (CCC)

作者： Chen, Yixiong Hu, Jin Zhang, Feng Li, Pengfeng Hou, Zengguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563835

In this paper, an artificial neural network is proposed to estimate knee joint angle in hybrid activation of knee extension motion, including voluntary muscle contraction and functional electrical stimulation (FES) induced contraction. Voluntary electromyography (EMG) signals of three muscles responsible for knee extension and FES parameter which describe the FES intensity are used as input vector of the neural network, while the estimated knee angle is the output. During the experiment, FES with different combinations of parameters (pulse amplitude and pulse width) was delivered to the rectus femoris muscle of a healthy male subject when the knee was in a periodic extension motion by voluntary muscle contraction. Raw EMG signals of three muscles, parameters of FES as well as the actual knee angle were recorded. Totally, there were 52,233 and 17,420 sampling points corresponding to 261 and 87 seconds used to train and validate the neural network. The result shows the trained network has a satisfactory performance on knee joint angle estimation whose output well follows the curve of actual knee angle. Root mean square error between estimated angle and actual angle is employed to represent the estimation accuracy which is 5.07 degree according to the experimental data.

关键词： Functional electrical stimulation Electromyography Neural network

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Fusing Global and Local Features for Face Verification

Fusing Global and Local Features for Face Verification

引用

5th International Conference on Digital Image Processing (ICDIP)

作者： Zhou, Ji Xiao, Baihua Wang, Chunheng Cai, Xinyuan Chen, Xue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9780819493057

In the literature of neurophysiology and computer vision, global and local features have both been demonstrated to be complementary for robust face recognition and verification. In this paper, we propose an approach for face verification by fusing global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from ten different component patches by a new image representation method named Histogram of Local Phase Quantization Ordinal Measures (HOLPQOM). Experimental results on the labeled Face in Wild (LFW) benchmark show the robustness of the proposed local descriptor, compared with other often-used descriptors.

关键词： global and local features face verification Fourier transform Local Phase Quantization ordinal measure

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Stable Iterative Optimal control for Discrete-Time Nonlinear systems Using Numerical controller

Stable Iterative Optimal Control for Discrete-Time Nonlinear...

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781479903801

This paper is concerned with a new iterative adaptive dynamic programming (ADP) algorithm to solve optimal control problems for infinite horizon discrete-time nonlinear systems using a numerical controller. The convergence conditions of the iterative ADP are developed considering the errors by the numerical controller which show that the iterative performance index functions can converge to the greatest lower bound of all performance indices within a finite error bound. Neural networks and digital computer are used to approximate the iterative performance index function and compute the numerically iterative control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method.

关键词： Adaptive critic designs adaptive dynamic programming approximate dynamic programming nonlinear systems optimal control neural networks reinforcement learning

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Generalized Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear systems: Convergence and Stability Analysis

Generalized Adaptive Dynamic Programming Algorithm for Discr...

引用

International Conference on Information Science and Technology (ICIST)

作者： Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467351379

This paper is concerned with a generalized iterative adaptive dynamic programming (ADP) algorithm for discrete-time nonlinear systems. The idea is to use iterative ADP algorithm to obtain iterative control laws which make the iterative performance index function reach the optimum. It is proved that for an arbitrary positive semi-definite function, the iterative performance index functions will converge to the optimum. For different initial functions, it shows that the convergence procedures of the iterative performance index functions are different. Stability properties of the system are analyzed in this paper to show that the iterative control can stabilize the system for suitable initial performance index functions. Admissible control properties of the iterative control laws obtained by the present generalized iterative ADP algorithm are also given in this paper.

关键词： Iterative methods

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