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Short-time-span EEG-based personalized emotion recognition with deep convolutional neural network
(2019-01-01)
Cheah, Kit Hwa
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Nisar, Humaira
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Yap, Vooi Voon
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Lee, Chen-Yi
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電子工程學系及電子研究所
Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signals/modalities as possible. Also, emotion-related neurological activities may be vastly different from person to person. Hence, this paper presents the single-modal EEG-based personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no pre-extracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotion-related EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3-class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3-class arousal classification test accuracy of 98.48% and 97.58%.
Design of Smart Brain Oxygenation Monitoring System for Estimating Cardiovascular Disease Severity
(2020-01-01)
Chou, Willy
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Wu, Pei-Jung
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Fang, Chih-Chieh
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Yen, Yun-Shan
;
Lin, Bor-Shyh
Clinically, cardiovascular disease (CVD) patients need physicians to suggest different exercise training and rehabilitation procedures to improve their cardiopulmonary function (CPF). In previous studies, several approaches, such as cardiopulmonary exercise testing (CPET), echocardiography and computed tomography angiography (CTA), were proposed to indirectly estimate the rehabilitation effect on CPF. However, the above approached require experienced operators and complex equipment. In this study, a smart and wearable brain oxygenation monitoring system without motion artifact and crosstalk is proposed to estimate the blood circulation state of brain tissue directly during incremental exercise. Moreover, the technique of neural network is also used for classifying different CPF groups from the indexes extracted from the measured hemoglobin parameters. The experimental results show that the defined indexes extracted from the hemoglobin parameters can present the state of CPF, and the proposed smart brain oxygenation monitoring system can also effectively and automatically classify different CPF groups from these indexes via artificial intelligence. The proposed system therefore may assist physicians in the clinical evaluation of the CVD severity and rehabilitation effect on CPF in the future.
Generation of Stereo Images Based on a View Synthesis Network
(2020-05-01)
Lo, Yuan-Mau
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Chang, Chin-Chen
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Way, Der-Lor
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Shih, Zen-Chung
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多媒體工程研究所
The conventional warping method only considers translations of pixels to generate stereo images. In this paper, we propose a model that can generate stereo images from a single image, considering both translation as well as rotation of objects in the image. We modified the appearance flow network to make it more general and suitable for our model. We also used a reference image to improve the inpainting method. The quality of images resulting from our model is better than that of images generated using conventional warping. Our model also better retained the structure of objects in the input image. In addition, our model does not limit the size of the input image. Most importantly, because our model considers the rotation of objects, the resulting images appear more stereoscopic when viewed with a device.
The Role of Accent and Grouping Structures in Estimating Musical Meter
(2020-04-01)
Lin, Han-Ying
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Huang, Chien-Chieh
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Chang, Wen-Whei
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Chien, Jen-Tzung
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電機工程學系
This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.
Adaptive Neural Predictive Control for Permanent Magnet Synchronous Motor Systems With Long Delay Time
(2019-01-01)
Wu, Bing-Fei
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Lin, Chun-Hsien
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電控工程研究所
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Institute of Electrical and Control Engineering
Since the permanent magnet synchronous motor system in this research needs about 40 ms to finish a control cycle, such a long delay in time strongly causes the bad performance for the conventional controllers, especially for position control. To well control the speed and position, an adaptive neural predictive control is proposed. A two-layer recursive neural network is employed as a speed predictor, and an extended Kalman filter is utilized to tune the parameters of the predictor adaptively. Chaos optimization algorithm and Newton-Raphson optimization are combined to solve the problem of predictive control. As for the speed control, the proposed method shows better performance. The position control is designed based on the speed control. Due to the physical limitation of the plant, the steady state error is still large. Hence, a fuzzy compensator is applied. From the experiment, the error is reduced obviously.
Discovering negative comments by sentiment analysis on web forum
(2019-05-01)
Hsu, Wei-Yun
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Hsu, Hui-Huang
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Tseng, Vincent S.
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資訊工程學系
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Department of Computer Science
Social media enables people to communicate with each other on the Internet in real-time and rich styles. In other words, there is a lot of information on the social media. If we can extract negative opinions of some brands, enterprises or politics, we can use these opinions to know the market demands and solve problems. In this paper, we propose a novel approach to extract negative-sentiment-oriented features and identify negative opinions in social media with text mining and machine learning techniques, support vector machine and neural network, as well as data collection with Web crawler. The experimental results show that our proposed methods can work effectively.
LiSiOX-Based Analog Memristive Synapse for Neuromorphic Computing
(2019-04-01)
Chen, Jia
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Lin, Chih-Yang
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Li, Yi
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Qin, Chao
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Lu, Ke
The progress in the neuromorphic computing hinges on the development of nanoscale analog artificial synapses. Here, we report a LiSiOX (LSO)-based memristive synapse with 100-level conductance states under identical pulses, representing synaptic potentiation and depression behaviors. The superior analog behaviors originate from the dynamic evolution of an electro-thermal modulation region with the motion of lithium and oxygen ions. A three-layer perceptron was constructed in simulation with LSO synapses, and a 91.97% recognition accuracy was achieved for handwritten digits. Moreover, the influences of several critical parameters, including device variability and weight precision, on the accuracy have been investigated. This letter provides guidelines for the optimization of synaptic device in robust memristive neural network.
Stochastic Recurrent Neural Network for Speech Recognition
(2017-01-01)
Chien, Jen-Tzung
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Shen, Chen
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電機工程學系
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Department of Electrical and Computer Engineering
This paper presents a new stochastic learning approach to construct a latent variable model for recurrent neural network (RNN) based speech recognition. A hybrid generative and discriminative stochastic network is implemented to build a deep classification model. In the implementation, we conduct stochastic modeling for hidden states of recurrent neural network based on the variational auto-encoder. The randomness of hidden neurons is represented by the Gaussian distribution with mean and variance parameters driven by neural weights and learned from variational inference. Importantly, the class labels of input speech frames are incorporated to regularize this deep model to sample the informative and discriminative features for reconstruction of classification outputs. We accordingly propose the stochastic RNN (SRNN) to reflect the probabilistic property in RNN classification system. A stochastic error backpropagation algorithm is implemented. The experiments on speech recognition using TIMIT and Aurora4 show the merit of the proposed SRNN.
Interaural Coherence Induced Ideal Binary Mask for Binaural Speech Separation and Dereverberation
(2016-01-01)
Chen, Yi-Ting
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Chen, Tzu-Hao
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Huang, Mao-Chang
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Chi, Tai-Shih
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電機工程學系
We have proposed a spatial-cue based binaural noise reduction algorithm for hearing aids. However, in that algorithm, decision parameters are empirically selected. In this paper, we extend the work and propose a supervised classification algorithm for binaural speech enhancement/separation and dereverberation using a modified ideal binary mask (mIBM) as the training target and simple neural networks (NNs) as classifiers. The low complexity of the simple NNs makes the proposed algorithm practical for binaural hearing aids. The interaural time difference (ITD) and the interaural level difference (ILD) of each T-F unit are extracted as the basic binaural features. For the purpose of dereverberation, the interaural coherence (IC) is also considered when building the target mIBM and training the NNs. For separation evaluations, our method yields comparable performance to a more complicated benchmark system, which cannot de-reverb the signal. For concurrent separation and dereverberation, our method offers 4 to 5 dB improvement on the frequencyweighted segmental speech-to-noise ratio (SNRfw) over unprocessed speech.
Asymptotic behaviors in a transiently chaotic neural network
(2003-04-01)
Chen, SS
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Shih, CW
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應用數學系
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Department of Applied Mathematics
We are interested in the asymptotic behaviors of a discrete-time neural network. This network admits transiently chaotic behaviors which provide global searching ability in solving combinatorial optimization problems. As the system evolves, the variables corresponding to temperature in the annealing process decrease, and the chaotic behaviors vanish. We shall find sufficient conditions under which evolutions for the system converge to a fixed point of the system. Attracting sets and uniqueness of fixed point for the system are also addressed. Moreover, we extend the theory to the neural networks with cycle-symmetric coupling weights and other output functions. An application of this annealing process in solving travelling salesman problems is illustrated.

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