Browsing by Author "Yang, Shih-Hung"

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A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex
(2016-12-09)
Yang, Shih-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Liao, Lun-De
;
Lu, Henry Horng-Shing
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (RCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, RCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 +/- 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 +/- 11.73 mm), OLE (20.17 +/- 6.43 mm), PCA (19.13 +/- 0.75 mm), SFS (22.75 +/- 2.01 mm), and NN (16.75 +/- 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.
An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications
(2012-06-01)
Yang, Shih-Hung
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Chen, Yon-Ping
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電機工程學系
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Department of Electrical and Computer Engineering
We propose a method for designing artificial neural networks (ANNs) for prediction problems based on an evolutionary constructive and pruning algorithm (ECPA). The proposed ECPA begins with a set of ANNs with the simplest possible structure, one hidden neuron connected to an input node, and employs crossover and mutation operators to increase the complexity of an ANN population. Additionally, cluster-based pruning (CBP) and age-based survival selection (ABSS) are proposed as two new operators for ANN pruning. The CBP operator retains significant neurons and prunes insignificant neurons on a probability basis and therefore prevents the exponential growth of an ANN. The ABSS operator can delete old ANNs with potentially complex structures and then introduce new ANNs with simple structures; thus, the ANNs are less likely to be trapped in a fully connected topology. The ECPA framework incorporates constructive and pruning approaches in an attempt to efficiently evolve compact ANNs. As a demonstration of the method, ECPA is applied to three prediction problems: the Mackey-Glass time series, the number of sunspots, and traffic flow. The numerical results show that ECPA makes the design of ANNs more feasible and practical for real-world applications. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
Face Liveness Detection Based on Skin Blood Flow Analysis
(2017-12-01)
Wang, Shun-Yi
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Yang, Shih-Hung
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Chen, Yon-Ping
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Huang, Jyun-We
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電子工程學系及電子研究所
Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications.
Grey Neural Network-Based Forecasting System for Vision-Guided Robot Trajectory Tracking
(2011)
Yang, Shih-Hung
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Chou, Chung-Hsien
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Chung, Chen-Fang
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Pai, Wen-Pang
;
Liu, Tse-Han
This paper presents a grey neural network-based forecasting system (GNNFS) in solving the prediction problem. GNNFS adopts a grey model to predict the signal and a neural network (NN) to forecast the prediction error of the grey model. A sequential batch learning (SBL) is developed to adjust the weights of the NN. The proposed GNNFS is applied to a binocular robot, called an Eye-Robot, for human-robot interaction which involved predicting the trajectory of a participant's hand and tracking the hand. By applying the SBL, the GNNFS can gradually learn to predict the trajectory of the hand and track it well. The experimental results show that the GNNFS can carry out the SBL in real-time for vision-guided robot trajectory tracking.
Integration of Grey Model and Neural Network for Robotic Application
(2011)
Yang, Shih-Hung
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Li, Jung-Che
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Chen, Yon-Ping
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper proposes an intelligent forecasting system based on a feedforward neural network aided grey model (FNAGM), integrating a first-order single variable grey model (GM(1,1)) and a feedforward neural network. The system includes three phases: initialization phase, GM(1,1) prediction phase, and FNAGM prediction phase. A number of parameters required for the FNAGM are selected in the initialization phase. A one-step ahead predictive value is generated in the GM(1,1) prediction phase, followed by the implementation of a feedforward neural network used to determine the prediction error of the GM(1,1) and compensate for it in the FNAGM prediction phase. We also adopted on-line batch training to adjust the network according to the Levenberg-Marquardt algorithm in real-time. According to the experimental results of a robot, the proposed intelligent forecasting system can provide high accuracy for both trajectory prediction and target tracking.
Intelligent Forecasting System Based on Grey Model and Neural Network
(2009)
Yang, Shih-Hung
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Chen, Yon-Ping
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電控工程研究所
;
Institute of Electrical and Control Engineering
This paper presents the design issues of two intelligent forecasting systems, feedforward-neural-network-aided grey model (FNAGM) and Elman-network-aided grey model (ENAGM). Both he FNAGM and ENAGM combine a first-order single variable grey model (GM(1,1)) and a neural network (NN). The GM(1,1) is adopted to predict signal, and the feedforward NN and the Elman network in the FNAGM and ENAGM respectively are used to learn the prediction error of the GM(1,1). Simulation results demonstrate that the intelligent forecasting systems with on-line learning can improve the prediction of the GM(1,1) and can be implemented in real-time prediction.
Intelligent Forecasting System Using Grey Model Combined with Neural Network
(2011-03-01)
Yang, Shih-Hung
;
Chen, Yon-Ping
;
電機工程學系
;
電控工程研究所
;
Department of Electrical and Computer Engineering
This paper proposes an intelligent forecasting system based on a feedforward-neural-network-aided grey model (FNAGM), which integrates a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNAGM prediction phase. First, some parameters required in the FNAGM are chosen in the initialization phase. Then, a one-step-ahead predictive value is generated in the GM(1,1) prediction phase. Finally, a feedforward neural network is used to learn the prediction error of the GM(1,1) and compensate it in the FNAGM prediction phase. Significantly, an on-line batch training is adopted to adjust the network according to the Levenberg-Marquardt algorithm in real-time. From the simulation results, the proposed intelligent forecasting system indeed improves the prediction error of the GM(1,1) and obtains more accurate prediction than other numerical methods.
Local hemostatic matrix for endoscope-assisted removal of intracerebral hemorrhage is safe and effective
(2018-01-01)
Luh, Hui-Tzung
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Huang, Abel Po-Hao
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Yang, Shih-Hung
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Chen, Chien-Ming
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Cho, Der-Yang
Background/Purpose: Minimally invasive endoscope-assisted (MIE) evacuation of spontaneous intracerebral hemorrhage (ICH) is simple and effective, but the limited working space may hinder meticulous hemostasis and might lead to rebleeding. Management of intraoperative hemorrhage is therefore a critical issue of this study. This study presents experience in the treatment of patients with various types of ICH by MIE evacuation followed by direct local injection of FloSeal Hemostatic Matrix (Baxter Healthcare Corp, Fremont, CA, USA) for hemostasis. Methods: The retrospective nonrandomized clinical and radiology-based analysis enrolled 42 patients treated with MIE evacuation of ICH followed by direct local injection of FloSeal Hemostatic Matrix. Rebleeding, morbidity, and mortality were the primary endpoints. The percentage of hematoma evacuated was calculated from the pre-and postoperative brain computed tomography (CT) scans. Extended Glasgow Outcome Scale (GOSE) was evaluated at 6 months postoperatively. Results: Forty-two ICH patients were included in this study, among these, 23 patients were putaminal hemorrhage, 16 were thalamic ICH, and the other three were subcortical type. Surgery-related mortality was 2.4%. The average percentage of hematoma evacuated was 80.8%, and the rebleeding rate was 4.8%. The mean operative time was 102.7 minutes and the average blood loss was 84.9 mL. The mean postoperative GOSE score was 4.55 at 6-months' follow-up. Conclusion: This study shows that local application of FloSeal Hemostatic Matrix is safe and effective for hemostasis during MIE evacuation of ICH. In our experience, this shortens the operation time, especially in cases with intraoperative bleeding. A large, prospective, randomized trial is needed to confirm the findings. Copyright (C) 2017, Formosan Medical Association. Published by Elsevier Taiwan LLC.
Neural Network Based Stereo Matching Algorithm Utilizing Vertical Disparity
(2010)
Yang, Shih-Hung
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Ho, Cheng-Yu
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Chen, Yon-Ping
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電機工程學系
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Department of Electrical and Computer Engineering
This paper presents a stereo matching algorithm utilizing vertical disparity (SMAVD) in solving the matching problem of stereo vision. SMAVD adopts a two-dimensional Hopfield neural network (HNN) to match the stereo pairs according to the energy function developed to describe three constraints including uniqueness, similarity and compatibility. The similarity of one matched pair is measured according to the difference of its neighboring pixels. The compatibility between two matched pairs is determined from not only smoothness and geometric comparisons but also vertical disparity comparison to improve the matching accuracy. Moreover, SMAVD uses a genetic algorithm to design the parameters of the nonlinear functions employed in the similarity and compatibility measures. By applying the updating rule, the HNN could obtain the correct matched pairs satisfying the constraints. The experimental results on the image pairs acquired from a binocular robot demonstrate that SMAVD could achieve high correct matching percentage with less computation time.
Symbiotic Neuron Evolution of a Neural-Network-Aided Grey Model for Time Series Prediction
(2011)
Yang, Shih-Hung
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Chen, Yon-Ping
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電控工程研究所
;
Institute of Electrical and Control Engineering
This paper introduces a symbiotic neuron evolution algorithm (SNEA) to determine the topology of a neural-network-aided grey model (NNAGM) for time series prediction problem. The SNEA uses an evolutionary approach to evolve partially connected neural networks (NNs) and determine the number of hidden neurons. To achieve symbiotic evolution, SNEA first establishes a neuron population where each neuron is randomly created, and evaluates the neurons by constructing NNs with different numbers of neurons. Each neuron shares fitness from participating NNs. This algorithm then performs evolution on the neuron population by crossover and mutation based on neuron fitness. An NNAGM designed by SNEA is applied to the prediction problems and compared with other methods. The experimental results show that SNEA can produce an NNAGM with appropriate topology and higher prediction performance than other methods.
Symbiotic Structure Learning Algorithm for Feedforward Neural-Network-Aided Grey Model and Prediction Applications
(2017-01-01)
Yang, Shih-Hung
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Huang, Wun-Jhu
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Tsai, Jian-Feng
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Chen, Yon-Ping
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電子工程學系及電子研究所
The learning ability of neural networks (NNs) enables them to solve time series prediction problems. Off-line training can be applied to design the structure and weights of NNs when sufficient training data are available. However, this may be inadequate for applications that operate in real time, possess limited memory size, or require online adaptation. Furthermore, the structural design of NNs (i.e., the number of hidden neurons and connected topology) is crucial. This paper presents a novel algorithm, called the symbiotic structure learning algorithm (SSLA), to enhance a feedforward neural-network-aided grey model (FNAGM) for real-time prediction problems. Through symbiotic evolution, the SSLA evolves neurons that cooperate well with each other, and constructs NNs from the neurons with hyperbolic tangent and linear activation functions. During construction, the hidden neurons with the linear activation function can be simplified to a few direct connections from the inputs to the output neuron, leading to a compact network topology. The NNs share the fitness value with participating neurons, which are further evolved through neuron crossover and mutation. The proposed SSLA was evaluated through three real-time prediction problems. Experimental results showed that the SSLA-derived FNAGM possesses a partially connected NN with few hidden neurons and a compact topology. The evolved FNAGM outperforms other methods in prediction accuracy and continuously adapts the NN to the dynamic changes of the time series for real-time applications.