Wai Keung Wong

Distributed Synchronization of Coupled Neural Networks via Randomly Occurring Control

In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertexs neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.

Approximate orthogonal sparse embedding for dimensionality reduction

Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1 -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: a case study based on the apparel industry

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each orders production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.

Stitching defect detection and classification using wavelet transform and BP neural network

In the textile and clothing industry, much research has been conducted on fabric defect automatic detection and classification. However, little research has been done to evaluate specifically the stitching defects of a garment. In this study, a stitching detection and classification technique is presented, which combines the improved thresholding method based on the wavelet transform with the back propagation (BP) neural network. The smooth subimage at a certain resolution level using the pyramid wavelet transform was obtained. The study uses the direct thresholding method, which is based on wavelet transform smooth subimages from the use of a quadrant mean filtering method, to attenuate the texture background and preserve the anomalies. The images are then segmented by thresholding processing and noise filtering. Nine characteristic variables based on the spectral measure of the binary images were collected and input into a BP neural network to classify the sample images. The classification results demonstrate that the proposed method can identify five classes of stitching defects effectively. Comparisons of the proposed new direct thresholding method with the direct thresholding method based on the wavelet transform detailed subimages and the automatic band selection for wavelet reconstruction method were made and the experimental results show that the proposed method outperforms the other two approaches.

Supervised optimal locality preserving projection

In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Unlike the unsupervised learning scheme of LPP, this paper follows the supervised learning scheme, i.e. it uses both local information and class information to model the similarity of the data. Based on novel similarity, we propose two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP). Optimal here means that the extracted features via SOLPP (or NL-SOLPP) are statistically uncorrelated and orthogonal. We compare the proposed SOLPP and NL-SOLPP with LPP, orthogonal locality preserving projection (OLPP) and uncorrelated locality preserving projection (ULPP) on publicly available data sets. Experimental results show that the proposed SOLPP and NL-SOLPP achieve much higher recognition accuracy.

Adaptive Time-Variant Models for Fuzzy-Time-Series Forecasting

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Intelligent production control decision support system for flexible assembly lines

In this study, a production control problem on a flexible assembly line (FAL) with flexible operation assignment and variable operative efficiencies is investigated. A mathematical model of the production control problem is formulated with the consideration of the time-constant learning curve to deal with the change of operative efficiency in real-life production. An intelligent production control decision support (PCDS) system is developed, which is composed of a radio frequency identification technology-based data capture system, a PCDS model comprising a bi-level genetic optimization process and a heuristic operation routing rule is developed. Experimental results demonstrated that the proposed PCDS system could implement effective production control decision-making for solving the FAL.

A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting

This paper investigates a flexible assembly line balancing (FALB) problem with work sharing and workstation revisiting. The mathematical model of the problem is presented, and its objective is to meet the desired cycle time of each order and minimize the total idle time of the assembly line. An optimization model is developed to tackle the addressed problem, which involves two parts. A bilevel genetic algorithm with multiparent crossover is proposed to determine the operation assignment to workstations and the task proportion of each shared operation being processed on different workstations. A heuristic operation routing rule is then presented to route the shared operation of each product to an appropriate workstation when it should be processed. Experiments based on industrial data are conducted to validate the proposed optimization model. The experimental results demonstrate the effectiveness of the proposed model to solve the FALB problem.

Sparse Approximation to the Eigensubspace for Discrimination

Two-dimensional (2-D) image-matrix-based projection methods for feature extraction are widely used in many fields of computer vision and pattern recognition. In this paper, we propose a novel framework called sparse 2-D projections (S2DP) for image feature extraction. Different from the existing 2-D feature extraction methods, S2DP iteratively learns the sparse projection matrix by using elastic net regression and singular value decomposition. Theoretical analysis shows that the optimal sparse subspace approximates the eigensubspace obtained by solving the corresponding generalized eigenequation. With the S2DP framework, many 2-D projection methods can be easily extended to sparse cases. Moreover, when each row/column of the image matrix is regarded as an independent high-dimensional vector (1-D vector), it is proven that the vector-based eigensubspace is also approximated by the sparse subspace obtained by the same method used in this paper. Theoretical analysis shows that, when compared with the vector-based sparse projection learning methods, S2DP greatly saves both computation and memory costs. This property makes S2DP more tractable for real-world applications. Experiments on well-known face databases indicate the competitive performance of the proposed S2DP over some 2-D projection methods when facial expressions, lighting conditions, and time vary.

A hybrid model using genetic algorithm and neural network for classifying garment defects

The inspection of semi-finished and finished garments is very important for quality control in the clothing industry. Unfortunately, garment inspection still relies on manual operation while studies on garment automatic inspection are limited. In this paper, a novel hybrid model through integration of genetic algorithm (GA) and neural network is proposed to classify the type of garment defects. To process the garment sample images, a morphological filter, a method based on GA to find out an optimal structuring element, was presented. A segmented window technique is developed to segment images into several classes using monochrome single-loop ribwork of knitted garment. Four characteristic variables were collected and input into a back-propagation (BP) neural network to classify the sample images. According to the experimental results, the proposed method achieves very high accuracy rate of recognition and thus provides decision support in defect classification.