Intelligent Facial Emotion Recognition

The system introduces an intelligent facial emotion recognition using arti?cial neural network (ANN). The concept ?rst takes modi?ed local binary patterns, which involve horizontal vertical and neighborhood pixel comparison, to produce initial facial representation. Then, a microgenetic algorithm(mGA) embedded Particle Swarm Optimization(PSO), is proposed for feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a sub dimension based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Arti?cial Neural Network is used as a classi?er for recognizing seven facial emotions. ANN is implemented as classi?er for pattern recognition. Based on a comprehensive study using within- and cross-domain images from the extended Japanese database. The empirical results indicate that our proposed system outperforms other state of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

Field Application of Cable Tension Estimation Technique Using the h-SI Method

This paper investigates field applicability of a new system identification technique of estimating tensile force for a cable of long span bridges. The newly proposed h-SI method using the combination of the sensitivity updating algorithm and the advanced hybrid microgenetic algorithm can allow not only avoiding the trap of local minimum at initial searching stage but also finding the optimal solution in terms of better numerical efficiency than existing methods. First, this paper overviews the procedure of tension estimation through a theoretical formulation. Secondly, the validity of the proposed technique is numerically examined using a set of dynamic data obtained from benchmark numerical samples considering the effect of sag extensibility and bending stiffness of a sag-cable system. Finally, the feasibility of the proposed method is investigated through actual field data extracted from a cable-stayed Seohae Bridge. The test results show that the existing methods require precise initial data in advance but the proposed method is not affected by such initial information. In particular, the proposed method can improve accuracy and convergence rate toward final values. Consequently, the proposed method can be more effective than existing methods in terms of characterizing the tensile force variation for cable structures.

Hybrid Fuzzy Skyhook Surface Control Using Multi-Objective Microgenetic Algorithm for Semi-Active Vehicle Suspension System Ride Comfort Stability Analysis

A polynomial function supervising fuzzy sliding mode control (PSFαSMC), which embedded with skyhook surface method, is proposed for the ride comfort of a vehicle semi-active suspension. The multi-objective microgenetic algorithm (MOμGA) has been utilized to determine the PSFαSMC controller’s parameter alignment in a training process with three ride comfort objectives for the vehicle semi-active suspension, which is called the “offline” step. Then, the optimized parameters are applied to the real-time control process by the polynomial function supervising controller, which is named “online” step. A two-degree-of-freedom dynamic model of the vehicle semi-active suspension systems with the stability analysis is given for passenger’s ride comfort enhancement studies, and a simulation with the given initial conditions has been devised in MATLAB. The numerical results have shown that this hybrid control method is able to provide real-time enhanced level of reliable ride comfort performance for the semi-active suspension system.