Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

A hybrid observer for characterization and compensation of periodic disturbances

Usage of frequency based error correction methods for repetitive disturbances becomes popular in data storage devices that use a rotating mechanical power plant. Two distinct disturbance cancellation methods, disturbance observer (DOB) and adaptive feed forward control (AFC) are the most popular approaches used for current drive servo integration. DOB reduces error using a predetermined function that is calculated thorough a complicated process. Generally the response to disturbance and initiation of control action of DOB is relatively fast even if the amount of error reduction is limited. Meanwhile the error elimination capability of AFC for the repetitive disturbance is normally better than DOB as it utilizes a Fourier coefficients updating feature that supports an enhanced fine adaptation of a certain frequency disturbance. Of course, AFC suffers for disturbances that have neighboring close frequencies due to the inherent fundamental frequency dependencies of AFC formulation. In the present work, an attempt for combining benefits of AFC and DOB is made. This might enables control action not only to generate fast response to disturbance but also perform an accurate error rejection. This feature also provides to the closed loop system a frequency selection for enabling a strategic selective error rejection. The proposed method named Hybrid Disturbance Observer (HDOB) is compared to the traditional AFC and DOB.     

Maximally equidistributed pseudorandom number generators via linear output transformations

This article discusses construction of maximally equidistributed (ME) linear feedback shift register generators by using linear output transformations. We introduce a new strategy to find linear output transformations attaining the ME property, and apply it to the WELL generators and the Mersenne Twister. As a result, we found a modified version of the WELL generators with the ME property, having fewer operations than the original ME WELL generators. We also found a ME Mersenne Twister which is faster than the WELL generators with a period of 2¹⁹⁹³⁷−1$2^{19937}-1$ on some platforms. The C codes of the obtained generators are available at http://www3.ocn.ne.jp/∼harase/megenerators.html.     

Towards an understanding of the consumer acceptance of mobile wallet

This study seeks to validate a comprehensive model of consumer acceptance in the context of mobile payment. It uses the unified theory of acceptance and use of technology (UTAUT) model with constructs of security, trust, social influence, and self-efficacy. Structural equation modeling is used to construct a predictive model of attitudes toward the mobile wallet. Individuals’ responses to questions about attitude and intention to adopt/use a mobile wallet were collected and analyzed with various factors modified from UTAUT. While the model confirms the classical role of technology acceptance factors (i.e., perceived usefulness and ease of use are key antecedents to users’ attitude), the results also show that users’ attitudes and intentions are influenced by perceived security and trust. In the extended model, the moderating effects of demographics on the relations among the variables were found to be significant. The proposed model brings together extant research on mobile payment and provides an important cluster of antecedents to eventual technology acceptance via constructs of behavioral intention to use and actual system usage.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection

DEA is a useful nonparametric method of measuring the relative efficiency of a DMU and yielding a reference target for an inefficient DMU. However, it is very difficult for inefficient DMUs to be efficient by benchmarking a target DMU which has different input use. Identifying appropriate benchmarks based on the similarity of input endowment makes it easier for an inefficient DMU to imitate its target DMUs. But it is rare to find out a target DMU, which is both the most efficient and similar in input endowments, in real situation. Therefore, it is necessary to provide an optimal path to the most efficient DMU on the frontier through several times of a proximity-based target selection process. We propose a dynamic method of stepwise benchmarking for inefficient DMUs to improve their efficiency gradually.The empirical study is conducted to compare the performance between the proposed method and the prior methods with a dataset collected from Canadian Bank branches. The comparison result shows that the proposed method is very practical to obtain a gradual improvement for inefficient DMUs while it assures to reach frontier eventually.     

Characterization of a novel antibody immobilization combining protein G with parylene-H for surface plasmon resonance immunosensors

Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications.     

An integer program and a hybrid genetic algorithm for the university timetabling problem

The university timetabling problem (UTP) has been studied by numerous research groups for decades. In addition to addressing hard and soft constraints, we extend the UTP by considering consecutiveness and periodicity constraints of multi-session lectures, which are common in many eastern Asian universities. Because schedulers can decide the consecutiveness and periodicity constraints for the multi-session lectures within a limited ratio, we consider these novel decision variables in our model. We develop a mixed integer linear program for the UTP. For the analysis, we convert the UTP into the three-dimensional container packing problem (3DCPP) and create a hybrid genetic algorithm (HGA), which has been shown to be efficient in solving the 3DCPP. We also develop a tabu search algorithm based on the existing UTP literature and compare the findings with that of our HGA. The results show that our HGA obtains a better solution than the tabu search algorithm in a reasonable amount of time.     

Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents

With the changes in the interface paradigm, a user may not be satisfied using only a behavior-based interface such as a mouse and keyboard. In this paper, we propose a real-time user interface with emotion recognition that depends on the need for skill development to support a change in the interface paradigm to one that is more human centered. The proposed emotion recognition technology may provide services to meet the need to recognize emotions when using contents. Until now, most studies on an emotion recognition interface have used a single signal, which was difficult to apply because of low accuracy. In this study, we developed a complex biological signal emotion recognition system that blends the ratio of an ECG for the autonomic nervous system and the relative power value of an EEG (theta, alpha, beta, and gamma) to improve the low accuracy. The system creates a data map that stores user-specific probabilities to recognize six kinds of feelings (amusement, fear, sadness, joy, anger, and disgust). It updates the weights to improve the accuracy of the emotion corresponding to the brain waves of each channel. In addition, we compared the results of the complex biological signal data set and EEG data set to verify the accuracy of the complex biological signal, and found that the accuracy had increased by 35.78%. The proposed system will be utilized as an interface for controlling a game and smart space for a user with high accuracy.