ANNs in ABC Multi-driver Optimization Based on Thailand Automotive Industry

The purpose of this research was to develop a method for Activity Based Costing (ABC) that provided accurate product production costs. ABC using Single Driver Activity Based Costing (SDABC) can result in distortion of the cost. A more accurate ABC cost calculation based on multiple cost drivers (CDs) in each activity has been devised and proven by considering the various cost drivers using the correlation coefficient or R2. The application of artificial neural networks (ANNs) to choose the CDs is Multiple Drivers Activity Based Costing (MDABC). The ANNs choose the CDs by algorithms including Multilayer Perceptron and Back-propagation. The transfer function for hidden layers is the Log-Sigmoid Function and for the output layer is the Pure Linear transfer function. The results have demonstrated that using MDABC results in more accurate cost calculations than when using SDABC. The study found that both of the extended ABC method, SDABC and MDABC provide more accurate actual cost of production, and both are applicable to products with low turnover or those in a state of loss condition. However, MDABC is better used in situations which include a variety of production activities, while the SDABC method is best used in situations of the factory operations not being very complex. Overall, the resolution, or accuracy, of the calculated production costs is better using the MDABC method, but is more complicated in its use and operation. Computer-based ANNs overcome this problem of complexity

Muscle Sensor Model Using Small Scale Optical Device for Pattern Recognitions

A new sensor system for measuring contraction and relaxation of muscles by using a PANDA ring resonator is proposed. The small scale optical device is designed and configured to perform the coupling effects between the changes in optical device phase shift and human facial muscle movement, which can be used to form the relationship between optical phase shift and muscle movement. By using the Optiwave and MATLAB programs, the results obtained have shown that the measurement of the contraction and relaxation of muscles can be obtained after the muscle movements, in which the unique pattern of individual muscle movement from facial expression can be established. The obtained simulation results, that is, interference signal patterns, can be used to form the various pattern recognitions, which are useful for the human machine interface and the human computer interface application and discussed in detail

Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints

Abstract Objective An effective training plan is an important factor in sports training to enhance athletic performance. A poorly considered training plan may result in injury to the athlete, and overtraining. Good training plans normally require expert input, which may have a cost too great for many athletes, particularly amateur athletes. The objectives of this research were to create a practical cycling training plan that substantially improves athletic performance while satisfying essential physiological constraints. Adaptive Particle Swarm Optimization using ɛ-constraint methods were used to formulate such a plan and simulate the likely performance outcomes. The physiological constraints considered in this study were monotony, chronic training load ramp rate and daily training impulse. Results A comparison of results from our simulations against a training plan from British Cycling, which we used as our standard, showed that our training plan outperformed the benchmark in terms of both athletic performance and satisfying all physiological constraints

Ycsc: a modified clustering technique based on lcs

This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System with a modification to the original YCS fitness function has been found to improve the identification of less-separated data sets. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number..

Brain Signal Monitoring Model Using THz Whispering Gallery Modes Generated by Micro-conjugate Mirror Probe

In this paper, a brain signal monitoring system using micro-optical conjugate mirror based on whispering gallery modes (WGMs) of light within a PANDA ring circuit is modeled and proposed. WGMs are generated by the PANDA ring circuit, which can be used to perform the brain signal connection using WGM probe, which is a 3D light probe. Simulation results obtained have shown that the THz WGMs can be generated and used as a probe to penetrate and connect to the brain cells and signals, which can be useful for brain signal monitoring and investigation. In applications, the various features such as smart multimedia devices, disability assisted and rehabilitation system, brain signal monitoring, robotic control and medical etc. using the THz 3D imaging probe can be plausible

A Learning Classifier Systems Approach to Clustering Learning Classifier Systems

Abstract- This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets.

MOESM1 of Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints

Additional file 1. Description of data: pseudocode of the proposed technique