Sang-Oh Shim

A Novel Model for Metabolic Syndrome Risk Quantification Based on Areal Similarity Degree

Metabolic syndrome (MS) refers to a clustering of specific cardiovascular disease (CVD) risk factors whose underlying pathology is thought to be related to insulin resistance. The risk factors include insulin resistance, obesity, dyslipidemia, and hypertension and it is known to increase the risk for CVD and type II diabetes. Since MS helps to identify individuals at high risk for both CVD and type II diabetes, it has become a major public healthcare issue in many countries. There has been much effort to establish diagnostic criteria for MS, but the current diagnostic criteria of MS have weaknesses, such as binary decision based on diagnostic criteria, equal weight among risk factors, and difficulty in estimating the temporal progress of the risk factors. To resolve these problems, this paper proposes a risk quantification model for MS, which is based on areal similarity degree analysis between weighted radar charts comprising MS diagnostic criteria and examination results of risk factors. The clinical effectiveness of the proposed model is extensively evaluated by using data of a large number of subjects obtained from the third Korea National Health and Nutrition Examination Survey. The evaluation results show that the proposed model can quantify the risk of MS and effectively identify a group of subjects who might be classified into a potential risk group for having MS in the future.

Short Communication: Generating subproblems in branch and bound algorithms for parallel machines scheduling problem

A branch and bound algorithm (B&B) has been widely used in various discrete and combinatorial optimization fields. To obtain optimal solutions as soon as possible for scheduling problems, three tools, which are branching, bounding and dominance rules, have been developed in the B&B algorithm. One of these tools, a branching is a method for generating subproblems and directly determines size of solution to be searched in the B&B algorithm. Therefore, it is very important to devise effective branching scheme for the problem. In this note, a survey of branching schemes is performed for parallel machines scheduling (PMS) problems with n independent jobs and m machines and new branching schemes that can be used for identical and unrelated PMS problems, respectively, are suggested. The suggested branching methods show that numbers of generated subproblems are much smaller than that of other methods developed earlier and therefore, it is expected that they help to reduce a lot of CPU time required to obtain optimal solutions in the B&B algorithm.

Multi-level job scheduling under processing time uncertainty

Abstract This study deals with a multi-level job scheduling problem in a single machine under processing time uncertainty. For the objectives of minimizing the total weighted tardiness of orders and the schedule deviation of the realized schedule from the baseline schedule, two surrogate measures for each of the two objectives are proposed to cope with the processing time uncertainty in the baseline scheduling phase. For the suggested surrogate measures, several optimal solution properties are developed to reduce the size of the solution space. Then, by using the solution properties a heuristic algorithm with two different order prioritization rules and a metaheuristic algorithm based on the discrete particle swarm optimization are developed for the order sequencing problem. Also, an iterative job sequencing algorithm is developed for the job sequencing problem that can be used for a given order sequence. From a series of computational experiments with a number of randomly generated problem instances with multiple bills of materials and normally distributed processing times of items, it is identified that the suggested metaheuristic algorithm with or without the iterative job sequencing algorithm shows good performance compared with the heuristic algorithm.

3D Electromagnetic Analysis of Magnetic Sensor for Improvement of Motor

This paper deals with an optimal angle error reduction method of magnetic hall sensor using hall effect elements with yoke. The magnetic position sensor is required to generate ideal sine and cosine waveforms from its hall effect elements according to rotation angle for precise angle information. However, the output signals are easy to include harmonics due to uneven magnetic field distribution from disturbance in the vicinity of hall effect elements. Thus, The paper studies a way which makes sine and cosine waveforms robust in disturbance and reduces harmonics by installing a yoke around Hall effect elements. The angle detection simulation for the magnetic hall sensor is performed by 3 dimensional finite element method and Taguchi method, one of the design of experiments. For the Taguchi method, three design parameters related to position of hall effect elements and shape of hall effect element yoke are selected.