• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.882-891
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• 2010

Many automated dynamic test data generation technique have been proposed. The techniques evaluate fitness of test data through executing instrumented Software Under Test (SUT) and then generate new test data based on evaluated fitness values and optimization algorithms. Previous researches and experiments have been showed that these techniques generate effective test data. However, optimization algorithms in these techniques incur much time to generate test data, which results in huge test case generation cost. In this paper, we propose a technique for reducing the time of evaluating a fitness of test data among steps of dynamic test data generation methods. We introduce the concept of Fitness Evaluation Program (FEP), derived from a path constraint of SUT. We suggest a test data generation method based on FEP and implement a test generation tool, named ConGA. We also apply ConGA to generate test cases for C programs, and evaluate efficiency of the FEP-based test case generation technique. The experiments show that the proposed technique reduces 20% of test data generation time on average.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.63-85
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• 1996

This paper describes the applications of genetic algorithm to nonlinear constrained optimization problems. Genetic algorithms are combinatorial in nature, and therefore are computationally suitable for treating continuous and idstrete integer design variables. For several problems , the conventional genetic algorithms are ill-defined , which comes from the application of penalty function , encoding and decoding methods, fitness scaling, and premature convergence of solution. Thus, we develope a hybrid genetic algorithm to resolve these problems and present two examples to demonstrate the effectiveness of the methodology developed in this paper.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.225-232
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• 2002

Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.493-503
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• 2018

Recently, as living standards have improved, many people are becoming more interested in health, and self-training is increasing through exercise to prevent and manage pre-illness. In general, an imbalance of muscles causes asymmetry of posture, which can cause various diseases by accompanying an adjustment force, circulation action, displacement of internal organs, etc.. In this study, the development of fitness software that can be self - training among smart wears has attracted considerable attention in recent years. In this study, a technology was proposed for the commercialization of self - trainer fitness wear by a simulation through Android - based applications. Self - trainer fitness software was developed by combining a conductive polymer, fashion design, sewing, and electric and electronic technology to monitor the unbalance of the muscles during exercise and make smart wear that can calibrate the asymmetry by oneself. In particular, a polymer sensor was fabricated by deriving the optimal MWCNT concentration, and the electrode signal was collected by attaching the electrode to the optimal position, where the electrode signal line using the conductive fiber was designed and attached to collect the signal. A signal module that converts the bio-signals collected through electrical signal conversion and transmits them using Bluetooth communication was designed and manufactured. Self-trainer fitness software that can be commercialized was developed by combining noise cancellation with Android-based self-training application using a software algorithm method.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.203-221
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• 2011

The purpose of this study is to investigate the applications of Structural Equation Modeling(SEM) into MIS area in recent years. Two methodologies, Lisrel and PLS, are adopted for the method comparison. A research model, based upon TAM(Technology Acceptance Model) is used for the analysis of the data set of a previous study. The research model includes six research variables that are composed of twenty-eight question items. 272 data are used for data analyses through Lisrel v.8.72 and Visual PLS v.1.04. This study shows the statistical results of Lisrel are the same to those of PLS. The contribution of this study can be suggested as the followings; (1) A theoretical comparison of two methodologies is shown, (2) A statistical analysis is done at a real-situated data set, and (3) Several implications are suggested.

• International Journal of Contents
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• v.10 no.3
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• pp.1-8
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• 2014

In this paper, we collect and analyze the status of mobile phone applications (hereafter apps) in the healthcare and fitness category of the Apple iTunes App Store and Google Play Store. We determine the number of apps and analyze statistical aspects such as classifications, age rating, fees, and user evaluation of the popular items. As of September 30, 2013, there were 236 popular apps available from iTunes. Google Play offered 720 apps. We discover that apps for healthcare and fitness are diverse. Apps for physical exercise have the greatest popularity. The proportions of apps that are suitable for all ages among the Google and iTunes popular apps are 55.8% and 89.4%, respectively. The user evaluation of apps in iTunes is relatively less positive. We determine that the proportion of paid apps to free apps in Google is higher than that of the apps in iTunes. We perform hypothesis tests and find statistically significant differences in age rating and perceived satisfaction between the apps of the Apple iTunes App Store and Google Play Store. However, we find no meaningful differences in the classification and price of the apps between the two app stores. We perform hypothesis tests to verify the differences in age rating and perceived satisfaction between the paid and free apps within and across the Google Play Store and iTunes App Store. There are statistically significant differences in the age rating between the paid and free apps in the Google play store, between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps. There are statistically significant differences in the perceived satisfaction between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.99-104
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• 2016

The walking exercise can alleviate stress and also it can improve health fortheir lifetime. Recent development in Information and Communication Technologies (ICT) has laid the foundation for Internet of Things (IoT) to become the future technology. IoT has many applications in industry automation, security, smart homes and cities, education, health etc. In personal health-care domain, IoT is mainly used for monitoring fitness condition by observing current activity of individual. In this paper, we have proposed a novel IoT based personal wellness care system. Proposed system not only keep track of current fitness level but also provide future activity prediction based on history data along with standard recommendations. Predicted activity helps in motivating the individual to achieve the desired fitness level. Initially, we consider only walking activity for testing purpose and later, other types of activities/exercise will be captured for improved health care support.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.69-77
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• 2015

The Microsoft Kinect is a motion sensing input device which is widely used for many motion recognition applications such as fitness, sports, and rehabilitation. Until now, most of remote rehabilitation systems with the Microsoft Kinect have allowed the user or patient to do rehabilitation or fitness by following the motion of a video screen. However in this paper we propose a smart remote rehabilitation system with the Microsoft Kinect motion sensor and a wearable ECG sensor which can allow patients to offer monitoring of the individual's performance and personalized feedback on rehabilitation exercises. The proposed noble smart remote rehabilitation is able to monitor and measure the state of the patient's condition during rehabilitation exercise, and transmits it to the prescriber. This system can give feedback to a prescriber, a doctor and a patient for improving and recovering motor performance. Thus, the efficient rehabilitation training service can be provided to patient in response to changes of patient's condition during exercise.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.1
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• pp.95-110
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• 1998

Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.86-93
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• 2008

The construction of ontology defines as complicated semantic relations needs precise and expert skills. For the well defined ontology in real applications, plenty of information of instances for ontology classes is very critical. In this study, crawling algorithm which extracts the fittest topic from the Web overflowing over by a great number of documents has been focused and developed. Proposed crawling algorithm made a progress to gather documents at high speed by extracting topic-specific Link using URL patterns. And topic fitness of Link block text has been represented by fuzzy sets which will improve a precision of the focused crawler.