• Journal of Digital Convergence
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• v.13 no.6
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• pp.327-339
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• 2015

The present study explored how 1) individuals' perception about attributes of exercise/fitness apps and 2) their motivation for exercise/fitness behaviors influence perceived usefulness and usage of the apps. A survey of 323 males and females in their 20's and 30's revealed that attributes of apps that satisfy their basic psychological needs are crucial in promoting perceived usefulness and usage of the apps. Participants were more likely to perceive exercise/fitness apps useful as they perceive them having more attributes related to autonomy and competence. The perception regarding relatedness attributes did not have the main effect on perceived usefulness but showed an interaction effect with relative autonomous motivation. This convergence study which examined health apps from the perspectives of psychology and public health provides practical implications to development and evaluation of health apps.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.282-289
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• 2009

This paper presents an estimation technique of the equivalent circuit parameters for an underwater acoustic piezoelectric transducer from the measured impedance. Estimated equivalent circuit can be used for the design of the impedance matching network of the sonar transmitter. A fitness function is proposed to minimize the error between the calculated impedance of the equivalent circuit and the measured impedance of the transducer. The equivalent circuit parameters are estimated by using the fitness function and the PSO(Particle Swarm Optimization) algorithm. The effectiveness of the proposed method is verified by the applications to a sandwich-type transducer and a dummy load. In addition, the impedance matching network is also designed by using the estimated equivalent circuit model.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.117-124
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• 2018

Wearable devices have become widespread. Fitness band is one of common wearable devices, providing useful functions. It helps users to monitor and collect their status such as heart rate and travel distance. Wearable devices, including fitness bands, are designed in small size and it ends up having small battery capacity. In that regard, it is necessary to expand the lifetime of wearable devices. Conventional power management scheme of wearable devices is based on DVFS Ondemand Governor and peripheral control by timeout event, such as turning off the LCD. In this paper, we propose a hybrid governor applying hardware supporting low power mode such as sleep mode to exploit the periodicity of fitness band task. In addition, we show hybrid governor outperforms in power consumption than conventional power management scheme of wearable devices based on Ondemand Governor through experiments.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.47-56
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• 2023

This paper presents an approach to exercise that utilizes motion capture through the Rokoko Smart Suit. With the emergence of Covid-19, physical fitness levels have declined due to restrictions on in-person fitness classes and gym closures. To maintain physical activity, many individuals have turned to mobile applications and streaming videos. However, home workouts often lack the motivation and experience found in gyms, classes, or community centers, particularly with the presence of coaches and instructors. Additionally, instructors find it challenging to convey precise postures to their online students, and vice versa. To address this issue, the researchers propose the use of a full-body tracking suit like the Rokoko Smart Suit, which enables instructors to present a more realistic approach to physical activity. The Rokoko Smart Suit offers a 3D view of the instructor, eliminating the limitations of camera scope when streaming on platforms like Zoom or MS Teams. This technology enhances the at-home workout experience, and the incorporation of 3D virtual reality features can further elevate the realism of a workout.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1246-1257
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• 2004

To find an optimal solution with genetic algorithm, it is desirable to maintain the population sire as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a novel genetic algorithm based on fuzzy clustering, which considerably reduces evaluation number without any significant loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness using membership matrix, since it is hard to distribute precise fitness values by hard clustering method to individuals which belong to multiple groups. Nine benchmark functions have been investigated and the results are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients as fitness distribution method.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.649-658
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• 2003

We propose a hierarchical architecture of ART2 Network for performance improvement and fast pattern classification model using fitness selection. This hierarchical network creates coarse clusters as first ART2 network layer by unsupervised learning, then creates fine clusters of the each first layer as second network layer by supervised learning. First, it compares input pattern with each clusters of first layer and select candidate clusters by fitness measure. We design a optimized fitness function for pruning clusters by measuring relative distance ratio between a input pattern and clusters. This makes it possible to improve speed and accuracy. Next, it compares input pattern with each clusters connected with selected clusters and finds winner cluster. Finally it classifies the pattern by a label of the winner cluster. Results of our experiments show that the proposed method is more accurate and fast than other approaches.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of Information Technology Applications and Management
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• v.11 no.4
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• pp.49-60
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• 2004

While the World-Wide Web offers an incredibly rich base of information, organized as a hypertext it does not provide a uniform and efficient way to retrieve specific information. Therefore, it is needed to develop an efficient web crawler for gathering useful information in acceptable amount of time. In this paper, we studied the order in which the web crawler visit URLs to rapidly obtain more important web pages. We also developed an internet agent for efficient web crawling using hyperlink structure and fitness of hypertext documents. As a result of experiment on a website. it is shown that proposed agent outperforms other web crawlers using BackLink and PageRank algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.574-579
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• 2006

The feature selection algorithms should broadly and efficiently explore the huge problem spaces to find a good solution. This paper attempts to gain insights on the fitness landscape of the spaces and to improve search capability of the algorithms. We investigate the solution spaces in terms of statistics on local maxima and minima. We also analyze behaviors of the existing algorithms and improve their solutions.