• KIPS Transactions on Computer and Communication Systems
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• v.4 no.8
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• pp.253-258
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• 2015

From the point of view of u-healthcare, heart rate is so useful for both illness for taking care of patients and wellness for improving the level of health and wellbeing. It is because heart rate is a significant clinical variable for all kinds of diseases as well as an indicator of the intensity of exercise. Recently, a number of various wearable heart rate monitors have been released to check people's status in the body by monitoring their heart rates. In addition, a number of smartphone applications have been released to conveniently monitor the status of exercise by using heart rate monitors. However, all of these applications are limited to a personal usage. In this paper, we will design a system to simultaneously monitor heart rates coming from multiple users in a real-time, and develop an Android application to apply the system. The application mainly features a simultaneous monitoring of heart rates coming from multiple users, allowing to be effectively applied to fitness centers.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• Journal of the Korea Society for Simulation
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• v.16 no.3
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• pp.49-56
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• 2007

There are many application areas of wireless sensor networks, such as combat field surveillance, terrorist tracking and highway traffic monitoring. These applications collect sensed data from sensor nodes to monitor events in the territory of interest. One of the important issues in these applications is the existence of the radio-jamming zone between source nodes and the base station. Depending on the routing protocol the transmission of the sensed data may not be delivered to the base station. To solve this problem we propose a genetic algorithm based routing method for reliable transmission while considering the balanced energy depletion of the sensor nodes. The genetic algorithm finds an efficient routing path by considering the radio-jamming zone, energy consumption needed fur data transmission and average remaining energy level. The fitness function employed in genetic algorithm is implemented by applying the fuzzy logic. In simulation, our proposed method is compared with LEACH and Hierarchical PEGASIS. The simulation results show that the proposed method is efficient in both the energy consumption and success ratio of delivery.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.57-67
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• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.93-98
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• 2015

IoT (Internet of Things) is recently burgeoning as business applications as well as ICT itself. Among the business applications of IoT, wearable devices are recognized as a leading area of customer devices. This research first identifies customer needs of activity trackers (fitness trackers), as one of representative wearable devices, and mapping the identified needs with the well-known marketing model of marketing mix (4 P's: Product, Price, Promotion, and Place). Survey was applied to university students for identifying current and potential needs for activity trackers. The needs were classified by 4 P's, and according to the results, different from other IT devices, activity trackers has more potential needs. Moreover, reliable distribution channels, offline and company owned shops were preferred, rather than online shopping mall by third parties. The results would provide some valuable implications to not only designers of activity trackers but also business management.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.307-311
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• 2022

With the development of Internet of Things (IoT) technologies, numerous people worldwide connect with various electronic devices via Human-Machine Interfaces (HMIs). Considering that HMIs are a new concept of dynamic interactions, wearable electronics have been highlighted owing to their lightweight, flexibility, stretchability, and attachability. In particular, wearable strain sensors have been applied to a multitude of practical applications (e.g., fitness and healthcare) by conformally attaching such devices to the human skin. However, the stretchable elastomer in a wearable sensor has an intrinsic stretching limitation; therefore, structural advances of wearable sensors are required to develop practical applications of wearable sensors. In this study, we demonstrated a 3-dimensional (3D), porous, and piezoelectric strain sensor for sensing body movements. More specifically, the device was fabricated by mixing polydimethylsiloxane (PDMS) and polyvinylidene fluoride nanoparticles (PVDF NPs) as the matrix and piezoelectric materials of the strain sensor. The porous structure of the strain sensor was formed by a sugar cube-based 3D template. Additionally, mixing methods of PVDF piezoelectric NPs were optimized to enhance the device sensitivity. Finally, it is verified that the developed strain sensor could be directly attached onto the finger joint to sense its movements.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.26-30
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• 2001

Multipopulation Genetic Algorithm(MPGA) is the modified form of Genetic Algorithm(GA), which was devised for covering for overing the defect of general GA. The core of MPGA is said to be the migration method. The fitness-based migration method and the random migration method are currently used. The random migration method is more general than the other because it keeps the diversity of the population. In this paper, a new migration method is suggested. This method has a merit that it can improve the speed of conergence, compared to the general migration method. This method is compared with the general migration method.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.213-229
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• 2005

Cost overrun or schedule delay of the software development project happens frequently despite that software developers continue to make every effort for the effective management of the projects. Previous researches have ascertained that these problems are ascribed to the uncertainty of projects and the improper management of the projects. The purposes of this research are to investigate the impacts of user participation and task interdependence on the performance of the projects and also to find out the appropriate project management method to improve the project performance. Even though the model fitness of the path model is proved to be very high, the verification of the hypotheses showed a variety of results including the four verifications and the one refutation of the hypotheses as well as the suggestion of one alternative hypothesis. The contribution of this research is that the integration model is proposed and verified, comprising the relationship among the user participation, the task interdependence, and the performance of software development projects. A project manager can utilize the implication of this research for an effective management of software development project.