• Journal of The Korea Institute of Healthcare Architecture
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• v.3 no.5
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• pp.41-52
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• 1997

The purpose of this thesis is to develop the new method of the expert system as a new approach of the architectural programming in general hospital. Theoretical and field investigations have been made for this study, and finally, the computer pro gram for the architectural programming of general hospital was established. The architectural programming of general hospital is more complex than any other types of buildings and has been changed rapidly according to the social, cultural and environmental factors. Hence, a new approach method is necessary for corresponding to the complexity and these factors. Therefore, the computer should be used as a tool in order to operate more logical and systematic programming.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.331-339
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• 2023

In programming classes, there is always a need to efficiently manage programming assignments. This is especially important as class sizes and assignment complexity grows. GitHub and GitHub Classroom makes the management of student assignments much simpler than uploading files and folders to a LMS or shared online drive. Additionally, git and GitHub are industry standard tools, so introducing students these tools in class provides them a good opportunity to start learning about how software is developed in the real-world. This study describes a workflow that uses both GitHub Pages and GitHub Classroom for more efficient classroom and assignment management. The workflow outlined in this study was used in two practical web programming classes in Spring 2023 with 46 third and fourth-year university students. GitHub Pages was used as a classroom website to distribute class announcements, assignments, lecture slides, study guides, and exams. GitHub Classroom was used as a class roster and assignment management platform. The workflow presented in this study is expected to assist other lecturers with the formidable tasks of distributing, collecting, grading, and leaving feedback on multiple students' multi-file programming assignments in practical programming classes.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.97-114
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• 2022

Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

• Journal of Communications and Networks
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• v.16 no.3
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• pp.293-300
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• 2014

As energy harvesting communication systems emerge, there is a need for transmission schemes that dynamically adapt to the energy harvesting process. In this paper, after exhibiting a finite-horizon online throughput-maximizing scheduling problem formulation and the structure of its optimal solution within a dynamic programming formulation, a low complexity online scheduling policy is proposed. The policy exploits the existence of thresholds for choosing rate and power levels as a function of stored energy, harvest state and time until the end of the horizon. The policy, which is based on computing an expected threshold, performs close to optimal on a wide range of example energy harvest patterns. Moreover, it achieves higher throughput values for a given delay, than throughput-optimal online policies developed based on infinite-horizon formulations in recent literature. The solution is extended to include ergodic time-varying (fading) channels, and a corresponding low complexity policy is proposed and evaluated for this case as well.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.81-86
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• 2015

This paper suggests heuristic algorithm with linear time complexity to decide the normal and optimal point at minimum loss/maximum profit maximum shortest scheduling problem with additional loss cost and bonus profit cost. This algorithm computes only the earliest ending time for each node. Therefore, this algorithm can be get the critical path and project duration within O(n) time complexity and reduces the five steps of critical path method to one step. The proposed algorithm can be show the result more visually than linear programming and critical path method. For real experimental data, the proposed algorithm obtains the same solution as linear programming more quickly.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.258-263
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• 2011

We study on a generalization of the two dimensional linear programming knapsack problem with the extended GUB constraint, which was presented in paper Won(2001). We identify some new parametric properties of the generalized problem and derive a solution algorithm based on the identified parametric properties. The solution algorithm has a worst case time complexity of order O($n^2logn$), where n is the total number of variables. We illustrate a numerical example.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.396-403
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• 2014

The multi-divisional knapsack problem is defined as a binary knapsack problem where each mutually exclusive division has its own capacity. In this paper, we present an extension of the multi-divisional knapsack problem that has generalized multiple-choice constraints. We explore the linear programming relaxation (P) of this extended problem and identify some properties of problem (P). Then, we develop a transformation which converts the problem (P) into an LP knapsack problem and derive the optimal solutions of problem (P) from those of the converted LP knapsack problem. The solution procedures have a worst case computational complexity of order $O(n^2{\log}\;n)$, where n is the total number of variables. We illustrate a numerical example and discuss some variations of problem (P).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.787-797
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• 2008

In this paper, we propose a fast disparity estimation method considering temporal and spatial redundancy based on a dynamic programming for stereo matching. For the first step, the dynamic programming is performed to estimate disparity vectors with correlation between neighboring pixels in an image. Next, we efficiently compensate regions, which disparity vectors are not allocated, with neighboring disparity vectors assuming that disparity vectors in same object are quite similar. Moreover, in case of video sequence, we can decrease a complexity with temporal redundancy between neighboring frames. For performance comparison, we generate an intermediate-view image using the estimated disparity vector. Test results show that the proposed algorithm gives $0.8{\sim}2.4dB$-increased PSNR(peak signal to noise ratio) compared to a conventional block matching algorithm, and the proposed algorithm also gives approximately 0.1dB-increased PSNR and $48{\sim}68%$-lower complexity compared to the disparity estimation method based on general dynamic programming.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.69-76
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• 2009

This paper proposes a teaching plan of programming language class for university of education students amusingly with LEGO Mindstorms NXT robot. The goal of class is not fragmentary knowledge acquirement but problem-solving of maze. This robot communicates with GUI named NXT-G installed in computer via USB. GUI is not text-based but icon-based programming tool. This paper designs a semester with 3 steps such as beginner, intermediate, high-rank. Beginner step is consists of learning of basic functions such as GUI usage and several sensors of robot. Intermediate step is consists of solving of maze problem with low complexity. High-rank step is consists of solving maze problem with medium and high complexity. All maze problem-solving have 3 process with algorithm, flowchart, and programming with stack.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.28-37
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• 2008

In this paper, we investigate the problem of minimizing the average transmission power of users while guaranteeing the average delay constraints in time-varying uplink channels. We design a scheduler that selects a user for transmission and determines the transmission rate of the selected user based on the channel and backlog information of users. Since it requires prohibitively high computation complexity to determine an optimal scheduler for multi-user systems, we propose a low-complexity scheduling scheme that can achieve near-optimal performance. In this scheme, we reduce the complexity by decomposing the multiuser problem into multiple individual user problems. We arrange the probability of selecting each user such that it can be determined only by the information of the corresponding user and then optimize the transmission rate of each user independently. We solve the user problem by using a dynamic programming approach and analyze the upper and lower bounds of average transmission power and average delay, respectively. In addition, we investigate the effects of the user selection algorithm on the performance for different channel models. We show that a channel-adaptive user selection algorithm can improve the energy efficiency under uncorrelated channels but the gain is obtainable only for loose delay requirements in the case of correlated channels. Based on this, we propose a user selection algorithm that adapts itself to both the channel condition and the backlog level, which turns out to be energy-efficient over wide range of delay requirement regardless of the channel model.