• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.490-493
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• 2006

본 논문은 Linux Cluster의 MPICH에서 MPI_Allgather Algorithm의 성능을 개선하고 실험을 통해 최대 30%의 성능향상을 증명하였다. MPICH의 기존 버전이 메시지의 크기와 실행 프로세스 수에 따라 Recursive Doubling, Bruck Algorithm, Ring Algorithm을 차등 적용했던 것을, 앞의 Algorithm을 개선하여 Double Bruck Algorithm, Double Ring Algorithm을 제안, 구현하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1761-1784
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• 2017

In order to improve the embedding rate of reversible watermarking algorithm for digital image and enhance the imperceptibility of the watermarked image, an adaptive reversible image watermarking algorithm based on DE is proposed. By analyzing the traditional DE algorithm and the generalized DE algorithm, an improved difference expansion algorithm is proposed. Through the analysis of image texture features, the improved algorithm is used for embedding and extracting the watermark. At the same time, in order to improve the embedding capacity and visual quality, the improved algorithm is optimized in this paper. Simulation results show that the proposed algorithm can not only achieve the blind extraction, but also significantly heighten the embedded capacity and non-perception. Moreover, compared with similar algorithms, it is easy to implement, and the quality of the watermarked images is high.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.373-376
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• 2002

K-Means 알고리즘은 재배치 기법의 일종으로 K 개의 초기 클러스터중심(centroid)를 중심으로 K 개의 클러스터가 될 때까지 클러스터링을 반복하는 것이다. K-Means 알고리즘은 특성상 초기 클러스터 중심과 새롭게 생성된 클러스터 중심에 따라 클러스터링 결과가 달라진다. 본 논문에서는 K-Means Algorithm 의 초기 클러스터중심 선택 방법과 새로운 클러스터 중심 결정 방법을 개선한 변형 K-Means Algorithm을 제안한다. SMART 시스템에서 제안한 16가지 가중치 계산 방식에 의하여 두 알고리즘의 성능을 평가한 결과 제안한 변형 알고리즘이 재현률과 F-Measure 에서 20%이상 향상된 결과를 얻을 수 있었으며 특정 주제 아래 문서가 할당되는 클러스터링 성능이 우수하였다.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.335-342
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• 2001

The problems in the loadflow study of the fixed voltage DC traction power supply system including the regeneration power is analyzed. And the computer algorithm to avoid the problem is developed. A computer program using the developed algorithm was developed. A test run of the computer program is conducted and the result shows the algorithm and the program developed is very effective for the loadflow study of the system including the regeneration power.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.25-32
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• 2015

The Genetic algorithm is a search algorithm using evaluation, genetic operator, natural selection to populational solution iteratively. The convergence and divergence characteristic of genetic algorithm are affected by selection strategy, generation replacement method, genetic operator when genetic algorithm is designed. This paper proposes fast convergence genetic algorithm for time-limited robot path planning. In urgent situation, genetic algorithm for robot path planning does not have enough time for computation, resulting in quality degradation of found path. Proposed genetic algorithm uses fast converging selection strategy and generation replacement method. Proposed genetic algorithm also uses not only traditional crossover and mutation operator but additional genetic operator for shortening the distance of found path. In this way, proposed genetic algorithm find reasonable path in time-limited situation.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.656-662
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• 2018

Pattern matching algorithm is widely used in many application fields such as bio-informatics, intrusion detection, etc. Among many string matching algorithms, KMP (Knuth-Morris-Pratt) algorithm is commonly used because of its fast execution time when using large texts. However, the processing speed of KMP algorithm is also limited when the text size increases significantly. In this paper, we propose a high throughput parallel KMP algorithm considering CPU-GPU memory hierarchy based on OpenCL in GPGPU (General Purpose computing on Graphic Processing Unit). We focus on the optimization for the allocation of work-times and work-groups, the local memory copy of the pattern data and the failure table, and the overlapping of the data transfer with the string matching operations. The experimental results show that the execution time of the optimized parallel KMP algorithm is about 3.6 times faster than that of the non-optimized parallel KMP algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1039-1048
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• 2011

Carrier Ethernet, which extend The algorithm based on constructing the mixed topology and performing link stretching, MT/s, has been proposed for designing cost-efficient Carrier Ethernet in optical network with multi-line-rate. However, the MT/s algorithm has high blocking ratio because the wavelength capacity is fully allocated without considering the load balance of network. In this paper, we propose an efficient mixed topology configuration (EMTC) algorithm by modifying MT/s algorithm. In order to reduce blocking ratio, we adapt a threshold for each link to restrict the link utilization so that traffic load can be distributed over whole network. We also apply the EMTC algorithm into optical hybrid switched network to evaluate the availability of our algorithm for different applications. The performance of the EMTC algorithm is compared with that of MT/s algorithm through OPNET simulation. The simulation results show that our algorithm achieve lower blocking ratio than the MT/s algorithm. Moreover, in hybrid switched network, our algorithm performs better than MT/s algorithm in terms of packet loss ratio and end-to-end delay.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1087-1091
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• 2009

The concept for algorithm needs to be established in various fields such as architecture, medicine and life science but understanding only theoretically has its limits. It is therefore educated using many multimedia factors but still is difficult to bring interests and carry out the higher level courses. For algorithm and programming, due to the subject's peculiarity it is very important to actually execute the program to understand the realization theory. In this paper, it aims to design a basic concept of algorithm using a flow chart of visualizing program and an algorithm education system to learn the basic programming C language, which is the fundamental element in algorithm learning.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.755-766
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• 2012

A Binary Decision Diagram (BDD) is a graph-based data structure that calculates an exact top event probability (TEP). It has been a very difficult task to develop an efficient BDD algorithm that can solve a large problem since its memory consumption is very high. Recently, in order to solve a large reliability problem within limited computational resources, Jung presented an efficient method to maintain a small BDD size by a BDD truncation during a BDD calculation. In this paper, it is first identified that Jung's BDD truncation algorithm can be improved for a more practical use. Then, a more efficient truncation algorithm is proposed in this paper, which can generate truncated BDD with smaller size and approximate TEP with smaller truncation error. Empirical results showed this new algorithm uses slightly less running time and slightly more storage usage than Jung's algorithm. It was also found, that designing a truncation algorithm with ideal features for every possible fault tree is very difficult, if not impossible. The so-called ideal features of this paper would be that with the decrease of truncation limits, the size of truncated BDD converges to the size of exact BDD, but should never be larger than exact BDD.