• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.5
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• pp.442-448
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• 1999

We introduce the systematization design method using similarity theory which is profitable in the c compatability and standardization of the developed products and the reduction of construction time and price to d develop and design a machine equipment. Systematization design method is to select the standard model for d designing and developing from the large machinery to the super precision one and then to induce the c characteristic of machines step by step in advance in case of miniaturizing and making largelongleftarrowscale. With this m method, we extract the peculiar characteristics through the close analysis on the physical and ttx:hnical part a and predict the characteristic experiment for the magnitude we desire by an머ogical mathematical analysis. At l last, we will get the design sample the users demand with the verification of the data on optimum design p previously. In this paper, we could predict the characteristic of the product the users rC'Quire in advance with the d design method applying similarity theor${\gamma}$ and suggested the design method which could meet the various r requirements the users want. Also, it is shown that the standardization design by the similarity theory is a available as comparing the characteristic values expc'Cted through the experiment of the actual actuator with t the theoretical character data of similarity theoη after selecting the linear actuator as a model.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.676-684
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• 2022

As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

• Microbiology and Biotechnology Letters
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• v.49 no.3
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• pp.432-439
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• 2021

Microbial community plays important roles in the culture environment of mud crab Scylla serrata. One of the environmental management efforts for the cultivation of S.serrata is by stabilizing microorganisms involved in nitrogen cycle process. The availability of dissolved inorganic nitrogen in its culture environment under a recirculating system closely relates to the nitrogen cycle, which involves both anaerobic and aerobic bacterial activities. Anaerobically, there are two major nitrogen compound degradation processes, i.e., denitrification and dissimilatory nitrate reduction to ammonium (DNRA). This study aimed to identify denitrifying and DNRA bacteria isolated from the recirculating cultivation of S. serrata. The water samples were collected from anaerobic filters called close filter system, which is anaerobically conditioned with the addition of varying physical filter materials in the recirculating mud crab cultures. The results showed that three denitrifying bacterial isolates and seven DNRA bacterial isolates were successfully identified. The phylogenetic analysis based on 16S rRNA gene of the denitrifying bacteria revealed that HIB_7a had the closest similarity to Stenotrophomonas daejeonensis strain MJ03. Meanwhile, DNRA bacterial isolate of HIB_92 showed a 100% similarity to Bacillus sonorensis strain N3, Bacillus vallismortis strain VITS-17, Bacillus tequlensis strain TY5, Geobacillus sp. strain DB24, Bacillus subtilis strain A1, and Bacillus mojavensis strain SSRAI21. This study provides basic information denitrifying and DNRA bacterial isolates identity which might have the potential to be applied as probiotics in aquaculture systems in order to maintain optimal environmental conditions.

• Journal of the Korean Society for information Management
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• v.18 no.2
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• pp.203-230
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• 2001

The purpose of this study is to develop a document clustering model for automatic classification of knowledge. Two test collections of newspaper article texts and journal article abstracts are built for the clustering experiment. Various feature reduction criteria as well as term weighting methods are applied to the term sets of the test collections, and cosine and Jaccard coefficients are used as similarity measures. The performances of complete linkage and K-means clustering algorithms are compared using different feature selection methods and various term weights. It was found that complete linkage clustering outperforms K-means algorithm and feature reduction up to almost 10% of the total feature sets does not lower the performance of document clustering to any significant extent.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.113-118
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• 2015

An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.205-214
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• 2020

With the recent development of hardware and software technology, interest in the development of wearable devices is increasing. In particular, wearable devices require algorithms suitable for low-power and low-capacity embedded devices. Among them, there is an increasing demand for a signal compression algorithm that reduces communication overhead, in order to increase the efficiency of storage and transmission of electrocardiogram (ECG) signals requiring long-time measurement. Because normal beats occupy most of the signal with similar shapes, a high rate of signal compression is possible if normal beats are represented by a template. In this paper, we propose an algorithm for determining the normal beat template using the template cluster and Pearson similarity. Also, the template is expressed effectively as a few vertices through linear approximation algorithm. In experiment of Datum 234 of MIT-BIH arrhythmia database (MIT-BIH ADB) provided by Physionet, a compression ratio was 33.44:1, and an average distribution of root mean square error (RMSE) was 1.55%.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.724-740
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• 2016

The performance issues of screening large database compounds and multiple query compounds in virtual screening highlight a common concern in Chemoinformatics applications. This study investigates these problems by choosing group fusion as a pilot model and presents efficient parallel solutions in parallel platforms, specifically, the multi-core architecture of CPU and many-core architecture of graphical processing unit (GPU). A study of sequential group fusion and a proposed design of parallel CUDA group fusion are presented in this paper. The design involves solving two important stages of group fusion, namely, similarity search and fusion (MAX rule), while addressing embarrassingly parallel and parallel reduction models. The sequential, optimized sequential and parallel OpenMP of group fusion were implemented and evaluated. The outcome of the analysis from these three different design approaches influenced the design of parallel CUDA version in order to optimize and achieve high computation intensity. The proposed parallel CUDA performed better than sequential and parallel OpenMP in terms of both execution time and speedup. The parallel CUDA was 5-10x faster than sequential and parallel OpenMP as both similarity search and fusion MAX stages had been CUDA-optimized.

• Journal of the Korean Society of Combustion
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• v.5 no.1
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• pp.19-30
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• 2000

The characteristics of NOx emissions in pure hydrogen nonpremixed flames with coaxial air are analyzed numerically for the three model cases of coaxial air flames classified by varying coaxial air velocity and/or fuel velocity. In coaxial air flames, the flame length is reduced by coaxial air and can be represented as a function of the ratio of coaxial air to fuel velocity. Coaxial air decreases flame reaction zone, resulting in reducing flame residence time significantly. Finally, the large reduction of EINOx is achieved by the decrease of the flame residence time. It is found that because coaxial air can break down the flame self-similarity law, appropriate scaling parameters, which are different from those in the simple jet flames, are recommended. In coaxial air flames, the flame residence time based on the flame volume produces better results than that based on a cube of the flame length. And some portion of deviations from the 1/2 scaling law by coaxial air may be due to the violation of the linear relationship between the flame volume and the flame reaction zone.