• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.889-900
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• 2006

Even though sensor networks are different in user requests and data characteristics depending on each application area, the existing researches on stream data transmission problem focus on the performance improvement of their methods rather than considering the original characteristic of stream data. In this paper, we introduce a hierarchical or distributed sensor network architecture and data model, and then evaluate the multivariate data reduction methods suitable for user requirements and data features so as to apply reduction methods alternatively. To assess the relative performance of the proposed multivariate data reduction methods, we used the conventional techniques, such as Wavelet, HCL(Hierarchical Clustering), Sampling and SVD (Singular Value Decomposition) as well as the experimental data sets, such as multivariate time series, synthetic data and robot execution failure data. The experimental results shows that SVD and Sampling method are superior to Wavelet and HCL ia respect to the relative error ratio and execution time. Especially, since relative error ratio of each data reduction method is different according to data characteristic, it shows a good performance using the selective data reduction method for the experimental data set. The findings reported in this paper can serve as a useful guideline for sensor network application design and construction including multivariate stream data.

• Resources Recycling
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• v.15 no.5 s.73
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• pp.17-25
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• 2006

Al-MCM-48 was used as a catalyst to decompose high density polyethylene(HDPE). Catalytic activity of Al-MCM-48 was compared with those of Al-MCM-41, Beta, and ZSM-5. Catalytic decomposition rate over Al-MCM-48 was much higher than at of non-catalytic pyrolysis only. Compared to other catalysts, Al-MCM-48 revealed the little higher activation energy value. The progressive deactivation behavior of the catalysts has also studied. ZSM-5 and Al-MCM-48 showed slower deactivation rates than Al-MCM-41 and Beta. Pyrolysis coupled with gas chromatographic separation and flame ionization detection (Py-GC/ FID) was also performed to assess the characteristics of pyrolysis products. ZSM-5 gave a higher fraction of gaseous products ($C_1-C_4$). Al-MCM-41 and Beta produced mainly $C_5-C_{12}$ products. The selectivity to oil product ($C_5-C_{22}$) obtained with Al- MCM-48 is higher an that with the other catalysts employed in this study.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.501-508
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• 2011

A knowledge map, which has been recently applied in various fields, is discovering characteristics hidden in a large amount of information and showing a tangible output to understand the meaning of the discovery. In this paper, we suggested a knowledge map for research trend analysis based on keyword-relation networks which are constructed by using a database of the domestic journal articles in the computer engineering field from 2000 through 2010. From that knowledge map, we could infer influential changes of a research topic related a specific keyword through examining the change of sizes of the connected components to which the keyword belongs in the keyword-relation networks. In addition, we observed that the size of the largest connected component in the keyword-relation networks is relatively small and groups of high-similarity keyword pairs are clustered in them by comparison with the random networks. This implies that the research field corresponding to the largest connected component is not so huge and many small-scale topics included in it are highly clustered and loosely-connected to each other. our proposed knowledge map can be considered as a approach for the research trend analysis while it is impossible to obtain those results by conventional approaches such as analyzing the frequency of an individual keyword.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.89-98
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• 2010

In current digital broadcasting systems, GEM(Globally Executable MHP)-based middlewares such as MHP(Multimedia Home Platform), OCAP(OpenCable Application Platform), ACAP(Advanced Common Application Platform) are the norm. Despite much of the common characteristics shared, such as MPEG-2 and DSM-CC(Digital Storage Media-Command and Control) protocols, the information and data structures they need are slightly different, which results in incompatibility issues. In this paper, in line with an effort to develop an integrated DTV middleware, we propose a general-purpose, reconfigurable DSM-CC architecture for supporting various standard GEM-based middlewares without code modifications. First, we identify DSM-CC components that are common and thus can be shared by all GEM-based middlewares. Next, the system is provided with middleware-specific information and data structures in the form of XML. Since the XML information can be parsed dynamically at run time, it can be interchanged either statically or dynamically for a specific target middleware. As for the performance issues, the response time and usage frequency of DSM-CC module highly contribute to the performance of STB(Set-Top-Box). In this paper, we also propose an efficient application cache management strategy and evaluate its performance. The performance result has shown that the cache strategy reflecting user preferences greatly helps to reduce response time for executing application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1032-1041
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• 2007

The global navigation satellite system (GNSS), which is the core technique for the location based service, adopts the direct sequence/spread spectrum (DS/SS) as its modulation method. The success of a DS/SS system depends on the synchronization between the received and locally generated pseudo noise (PN) signals. As a step in the synchronization process, the tacking scheme performs fine adjustment to bring the phase difference between the two PN signals to zero. The most widely used tracking scheme is the delay locked loop with early minus late discriminator (EL-DLL). In the ideal case, the EL-DLL is the best estimator among various DLL. However, in the band-limited multipath environment, the EL-DLL has tracking bias. In this paper, the timing offset range of correlation function is divided into advanced offset range (AOR) and delayed offset range (DOR) centering around the correct synchronization time point. The tracking bias results from the following two reasons: symmetry distortion between correlation values in AOR and DOR, and mismatch between the time point corresponding to the maximum correlation value and the synchronization time point. The former and latter are named as the type I and type II tracking bias, respectively. In this paper, when the receiver has finite bandwidth in the presence of multipath signals, it is shown that the type II tracking bias becomes a more dominant error factor than the type I tracking bias, and the correlation values in AOR are not almost changed. Exploiting these characteristics, we propose a novel tracking bias mitigation scheme and demonstrate that the tracking accuracy of the proposed scheme is higher than that of the conventional scheme, both in the presence and absence of noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.172-179
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• 2014

HEVC (High Efficiency Video Coding) fine-tuned many existing coding tools and adopted also many new coding techniques. As a result, HEVC has accomplished about 2 times of compression efficiency enhancement compared to the existing video coding standard of H.264/AVC. One of the newly adopted tools in HEVC is the transform skip scheme which performs quantization without transform. This technique improves coding efficiency especially with computer-generated images. However, the unavailability of global or local properties of general video signals demands encoder to decide whether performing transform or not for each TU (Transform Unit). The necessity of computing rate-distortion costs for this decision is one reason to increase encoder complexity. In this paper, a fast transform skip mode decision method is proposed, which is based on the fast decision of rate-distortion cost calculation for transform skip mode, by considering frequency characteristics of residual signal. The proposed method can reduce $4{\times}4$ TU encoding time by about 27.1% with only about 0.03% consequential decrement in BDBR.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1709-1717
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• 2013

This study was to identify the frequency and related factors of advanced airway management for patients with cardiac arrest by the Level 1 emergency medical technicians in the 119 Emergency medical service. 95 level-1 medical technicians belonging to Gwangju Fire Service Headquarter were surveyed with structured questionnaire composed of general and job-related characteristics, self-efficacy, barrier factors to performing the advanced airway for patients with cardiac arrest. From January 1, 2010 to June 30, 2011, data obtained from the Gwangju Fire Service Headquarter by performance of advanced airway. Self-efficacy toward the necessity of advanced airway revealed positive correlation with self-confidence. The barrier factor to advanced airway performance had negative correlation with the frequency of performance. The most important factor of advanced airway performance was necessity and self-efficacy. In conclusion, it is necessary to develop the field-based practice education program and to improve self-efficacy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5672-5681
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• 2013

The aim of this study was to test the correlation of ethical values and perception of biomedical ethics in nursing students. Data was collected from September 10th to October 5th 2012, was analyzed using the t-test, the one-way ANOVA, the Scheffe, the Multiple regression and Pearson's correlation. The average point of consciousness of biomedical ethics was 2.98(${\pm}0.2$). The highest score of 3.27(${\pm}0.5$) was seen in the category of the ethics for 'right to life of the fetus', the lowest score of 2.70(${\pm}0.4$) was seen in the 'organ transplantation'. As a result of analyzing on general and specific aspects, the difference was found in gender and ethical values(F=6.148, p=<.001). In gender, the score of biomedical ethics(r=0.108, p=.003) and ethical values(r=-0.185, p=.002) were showing a positive correlation. The predictors of biomedical ethics was gender(${\beta}$=0.205, t=3.399, p=<.001), the possibility of biomedical ethics problems depend on the development of science (${\beta}$=0.142, t=2.261, p=0.025) and ethical values(${\beta}$=0.189, t=3.119, p=0.002). These results can provide a basic data to enhance the consciousness of biomedical ethics and establish a educational program of biomedical ethics for nursing students.