• Journal of Preventive Medicine and Public Health
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• v.40 no.4
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• pp.259-264
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• 2007

Effective communicable disease surveillance systems are the basis of the national disease prevention and control. Following the increase in emerging and re-emerging infectious diseases since late 1990s, the Korean government has strived to enhance surveillance and response system. Since 2000, sentinel surveillance, such as influenza sentinel surveillance, pediatric sentinel surveillance, school-based sentinel surveillance and ophthalmological sentinel surveillance, was introduced to improve the surveillance activities. Electronic reporting system was developed in 2000, enabling the establishment of national database of reported cases. Disweb, a portal for sharing communicable disease information with the public and health care workers, was developed. In general, the survey results on usefulness and attributes of the system, such as simplicity, flexibility, acceptability, sensitivity, timeliness, and representa-tiveness, received relatively high recognition. Compared to the number of paid cases of national health insurance, reported cases by national notifiable disease surveillance system, and various sentinel surveillance system, the result of the correlation analysis was high. According to the research project conducted by KCDC, the reporting rate of physicians in 2004 has also greatly improved, compared with that in 1990s. However, continuous efforts are needed to further improve the communicable disease surveillance system. Awareness of physicians on communicable disease surveillance system must be improved by conducting education and information campaigns on a continuous basis. We should also devise means for efficient use of various administrative data including cause of death statistics and health insurance. In addition, efficiency of the system must be improved by linking data from various surveillance system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.57-62
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• 2004

We have proposed an efficient method of word separation in a handwritten legal amount on bank check based on the spatial gaps between the connected components. The previous gap measures all suffer from the inherent problem of underestimation or overestimation that causes a deterioration in separation performance. In order to alleviate such burden, we have developed a modified version of each distance measure. Also, 4 class clustering based method of integrating three different types of distance measures has been proposed to compensate effectively the errors in each measure, whereby further improvement in performance of word separation is expected. Through a series of word separation experiments, we found that the modified distance measures show a better performance with over 2 - 3% of the word separation rate than their corresponding original distance measures. In addition, the proposed combining method based on 4-class clustering achieved further improvement by effectively reducing the errors common to two of three distance measures as well as the individual errors.

• Journal of the Korea Institute of Building Construction
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• v.9 no.4
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• pp.131-137
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• 2009

The rate of recycling of waste concrete, which represents the majority of construction-related waste, is increasing. However, a general recognition of the inferior qualify of recycled aggregates and their lower grade of compressive strength, bending strength, shear strength, frost resistance and ductility make the application of recycled aggregates to structures insufficient. Therefore, this study conducted material and member experiments by adding steel fiber for the purpose of improving the properties of recycled aggregate concrete. To synthesize the experimental results, it was found that specimens with a 30% steel fiber admixture had levels of compressive strength, tensile strength and frost resistance that were equivalent to or higher than the standard specimen, and that concrete that had a 30% replacement of recycled aggregates with steel fiber was suitable for application to actual structures.

• KIEAE Journal
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• v.5 no.3
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• pp.41-48
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• 2005

Before occupation of an apartment housing, the builders are required to inform the test result of IAQ to the public. However, there is no simplified method to predict IAQ before measurement of pollutant concentration. In this study, a simplified way of predicting IAQ based on the distribution of indoor pollutant concentration is proposed. 7 different cases of air change rate have been simulated through CFD analysis to get the distribution ratio of each pollutant material and then simplified functions were used with CRIAQ1 values derived from CFD simulation to evaluate by comparing the influence of each material in the indoor pollutant concentration. Again, a lot of efforts which can improve the indoor air quality have been performed. Materials used in indoor space are labeled with their pollutant emission level. Installation of ventilation system in residential buildings will be regulated by a building codes sooner or later. But it is important to understand the fact that layout of walls, location or size of openings will influence the indoor air flow and pollutant concentration. And location of emitting material influences to indoor air pollutants distribution. But until now there is few recognition and consideration of these factors. Therefore, in this paper the effects of these factors is proved and some kind of guideline is made for designers after a comparison of typical apartment floor plan and a new type plan with their average pollutant concentration and its distribution of each room. CFD(Computational Fluid Dynamics) program was used to show the indoor air flow and pollutant concentration distribution. For this purpose, a typical $100m^2$ apartment floor plan was chosen as a case study model and several alternatives were reviewed to improve the IAQ performance. The simulation took place in the condition of natural ventilation through windows.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1120-1131
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• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• KSBB Journal
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• v.18 no.4
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• pp.306-311
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• 2003

Solid-phase refolding of immobilized proteins can be an effective way to reuse an immobilized enzyme column. Oriented immobilization methods are known to provide higher activity of the immobilized enzymes. In this study, using recombinant EK (enterokinase) as a model enzyme and a fusion protein, that consisted of recombinant human growth hormone and six His tag that was linked by the peptide of EK-specific recognition sequence, as a model substrate, we evaluated two oriented immobilization methods, i. e., reductive alkylation of N-terminus ${\alpha}$-amine and affinity interaction between poly-histidine tag and Ni-NTA (nickel-nitrilotriacetic acid). The immobilization yield, activity and cleavage of the immobilized enzymes, and the yield of solid-phase refolding were compared. The Ni affinity immobilization and the covalent immobilization yields were about 100％ and 65％, respectively. But the specific activities were the same, about 50％ of that of the soluble enzyme. The cleavage rate by the covalently immobilized EK was higher than the soluble enzyme and the side reaction of cryptic cleavage was significantly decreased. Covalently immobilized EK showed almost 100％ refolding yield but the affinity immobilized EK showed only 70％ yield, which suggested the covalent conjugation provided more rigid ‘reference structure’ for the solid-phase refolding. The monomeric hGH could be easily obtained by capturing the cleaved poly Histidine tag by the Ni affinity column.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.123-130
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• 2005

In this paper, fingerprint identification algorithm using pixel direction factor in blocks is proposed to minimize false acceptance ratio and to apply security system. The proposed algorithm is that a fingerprint image is divided by 16 blocks, then feature parameters which have direct factors of $0^{\circ},\;45^{\circ},\;90^{\circ}\;and\;135^{\circ}$ is extracted for each block. Membership function of a reference fingerprint and an input fingerprint for the extracted parameters is calculated, then identification of two fingerprint is distinguished using fuzzy inference. False acceptance ratio is evaluated about different fingerprints of In kinds regardless of sex and shape which are obtained from adults, and false rejection ratio is evaluated about fingerprints which are obtained by adding fingerprints of 10 kinds on different fingerprints of 100 kinds. The experiment results is that false acceptance ratio is average $0.34\%$ about experiment of 4,950 times, and false rejection ratio is average $3.7\%$ about experiment of 1,000 times. The proposed algerian is excellent for recognition rate and security.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.1-9
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• 2002

Evolutionary artificial neural networks (EANNs) are towards the near optimal ANN using the global search of evolutionary instead of trial-and-error process. However, many real-world problems are too hard to be solved by only one ANN. Recently there has been plenty of interest on combining ANNs in the last generation to improve the performance and reliability. This paper proposes a new approach of constructing multiple ANNs which complement each other by speciation. Also, we develop a multiple ANN to combine the results in abstract, rank, and measurement levels. The experimental results on Australian credit approval data from UCI benchmark data set have shown that combining of the speciated EANNs have better recognition ability than EANNs which are not speciated, and the average error rate of 0.105 proves the superiority of the proposed EANNs.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1124-1134
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• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1100-1105
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• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.