• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• Journal of the Korean Institute of Gas
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• v.21 no.2
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• pp.41-49
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• 2017

Recently, as the number of facilities using chemicals is increasing, the amount of handling is rapidly increasing. However, chemical spills are occurring steadily, and if large quantities of chemicals are leaked in time, they are likely to cause major damage. These industrial complexes use information obtained from a number of sensors to detect and monitor leaking areas, and are used in industrial fields by applying existing fixed sensors to robots and drones. Therefore, it is necessary to propose a sensor placement method at the interface for rapid detection and response based on various leaking scenarios reflecting leaking conditions and environmental conditions of the chemical handling process. In this study, COMSOL was used to analyze the actual accident scenarios by applying the medium parameter to the case of chemical leaks. Based on the accident scenarios, the objective function is selected so that the velocity of each robot is calculated by attaching importance to each item of sensor detection probability, sensing time and sensing scenario number. We also confirmed the feasibility of this method of reliability analysis for unexpected leak accidents. Based on the above results, it is expected that it will be helpful to trace back the leakage source based on the concentration data of the portable sensor to be applied later.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.309-319
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• 2016

There is a need to develop a data quality management algorithm in order to improve the quality of health care data. In this study, we developed a data quality control algorithms associated diseases related to diabetes in patients with hypertension. To make a data quality algorithm, we extracted hypertension patients from 2011 and 2012 discharge damage survey data. As the result of developing Data quality management algorithm, significant factors in hypertension patients with diabetes are gender, age, Glomerular disorders in diabetes mellitus, Diabetic retinopathy, Diabetic polyneuropathy, Closed [percutaneous] [needle] biopsy of kidney. Depending on the decision tree results, we defined Outlier which was probability values associated with a patient having diabetes corporal with hypertension or more than 80%, or not more than 20%, and found six groups with extreme values for diabetes accompanying hypertension patients. Thus there is a need to check the actual data contained in the Outlier(extreme value) groups to improve the quality of the data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.781-793
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• 2014

A Public Key Infrastructure (PKI) is security standards to manage and use public key cryptosystem. A PKI is used to provide digital signature, authentication, public key encryption functionality on insecure channel, such as E-banking and E-commerce on Internet. A soft-token private key in PKI is leaked easily because it is stored in a file at standardized location. Also it is vulnerable to a brute-force password attack as is protected by password-based encryption. In this paper, we proposed a new method that detects private key compromise and is probabilistically secure against a brute-force password attack though soft-token private key is leaked. The main idea of the proposed method is to use a genuine signature key pair and (n-1) fake signature key pairs to make an attacker difficult to generate a valid signature with probability 1/n even if the attacker found the correct password. The proposed method provides detection and notification functionality when an attacker make an attempt at authentication, and enhances the security of soft-token private key without the additional cost of construction of infrastructure thereby extending the function of the existing PKI and SSL/TLS.

• Journal of Preventive Medicine and Public Health
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• v.39 no.4
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• pp.346-352
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• 2006

Objectives: The purposes of this study were to propose a screening schedule for the early detection of breast cancer among Korean women, as based on the statistical model, and to compare the efficacy of the proposed screening schedule with the current recommendations. Methods: The development of the screening schedule for breast cancer closely followed the work of Lee and Zelen (1998). We calculated the age-specific breast cancer incidence rate from the Korea Central Cancer Registry (2003), and then we estimated the scheduling of periodic examinations for the early detection of breast cancer, using mammography, and based on the threshold method. The efficacy of the derived screening schedule was evaluated by the schedule sensitivity. Results: For estimating the screening schedule threshold method, we set the threshold value as the probability of being in the preclinical stage at age 35, the sensitivity of mammography as 0.9 and the mean sojourn time in the preclinical stage as 4 years. This method generated 14 examinations within the age interval [40, 69] of 40.0, 41.3, 42.7, 44.1, 45.4, 46.7, 48.0, 49.3, 51.0, 53.2, 55.3, 57.1, 59.0 and 63.6 years, and the schedule sensitivity was 75.4%. The proposed screening schedule detected 85.2% (74.5/87.4) of the cases that could have been detected by annual screening, but it required only about 48.7% (14.0/30.0) of the total number of examinations. We also examined the threshold screening schedules for a range of sensitivities of mammography and the mean sojourn time in the preclinical stage. Conclusions: The proposed screening schedule for breast cancer with using the threshold method will be helpful to provide guidelines for a public health program for choosing an effective screening schedule for breast cancer among Korean women.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.349-356
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• 2007

Conclusions below are drawn after survey date from 1,969 samplers of mammography and 1,531 breast ultrasound for 10 months, from 1 July 2006 to 30 April 2007. 1. Ages between 40 and 50 of samplers take the largest part of age distribution, and 68.57% of mammography and 71.32% of samplers are fallen under the category. 2. Samplers judged by diseased patients are 31.95% samplers of mammography and 45.79% samplers of breast ultrasound. 3. Age distributions of diseased patients were from 30 to 60 in mammography, 30 to 50 in breast ultrasound. 4. Breast ultrasound shows little difference between left side and right side of diseased part, but mammography shows significant diseased part in both sides. 5. As a result of reading examination, there is higher probability of detection in order of Calcification, Nodular, Mass in mammography. And Cyst, Nodular, Mass in breast ultrasound. 6. As a reading examinations, probability of judging a certain disease in high in mammography, but breast ultrasound shows 1 or 2 kinds of disease.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.3
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• pp.233-240
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• 2018

Radar cross section (RCS) analysis of chaff clouds is essential for the accurate detection and tracking of missile targets using radar. For this purpose, we compare the performance of two existing methods of predicting RCS of chaff clouds. One method involves summing up the RCS values of individual chaffs in a cloud, while the other method predicts the RCS values using aerodynamic models based on the probability density function. In order to compare and analyze the two techniques more precisely, the RCS of a single chaff computer-aided design model consisting of a half wavelength dipole was calculated using the commercial electromagnetic numerical analysis software, FEKO 7.0, to estimate the RCS values of chaff clouds via simulation. Thus, we verified that our method using the probability density distribution model is capable of analyzing the RCS of chaff clouds more efficiently.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.61-72
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• 2010

Spectrum scarcity problem and increasing spectrum demand for new wireless applications have embossed the importance of cognitive radio technology; the technology enables the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis after sensing the vacant channel. To enhance the accuracy of sensing, distributed spectrum sensing is proposed. However, it is necessary to provide the robustness against the compromised sensing nodes in the distributed spectrum sensing. RDSS, a fusion mechanism based on the reputation of sensing nodes and WSPRT (weighted sequential probability ratio test), was proposed. However, in RDSS, the execution number of WSPRT could increase according to the order of inputted sensing values, and the fast defense against the forged values is difficult. In this paper, we propose an enhanced fusion mechanism to input the sensing values in reputation order and exclude the sensing values with the high possibility to be compromised, using the trend of reputation variation. We evaluate our mechanism through simulation. The results show that our mechanism improves the robustness against attack with the smaller number of sensing values and more accurate detection ratio than RDSS.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.