• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1025-1034
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• 2009

In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number(SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT(Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor(NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.374-381
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• 2017

Concrete sleepers, installed on majority of railroad track in this nation can, if not maintained properly, threaten the safety of running trains. In this paper, an algorithm for automatically identifying cracks in a sleeper image, taken by high-resolution camera, is developed based on Adaboost, known as the strongest adaptive algorithm and most actively utilized algorithm of current days. The developed algorithm is trained using crack characteristics drawn from the analysis results of crack and non-crack images of field-installed sleepers. The applicability of the developed algorithm is verified using 48 images utilized in the training process and 11 images not used in the process. The verification results show that cracks in all the sleeper images can be successfully identified with an identification rate greater than 90%, and that the developed automatic crack identification algorithm therefore has sufficient applicability.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.742-750
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• 2022

This study aimed to monitor the distribution of Cobitidae in Korea by the identification of species using genetic analysis. Based on the genetic analysis, Cobitidae species in four of five domestic fish farms consisted of only Chinese muddy loach Misgurnus mizolepis, but muddy loach Misgurnus anguillicaudatus was also present it in one fish farm. In the case of imported Cobitidae species, in addition to Chinese muddy loach and muddy loach, the harmful species Paramisgurnus dabryanus, was also present. Chinese muddy loach accounted for 20%, 67%, and 60% of the S6, S7, and S8 samples, respectively. An analysis of the total length, body length, and weight showed that domestic Chinese muddy loach showed higher values than imported muddy loach, and imported Chinese muddy loach showed similar values to P. dabryanus. There were no significant differences in the country of origin of the three species. Thus, the mitochondrial cytochrome c oxidase subunit I gene sequence was analyzed and compared the verification of species identification. The three species of Cobitidae were genetically divided into three groups and determined to have genetic differences. These results indicate that it is necessary to reduce the heterogeneous mixing rate through discriminating species by genetic analysis.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.737-746
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• 2013

This research aim is to investigate the rate and pattern of re-using the four-digit personal identification numbers(PINs). 1313 types of PINs were observed by 224 students who took this author's classes from last 2006 to 2011 at A-university. Some students used PINs as few as 3-4 and as many as 12-13. The average is 5.86 per person. The rates of re-using PINs were calculated by each student. 87%(195/224) of students reused PINs and 64% of them reused with just only one type of PINs, 20% reused with 2 types, and about 3% reused with 3-4 types. With respect to PINs, 884 out of the total 1313 PINs were reused, that is around 64.3%. In a broad sense, a pair of slight modification of PINs were also observed, that is, new PINs were partly matched in position or size of numbers of previous PINs. And if the reuse rate falling under the slight modification of PINs, 10.4% is added, about 75% of the PINs were reused in a broad sense. The re-using rate of male students is higher than the one of female students. This paper's results may provide to make plan for hacks of passwords.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.1-6
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• 2019

As mobile technologies such as Internet of Things (IoT)-based smart homes and financial technologies (FinTech) are developed, authentication by smart devices is used everywhere. As a result, presence-based biometric authentication using smart devices has become a new mainstream in knowledge-based authentication methods like the existing passwords. The electrocardiogram (ECG) is less prone to forgery, and high-level personal identification is its unique feature from among various biometric authentication methods, such as the pulse, fingerprints, the face, and the iris. Biometric authentication using an ECG is receiving a great deal of attention due to its uses in healthcare and FinTech. In this study, we implemented an ECG authentication system that allows users to easily measure and authenticate their ECG waveforms using a miniaturized wearable device, rather than a large and expensive measurement device. The implemented ECG authentication system identifies ECG features through P-Q-R-S-T feature point identification, and was user-certified under the proposed authentication protocols. Finally, assessment of measurements in a majority of adult males showed a relatively low false acceptance rate of 1.73%, and a low false rejection rate of 4.14%, in a stable normal state. In a high-activity state, the false acceptance rate was 13.72%, and the false rejection rate was 21.68%. In a high-heart rate state, the false acceptance rate was 10.48%, and the false rejection rate was 11.21%.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.4
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• pp.407-412
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• 2017

The aim of this study was to shorten the time required for subculture and bacterial identification and obtain a simple and rapid identification method for new test methods for bloodstream infections. The following results were obtained using a mass spectrometer. In Vitek 2, 208 (81.8%) cases were well-identified and 45 isolates were not identified in blood cultures. Among 208 cases, 146 (57.5%) were Gram positive bacteria and 108 (42.5%) were Gram negative bacteria. In total, 233 were identified to the species level and 21 were identified to the genus level. The identification error was found to be Propionibacterium acnes as Clostridium bifermentans. The accuracy of Enterobacteriaceae, glucose non-fermentative bacilli (GNFB), and staphylococci were 81/83 (97.6%), 12/15 (80.0%), and 72/85 (84.7%), respectively. The concordance rate of Vitek 2 and Vitek MS by the direct method was 81.8% and 45 isolates were not identified. Most of the unidentified bacteria were Gram positive bacteria (N=37). The Gram positive bacteria were streptococci (14), coagulase-negative staphylococci (CNS) (11), enterococci (3), Staphylococcus aureus (2), Micrococcus spp. (2), Bacillus spp. (2) and Actinomyces odontolyticus, Finegoldia magna, and Peptostreptococcus spp. The results reporting time was reduced to 24~72 hours compared to the conventional method. The rate of identification of the aerobic and anaerobic cultures was similar, but the use of an anaerobic culture did not require a dissolution process, which could shorten the sample preparation time. These results suggest that the method of direct identification in blood cultures is very useful for the treatment of patients. In further studies, it might be necessary to further improve the method for identifying streptococci and CNS, which were lacking in accuracy in this study.

• The Journal of Internal Korean Medicine
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• v.38 no.6
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• pp.955-970
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• 2017

Objectives: The aim of this observational study was to identify and analyze the patterns to compare the characteristics of asthma patients. Methods: The subjects were 40 asthma patients who had satisfied the inclusion and exclusion criteria. They were divided into deficiency syndrome and excess syndrome groups, and cold syndrome and heat syndrome groups. Their quality of life was measured by the quality of life questionnaire for adult Korean asthmatics (QLQAKA) and VAS. Heart rate variability (HRV) was measured, and the degree of obesity was evaluated by body mass index (BMI). Hematological, biochemical, and immunoglobulin (Ig) E laboratory tests were included. Results: Based on pattern identification, the 40 asthma patients could be divided into two categories of groups: 1) the deficiency syndrome (N=18) and the excess syndrome (N=22) groups: 2) the cold syndrome (N=35) and the heat syndrome (N=5) groups. The mean value of HF differed significantly between the deficiency and excess syndrome groups. The mean value of IgE in blood tests of asthmatics was greater than four times the reference value. For BMI, the subjects were classified into three groups: normal weight (N=12), overweight (N=12), and obese (N=16). Conclusions: Development of a more accurate asthma-specific pattern identification tool could play a crucial role in asthma control. In addition, good control of asthma can improve the quality of life. Obesity is one of the factors associated with asthma exacerbation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.110-123
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• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.