• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.71-79
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• 2008

High voltage distribution lines in the electric railway system placed according track with communication lines and signal equipments. Case of the over head lines is occurrence the many fault because lightning, rainstorm, damage from the sea wind and so on. According this fault caused protection device to wrong operation. One line ground fault that occurs most frequently in railway high voltage distribution lines and sort of faults is line short, three line ground breaking of a wire, and so on. For this reason we need precise maintenance for prevent of the faults. The most important is early detection and fast restoration in time of fault for a safety transit. In order to develop an advanced fault location device for 22.9[kV] distribution power network in electric railway system this paper deals with new fault locating algorithm using flow technique which enable to determine the location of the fault accurately. To demonstrate its superiorities, the case studies with the algorithm and the fault analysis using PSCAD/EMTDC (Power System Computer Aided Design/Electro Magnetic Transients DC Analysis Program) were carried out with the models of direct-grounded 22.9[kV] distribution network which is supposed to be the grounding method for electric railway system in Korea.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.465-472
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• 2017

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.127-133
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• 2021

In industrial IoT environments, sensors generate data for their detection targets and deliver the data to IoT gateways. Therefore, managing large amounts of real-time sensor data is an essential feature for IoT gateways, and key-value storage engines are widely used to manage these sensor data. However, key-value storage engines used in IoT gateways do not take into account the characteristics of sensor data generated in industrial IoT environments, and this limits the performance of key-value storage engines. In this paper, we optimize the key-value storage engine by utilizing the features of sensor data in industrial IoT environments. The proposed optimization technique is to analyze the key, which is the input of a key-value storage engine, for further indexing. This reduces excessive write amplification and improves performance. We implement our optimization scheme in LevelDB and use the workload of the TPCx-IoT benchmark to evaluate our proposed scheme. From experimental results we show that our proposed technique achieves up to 21 times better than the existing scheme, and this shows that the proposed technique can perform high-speed data ingestion in industrial IoT environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.1-12
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• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.103-110
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• 2023

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication between people, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past, administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased, they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent malicious writing from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in a bypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by users in real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, not from a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificial intelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imaged using a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only be understood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmed that the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposed preprocessing technique.

• Journal of Yeungnam Medical Science
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• v.14 no.1
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• pp.155-167
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• 1997

The identification of serum HBV DNA is very important for the assessment of the disease activity in persistent infection, for the evaluation of the infectivity of an individuals blood. The dot blot, however, has limited sensitivity and sometimes inconsistent with other serological markers and clinical settings. Using the most important recent advance in molecular biology, the polymerase chain reaction(PCR), specific DNA sequences can be amplified more than a million-fold in a few hours and with this technique the detection of the extreme low level of DNA is possible. This study was to determine sensitivity of the PCR for the detection of serum HBV DNA in comparison with dot blot analysis and to investigate the serum HBV DNA status and clinical significance of PCR in patients with chronic HBsAg positive liver disease. The subjects of this study were 17 patients with asymptomatic HBsAg carriers(9 HBeAg positive patients, 8 anti-HBe positive patients), 91 chronic hepatitis B(50 HBeAg positive patients, 41 anti-HBe positive patients), 57 liver cirrhosis(21 HBeAg positive patients, 36 anti-HBe positive patients), 27 hepatocellular carcinoma(10 HBeAg positive patients, 17 anti-HBe positive patients). The results were summerized as following; The detection rates of HBV DNA by dot blot, PCR were 58.9%, 72.2% in HBeAg positive patients, 34.3%, 53.9% in anti-HBe positive patients. The detection rates of HBV DNA by PCR in HBeAg negative patients were 25.0% in asymptomatic HBsAg carriers, 61.0% in chronic hepatitis B, 52.8% in liver cirrhosis, 52.9% in hepatocellular carcinoma. The positive rate for HBV DNA is a significant difference between HBeAg positive and negative asymptomatic HBsAg carriers, but not significantly difference in other groups. In conclusions, this study confirmed that the PCR is much more sensitive than the dot blot analysis in detecting the HBV DNA in the sera of patients with chronic liver disease. The presence of HBV DNA in the serum was detected by PCR with higher sensitivity and it suggested that active viral replication is still going on in most patients with chronic HBsAg positive liver disease irrespective of HBeAg/anti-HBe status, and PCR may be used as a prognostic factor in asymptomatic HBsAg carriers.

• Journal of Food Hygiene and Safety
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• v.30 no.2
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• pp.166-172
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• 2015

This study was performed to develop treatment method for reducing microbial contamination on Daruma (a semi-processed product of seasoned and dried squid) by combination of strong acidic hypochlorous water (SAHW) and ultrasonic waves (UW). The available chlorine concentration, oxidation reduction potential (ORP) and pH of SAHW were $69.67{\pm}0.58ppm$, $1071.33{\pm}4.16mV$ and 2.79, respectively. The 1.49 log CFU/g of viable cell count and 1.32 log CFU/g of Staphylococcus aureus was reduced, and Escherichia coli was reduced below detection limit when the Daruma was treated with 20 times (w/v) of sodium hypochlorite solution (SHS) for 120 min. The 3.62 log CFU/g of viable cell count and 3.22 log CFU/g of Staphylococcus aureus was reduced, and Escherichia coli was reduced below detection limit when the Daruma was treated with 20 times (w/v) of SAHW for 120 min. The antibacterial effects of SAHW were stronger than those of SHS at same available chroline concentration. SAHW treatment after washing strongly alkalic electrolyzed water (SAEW) showed better bactericidal effects than SAHW treatment only. The 4.0 log CFU/g of viable cell count was reduced, S. aureus was reduced below regulation limit (Log 2.0 CFU/g), and E. coli was reduced below detection limit when the Daruma was treated with 20 times (w/v) of SAHW for 90 min after washing with 20 times (w/v) of SAEW for 60 min. The viable cell number was reduced below detection limit and S. aureus was reduced below regulation limit when the Daruma was treated with 20 times (w/v) of SAHW for 60 min in ultrasonic washer. E. coli was reduced below detection limit when the Daruma was treated with 20 times (w/v) of SAHW for 10 min in ultrasonic washer. These results suggest that combination of SAHW and UW may be a good technique to reduce the microbial contamination in daruma.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.