• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Convergence Security Journal
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• v.4 no.3
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• pp.1-8
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• 2004

Large distributed systems such as computational and data grids require that a substantial amount of monitoring data be collected for various tasks such as fault detection, performance analysis, performance tuning, performance prediction, security analysis and scheduling. to cope with this problem, they are needed network monitoring architecture which can collect various network characteristic and analyze network security state. In this paper, we suggest network performance and security analysis system based on network monitoring. The System suggest that users can see distance network state with tuning network parameters.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.175-188
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• 2017

Radio-frequency identification (RFID) is being used in various fields as a technology for identifying objects (people, things etc.) using radio frequencies. In the past, there was an attempt to apply RFID into national defense, but failed to spread RFID in the defense field because of some limitations of RFID in a specific situation (e.g., low recognition rate). Therefore, in this paper, we propose how to overcome the limitation of RFID by adopting the Internet of Things (IoT) technology which is considered as an important technology of the future. Furthermore, we propose four scenarios (i.e., healcare band and RFID, identification and anormal state detection, access control, and confidential document management) that can be used for enhancing national defense security. In addition, we analyze the basic characteristics and security requirements of RFID and IoT in order to effectively apply each technology and improve security level.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.257-261
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• 2022

Diseases of agricultural plants in recent years have spread greatly across the regions of the Kyrgyz Republic and pose a serious threat to the yield of many crops. The consequences of it can greatly affect the food security for an entire country. Due to force majeure, abnormal cases in climatic conditions, the annual incomes of many farmers and agricultural producers can be destroyed locally. Along with this, the rapid detection of plant diseases also remains difficult in many parts of the regions due to the lack of necessary infrastructure. In this case, it is possible to pave the way for the diagnosis of diseases with the help of the latest achievements due to the possibilities of feedback from the farmer - developer in the formation and updating of the database of sick and healthy plants with the help of advances in computer vision, developing on the basis of machine and deep learning. Currently, model training is increasingly used already on publicly available datasets, i.e. it has become popular to build new models already on trained models. The latter is called as transfer training and is developing very quickly. Using a publicly available data set from PlantVillage, which consists of 54,306 or NewPlantVillage with a data volumed with 87,356 images of sick and healthy plant leaves collected under controlled conditions, it is possible to build a deep convolutional neural network to identify 14 types of crops and 26 diseases. At the same time, the trained model can achieve an accuracy of more than 99% on a specially selected test set.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.53-59
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• 2018

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1581-1587
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• 2021

As embedded system are used widely and variously, multi-edge system, which multiple edges gather and perform complex operations together, is actively operating. In a multi-edge system, it often occurs that an abnormal operation at one edge is transferred to another edge or the entire system goes down. It is necessary to determine and control edge anomalies in order to prevent system down, but this can be a heavy burden on the resource-limited edge. As a solution to this, we use power consumption data to check the state of the edge device and transmit it based on a QRcode to check and control errors at the server. The architecture proposed in this paper is implemented using 'chip-whisperer' to measure the power consumption of the edge and 'Raspberry Pi 3' to implement the server. As a result, the proposed architecture server showed successful data transmission and error determination without additional load appearing at the edge.

• Journal of Preventive Medicine and Public Health
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• v.27 no.4 s.48
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• pp.747-761
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• 1994

Workers' periodic health examination is the main tools used to manage the health problems of most workers in Korea. The most common health problem found in workers' periodic health examination is liver disorder. Liver disorder is also one of the most common health problems in general population and one of the leading causes of mortality in adult population. Regulation proposed by government (No. 207, Ministry of Labor, 1992) defines the criteria for selection of workers with the liver dysfunction for further evaluative examination and the examination items used for diagnosis of the workers with liver dysfunction. This study was designed to evaluate the proficiency of each examination items presently defined in Regulation and propose the optimal examination items for detection of the liver disorders found by workers' periodic health examination. Study subjects are 186 workers with abnormal liver function tests in screening examination of workers' periodic health examination. Questionnaire survey including past history of liver disorder, drinking history, height and weight was done. Physical examination by physician, routine test items defined by Regulation (SGOT, SGPT, $\gamma$-GTP, protein, albumin, total and direct bilirubin, alkaline phosphatase, $\alpha$-feto protein, HBsAg and anti-HBs), anti-HCV antibody test and liver ultrasonography were done. Results are as follows; 1. Result of evaluative examination utilizing only the items defined in Regulation was; There were 75 workers with suspected live. disorder(40.3%), 63 with no liver dysfunction (33.9%), 13 with suspected hepatitis B(7.0%), 10 workers with hepatitis B(5.4%), 10 workers with hepatitis B carrier state(5.4%), 10 with alcoholic liver disorders(5.4%), 5 with fatty liver(2.7%). When alternative diagnostic criteria applying additional examination items (drinking history, body mass index, anti-HCV antibody and ultrasonography) diagnosability of liver disorder was increased. When all four items were included, final results were; 23 workers (17.8%) with hepatitis B (10 carriers, 13 suspects and 10 hepatitis B), 10 (5.4%) with hepatitis C(4 carriers, 5 suspects and 1 hepatitis C), 13(7.0%) with alcoholic liver disorder, 45(24.2%) with fatty liver (40 suspects, 5 fatty liver), 410%) with suspected liver disorders and 44 (23.7%) with normal liver. 2. Of examination items defined by Regulation, only SGOT, SGPT, $\gamma$-GTP and HBsAg were significantly different in abnormal rate and mean value, and all other laboratory findings did not showed significant difference between two groups. Drinking history, body mass index and anti-HCV antibody test which are the items that authors included in this study, also showed significant difference between two groups. Utilization of body mass index (BMI) for abnormal liver function group in diagnosis of fatty liver had high specificity (97.6%) but sensitivity (22.3%) was low. Therefore we suggest that SGOT, SGPT, $\gamma$-GTP, HBsAg, alcohol drinking history, BMI and anti-HCV Ab were useful for diagnosis of liver disorders among worker's periodic health examination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1274-1292
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• 2011

Due to the variety of household electric devices and different power consumption habits of consumers at present, general home energy management (HEM) systems suffer from the lack of dynamic identification of various household appliances and a unidirectional information display. This study presented a set of intelligent interconnection network systems for electric appliances, which can measure the power consumption of household appliances through a current sensing device based on OSGi platform. The system establishes the characteristics and categories of related electric appliances, and searches the corresponding cluster data and eliminates noise for recognition functionality and error detection mechanism of electric appliances by applying the clustering algorithm. The system also integrates household appliance control network services so as to control them according to users' power consumption plans or through mobile devices, thus realizing a bidirectional monitoring service. When the system detects an abnormal operating state, it can automatically shut off electric appliances to avoid accidents. In practical tests, the system reached a recognition rate of 95%, and could successfully control general household appliances through the ZigBee network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.80-84
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• 1996

This paper aims at reviewing the Possibility application over normal or abnormal, detection used by AE and the wear characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and hE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece; arcyl and bovine. According to the experiment results, the following can be expected; AE has the possibility to detect the state normality and abnormality. However, the grinding resistance measuring can find it difficult to detect it. It can be accurately excerpted from AE occurrence pattern in contact start point of diamond bur and bovine, grinding condition and derailment point. It is known that AE$\_$rms/ is well compatible with grinding resistance. According to the increase of the material removal rate, the specific energy of the diamond bur is inclined to decrease and the grinding resistance has a tendency to increase.