• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.6
• /
• pp.283-288
• /
• 2016

In recent years, there have been many research efforts on Big Data, and many companies developed a variety of relevant products. Accordingly, we are able to store and analyze a large volume of log data, which have been difficult to be handled in the traditional computing environment. To handle a large volume of log data, which rapidly occur in multiple servers, in this paper we design a new data storage architecture to efficiently analyze those big log data through Apache Hive. We then design and implement anomaly detection methods, which identify abnormal status of servers from log data, based on moving average and 3-sigma techniques. We also show effectiveness of the proposed detection methods by demonstrating that our methods identifies anomalies correctly. These results show that our anomaly detection is an excellent approach for properly detecting anomalies from Hadoop log data.

• Korean Journal of Acupuncture
• /
• v.37 no.3
• /
• pp.159-171
• /
• 2020

Objectives : This study aimed to understand the characteristics of the cases covered in the case studies on traditional Korean medicine (TKM) and furthermore, to provide basic information that can lead the discussion on 'what cases are worth reporting' in future case reports. Methods : Case reports on TKM were searched using the OASIS. The searched researches were analyzed according to the type of case, including information on disease/symptoms and intervention. Results : A total of 940 researches were searched. The most frequently reported type of case study was the report on the effectiveness of intervention. Case reports, which were only two cases in the 1970s, increased rapidly in the 2000s, and in particular, 314 cases within the last five years accounted for about 33% of the total literature. As for the number of studies by disease, the cases dealing with musculoskeletal diseases such as spine, shoulder and knee joint disorders were the most prevalent. Besides, there were many case reports related to cardiovascular, gynecological, cancer, psychiatric, and dermatological diseases. In a total of 51.9% of the included case reports, a combination of two or more Korean medical treatments such as acupuncture and herbal medicine was used at once, and western treatment was used with Korean medical treatment in 28.2% of the studies. The types of Korean medical treatments were varied, such as acupuncture, moxibustion, pharmacopuncture, electroacupuncture, Chuna, acupotomy, herbal medicine, external preparation, and psychotherapy. The main purpose of the publication of the included case reports was analyzed as a report of TKM treatment for rare diseases, or the application of TKM treatment to diseases or symptoms that are "uncommon in TKM treatment" even if it is not a rare disease. Conclusions : Case reports have the strength of generating new scientific hypotheses by detecting the basic needs and novelty of medicine. The current case studies of TKM do not seem to be sufficient to highlight these strengths. It is necessary to discuss which cases are reported as cases of patients worth publishing, and based on this, it is necessary to activate case studies of TKM by utilizing diagnostic tools and science technology.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.125-132
• /
• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Korean Journal of Clinical Laboratory Science
• /
• v.51 no.4
• /
• pp.414-419
• /
• 2019

JC polyomavirus (JCPyV) is a human pathogenic virus belonging to the family Polyomaviridae, a viral group containing dsDNA nucleic acid. A recent recommendation is to apply the presence of JCPyV as a fecal indicator for water contamination in environments like sewage, and techniques to monitor JCPyV in water are being proposed. To date, the conventional PCR system has been applied as a diagnostic method for detecting JCPyV. There is a need for a more rapid and sensitive JCPyV diagnostic detection method in clinical and environmental samples. In this study, we developed a loop-mediated isothermal amplification (LAMP) primer set for the detection of JCPyV. Our results indicate that the LAMP method using a specific primer set shows about 10-fold higher detection sensitivity than the conventional PCR system. The effectiveness of the LAMP method developed in this study has been validated by PCR product digestion using the HaeIII restriction enzyme. We, therefore, propose that the LAMP method using a specific primer set can be applied as a rapid and sensitive detection method for monitoring JCPyV in clinical and environmental samples.

• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.3
• /
• pp.27-34
• /
• 2013

As smart phone is daily used by most people, various location-based applications have been developed. Moreover, it is increased the requirements of location-based services that utilize Google maps, GPS sensing information and etc.. In this paper, we develop a smart phone application providing notification services by detecting the daily route deviation. We develop the server system which receives GPS information gathering from user's smart phone and analyse the user's regular routes. With the results from the analyzation the GPS coordinates, the application on a smart phone detects the case of a breakaway from regular routes. And then it can inform automatically and immediately the designed person such as guardians of children or the old and the infirm of the route deviation so will be helpful to protect such person. This is one example of the effectiveness of the application. Moreover, the application is developed on the client-server framework, thus it is very useful to judge the user's daily route deviation in realtime by using the accumulated information on the corresponding DB. Finally we evaluated the proposed methods which had been implemented on smart phones and show the utilities of the application.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.741-747
• /
• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• Smart Structures and Systems
• /
• v.18 no.3
• /
• pp.501-523
• /
• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.37-45
• /
• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.275-285
• /
• 2019

Web-assemblies are a new binary standard designed to improve the performance of Web browser JavaScript. Web-assemblies are becoming a new web standard that can run at near native speed with efficient execution, concise representation, and code written in multiple languages. However, current Web-assembly vulnerability verification is limited to the Web assembly interpreter language, and vulnerability verification of Web-assembly binary itself is insufficient. Therefore, it is necessary to verify the safety of the web assembly itself. In this paper, we analyze how to operate the web assembly and verify the safety of the current web-assembly. In addition, we examine vulnerability of existing web -assembly and analyze limitations according to existing safety verification method. Finally, we introduce web-assembly API based fuzzing method to overcome limitation of web-assembly safety verification method. This verifies the effectiveness of the proposed Fuzzing by detecting crashes that could not be detected by existing safety verification tools.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.944-945
• /
• 2016

Gamma-ray Detector for gamma-ray imaging device is composed of a shielding body for shielding gamma-rays incident from the radiation source. Distribution of the gamma ray can be represented by the distribution information on the direction in which the detecting section and the signal through the incident hole of collimator. The role of the shield is important because all signals should be treated as noise except for the signal from the incident hole.In this paper In this paper, we have produced a compact, lightweight and Collimator shield by changing the structure and physical properties with respect to the collimator and shielding of lead-based gamma-ray detectors. And we analyzed the shielding effectiveness relative to the incident gamma ray sphere measured signal value through the gamma irradiation test facility. The results confirmed that the production and Collimator shielding the imaging device Implementing more efficient to implement.