• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.297-304
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• 2021

Commercial obfuscation tools (protectors) aim to create difficulties in analyzing the operation process of software by applying obfuscation techniques and Anti-reversing techniques that delay and interrupt the analysis of programs in software reverse engineering process. In particular, in case of virtualization detection and anti-debugging functions, the analysis tool exits the normal execution flow and terminates the program. In this paper, we analyze Anti-reversing techniques of executables with Debugger Detection and Viralization Tools Detection options through VMProtect 3.5.0, one of the commercial obfuscation tools (protector), and address bypass methods using Pin. In addition, we predicted the location of the applied obfuscation technique by finding out a specific program termination routine through API analysis since there is a problem that the program is terminated by the Anti-VM technology and the Anti-DBI technology and drew up the algorithm flowchart for bypassing the Anti-reversing techniques. Considering compatibility problems and changes in techniques from differences in versions of the software used in experiment, it was confirmed that the bypass was successful by writing the pin automation bypass code in the latest version of the software (VMProtect, Windows, Pin) and conducting the experiment. By improving the proposed analysis method, it is possible to analyze the Anti-reversing method of the obfuscation tool for which the method is not presented so far and find a bypass method.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.119-125
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• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.1-18
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• 2023

Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAO approach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Software product line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematic reuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding, integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and their structures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thus requires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifies a set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, which has evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and the number of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.9-16
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• 2023

Robots are expected to expand their scope of application to the military field and take on important missions such as surveillance and enemy detection in the coming future warfare. Swarm robots can perform tasks that are difficult or time-consuming for a single robot to be performed more efficiently due to the advantage of having multiple robots. Swarm robots require mutual recognition and collaboration. So they send and receive vast amounts of data, making it increasingly difficult to verify SW. Hardware-in-the-loop simulation used to increase the reliability of mission verification enables SW verification of complex swarm robots, but the amount of verification data exchanged between the HILS device and the simulator increases exponentially according to the number of systems to be verified. So communication overload may occur. In this paper, we propose a digital twin-based communication optimization technique to solve the communication overload problem that occurs in mission verification of swarm robots. Under the proposed Digital Twin based Multi HILS Framework, Network DT can efficiently allocate network resources to each robot according to the mission scenario through the Network Controller algorithm, and can satisfy all sensor generation rates required by individual robots participating in the group. In addition, as a result of an experiment on packet loss rate, it was possible to reduce the packet loss rate from 15.7% to 0.2%.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.305-316
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• 2022

With the flood of digital data owing to the Internet of Things and big data, cloud service providers that process and store vast amount of data from multiple users can apply duplicate data elimination technique for efficient data management. The user experience can be improved as the notion of edge computing paradigm is introduced as an extension of the cloud computing to improve problems such as network congestion to a central cloud server and reduced computational efficiency. However, the addition of a new edge device that is not entirely reliable in the edge computing may cause increase in the computational complexity for additional cryptographic operations to preserve data privacy in duplicate identification and elimination process. In this paper, we propose an efficiency-improved duplicate data elimination protocol while preserving data privacy with an optimized user-edge-cloud communication framework by utilizing a trusted execution environment. Direct sharing of secret information between the user and the central cloud server can minimize the computational complexity in edge devices and enables the use of efficient encryption algorithms at the side of cloud service providers. Users also improve the user experience by offloading data to edge devices, enabling duplicate elimination and independent activity. Through experiments, efficiency of the proposed scheme has been analyzed such as up to 78x improvements in computation during data outsourcing process compared to the previous study which does not exploit trusted execution environment in edge computing architecture.

• Explosives and Blasting
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• v.40 no.3
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• pp.19-32
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• 2022

Hydrogen is a fuel having the highest energy compared with other common fuels. This means hydrogen is a clean energy source for the future. However, using hydrogen as a fuel has implication regarding carrier and storage issues, as hydrogen is highly inflammable and unstable gas susceptible to explosion. Explosions resulting from hydrogen-air mixtures have already been encountered and well documented in research experiments. However, there are still large gaps in this research field as the use of numerical tools and field experiments are required to fully understand the safety measures necessary to prevent hydrogen explosions. The purpose of this present study is to develop and simulate 3D numerical modelling of an existing hydrogen gas station in Jeonju by using handheld LiDAR and Ansys AUTODYN, as well as the processing of point cloud scans and use of cloud dataset to develop FEM 3D meshed model for the numerical simulation to predict peak-over pressures. The results show that the Lidar scanning technique combined with the ANSYS AUTODYN can help to determine the safety distance and as well as construct, simulate and predict the peak over-pressures for hydrogen refueling station explosions.