• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.115-123
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• 2017

Dispite the fact that our military has outwardly made notable accomplishments such as the development of weapon systems like tanks, self-propelled artillery, and missiles, there has been a lack of attention to producing weapon effectiveness data that suggests a standard as to what effects the developed weapons will demonstrate on the battlefield. For such reasons, most of the weapon effectiveness data utilizes JMEM data introduced by the United States and as for the rest of the data that cannot be acquired, respective branches create and utilize their own data through research. This research aims to develop a reliable methodology that can meet the requirements of the requesting branches in a short span of time and at a low cost by studying the existing weapon effectiveness data production methodologies such as that of JMEM. As a result I have developed a method that calculates the vulnerable area and the probability of kill of the weapon system that one wants to calculate by applying statistical technique and simulation technique based on weapon effectiveness data of similar weapon systems in JMEM and live test data.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.793-799
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• 2018

As interest in artificial intelligence has increased, artificial intelligence has been actively studied in the medical field. In Korea, artificial intelligence has been applied to medical imaging devices such as X-ray imaging, Computer Tomography and Magnetic Resonance Imaging and artificial intelligence capable of acquiring radiation images of patients without radiologists in the future Medical devices are expected to be invented. This study was an initial study on the automation of patient positioning in X - ray imaging. We used x-ray equipment and human phantoms to evaluate the positioning. The program used Visual Studio 2010 MFC and the image was in the size $1450{\times}1814$. The pixel values were converted to contrasts with values of 0 to 255 that can be visually recognized and output to the monitor. We developed a procedure algorithm program that predicts the angle of the output image through three pixel coordinate values and induces the patient to perform correct positioning according to the voice guidance according to the angle. In the next study, we will study the artificial intelligence to grasp the structure itself and calculate the angle, rather than conveying the reference of coordinates to artificial intelligence. In the future, it is expected that it will be helpful in the study of artificial intelligence from shooting to positioning through the automation of positioning.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.781-788
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• 2021

This paper is the study of a design to eliminate the communication error that occurs between the main controller and the servo controller of the K56 ammunition-carrying armored vehicle. The K56 assists in the operation of the K-55A1 self-propelled gun by automating the supply and loading of ammunition. The CAN communication board of the ammunition carrier is a key-function product mounted inside the main controller and installed for communication with the servo controller. It was confirmed that an undefined error would occur intermittently in the existing CAN communication board, interrupting the operation of the ammunition supply system during the loading process. In this paper, in order to solve the problem, the cause of the failure is identified through analysis and a functional test of the communication signal between the main controller and the servo controller. The error was resolved by redesigning and improving the Read/Write algorithm. Finally, the proposed cause analysis and design effectiveness were verified through the CAN communication board single item test and a system equipment application test. It is expected that this study will serve as a reference for improving defense capabilities through improving the reliability of CAN communication boards and by improving the reliability of the overall electronic equipment using DPRAM.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.839-846
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• 2022

Purpose: To secure the safety of firefighters who are dispatched to emergency activities for patients with suspected infectious diseases during an epidemic, and to identify the current status of suspected infectious disease patients by region based on the information collected at the site, and manage firefighting infectious diseases that can be controlled and supported I want to develop a system. Method: Develop a smartphone app that can classify suspected infectious disease patients to check whether an infectious disease is suspected, and develop a disposable NFC tag for patient identification to prevent infection from suspected infectious disease patients. Develop a management system that collects and analyzes data related to emergency patients with suspected infectious disease input from the field and provides them to relevant business personnel to evaluate whether the transport of emergency patients with suspected infectious disease is improved. Result: As a result of the experiment, it was possible to determine whether an infectious disease was suspected through the algorithm implemented in the smartphone app, and the retransfer rate was significantly reduced by transferring to an appropriate hospital. Conclusion: Through this study, the possibility of improving emergency medical services by applying ICT technology to emergency medical services was confirmed. It is expected that the safety of paramedics will be actively secured.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.97-106
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• 2022

In this study, H&E staining is necessary to distinguish cells. However, dyeing directly requires a lot of money and time. The purpose is to convert the phase image of unstained cells to the amplitude image of stained cells. Image data taken with FPM was created with Phase image and Amplitude image using Matlab's parameters. Through normalization, a visually identifiable image was obtained. Through normalization, a visually distinguishable image was obtained. Using the GAN algorithm, a Fake Amplitude image similar to the Real Amplitude image was created based on the Phase image, and cells were distinguished by objectification using MASK R-CNN with the Fake Amplitude image As a result of the study, D loss max is 3.3e-1, min is 6.8e-2, G loss max is 6.9e-2, min is 2.9e-2, A loss max is 5.8e-1, min is 1.2e-1, Mask R-CNN max is 1.9e0, and min is 3.2e-1.