• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.847-855
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• 2018

GMTI (Ground Moving Target Indicator) is a kind of airborne radar function that is used widely in military applications to detect the moving targets on the ground. In this paper, GMTI signal processing technique was presented and its performance was verified using sum and difference channels raw data obtained by the captive flight test.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.333-334
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• 2016

Recently, with the development of technology for the virtual reality services, the virtual reality technology has been to use various application services. However, because it is difficult to secure a non-Augmented Reality Virtual Reality in this case it has a vision problem that can be fixed in a service only, not removable. In this paper, we propose a technique of application iBeacon signal by applying the technology of the indoor location-based service using a virtual reality system described in iBeacon ensure the mobility in a virtual space, and can detect the collisions with other moving objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.741-742
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• 2016

Secure Coding is in the development phase, removing a potential security vulnerability that could lead to attacks such as hacking in advance, says the technique to develop secure software from external attacks. In this paper, we'll learn about the needs and expectations of the effectiveness of these security software development. Due to this, the threat to the safe software development project, and there is an effect to improve quality.

• Convergence Security Journal
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• v.18 no.3
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• pp.27-35
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• 2018

The certificate is electronic information issued by an accredited certification body to certify an individual or to prevent forgery and alteration between communications. Certified certificates are stored in PCs and smart phones in the form of encrypted files and are used to prove individuals when using Internet banking and smart banking services. Among the rapidly growing Android-based malicious applications are malicious apps that leak personal information, especially certificates that exist in the form of files. This paper proposes a method for judging whether malicious codes leak certificates by using DroidBox, an Android-based dynamic analysis tool.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.238-246
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• 2021

Objective : To retrospectively analyze the clinical characteristics and surgical experience of 10 adults with spinal extradural meningeal cysts (SEMCs) in the thoracolumbar spine which may further provide evidence for surgical decision-making. Methods : Ten adults with SEMCs in the thoracolumbar spine were surgically treated and enrolled in this study. Clinical manifestations, imaging data, intraoperative findings and postoperative outcome were recorded. Results : Clinical manifestations of SEMCs included motor and sensory dysfunction of the lower limbs and urination and defecation disturbance. The cysts presented as intraspinal occupying lesions dorsal to the spine, ranging from the T8 to L3 level. Defects of eight cases were found on preoperative magnetic resonance imaging (MRI). Selective hemilaminectomy or laminectomy were used to reveal the defect within the cyst, which was further sutured with microscopic technique. The final outcome was excellent or good in seven cases and fair in three cases. No recurrence was observed during follow-up. Conclusion : SEMCs are rare intraspinal cystic lesions. Radiography and MRI are clinically practical methods to assess defects within SEMCs. Selective hemilaminectomy or laminectomy may reduce surgical trauma. Detection and microscopic suturing of the defects are the key steps to adequately decompress the nervous tissue and prevent postoperative recurrence.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.13-20
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• 2021

In this paper, a new algorithm is proposed to estimate the damage size by combining the reflected area with the reflected position and extracting contours in proportion to the maximum value of pixels from the visible image. The cumulative summation feature vector algorithm is used to obtain the area of the reflected signal. To get the position of the reflected signal, the signal correlation algorithm is used to decompose the reflected signal from the damage. The proposed algorithm is tested and validated for composite panels. Repetitive experiments are performed and it is confirm that the proposed algorithm is reproducible. Further, it is verified that the damage size can be estimated appropriately by the proposed algorithm.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.