• Journal of the Korean Geotechnical Society
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• v.35 no.10
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• pp.17-31
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• 2019

This study describes a prediction method for rainfall-induced landslides and subsequently debris flows in a regional scale areas. Special attention is given to the calculation of the propagation of debris flows by considering rainfall infiltration into soil slopes and soil entrainments by debris flows. The proposed method was verified by comparing the analytical results and the measured ones reported by the previous research. As a result, predictions and observations were quite similar in terms of the front position, the velocity, volume and momentum of debris flows. Even when applied to natural mountain slope with complicated terrain, numerical results and observations were similar. At last, the combined analysis of landslides and debris flows were conducted. The landslides prediction showed a predictive rate of about 83%, and the result of the final volume of debris flow showed an error rate of 3%. As a result, the proposed combined method for landslides and debris flows overcomes the problem of separating the landslides analysis and the debris flows simulation. Especially, the proposed method can analyze the effects of rainfall on entrainments by debris flows as well as rainfall-induced landslides and the behavior of debris flows.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.571-581
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• 2020

Vegetation affects water level change and flow resistance in rivers and impacts waterway ecosystems as a whole. Therefore, it is important to have accurate information about the species, shape, and size of any river vegetation. However, it is not easy to collect full vegetation data on-site, so recent studies have attempted to obtain large amounts of vegetation data using terrestrial laser scanning (TLS). Also, due to the complex shape of vegetation, it is not easy to obtain accurate information about the canopy area, and there are limitations due to a complex range of variables. Therefore, the physical structure of vegetation was analyzed in this study by reconfiguring high-resolution point cloud data collected through 3-dimensional terrestrial laser scanning (3D TLS) in a voxel. Each physical structure was analyzed under three different conditions: a simple vegetation formation without leaves, a complete formation with leaves, and a patch-scale vegetation formation. In the raw data, the outlier and unnecessary data were filtered and removed by Statistical Outlier Removal (SOR), resulting in 17%, 26%, and 25% of data being removed, respectively. Also, vegetation volume by voxel size was reconfigured from post-processed point clouds and compared with vegetation volume; the analysis showed that the margin of error was 8%, 25%, and 63% for each condition, respectively. The larger the size of the target sample, the larger the error. The vegetation surface looked visually similar when resizing the voxel; however, the volume of the entire vegetation was susceptible to error.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Journal of Korean Society of Disaster and Security
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• v.14 no.1
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• pp.1-8
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• 2021

A corrugated pipe is widely used in firefighting equipment and sprinkler pipes because of its elasticity, which is less damaged by deformation and convenient facilities. However, the corrugated shape of the wall results in complex internal turbulent flow, and it is difficult to predict the pressure drop, which is an important design factor for pipe flow. The pressure drop in the corrugated tube is a function of the shape factors of the pipe wall, such as groove height, length, and pitch. Existing studies have only shown a study of pressure drop due to length changes in the case of D-shaped tubes with less than 5 pitch (P) and height (K) of the rectangular grooves in the tube. In this work, we conduct a numerical study of pressure drop for P/Ks with length and height changes of 2.8, 3.5 and 4.67 with Re Numbers of 55,000, 70,000 and 85,000. The pressure drop in the corrugated tube was interpreted to decrease with smaller P/K. We show that the pressure drop is affected by the change in the groove aspect ratio, and the increase in the height of the groove increases the recirculation area, and the larger the Reynolds number, the greater the pressure drop.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.245-256
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• 2021

The Fourth Industrial Revolution brought the quantitative value of data across the industry and entered the era of 'Big Data'. This is due to both the rapid development of information & communication technology and the diversity & complexity of customer purchasing tendencies. An enterprise's core competence in the Big Data Era is to analyze and utilize the data to make strategic decisions for enterprise. However, most of traditional studies on Big Data have focused on technical issues and future potential values. In addition, these studies lacked interest in managing the quality and utilization levels of internal & external customer Big Data held by the entity. To overcome these shortages, this study attempted to derive influential factors by recognizing the quality management information systems and quality management of the internal & external Big Data. First of all, we conducted a survey of 204 executives & employees to determine whether Big Data quality management, Big Data utilization, and level management have a significant impact on corporate work efficiency & corporate management performance. For the study for this purpose, hypotheses were established, and their verifications were carried out. As a result of these studies, we found that the reasons that significantly affect corporate management performance are support from the management class, individual innovation, changes in the management environment, Big Data quality utilization metrics, and Big Data governance system.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.49-55
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• 2022

In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.35-42
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• 2022

Visible light communication (VLC) is a method of transmitting data through LED blinking and is vulnerable to eavesdropping because the illumination affects the wide range of area. IEEE standard 802.15.7 defines On-Off Keying (OOK), Variable Pulse Position Modulation (VPPM), and Color Shift Keying (CSK) as modulation. In this paper, we propose an encryption method in VPPM for secure communication. The VPPM uses an encoding method called 4B6B where 16 different outputs are represented with 6-bit. This paper extends the number of outputs to 20, to add complexity while not violating the 4B6B generation conditions. Then each entry in the extended 4B6B table is scrambled using vigenère cipher. The probability of decrypting each 6-bit data is $\frac{1}{20}$. Eavesdropper should perform $\sum\limits_{k=1}^{n}20^k$ number of different trials to decrypt the message if the number of keys is n. The proposed method can be applied to OOK of PHY II and CSK of PHY III. We further discuss the secure encoding that can be used in OOK and CSK without performance degradation.

• Journal of Conservation Science
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• v.37 no.3
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• pp.209-219
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• 2021

In this study, the wind environment in an urban area near Sungneymun gate was numerically investigated in the cases of inflow directions. The wind fields for the target area were simulated using Geographic Information System data and Computational Fluid Dynamics model. Results, including vector fields, three-dimensional wind velocity components, and wind speeds, were analyzed to examine flow characteristics. Wind direction variability affected by buildings was shown in the target area. The complex flows around Sungneymun did not depend on the inflow direction as a boundary condition. The wind speed around Sungneymun was generally 3 times stronger at 14 m above ground level (AGL) compared to the surface wind at 2 m AGL and relatively high in the case of easterly inflow. The effect of wind was also analyzed to be relatively significant at the southeast side of Sungneymun. Thus, it was suggested that the assessment of wind environment affected by high-rise and high-density buildings should be necessary for the architectural heritage in urban areas.

• Journal of IKEEE
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• v.26 no.4
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• pp.736-741
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• 2022

Lattice-based cryptography is the most practical post-quantum cryptography because it enjoys strong worst-case security, relatively efficient implementation, and simplicity. Ring learning with errors (R-LWE) is a public key encryption (PKE) method of lattice-based encryption (LBC), and the most important operation of R-LWE is the modular polynomial multiplication of rings. This paper proposes a method for optimizing modular multipliers based on approximate computing (AC) technology, targeting the medium-security parameter set of the R-LWE cryptosystem. First, as a simple way to implement complex logic, LUT is used to omit some of the approximate multiplication operations, and the 2's complement method is used to calculate the number of bits whose value is 1 when converting the value of the input data to binary. We propose a total of two methods to reduce the number of required adders by minimizing them. The proposed LUT-based modular multiplier reduced both speed and area by 9% compared to the existing R-LWE modular multiplier, and the modular multiplier using the 2's complement method reduced the area by 40% and improved the speed by 2%. appear. Finally, the area of the optimized modular multiplier with both of these methods applied was reduced by up to 43% compared to the previous one, and the speed was reduced by up to 10%.