• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Journal of Forest and Environmental Science
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• v.37 no.2
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• pp.116-127
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• 2021

Predicting carbon distribution of soil aggregates is difficult due to complexity in organo-mineral formation. This limits global warming mitigation through soil carbon sequestration. Therefore, knowledge of land use effect on carbon stabilization requires quantification of soil mineral cations. The study was conducted to quantify carbon and base cations on soil mineral fractions in Natural Forest, Plantation Forest and Farm Land. Five 0.09 ha were demarcated alternately along 500 m long transect with an interval of 50 m in Natural Forest (NF), Plantation Forest (PF) and Farm Land (FL). Soil samples were collected with soil cores at 0-15, 15-30 and 30-45 cm depths in each plot. Soil core samples were oven-dried at 105℃ and soil bulk densities were computed. Sample (100 g) of each soil core was separated into >2.0, 2.0-1.0, 1.0-0.5, 0.5-0.05 and <0.05 mm aggregates using dry sieve procedure and proportion determined. Carbon concentration of soil aggregates was determined using Loss-on-ignition method. Mineral fractions of soil depths were obtained using dispersion, sequential extraction and sedimentation methods of composite soil samples and sieved into <0.05 and >0.05 mm fractions. Cation exchange capacity of two mineral fractions was measured using spectrophotometry method. Data collected were analysed using descriptive and ANOVA at α_0.05. Silt and sand particle size decreased while clay increased with increase in soil depth in NF and PF. Subsoil depth contained highest carbon stock in the PF. Carbon concentration increased with decrease in aggregate size in soil depths of NF and FL. Micro- (1-0.5, 0.5-0.05 and <0.05 mm) and macro-aggregates (>2.0 and 2-1.0 mm) were saturated with soil carbon in NF and FL, respectively. Cation exchange capacity of <0.05 mm was higher than >0.05 mm in soil depths of PF and FL. Fine silt (<0.05 mm) determine the cation exchange capacity in soil depths. Land use and mineral size influence the carbon and cation exchange capacity of Gambari Forest Reserve.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.53-60
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• 2024

Security assessment is an indispensable process for a secure network, and appropriate performance indicators must be present to manage risks. The most widely used quantitative indicator is CVSS. CVSS has a problem that it cannot consider context in terms of subjectivity, complexity of interpretation, and security risks. To compensate for these problems, we propose indicators that itemize and quantify four things: attackers, threats, responses, and assets, taking into account the security context of ISO/IEC 15408 documents. Vulnerabilities discovered through network scanning can be mapped to MITREATT&CK's technology by the connection between weaknesses and attack patterns (CAPEC). We use MITREATT&CK's Groups, Tactic, and Mitigations to produce consistent and intuitive scores. Accordingly, it is expected that security evaluation managers will have a positive impact on strengthening security such as corporate networks by expanding the range of choices among security indicators from various perspectives.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.22-28
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• 2007

Spectrum spreading, in its general form, can be conceived as an artificial expansion of the signal bandwidth with respect to the minimum Nyquist band required to transmit the desired information. Spreading can be functional to several objectives, including resilience to interference and jammers and reduction of power spectral density levels. In the paper, signal spreading is manly used for increasing the received energy, thus satisfying link budget constraints, for terminals with low aperture antennas, without increasing the transmitted EIRP. As a matter of fact, in many mobile scenarios, even when MODCOD configurations with very low spectral efficiency (i.e. QPSK-1/4) in DVB-S2 standard, are used, the link budget cannot be closed. Spectrum spreading has been recently proposed as a technique to improve system performance without introducing additional MODCOD configurations under the constraint of fixed power spectrum density level at the transmitter side. To this aim, the design of spectrum spreading techniques shall keep into consideration requirements such as spectrum mask, physical layer performance, link budget, hardware reuse, robustness, complexity, and backward compliance with existing commercial equipments. The proposed implementation allows to fully reuse the standard DVB-S2 circuitry and is inserted as an 'inner layer' in the standard DVB-S2 chain.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.