• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.629-635
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• 2022

Cyber threats are also increasing with recent social changes and the development of ICT technology. Malicious codes used in cyber threats are becoming more advanced and intelligent, such as analysis environment avoidance technology, concealment, and fileless distribution, to make analysis difficult. Machine learning technology is being used to effectively analyze these malicious codes, but a lot of effort is needed to increase the accuracy of classification. In this paper, we propose a malicious code detection technology based on API call interval characteristics to improve the classification performance of machine learning. The proposed technology uses API call characteristics for each section and entropy of binary to separate characteristic factors into sections based on the extraction malicious code and API call order of normal binary. It was verified that malicious code can be well analyzed using the support vector machine (SVM) algorithm for the extracted characteristic factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.81-94
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• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.436-436
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• 2023

산업혁명을 거치면서 높은 화석연료를 사용하는 제조업 중심의 산업구조와 많은 자원을 필요로 하는 도시의 집중 현상으로 지구 온난화에 따른 이상기후 발생이 증가하고 있다. 이러한 기후변화는 홍수, 태풍, 폭염 및 폭설 등의 자연재해 발생 빈도 및 규모를 증가시켜 피해가 커지고 있다. 특히 인구 및 시설들이 집중해 있어 도시의 집중 현상은 이러한 재해에 더욱 취약한 구조가 됨에 따라 피해의 규모를 가중 시키고 있는 실정이다. 전 세계적으로 기후변화 문제의 심각성을 인식하고 이를 해결하기 위해 선신국에 의무를 부여하는 교토의정서(1997년) 채택에 이어, 선진국과 개도국이 모두 참여하는 파리협정(2015년)을 채택하였고 2016년 협정이 발효되었다. 파리협정의 목표는 산업화 이전 대비 지구 평균온도 상승을 2℃보다 아래로 유지하고, 나아가 1.5℃로 억제하기 노력하는 것을 강제하는 것으로 2050년까지 탄소 순배출량을 '0'으로 만든다는 탄소중립사회로의 전환이 본격적으로 시작되었다. 본 연구에서는 기후변화로 인한 물부족 및 수실오염과 같은 도시의 수자원 문제 해결을 위해 IoT 기반 센서 및 네트워크 기반 수자원 플랫폼을 개발하였다. 도시 수자원 시설 데이터를 기반으로 대체 수자원 확보 및 수요 중심의 물 관리를 통해 효율적인 물 배분이 될 수 있도록 하였으며 이러한 스마트 물 관리에 따른 대체 수자원 확보 및 효율적 물 배분이 탄소 저감에 미치는 효과에 대해 분석하였다. 연구대상 지역은 세종 6-4구역으로 LID 특화지구로 조성되었으며 1,000 세대의 주민이 생활하는 공동주택이다. 물 순환(LID) 시설에서 확보된 물을 물 공급 시설과 연계하여 공동주택에서 활용함으로써 감소된 상수 사용량을 온실가스 배출량으로 환산하여 탄소 저감량을 계산하였다. 실제 주민들(1,000세대)이 사용하고 있는 상수량 데이터와 전력거래소 온실가스 배출계수를 활용하였으며 물순환(LID) 시설로 확보하여 대체할 수 있는 상수량은 10%로 가정하였다. 연구대상 지역(1,000세대)의 연간 상수공급량은 331,603m³이며, 연간 전력사용량은69,637kWh이다. 온실가스 배출량은 31.963tCO₂eq이며, 온실가스 저감량은 3.2tCO₂eq로 산정되었다. 추후 LID 시설에 대한 상수 대체량과 온실가스 저감효과 정량화가 필요하다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.208-210
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• 2021

As the number of objects connected to the Internet increases rapidly, intelligent device development projects are gradually expanding that provide direct value to humans, away from simple monitoring functions, including sensors and communication functions, or delivery to servers.It is expected that the device will develop a technology that analyzes surrounding sensing information and changes the surrounding environment in consideration of users' preferences or safety. By establishing a biosignal measurement system in a developed product that can bring various effects using air, it will be possible to grasp the user's condition through a pattern of change in pressure distribution when seated. This paper proposes a construction system that enhances the comfort of using an air car seat through contact between a temperature measurement sensor and a user, and enables effective management of measured biosignals by linking them with an air pump control system.

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Structural Engineering and Mechanics
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• v.90 no.3
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• pp.253-261
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• 2024

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam's directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.11-23
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• 2014

Wireless Mesh Network(WMN) has drawn much attention due to easy deployment and good scalability. Recently, major power utilities have been focusing on R&D to apply WMN technology in Smart Grid Network. Smart Grid is an intelligent electrical power network that can maximize energy efficiency through bidirectional communication between utility providers and customers with ICT(Information Communication Technology). It is necessary to guarantee QoS of some important data in Smart Grid system such as real-time data delivery. In this paper, we suggest QoS enhancement method for WMN based Smart Grid system using IEEE 802.11s. We analyze Smart Grid Application characteristics and apply IEEE 802.11s WMN scheme for Smart Grid in domestic power communication system. Performance evaluation is progressed using NS-2 simulator implementing IEEE 802.11s. The simulation results show that the QoS enhancement scheme can guarantee stable bandwidth irrespective of traffic condition due to IEEE 802.11s reservation mechanism.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.75-93
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• 2006

The ship selection by consignors has two steps to carry their cargo. The first step is to select according to time schedule of ships and amount of cargo, and the second one is re-selection by concentrating different consignors' cargo into a unit that can be carried by single ship. Up to now, these steps are usually done by hands leading to inefficiency. The purpose of our paper is to form a logistics chain to minimize the overall sum of logistics cost by selecting ships for consignors' cargo using negotiation methodology between agents. Through concentration and distribution of cargo, maximization of global profit derived from searching optimal point in trade-off between inventory cost and freight rate cost. It is settled by the negotiation between consignors. In the experiments, two methods of the first-step of ship selection: EPDS(Earliest Possible Departure-Date Scheduling) and LPDS(Latest Possible Departure-Date Scheduling) coupled with the second-step ship concentration method using the negotiation were shown. From this, we deduced inventory cost, freight rates and logistics cost according SBF(Scheduling Bundle Factor) and analyzed the result. We found it will minimize the total logistics cost if we use negotiation method with EPDS.

• Journal of the Korean association of regional geographers
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• v.14 no.6
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• pp.736-751
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• 2008

Although a growing number of studies have commonly used a simple areal weighting interpolation method to quantify demographic characteristics of impacted areas in environmental equity analysis, the results obtained are inevitably imprecise because of the method's unrealistic assumption that population is evenly distributed within a census enumeration unit. Two alternative areal interpolation methods such as intelligent areal weighting and regression methods can account for the distributional biases in the estimation of impacted populations by making use of additional information about the geographic distribution of population. This research explores five areal interpolation methods for estimating the population characteristics of impacted areas in environmental equity analysis and evaluates the sensitivity of the outcomes of environmental equity analysis to areal interpolation methods. This study used GIS techniques to allow areal interpolation to be informed by the distribution of land cover types, as inferred from a satellite image. in both the source and target units. Independent samples t-test statistics were measured to verify the environmental equity hypothesis while coefficients of variation were calculated to compare the relative variability and consistency in the socioeconomic characteristics of populations at risk over different areal interpolation methods. Results show that the outcomes of environmental equity analysis in the study area are not sensitive to the areal interpolation methods used in estimating affected populations, but the population estimates within the impacted areas are largely variable as different areal interpolation methods are used. This implies that the use of different areal interpolation methods may to some degree alter the statistical results of environmental equity analysis.