• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3789-3809
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• 2014

Radio environment maps (REMs) and geolocation database represent an important source of information for the operation of cognitive radio networks, replacing or complementing spectrum sensing information. This paper provides a survey of methods for constructing the radio frequency layer of radio environment map (RF-REM) using distributed measurements of the signal levels at a given frequency in space and time. The signal level measurements can be obtained from fixed or mobile devices capable of sensing radio environment and sending this information to the REM. The signal measurements are complemented with information already stored in different REM content layers. The combined information is applied for estimation of the RF-REM layer. The RF-REM construction methods are compared, and their advantages and disadvantages with respect to the spatial distribution of signal measurements and computational complexity is given. This survey also indicates possible directions of further research in indirect RF-REM construction methods. It emphasizes that accurate RF-REM construction methods should in the best case support operation with random and clustered signal measurements, their operation should not be affected by measurements outliers, and it must estimate signal levels comparably on all RF-REM locations with moderate computational effort.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• Journal of Digital Contents Society
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• v.5 no.3
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• pp.219-224
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• 2004

In This paper, we have analyzed the power spectrum of DS-TH Ulhawideband (Direct Sequence-Time Hopping UWB) system which used pseudo-noise (PN) code. The DS-TH UWB system proposed in this paper multiplies the information signal with PN code to construct pulse train with random pattern and then the chips in pulse train are bundled into several groups to map to the particular value. The (+)/(-) pulse is tented in the time slot of frame by comparing a particular value with timing information that was stored in the lookup table. Thus, the energy spark (Comb Line) which is generated certainly in convantional system can be suppressed efficiently by PN code. And we knew that the proposed DS-TH UWB System even could have very smoothing power spectrum ctaracteristic without applying high speed Time-Hopping code.

• Journal of the Korean Society of Combustion
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• v.5 no.2
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• pp.19-27
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• 2000

A turbulent combustion model, based on edge flame dynamics, is discussed in order to predict global extinction of turbulent flames. The model is applicable to the broken flamelet regime of turbulent combustion, in which global extinction of turbulent flame is achieved by gradual expansion of flame holes. The edge flame dynamics is the key mechanism to describe the flame hole expansion or contraction. For flames with Lewis numbers near unity, there is a $Damk{\ddot{o}}hler$ number, namely the crossover $Damk{\ddot{o}}hler$ number, at which edge flame changes its direction of propagation. The parametric region between the quasi-steady extinction condition and the edge-flame crossover condition is a metastable region, in that flames without edge can stay in their burning states while flames with edge have to retract to expand quenching holes. Using the above properties of edge flame, Hartley and Dold proposed a Lagrangian hole dynamics, which allows us to simulate transient variation of quenching holes. In their model, each stoichiometric surface is subjected to a random sequence of scalar dissipation rate compatible to the equilibrium turbulence. Then, each stoichiometric surface will evolve, according to the combustion map, dependent on the scalar dissipation rate and existence of flame edge, If all the burning surfaces are annihilated, the event can be declared as a global extinction. The consequence obtained from the above model also can be used as a subgrid model to determine local extinction occurring in a calculation grid.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.214-217
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• 2004

숭화법을 이용한 탄화규소(Silicon carbide) 단결정 성장시 사용되는 원료의 상(phase)이 단결정 성장에 미치는 영향을 알아보기 위해 알파형 탄화규소 분말(${\alpha}-SiC$ powder)과 베타형 탄화규소 분말(${\beta}-SiC$ powder)을 각각 사용하였다. 알파형 탄화규소 분말을 사용한 경우에 단결정(single-crystal)을 성장할 수 있었으나, 베타형 탄화규소 분말을 사용하였을 때에는 다결정(poly-crystal)이 성장되었다. 다결정 형성요인에 관한 EPMA 분석결과, 베타형 탄화규소 분말의 탄소에 대한 실리콘의 원소조성비$(N_{Si}/N_C\;=\;1.57)$가 알파형 탄화규소 분말의 경우보다$(N_{Si}/N_C\;=\;0.81)$ 높음을 확인하였다. 따라서 흑연도가니(crucible) 내부의 실리콘 원자가 알파형 탄화규소 분말을 사용하는 경우보다 높은 과포화상태가 되어 종자정 표면에 미세한 실리콘 액적(droplet)이 중착되고 이것으로부터 일정하지 않은 방향성(random orientation)을 갖는 탄화규소 다결정(다양한 방향성을 갖는 다형 포함)이 성장한 것으로 실리콘과 탄소 원소에 대한 EPMA 지도(map) 결과를 통해 확인할 수 있었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.23-33
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• 2006

Key Agreement protocols are among the most basic and widely used cryptographic protocols. In this paper we present an efficient O-based authenticated key agreement (AKA) protocol by using bilinear maps, especially well suited to unbalanced computing environments : an ID-based AKA protocol for Server and Client. Particularly, considering low-power clients' devices, we remove expensive operations such as bilinear maps from a client side. Our protocol uses signcryption and provide security in random oracle model.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.293-299
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• 2021

Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.

• ETRI Journal
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• v.45 no.5
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• pp.822-835
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• 2023

Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.