• Journal of the Korean Geotechnical Society
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• v.23 no.11
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• pp.67-76
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• 2007

The characteristics of rockfall are determined by virtually all factors and conditions e.g. the physical figure of the slope such as inclination, height, roughness, the elemental figure of the slope such as vegetation and material deposited, and the shape and weight of the rockfall itself. Although it is one of the major factors to be considered in rockfall simulation, little attention has been given to the weight of the rockfall. And, since the size of the rockfall is dominated by joint spacing, the distribution of the rockfall block weight can be predicted as a function of the joint spacing. In this study, the weight distribution of rockfall was estimated by using the method of volumetric joint count, $J_{\nu}$, based on joint spacing, and $RQD-J_{\nu}$. The results indicate that the weight distributions were analogous in two methods, and the distribution was to be $75.3{\sim}76.7%$ for 200 kilograms or lesser, $15.0{\sim}16.6%$ for $200{\sim}400$ kilograms, and $6.7{\sim}9.7%$ for 400 kilograms or more, which show good matches with the actual on-site weight distribution. Therefore, the weight distribution of rockfall suggested in this paper is able to be considered as appropriate data for rockfall simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.32-41
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• 2024

In the construction industry, not only safety accidents, but also various complex risks such as construction delays, cost increases, and environmental pollution occur, and management technologies are needed to solve them. Among them, process risk management, which directly affects the project, lacks related information compared to its importance. This study tried to develop a MATM tunnel process risk classification system to solve the difficulty of risk information retrieval due to the use of different classification systems for each project. Risk collection used existing literature review and experience mining techniques, and DB construction utilized the concept of natural language processing. For the structure of the classification system, the existing WBS structure was adopted in consideration of compatibility of data, and an RBS linked to the work species of the WBS was established. As a result of the research, a risk classification system was completed that easily identifies risks by work type and intuitively reveals risk characteristics and risk factors linked to risks. As a result of verifying the usability of the established classification system, it was found that the classification system was effective as risks and risk factors for each work type were easily identified by user input of keywords. Through this study, it is expected to contribute to preventing an increase in cost and construction period by identifying risks according to work types in advance when planning and designing NATM tunnels and establishing countermeasures suitable for those factors.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.121-130
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• 2024

In this paper, we propose and design an NCS-based job matching system for individuals with disabilities. This system allows users with disabilities to access it, input basic information (personal and disability-related details), and take a simple test related to job performance. The system then provides NCS job-related information appropriate to their type and degree of disability. To effectively link various NCS-based jobs, it is essential to consider the degree of disability for each type of disability. However, most evaluation tools target specific types of disabilities or assess the vocational abilities of individuals with disabilities in a limited manner, focusing only on cognitive levels or certain physical functions. This makes it challenging to apply these tools to an NCS-based job matching system for individuals with disabilities. Therefore, in this paper, we utilize the ICF coresets for VR to assess the cognitive levels or physical functions required for performing specific jobs. Additionally, we use the NCS vocational competency evaluation tools to determine the levels of vocational competencies required for performing specific jobs. By doing so, we match NCS-based jobs according to the type and degree of disability. The proposed NCS-based job matching system relies on the user's interaction with the system, which may pose challenges for visually impaired individuals or those with intellectual and autism spectrum disabilities who have low literacy levels. Enhancing the accessibility of this system could enable individuals with disabilities to receive recommendations for NCS-based jobs that suit their vocational abilities.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.420-430
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• 2001

The object of this study is to develop a computer program for the arrangement of the forest-road network maximizing the investment effect in forest-road construction with factors such as terrains, forest physiognomy, management plan, logging system, cost of forest-road construction, capacity of inputted labour, capacity of timber production and so on. The operating system developed by this study is Korean Windows 95/98 and Microsoft Visual Basic ver. 5.0. User interface was designed as systematic structure, it is presented as a kind of GUI(graphic user interface). The developed program has result of the most suitable forest-road arrangement, has suitable forest-road density calculated with cost of logging, cost of forest-road construction, diversion ratio of forest-road, cost of walking in forest. And the most suitable forest-road arrangement was designed for forest-road arrangement network which maximized investment effect through minimizing the sum of cost of logging and cost of forest-road construction. Input data were divided into map data and control data. Digital terrain model, division of forest-road layout plan, division of forest function and the existing road network are obtained from map data. on the other hand, cost of logging related terrain division, diversion ratio of forest-road and working road, cost of forest-road construction, cost of walking, cost of labor, walking speed, capacity of inputted labor, capacity of timber production and total distance of forest-road are inputted from control data. And map data was designed to be inputted by mesh method for common matrix. This program can be used to construct a new forest-road or vice forest-road which compensate already existing forest-road for the functional forestry.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.12 no.3
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• pp.217-233
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• 2014

Securing the radiological safety is a prerequisite for the safe management of the naturally occurring radioactive materials (NORM) which cannot be reused. This becomes a crucial focus of our R&D efforts upon the implementation of the Act on Protective Action Guidelines against Radiation in the Natural Environment. To secure the safety, the establishment of technical bases and procedures for securing radiological safety related to the disposal of NORM is required. Thus, it is necessary to analyze the characteristics, to collect the data, to have the radiological safety assessment methodologies and tools, to investigate disposal methods and facilities, and to study the effects of the input data on the safety for the NORM wastes. Here, we assess the environmental impact of the NORM waste disposal with respect to the major domestic and foreign NORM characteristics. The data associated with major industries are collected/analyzed and the status of disposal facilities and methodologies relevant to the NORM wastes is investigated. We also suggest the conceptual design concept of a landfill disposal facility and the management plan with respect to the major NORM wastes characteristics. The radionuclide pathways are identified for the atmospheric transport and leachate release and the environmental impact assessment methodology for the NORM waste disposal is established using a relevant code. The assessment and analysis on the exposure doses and excessive cancer risks for the NORM waste disposal are performed using the characteristics of the representative domestic NORM wastes including flying ash, phosphor gypsum, and redmud. The results show that the exposure dose and the excessive cancer risks are very low to consider any radiation effects. This study will contribute to development in the areas of the regulatory technology for securing radiological safety relevant to NORM waste disposal and to the implementation technology for the Act.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1210-1230
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• 1997

In this paper, we suggest an architecture of DS/CDMA tranceiver composed of one pilot channel used as reference and multiple traffic channels. The pilot channel-an unmodulated PN code-is used as the reference signal for synchronization of PN code and data demondulation. The coherent demodulation architecture is also exploited for the reverse link as well as for the forward link. Here are the characteristics of the suggested DS/CDMA system. First, we suggest an interlaced quadrature spreading(IQS) method. In this method, the PN coe for I-phase 1st channel is used for Q-phase 2nd channels and the PN code for Q-phase 1st channel is used for I-phase 2nd channel, and so on-which is quite different from the eisting spreading schemes of DS/CDMA systems, such as IS-95 digital CDMA cellular or W-CDMA for PCS. By doing IQS spreading, we can drastically reduce the zero crossing rate of the RF signals. Second, we introduce an adaptive threshold setting for the synchronization of PN code, an initial acquistion method that uses a single PN code generator and reduces the acquistion time by a half compared the existing ones, and exploit the state machines to reduce the reacquistion time Third, various kinds of functions, such as automatic frequency control(AFC), automatic level control(ALC), bit-error-rate(BER) estimator, and spectral shaping for reducing the adjacent channel interference, are introduced to improve the system performance. Fourth, we designed and implemented the DS/CDMA MODEM to be used for variable transmission rate applications-from 16Kbps to 1.024Mbps. We developed and confirmed the DS/CDMA MODEM architecture through mathematical analysis and various kind of simulations. The ASIC design was done using VHDL coding and synthesis. To cope with several different kinds of applications, we developed transmitter and receiver ASICs separately. While a single transmitter or receiver ASC contains three channels (one for the pilot and the others for the traffic channels), by combining several transmitter ASICs, we can expand the number of channels up to 64. The ASICs are now under use for implementing a line-of-sight (LOS) radio equipment.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.