• Title/Summary/Keyword: flood item.

Search Result 16, Processing Time 0.024 seconds

Study on Guideline for the Selection of Small Stream Implementation Projects (소하천정비사업 우선순위 선정기준에 관한 연구)

  • Cheong, Tae-Sung;Kang, Byung-Hwa;Jeong, Sang-Man
    • Journal of the Korean Society of Hazard Mitigation
    • /
    • v.11 no.2
    • /
    • pp.163-170
    • /
    • 2011
  • Natural stream disasters due to a localized torrential and flash flood has occurring in a small stream especially un-implemented small stream. The survey results during ten years from 2001 to 2010 show that the small stream implementation projects (SSIPs) expenses is increasing with the damages is generally decreasing with variableness in which SSIPs is contributing to disaster prevention in a small stream. This study develop guideline for the selection of SSIPs to support high risk stream at first and save the small streams located on the mountainous area, prevention area and agricultural area which streams have no implementation effects. Developed sub items in guideline are evaluated by stream data collected from 212 small streams where it is proved that sub distance of each item are well arranged by normal distribution. This SSIPs is useful for selecting high risk small stream at first to maximize disaster risk reduction with minimum SSIPs expenses. Also, this SSIPs is used for leading to save small stream on the upstream to minimize flood damages on the down stream with selection a SSIP purchasing agricultural land for preparing flood plane.

Development and Application of Evaluation Technique for Revetment for Nature-Friendly River Improvement (자연 친화적 하천정비를 위한 호안평가기법의 개발 및 적용)

  • Kim, Yun-Hwan;Park, Nam-Hee;Jin, Young-Hoon;Kim, Chul
    • Journal of Korea Water Resources Association
    • /
    • v.40 no.12
    • /
    • pp.1007-1014
    • /
    • 2007
  • Recently, existing river improvement methods for flood control purpose have changed into nature friendly river improvement methods and the efforts to improve the river environment including river restoration have been made, and close-to-nature river improvement and nature friendly river restoration are actively conducted all over the country. In the present situation where various revetment methods are used after the introduction of the concept of close-to-nature river improvement, the environmental characteristics of rivers need to be considered to apply more suitable revetment methods. Therefore, as a precedent study for the development of revetment evaluation techniques and methods for close-to-nature river improvement, the present study suggested evaluation techniques using detailed survey items through field survey. Evaluation items of hydraulic stability consist of mode of streamline, stream bed gradient, flow velocity and tractive force ratio and those of environmental efficiency consist of revetment of vegetation, state of river water, land use of the terrace land on the river, vegetation and materials of the terrace land on the river. Each item was graded with the point 1 through 5. Hydraulic stability and environmental efficiency was evaluated by the points which were averaged in each items. As the result of the application of the proposed evaluation technique, it was found that a number of existing revetments excessively focus on hydraulic stability with little consideration about environmental term. It is expected that the proposed technique in the present study can be used as a base for providing guidelines to construct the design and construction of revetments in the future.

A Study on the Test and Installation Standards of the Video Fire Detector (영상화재감지기 시험과 설치기준에 관한 연구)

  • Lee, Jeong-Hyun;Baek, Dong-Hyun
    • Fire Science and Engineering
    • /
    • v.30 no.4
    • /
    • pp.1-5
    • /
    • 2016
  • This research performed tests of Video Fire Detector and criteria of installation to make suggestions regarding the criteria that must be reflected in NFSC 203 by comparing the standards of FM Approvals, UL, ISO7240 and NFPA 72. FM Standard related to Video Fire Detector test has been classified as Smoke, Flame type, but the UL Standard has classified only as a Smoke type. This research examined 6 cases of fire phenomenon detection case in ISO 7240 and 3 cases in NFPA 72, respectively. There are 15 items required for the installation standard of a Video Fire Detector and each field standard is presented as a per installation method. To apply a Video Fire Detector, the pertinent items (the definition of term, detector's classification, structure and function among its test item) must be inserted. In addition, 7 items of the fire test, i.e., the sensitivity adjustment, prevent false alarm, ambient temperature test, the effective sensitivity and detection distance and viewing angle, aging test, flood test, must be applied to the actual test. For installation in the field, the operation environment and levels of illumination, and NFSC 203 must be set, and standards relevant to the sound system, indicators' installation distance, etc. need to be inserted.

A CF-based Health Functional Recommender System using Extended User Similarity Measure (확장된 사용자 유사도를 이용한 CF-기반 건강기능식품 추천 시스템)

  • Sein Hong;Euiju Jeong;Jaekyeong Kim
    • Journal of Intelligence and Information Systems
    • /
    • v.29 no.3
    • /
    • pp.1-17
    • /
    • 2023
  • With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

Review of applicability of Turbidity-SS relationship in hyperspectral imaging-based turbid water monitoring (초분광영상 기반 탁수 모니터링에서의 탁도-SS 관계식 적용성 검토)

  • Kim, Jongmin;Kim, Gwang Soo;Kwon, Siyoon;Kim, Young Do
    • Journal of Korea Water Resources Association
    • /
    • v.56 no.12
    • /
    • pp.919-928
    • /
    • 2023
  • Rainfall characteristics in Korea are concentrated during the summer flood season. In particular, when a large amount of turbid water flows into the dam due to the increasing trend of concentrated rainfall due to abnormal rainfall and abnormal weather conditions, prolonged turbid water phenomenon occurs due to the overturning phenomenon. Much research is being conducted on turbid water prediction to solve these problems. To predict turbid water, turbid water data from the upstream inflow is required, but spatial and temporal data resolution is currently insufficient. To improve temporal resolution, the development of the Turbidity-SS conversion equation is necessary, and to improve spatial resolution, multi-item water quality measurement instrument (YSI), Laser In-Situ Scattering and Transmissometry (LISST), and hyperspectral sensors are needed. Sensor-based measurement can improve the spatial resolution of turbid water by measuring line and surface unit data. In addition, in the case of LISST-200X, it is possible to collect data on particle size, etc., so it can be used in the Turbidity-SS conversion equation for fraction (Clay: Silt: Sand). In addition, among recent remote sensing methods, the spatial distribution of turbid water can be presented when using UAVs with higher spatial and temporal resolutions than other payloads and hyperspectral sensors with high spectral and radiometric resolutions. Therefore, in this study, the Turbidity-SS conversion equation was calculated according to the fraction through laboratory analysis using LISST-200X and YSI-EXO, and sensor-based field measurements including UAV (Matrice 600) and hyperspectral sensor (microHSI 410 SHARK) were used. Through this, the spatial distribution of turbidity and suspended sediment concentration, and the turbidity calculated using the Turbidity-SS conversion equation based on the measured suspended sediment concentration, was presented. Through this, we attempted to review the applicability of the Turbidity-SS conversion equation and understand the current status of turbid water occurrence.

A Study on Knowledge Entity Extraction Method for Individual Stocks Based on Neural Tensor Network (뉴럴 텐서 네트워크 기반 주식 개별종목 지식개체명 추출 방법에 관한 연구)

  • Yang, Yunseok;Lee, Hyun Jun;Oh, Kyong Joo
    • Journal of Intelligence and Information Systems
    • /
    • v.25 no.2
    • /
    • pp.25-38
    • /
    • 2019
  • Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.