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Development of Hazard-Level Forecasting Model using Combined Method of Genetic Algorithm and Artificial Neural Network at Signalized Intersections (유전자 알고리즘과 신경망 이론의 결합에 의한 신호교차로 위험도 예측모형 개발에 관한 연구)

  • Kim, Joong-Hyo;Shin, Jae-Man;Park, Je-Jin;Ha, Tae-Jun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.4D
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    • pp.351-360
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    • 2010
  • In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

An Examination of the Effectiveness of Crisis Response Strategies for Repairing Competence and Integrity Violations

  • Sung, Yen-yi;Lee, Han-joon;Park, Jong-chul
    • Asia Marketing Journal
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    • v.15 no.1
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    • pp.129-154
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    • 2013
  • Product-harm crises, which are connected to defective or dangerous products, are perceived as the most common threats to a company. Product harm crises can distort long standing favorable equality perceptions, tarnish a company's reputation, cause major revenue and market-share losses, lead to costly product recalls, and devastate a carefully nurtured brand equity. However, in spite of the devastating impact of product-harm crises, little systematic research exists to asses its marketing consequences. So, the purpose of this study is to investigate how Koreans react to the crisis response in the aftermath of different crises(competence violation vs. integrity violation) and inspire additional research in crisis communication. This study has three main findings which run counter to the assumptions of Kim et al.(2007). Namely, the current study expands on the research of Kim et al. (2004, 2007) by examining how companies repair customers' trust and corporate attitude after crises. Different from previous studies, this study assumes that apology for an integrity-based crisis is the most appropriate way to repair consumer trust and corporate attitude. As for competence-based crisis, similarly, apology for competence-based crisis can be more successful repairing consumer trust and corporate attitude. Concerning silence strategy, remaining silent dose not admit or deny guilt right away, but instead of asking the perceiver to withhold judgment, suggesting that, silence could be expected to be superior to apology but inferior to denial. Finally, apology for competence violation will be expected to bemore effective than apology for integrity violation. Research conceptual model was as follows: According to the results, apology is found to be the most effective strategy to repair corporate attitude no matter the crisis is perceived as a violation of competence or integrity. Second, company may consider keeping silent as a desirable response because they does not admit nor deny responsibility but ask the public to withhold judgment. However, the result of this study shows that, in the overall crisis situations, silence strategy did not differ significantly from the denial strategy, which suggested that the public wants explanation instead of uncertainty. Third, there was the interaction effect between crisis type and crisis response strategies. In this study, apology is more effective for the competence violated situation in terms of regaining consumer trust and repairing their attitude toward company, while the apology's effectiveness is lower for the integrity-violated situation. More specifically, when the crisis is perceived due to company's lack of ability(competence violation), consumer's trust belief and attitude toward the company is more easily to repair when the company issued a sincere apology. Damaged product is perceived less intentional so participants are more likely to give the company second chance when they apology to the public. By contrast, exaggerated advertisement(integrity violation) is perceived intentionally and thus makes participants angrier toward the accused company. Although apology is perceived as the most effective strategy, when issuing apology, it also means the company admitted their intention. Therefore, in this kind of crisis situation, trust repair needs not only a sincere apology but additional efforts.

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The Effects of Corporate Corresponding Time on the Negativity Publicity (부정적 언론보도에 대한 기업의 대응시점 효과)

  • Jongchul Park;Woojun An;Hanjun Lee
    • Asia Marketing Journal
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    • v.12 no.4
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    • pp.113-136
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    • 2011
  • Product harm crises can distort long standing favorable equality perceptions, tarnish a company's reputation, cause major revenue and market-share losses, lead to costly product recalls, and devastate a carefully nurtured brand equity. However, in spite of the devastating impact of product-harm crises, little systematic research exists to asses its marketing consequences. So, this study focuses on the negative publicity about companies and their products. Namely, this study presented how inclusion effect supported the relationship between negative publicity and consumers' response, market performance. According to the results, after negativity publicity was happened, it was appeared that the negativity image spread into other product lines(spillover effect; inclusion effect). Also, when they contact with the negative publicity, respondents negatively evaluated both production evaluation and corporate evaluation. And, in that case of the products with negativity publicity, compared with refutation strategy(defense strategy<study 2>), improving strategy(correction notice) had positive influence on recovery of sales, product evaluation, and corporate evaluation. Finally, as the reaction time toward negativity publicity was faster, the market performance got worse. Especially, according to two-way interaction, when the reaction time was fast, the difference between refutation strategy(defense strategy<study 2>) and improving strategy was not existed in product evaluation and corporate evaluation. However, when the reaction time was late(after a month), improving strategy had more positive evaluation than defense strategy in product evaluation, and corporate evaluation.

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A Study on the Application of the Price Prediction of Construction Materials through the Improvement of Data Refactor Techniques (Data Refactor 기법의 개선을 통한 건설원자재 가격 예측 적용성 연구)

  • Lee, Woo-Yang;Lee, Dong-Eun;Kim, Byung-Soo
    • Korean Journal of Construction Engineering and Management
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    • v.24 no.6
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    • pp.66-73
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    • 2023
  • The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

Selection Method for Installation of Reduction Facilities to Prevention of Roe Deer(Capreouls pygargus) Road-kill in Jeju Island (제주도 노루 로드킬 방지를 위한 저감시설 대상지 선정방안 연구)

  • Kim, Min-Ji;Jang, Rae-ik;Yoo, Young-jae;Lee, Jun-Won;Song, Eui-Geun;Oh, Hong-Shik;Sung, Hyun-Chan;Kim, Do-kyung;Jeon, Seong-Woo
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.26 no.5
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    • pp.19-32
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    • 2023
  • The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

National Disaster Management, Investigation, and Analysis Using RS/GIS Data Fusion (RS/GIS 자료융합을 통한 국가 재난관리 및 조사·분석)

  • Seongsam Kim;Jaewook Suk;Dalgeun Lee;Junwoo Lee
    • Korean Journal of Remote Sensing
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    • v.39 no.5_2
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    • pp.743-754
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    • 2023
  • The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

Development of SVM-based Construction Project Document Classification Model to Derive Construction Risk (건설 리스크 도출을 위한 SVM 기반의 건설프로젝트 문서 분류 모델 개발)

  • Kang, Donguk;Cho, Mingeon;Cha, Gichun;Park, Seunghee
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.6
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    • pp.841-849
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    • 2023
  • Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

A Case Study on Predicting and Analyzing Inflow Sources of Underground Water in a Limestone Mine (석회석 광산 갱내수 유입원 예측분석 사례연구)

  • Minkyu Lee;Sunghyun Park;Hwicheol Ko;Yongsik Jeong;Seon-hee Heo
    • Tunnel and Underground Space
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    • v.33 no.5
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    • pp.388-398
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    • 2023
  • The changes in groundwater flow due to mining development act as a contributing factor to major issues such as ground subsidence, strength reduction and collapse. For the sustainable mining development, measures for dealing with fluctuations in seasonal underground water inflow, power losses, pump damage, and unexpected increases in inflow must be put in place. In this study, the aim is to identify the causes of underground seepage through the examination of hydrological connectivity between the study area and nearby limestone mine. A tracer tes for assessing subsurface connectivity has been planned. A variety of tracers, such as dyes and ions, were applied in lab test to select the optimal tracer material, and a hydrological model of the study area was implemented through field test. Finally, the hydrological connectivity between the external stream and underground water in the mine was analyzed.

Automatic Detection of Type II Solar Radio Burst by Using 1-D Convolution Neutral Network

  • Kyung-Suk Cho;Junyoung Kim;Rok-Soon Kim;Eunsu Park;Yuki Kubo;Kazumasa Iwai
    • Journal of The Korean Astronomical Society
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    • v.56 no.2
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    • pp.213-224
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    • 2023
  • Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

A global-scale assessment of agricultural droughts and their relation to global crop prices (전 지구 농업가뭄 발생특성 및 곡물가격과의 상관성 분석)

  • Kim, Daeha;Lee, Hyun-Ju
    • Journal of Korea Water Resources Association
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    • v.56 no.12
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    • pp.883-893
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    • 2023
  • While South Korea's dependence on imported grains is very high, droughts impacts from exporting countries have been overlooked. Using the Evaporative Stress Index (ESI), this study globally analyzed frequency, extent, and long-term trends of agricultural droughts and their relation to natural oscillations and global crop prices. Results showed that global-scale correlations were found between ESI and soil moisture anomalies, and they were particularly strong in crop cultivation areas. The high correlations in crop cultivation areas imply a strong land-atmosphere coupling, which can lead to relatively large yield losses with a minor soil moisture deficits. ESI showed a clear decreasing trend in crop cultivation areas from 1991 to 2022, and this trend may continue due to global warming. The sharp increases in the grain prices in 2012 and 2022 were likely related to increased drought areas in major grain-exporting countries, and they seemed to elevate South Korea's producer price index. This study suggests the need for drought risk management for grain-exporting countries to reduce socioeconomic impacts in South Korea.