• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.184-194
• /
• 2023

Purpose: The purpose of this study is to identify the characteristics of C02 emissions by road before establishing a policy to reduce greenhouse gas emissions. Method: As for the analysis method, the traffic volume and speed of the road were estimated using the traffic Assignment model targeting 27 arterial road axes in Incheon Metropolitan City. And, after estimating CO2 emissions by road axis by applying this, the characteristics of each group were analyzed through cluster analysis. Result: As a result of cluster analysis using total CO2 emissions, CO2 emissions by truck vehicles, and the ratio of truck vehicle emissions to total carbon dioxide emissions, four clusters were classified. When examining the characteristics of each road included in each group, it was analyzed that the characteristics of each group appeared according to the level of impact by CO2 emissions and truck vehicles. Conclusion: It is judged that it is necessary to establish a plan in consideration of CO2 emission characteristics for road CO2 management for greenhouse gas reduction.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.86-86
• /
• 2021

온천천은 부산광역시 금정구, 동래구, 연제구를 흐르는 도심 하천으로 부산 시민들의 도심 속 산책길, 자전거 길 등으로 활용되는 도시하천이다. 그러나 온천천 양안의 동래 곡저 평야가 시가지화 되고 온천천 발원지인 금정산 주변에서 무허가 상수도를 사용하고 각종 쓰레기와 하수의 유입으로 인해 하천 전체가 하수관으로 변해왔다. 이에 따라 부산광역시는 온천천 정비 계획을 시행하여 하천 정비와 함께 자동측정망을 설치하여 하천의 DO (dissolved oxygen), 탁도, TDS농도 등 자료를 수집하고 있다. 그러나 자동측정망으로 쌓여가는 데이터를 활용하여 DO농도 예측은 거의 이뤄지지 않고 있다. DO는 하천의 수질 오염 정도를 판단하는 수질인자로 역사적으로 하천 연구의 주요 연구 대상이 되어 왔다. 본 연구에서는 일 자료 뿐만 아니라 시 자료를 기반으로 RNN-LSTM 알고리즘을 활용한 DO예측을 시도하였다. RNN-LSTM은 시계열 학습에 뛰어난 알고리즘으로 인공신경망의 발전된 형태인 순환신경망이다. 연구에 앞서 부산광역시 보건환경정보 공개시스템으로부터 받은 자료 중에서 교정, 보수 중, 비사용, 장비전원단절 등으로 인해 누락데이터를 2014년 1월 1일부터 2018년 12월 31일의 데이터 전수조사 후 이상데이터를 확인하여 선형 보간하여 데이터를 사용하였다. 연구에서는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 부산광역시 금정구 부곡동에 위치한 부곡교 관측소의 DO농도를 시간 또는 일 예측을 하였다. 일 예측 학습에는 2014년~ 2018년의 기상자료(기온, 상대습도, 풍속, 강수량), DO농도 자료를 사용하였고, 시 예측 학습에는 연속된 자료가 가장 많은 2015년 3월 ~ 12월까지의 데이터를 활용하여 연구를 진행하였다. 모형의 검증을 위해 결정계수(R square)를 이용하여 통계분석을 실시하였다.

• Proceedings of the KOR-KST Conference
• /
• 1998.10b
• /
• pp.295-295
• /
• 1998

고속도로에서 교통류의 특성에 파악하여 교통류의 특성을 파악하여 동적행태로 교통상황을 분석하고 효과적인 제어전략, 시뮬레이션, 그리고 기하구조 개선등의 효율적이고 실용적인 적용을 위해서는 교통류의 정확한 모사가 필요하다. 시공간으로 표현되는 상태방정식을 포함하는 거시적 시뮬레이션 모델에 사용되는 연속류 모델은 이러한 교통류 특성을 모사하는데 적절하다. Lighthill과 Whitham(1955), Richard(1956)에 의해 일계도함수의 형태를 가지는 단순모델이 제시된 이후 모델의 결점을 보완하기 위해 많은 고계도 모델이 제시되었지만 고계도 모델이 가진 이론적인 결점에 대해서는 여러 연구들이 제시되어 있다. 또한 고계도 모델은 운동량 방정식의 유도, 정산, 구현의 어려움으로 널리 사용되기 힘들다는 단점을 가지고 있다. 만일 적절히 구현할 수 있다면 적용이 간단한 단순모델로도 보다 정확한 교통류 상황 모사가 가능하다. Ansorge는 혼잡교통류상황을 보다 정확하게 모사하기 위해 단순모델에 엔트로피 조건을 결합시킨 모델을 제시했다. Bui는 이 제안된 모델이 적절한 시뮬레이션 결과를 나타낸다는 것을 밝혔다. 그러나 이 모델은 차량의 재가속이 이루어지는 교통상황-stop-start wave의 경우 비현실적인 값을 가진다. 엔트로피조건에 의해 구해진 해는 실제보다 과다한 교통량을 추정하게 되는데 이런 결과는 위와 같은 교통상황에서 중요한 요소로 작용하는 가속효과가 무시되고 있기 때문이다. 따라서 본 연구에서는 stop-start wave 조건에서 가속도에 경계치를 부여하여 교통류율을 상한경계조건을 제시함으로써 교통상황에 맞는 교통류율을 산정하는 방법에 대해 제안하고자 한다.환승이라는 특정대안변수(Specific alternative variable)를 첨가하여 그것이 수단선택에 미치는 영향을 분석한다. 또한, 대중교통의 속성을 가지고 있는 지하철과 버스를 하나의 대안으로 묶어서 효용함수를 구한 다음 다시 승용차, 택시, 대중교통을 독립된 대안으로 두고 모형을 정립하는 NESTED LOGIT모형으로 파라메타를 추정하여 대중교통의 효용에 관해 분석·비교하였다. 본 논문에 이용된 자료는 공항을 이용하는 이용객들을 대상으로 직접 설문·면접조사한 자료이며 대상 교통수단은 승용차, 택시, 지하철, 버스로 설정하였다. 결과 적응형 알고리즘이 개개인의 최단시간 경로를 제공하는 사용자 평형 경로안내전략에 비해 교통혼잡도와 정체시간의 체류정도에 따라 3%에서 10%까지 전체통행시간을 절약할 수 있다는 결론을 얻었다.출발참, 구성대외개방선면축심, 실현국제항선적함접화국내항반적전항, 형성다축심복사식항선망; 가강기장건설, 개피포동제이국제기장건설, 괄응포동개발경제발전적수요. 부화개시일은 각 5월 26일과 5월 22일이었다. 11. 6월 중순에 애벌레를 대상으로 처리한 Phenthoate EC가 96.38%의 방제가로 약효가 가장 우수하였고 3월중순 및 4월중순 월동후 암컷을 대상으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압주입시험에서도 확인된다.. It was result

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.161-177
• /
• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.3
• /
• pp.249-257
• /
• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of Korean Society of Forest Science
• /
• v.99 no.4
• /
• pp.517-527
• /
• 2010

The purpose of this study was to investigate the causal relationship among perceived value, satisfaction and revisit intention for visitors of the Korea National Arboretum. Three hundred respondents were selected by quota sampling, and well-known measurement scales utilized in marketing field were adopted in order to measure three variables with some modification to apply for arboretum visitors. Results of structural equation model showed that perceived value affected visitors' satisfaction, which in turn influenced revisit intention. Specifically, "emotional value" had the most significant influence on satisfaction, followed by "value for money" and "novelty value"; however, "social value" was not an influential construct. Based on calibration results, several strategies were suggested for the effective management. Authors advised that (a)improvement of measurement scale of value, (b)inclusion of self-congruity as antecedent variable to satisfaction, (c)changing revisit intention to word-of-mouth communication as a post-purchase behavioral intention, and (d)implementation of market segmentation should be considered for future research.

• Geophysics and Geophysical Exploration
• /
• v.11 no.1
• /
• pp.52-59
• /
• 2008

We have developed a new deep-towed marine DC resistivity survey system. It was designed to detect the top boundary of the methane hydrate zone, which is not imaged well by seismic reflection surveys. Our system, with a transmitter and a 160-m-long tail with eight source electrodes and a receiver dipole, is towed from a research vessel near the seafloor. Numerical calculations show that our marine DC resistivity survey system can effectively image the top surface of the methane hydrate layer. A survey was carried out off Joetsu, in the Japan Sea, where outcrops of methane hydrate are observed. We successfully obtained DC resistivity data along a profile ${\sim}3.5\;km$ long, and detected relatively high apparent resistivity values. Particularly in areas with methane hydrate exposure, anomalously high apparent resistivity was observed, and we interpret these high apparent resistivities to be due to the methane hydrate zone below the seafloor. Marine DC resistivity surveys will be a new tool to image sub-seafloor structures within methane hydrate zones.