• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.298-300
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• 2003

ea­RED(Efficient Adaptive RED)[1][2]는 다수의 TCP 커넥션이 경쟁하는 병목구간에서 인터넷 라우터 버퍼를 능동적으로 관리하는 다양한 AQM(Active Queue Management) 알고리즘 중의 하나로 RED 라우터 버퍼 관리 알고리즘의 성능을 개선한 라우터 버퍼 관리 알고리즘이다. RED 라우터가 TD 라우터와 같은 네트워크 퍼포먼스를 유지하면서 TCP 커넥션 간 페어니스를 향상시키기 위해서는 link bandwidth. active 커넥션 수. congestion level 등에 대한 네트워크 상태를 고려하여 파라미터에 적절한 값을 설정해야만 한다. 문제는 다이내믹하게 변하는 네트워크 상황에 적합한 파라미터 값을 초기에 설정해주는 것이 매우 어렵다는 점이다. [3]. ea­RED는 max threshold와 min threshold 값을 네트워크 상황에 따라 동적으로 조절함으로써 이런 문제를 해결했고, 기존 RED에 비해 라우터 버퍼는 50% 정도만 사용하면서도, 페어니스 인덱스(Fairness Index)[4]가 최대 41.42% 개선되었다. [1] [2] 그러나 송신 TCP 커넥션의 수가 늘어날수록 성능향상에 대한 효과가 감소되었고, 드롭 패킷수가 TD나 RED 라우터 버퍼관리 알고리즘에 비해 많았기 때문에 라우터의 출력(output) 총 패킷 용량이 최대 약 2.3% 정도 TD나 RED 라우터 버퍼관리 알고리즘에 비해 적었다. 이 부분을 개선하기 위해 기존 ea­RED 알고리즘에 LR_Lines 예측 알고리즘을 적용한 ea­RED++ 알고리즘을 구현하였고, 실험 결과 페어니스 인덱스는 기존 ea­RED에 비해 최대 약 30% 정도 향상되었고, 총 output 패킷 용량의 손실률은 최대 50%정도 감소하여 기존 ea­RED에 비해 향상된 성능을 보여주었다.웍스 네트워크상의 다양한 디바이스들간의 네트워크 다양화와 분산화 기능을 얻을 수 있었고, 기존의 고가의 해외 솔루션인 Echelon사의 LonMaker 소프트웨어를 사용하지 않고도 국내의 순수 솔루션인 리눅스 기반의 LonWare 3.0 다중 바인딩 기능을 통해 저 비용으로 홈 네트워크 구성 관리 서버 시스템 개발에 대한 비용을 줄일 수 있다. 기대된다.e 함량이 대체로 높게 나타났다. 점미가 수가용성분에서 goucose대비 용출함량이 고르게 나타나는 경향을 보였고 흑미는 알칼리가용분에서 glucose가 상당량(0.68%) 포함되고 있음을 보여주었고 arabinose(0.68%), xylose(0.05%)도 다른 종류에 비해서 다량 함유한 것으로 나타났다. 흑미는 총식이섬유 함량이 높고 pectic substances, hemicellulose, uronic acid 함량이 높아서 콜레스테롤 저하 등의 효과가 기대되며 고섬유식품으로서 조리 특성 연구가 필요한 것으로 사료된다.리하였다. 얻어진 소견(所見)은 다음과 같았다. 1. 모년령(母年齡), 임신회수(姙娠回數), 임신기간(姙娠其間), 출산시체중등(出産時體重等)의 제요인(諸要因)은 주산기사망(周産基死亡)에 대(對)하여 통계적(統計的)으로 유의(有意)한 영향을 미치고 있어 $25{\sim}29$세(歲)의 연령군에서, 2번째 임신과 2번째의 출산에서 그리고 만삭의 임신 기간에, 출산시체중(出産時體重) $3.50{\sim}3.99kg$사이의 아이에서 그 주산기사망률(周産基死亡率)이 각각 가장 낮았다. 2. 사산(死産)과 초생아사망(初生兒死亡)을 구분(區分)하여 고려해 볼때 사산(死産)은 모성(母性)의 임신력(姙娠歷)과 매우 밀접한 관련이 있는 것으로 사료(思料)되었고 초생아사망(初生兒死亡)은 미숙아(未熟兒)와 이에 관련된 병

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.179-188
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• 2017

It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.2
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• pp.325-330
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• 2009

In Korea, medical diagnostic equipments and biochemical examination can not be used in order for diagnosing sub-healthy state or ante-disease state in oriental medicine clinic. So morphic analogical method used in oriental medicine can be a good tool as a disease-predictable signs in order to enable preventive diagnosis and therapy. Therefore the four geometrical subjects; Essence, Pneuma, Spirit, Blood(四科;精氣紳血) and the four taxonomical species; Pisces, Quadruped, Aves, Carapaces(四類;魚走鳥甲) are chosen as morphic models in this paper. The differences of two classifying methods with four subjects and four species were as follows. The diagnostic category was meta-medical and synthetic against medical specific. The diagnostic object was body in contrast with face. They were able to be applicant in psychology and classification of characteristics against diagnostics and therapeutics directly in oriental medicine. The theoretical basis was basic diagrams of four unit-fluids of body and morphological analogy with four animal species respectively. And the therapeutic aims were systemic pathogenesis following five phase theory against congestion and deficiency of Essence, Pneuma, Spirit, Blood. The four subjects and four species are mixed each other practically in clinic. But it should be used limitedly because of the above reasons described and must divide the principal and secondary factors and follow the pathology of principal shape factor. In order to improve the diagnostic value of ante-disease state, the discriminable standards, measurement methods, limit of interrelating interpretation and the criteria of abnormal disproportion were needed to be defined more clearly in advance.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.135-142
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• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.77-90
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• 2015

An increasing number of vehicles is causing various traffic problems such as chronic congestion of highways and air pollution. Local governments have been managing traffic by constructing systems such as Intelligent Transport Systems (ITS) and Advanced Traffic Management Systems (ATMS) to relieve such problems, but construction of an infrastructure-based traffic system is insufficient in resolving chronic traffic problems. A more sophisticated system with enhanced operational management capabilities added to the existing facilities is necessary at this point. As traffic patterns of the urban traffic flow is time-specific due to the different vehicle populations throughout the time of the day, a local network-wide signal operation plan that can manage such situation-specific traffic patterns is deemed to be necessary. Therefore, this study is conducted for the purpose of establishment of a plan for contextual signal control management through signal optimization at the network level after setting the Frame Signal in accordance to the traffic patterns gathered from the short-term traffic forecast data as a means to mitigate the problems with existing standardized signal operations.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.2
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• pp.114-128
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• 2013

Rapid industrialization and economic growth have led to serious problems including reduced open space, environmental degradation, traffic congestion, and urban sprawl. These problems have been exacerbated by the absence of effective conservation and governance, and have resulted in various social conflicts. In response to these challenges, many scholar and government hope to achieve sustainable development through the establishment and management of environment-friendly planning. For this purpose, we would like to analyze functional change for ecosystem by future land-use/cover changes in South Korea. Toward this goal, we predicted land-use/cover changes from 2010 to 2060 using the future population of Statistics Korea and urban growth probability map created by logistic regression analysis and analyzed ecosystem service value using costanza's coefficient. In the case of scenario 1, ecosystem service value represented 6,783~7,092 million USD. In the case of scenario 2, ecosystem represented 6,775~7,089 million USD, 2.9~7.6 million USD decreased compared by scenario 1. This was the result of area reduction for farmland and wetland which have high environmental value relatively according to urban growth by development point of view. The results of this analysis indicate that environmentally sustainable systems and urban development must be applied to achieve sustainable development and environmental protection. Quantitative analysis of environmental values in accordance with environmental policy can help inform the decisions of policy makers and urban developers. Furthermore, forecasting urban growth based on future demand will provide more precise predictive analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.287-296
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• 2015

Recent navigation systems provide quick guide services, based on processing real-time traffic information and past traffic information by applying predictable pattern for traffic information. However, the current pattern for traffic information predicts traffic information by processing past information that it presents an inaccuracy problem in particular circumstances(accidents and weather). So, this study presented a more precise predictive traffic information system than historical traffic data first by analyzing route search data which the drivers ask in real time for the quickest way then by grasping traffic congestion levels of the route in which future drivers are supposed to locate. First results of this study, the congested route from Yang Jae to Mapo, the analysis result shows that the accuracy of the weighted value of speed of existing commonly congested road registered an error rate of 3km/h to 18km/h, however, after applying the real predictive traffic information of this study the error rate registered only 1km/h to 5km/h. Second, in terms of quality of route as compared to the existing route which allowed for an earlier arrival to the destination up to a maximum of 9 minutes and an average of up to 3 minutes that the reliability of predictable results has been secured. Third, new method allows for the prediction of congested levels and deduces results of route searches that avoid possibly congested routes and to reflect accurate real-time data in comparison with existing route searches. Therefore, this study enabled not only the predictable gathering of information regarding traffic density through route searches, but it also made real-time quick route searches based on this mechanism that convinced that this new method will contribute to diffusing future traffic flow.

• Korea Real Estate Review
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• v.28 no.2
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• pp.35-47
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• 2018

Securing a stable residential location is one of the most important decisions that must be made in the modern society. On this matter, both individuals and their families must decide on where to live after taking into consideration various analyses. Contributing attributes in the selection of our dwelling place are crucial. In this research, influencing variables were derived from the intention to move by focusing on the characteristics of the household and traffic conditions, while implications were suggested through a comparison of urban characteristics. Suwon was selected as the case study. The result of the analysis showed the city of Suwon has longer communal satisfaction, relies on self-sufficiency, and is conscious of parking regulation. Preferences for rental housing, having infants and elementary school kids, high savings, and commuter convenience in Suwon and Gyeonggi-do ranked higher in the hierarchy of the intention to move. Compared to Gyeonggi-do, Suwon was influenced by commuters in the city and parking regulation-related variables. Meanwhile, Gyeonggi-do was affected by the lack of public transportation facilities and traffic congestion. Suwon, on the other hand, has a high share of passenger car ownership, so it seems that the psychological stability of parking space is significant. This research will contribute in the policy-making of Suwon, especially on the subject of migration prediction of citizens and real estate location selection, through analyses of variables related to the intention to move to a new residence.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.