• Title/Summary/Keyword: route prediction

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Performance Assessment of Two-stream Convolutional Long- and Short-term Memory Model for September Arctic Sea Ice Prediction from 2001 to 2021 (Two-stream Convolutional Long- and Short-term Memory 모델의 2001-2021년 9월 북극 해빙 예측 성능 평가)

  • Chi, Junhwa
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1047-1056
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    • 2022
  • Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

Accurate prediction of lane speeds by using neural network

  • Dong hyun Pyun;Changwoo Pyo
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.5
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    • pp.9-15
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    • 2023
  • In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

Base Location Prediction Algorithm of Serial Crimes based on the Spatio-Temporal Analysis (시공간 분석 기반 연쇄 범죄 거점 위치 예측 알고리즘)

  • Hong, Dong-Suk;Kim, Joung-Joon;Kang, Hong-Koo;Lee, Ki-Young;Seo, Jong-Soo;Han, Ki-Joon
    • Journal of Korea Spatial Information System Society
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    • v.10 no.2
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    • pp.63-79
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    • 2008
  • With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

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Long-Term Arrival Time Estimation Model Based on Service Time (버스의 정차시간을 고려한 장기 도착시간 예측 모델)

  • Park, Chul Young;Kim, Hong Geun;Shin, Chang Sun;Cho, Yong Yun;Park, Jang Woo
    • KIPS Transactions on Computer and Communication Systems
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    • v.6 no.7
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    • pp.297-306
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    • 2017
  • Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

Comparative Analysis of the Accuracy of Severity Scoring Systems for the Prediction of Healthcare Outcomes of Intensive Care Unit Patients (중환자실 환자의 건강결과 예측을 위한 중증도 평가도구의 정확도 비교분석)

  • Seong, Ji-Suk;So, HeeYoung
    • Journal of Korean Critical Care Nursing
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    • v.8 no.1
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    • pp.71-79
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    • 2015
  • Purpose: The purpose of this study was to compare the applicability of the Charlson Comorbidity Index (CCI) and Acute Physiology, Age, Chronic Health Evaluation III (APACHE III) to the prediction of the healthcare outcomes of intensive care unit (ICU) patients. Methods: This research was performed with 136 adult patients (age>18 years) who were admitted to the ICU between May and June 2012. Data were measured using the CCI score with a comorbidity index of 19 and the APACHE III score on the standard of the worst result with vital signs and laboratory results. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under an ROC curve (AUC). Calibration was performed using logistic regression. Results: The overall mortality was 25.7%. The mean CCI and APACHE III scores for survivors were found to be significantly lower than those of non-survivors. The AUC was 0.835 for the APACHE III score and remained high, at 0.688, for the CCI score. The rate of concordance according to the CCI and the APACHE III score was 69.1%. Conclusion: The route of admission, days in ICU, CCI, and APACHE III score are associated with an increased mortality risk in ICU patients.

A Study on the Identification of Risk Factors for unplanned Readmissions in a University Hospital (계획되지 않은 재입원에 대한 위험요인분석)

  • Hwang Jeong Hae;Rhee Seon Ja
    • Journal of Korean Public Health Nursing
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    • v.16 no.1
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    • pp.201-212
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    • 2002
  • This study was designed to identify the risk factors of unplanned readmission in a university hospital. The six-month discharge information from January to June, 2000 in a tertiary university hospital was used as a source of data through the medical record and hospital information system. To increase the effect of comparison. the data were collected by sampling 192 couples (384 patients) of unplanned readmission group through the matching by its disease groups, sex, and age. The accuracy of prediction for unplanned readmission was analyzed by constructing the predicted model of unplanned readmission through the logistic regression. The study results are as follows. The conditional logistic regression analysis was performed with nine variables at the significance level 0.05 through univariate analysis including residence, days after discharge, initial admission route, previous admission, transfer to special care unite, hospital stay days, medical care expenses, special cares, and laboratory and imaging services. As a result, the closer the patients live in Seoul and Gyeong-in area (Odds ratio=2.529, p=0.003), the shorter the days after discharge was (Odds ratio=0.600, p=0.000), and the more frequent admission rate was (Odds ratio=2.317, p=0.004), the more unplanned readmission was resulted. Also, the accuracy of prediction for data classification of this regression model showed $70.3\%$(032+83/306).

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A Propagation Prediction Model for Planning a Cell in the PCS System (PCS 시스템 셀설계를 위한 전파예측 모델)

  • 김송민
    • Journal of the Korean Institute of Telematics and Electronics T
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    • v.35T no.3
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    • pp.103-112
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    • 1998
  • This paper proposes a propagation prediction model which can calculate a propagation path loss easily at option point in case of the propagation processing by repeat reflection when we analysis a propagation route, it makes the calculation speed which is the defect of a geometrical of image method and a ray-launching method improve and we develop and apply the algorithms which can do an angle of incidence, an angle of reflection with a propagation direct path, a reflection path and a maximum reflection number arithmetic process synchronously. Finally we choose as a sample which is the real road condition where is around SK telecoms chunnam branch office in wolgok-dong, kwangsan-ku, kwangju and simulate proposition model then we demonstrate the relative superiority with comparing the results.

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Development of destination arrival time prediction system for bus that applied smart-phone based real-time traffic information (스마트폰 기반 실시간 교통정보를 반영한 버스의 목적지 도착 시간 예측 시스템 개발)

  • Wang, Jong Soo;Kim, Dae Young
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.9 no.4
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    • pp.127-134
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    • 2013
  • While there are many services that can check current traffic condition and application program such as bus arrival alarm are developed, since it only provide simple alarm and check level of information, it is still insufficient in many senses. Therefore, the program that try to develop in this study is the system that predict arrival time to destination and inform the bus passengers by applying real time traffic information. The system developed related to this study is still very inadequate. In the system developed in this thesis, when the user input the current bus number and destination using smart-phone, relevant server acquire the bus route information from bus information DB, and analyze real time traffic information based on the information from traffic information DB, and inform customer of expected arrival time to destination. In this thesis, traffic congestion can be eased off and regular operation of public transportation can be improved with reliable destination arrival alarm. Also, it is considered that pattern of bus users can be analyzed by using these information, and analyzing average transport speed and time of public transportation, travel time depending on various situation can give a boost to study related to transportation information and its development.

Validation of KREAM Based on In-Situ Measurements of Aviation Radiation in Commercial Flights

  • Hwang, Junga;Kwak, Jaeyoung;Jo, Gyeongbok;Nam, Uk-won
    • Journal of Astronomy and Space Sciences
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    • v.37 no.4
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    • pp.229-236
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    • 2020
  • There has been increasing necessity of more precise prediction and measurements of aviation radiation in Korea. For our air crew and passengers' radiation safety, we develop our own radiation prediction model of KREAM. In this paper, we validate the KREAM model based on comparison with Liulin observations. During early three months of this year, we perform total 25 experiments to measure aviation radiation exposure using Liulin-6K in commercial flights. We found that KREAM's result is very well consistent with Liulin observation in general. NAIRAS shows mostly higher results than Liulin observation, while CARI-6M shows generally lower results than the observations. The percent error of KREAM compared with Liulin observation is 10.95%. In contrast, the error for NAIRAS is 43.38% and 22.03% for CARI-6M. We found that the increase of the altitude might cause sudden increase in radiation exposure, especially for the polar route. As more comprehensive and complete analysis is required to validate KREAM's reliability to use for the public service, we plan to expand these radiation measurements with Liulin and Tissue Equivalent Proportional Counter (TEPC) in the near future.

Several models for tunnel boring machine performance prediction based on machine learning

  • Mahmoodzadeh, Arsalan;Nejati, Hamid Reza;Ibrahim, Hawkar Hashim;Ali, Hunar Farid Hama;Mohammed, Adil Hussein;Rashidi, Shima;Majeed, Mohammed Kamal
    • Geomechanics and Engineering
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    • v.30 no.1
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    • pp.75-91
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    • 2022
  • This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.