• 제어로봇시스템학회논문지
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• 제13권3호
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• pp.268-272
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• 2007

These days many scientists make studies of feedback control system for stability on non-linear state and for the maneuver of flying objects. These feedback control systems have to satisfy trajectory condition and angular conditions, that is to say, controllability and stability simultaneously to achieve mission. In this paper, a design methods using model based control system which consists of spiral predictive model, Q-function included into generalized-work function is shown. It is made a clear that the proposed algorithm using SPM maneuvers for controllability and stability at the same time is successful in attaining our purpose. The feature of the proposed algorithm is illustrated by simulation results. As a conclusion, the proposed algorithm is useful for the control of moving objects.

• 자동차안전학회지
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• 제14권4호
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• pp.35-42
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• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.123-130
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• 2024

This paper proposes an EMPC (Explicit Model Predictive Controller) for temperature tracking control based on heat load prediction by an ESO (Extended State Observer) for a variable cooling circulation system with multiple indoor units connected to one outdoor unit. In this system, heat transfer and heat loss relative to the input temperature are modeled using system dynamics. Using this model, we design an EMPC based on an ESO that is robust to temperature changes and depends on airflow. To determine the stability of both the controller and the observer, asymptotic stability is verified through Lyapunov stability analysis. Finally, to validate the performance of the proposed controller, simulations are conducted under three scenarios with varying airflow, set temperature, and heat load.

• 디지털융복합연구
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• 제16권9호
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• pp.147-157
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• 2018

구글의 인플루엔자 의사환자(ILI) 예측 서비스 시작 이래로 웹 검색 정보를 활용한 ILI 예측 연구들이 급속도로 확산되고 있는 가운데, 본 연구는 ILI 자료와 웹 검색 정보를 활용한 한국 ILI 단기 예측 모형을 개발해 성능을 평가해 보고자 한다. 한국에 특화된 ILI 예측 모형 개발을 위해 한국질병관리본부의 ILI 감시 자료와 구글 및 네이버의 한국어 검색정보를 ARIMA 모형과 함께 사용하였다. 모형1은 ILI 자료만 사용하였으며, 모형 2와 3은 모형1에 구글과 네이버의 검색자료를 각각 추가하였다. 모형4는 모형 2와 3의 공통 검색어를 모형1에 추가하였다. 모형 훈련기간 동안 모든 예측모형들이 95%($R^2$) 이상의 높은 적합도를 보였으며, 예측기간 1과 2에서 모형1이 가장 우수한 예측력(99.98%, 96.94%)을 보였다. 모형 3(a)와 4(b, c)는 전체 예측기간에서 90% 이상의 안정적인 예측력을 보였지만, 모형1의 성능에는 미치지 못하였다. 본 연구에서 정확하고 안정적인 예측력을 보인 모형들은 성능개선에 관한 보완적 연구와 더불어 국내 인플루엔자 유행 조기경보시스템에 활용 가능하다.

• 대한간호학회지
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• 제49권5호
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• pp.575-585
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• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

• 대한안전경영과학회지
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• 제25권4호
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

• 대한지리학회지
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• 제51권2호
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• pp.219-233
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• 2016

수리 또는 계량적 모형을 사용하는 사회과학연구에서 분석의 초점은 종속변수와 설명변수의 관계를 밝히는 것, 즉 설명 중심의 모형(explanatory modeling)이 지금까지 주류를 이루었다. 반면 예측(prediction) 능력 제고에 초점을 맞춘 분석은 드물었다. 본 연구에서는 이론 및 가설을 검증하거나 변수 간의 관계를 밝히는 설명 중심의 모형이 아니라 신규 관찰치에 대한 예측 오차를 줄이는, 예측 중심의 비모수 모형(non-parametric model)을 검토하였다. 서울시 강남구를 사례지역으로 선정한 후, 2011년부터 2014년까지 신고된 단독주택 실거래가를 기초자료로 하여 주택가격을 추정하였다. 적용한 비모수 모형은 기계학습 분야에서 제시된 일반가산모형(generalized additive model), 랜덤 포리스트, MARS(multivariate adaptive regression splines), SVM(support vector machines) 등이며 비교적 최근에 개발된 MARS나 SVM의 예측력이 뛰어남을 확인할 수 있었다. 마지막으로 이러한 비모수 모형에 공간적 자기상관성을 추가적으로 반영한 결과, 모형의 가격 예측력이 보다 개선되었음을 알 수 있었다. 본 연구를 계기로 그간 모수 모형에 집중되었던 부동산 가격추정 방법론이 비모수 모형으로 확대 및 다양화되기를 기대한다.

• 수산경영론집
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• 제52권4호
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• International Journal of Air-Conditioning and Refrigeration
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• 제15권3호
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• pp.129-136
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• 2007

In this paper, we suggest PI control with the Smith predictive controller to improve transient response of a variable speed refrigeration system (VSRS). As the refrigeration system has long dead time inherently, it is difficult to get fast responses of super-heat and reference temperature. We incorporated the Smith predictive controller into PI to compensate the effect of the long dead time of the system. At first, we introduced the decoupling model of the system to control capacity and superheat simultaneously and independently. Next, we designed the predictive controller of the superheat based on PI control law. Finally, the control performance by the proposed method was investigated through some numerical simulations and experiments. The results of the simulations and experiments showed that the proposed PI control with the predictive controller could obtain acceptable transient behaviour for the system.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.221-232
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.