• International Journal of Highway Engineering
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• v.16 no.5
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• pp.99-108
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• 2014

PURPOSES : The travel times of expressway buses have been estimated using the travel time data between entrance tollgates and exit tollgates, which are produced by the Toll Collections System (TCS). However, the travel time data from TCS has a few critical problems. For example, the travel time data include the travel times of trucks as well as those of buses. Therefore, the travel time estimation of expressway buses using TCS data may be implicitly and explicitly incorrect. The goal of this study is to improve the accuracy of the expressway bus travel time estimation using DSRC-based travel time by identifying the appropriate analysis period of input data. METHODS : All expressway buses are equipped with the Hi-Pass transponders so that the travel times of only expressway buses can be extracted now using DSRC. Thus, this study analyzed the operational characteristics as well as travel time patterns of the expressway buses operating between Seoul and Dajeon. And then, this study determined the most appropriate analysis period of input data for the expressway bus travel time estimation model in order to improve the accuracy of the model. RESULTS : As a result of feasibility analysis according to the analysis period, overall MAPE values were found to be similar. However, the MAPE values of the cases using similar volume patterns outperformed other cases. CONCLUSIONS : The best input period was that of the case which uses the travel time pattern of the days whose total expressway traffic volumes are similar to that of one day before the day during which the travel times of expressway buses must be estimated.

• KIEAE Journal
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• v.15 no.4
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.864-865
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• 2011

Battery Disconnecting System (BDS) is the important equipment in electric vehicle system. Therefore, most of electric vehicle companies, i.e. Hyundai Motors, Renault Motors, General Motors, want to have the reliability of 15 years - 150, 000 miles. Recently, reliability prediction through Siemens Norm SN 29500 is considered without testing. In this paper, we will introduce the standard and various input parameters. Also the case study will be shown for BDS. Prediction model is constructed by listing all the components of BDS. It calculates the $\pi$ factors for each components using the conversion equation in the standard and converts the reference failure rates to the expected operating failure rates. According to the result, the parts which will be improved are EV-Relays.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.193-200
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• 2002

A study was conducted to investigate the availability of Fitts' Law to Hangul input systems on mobile phones. Three different Hangul input systems were experimented to measure the performance time to evaluate the physical interface of all. The measured performance time was found to be well fitted with the modified Fitts' Law by Hangul input systems on mobile phones. As a result, the physical interfaces for Hangul input systems could be evaluated quantitatively with the prediction of the performance time by Fitts' Law.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.135-142
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• 2021

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers' locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasksin shipyards.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.131-140
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• 2015

The purpose of this study is to predict the fatigue life of a planet carrier of a slewing reducer for a tower crane. To predict the fatigue life of the carrier, the inertia endurance test was carried out, and then the input torque profile for the reducer was obtained. The load profile acting on the planet pins that assembled the carrier was calculated from the measured input torque profile using commercial gearbox analysis software. The stress profiles of the carrier weak points were analyzed from the calculated load profile and boundary conditions using commercial FE software, and the stress cycles were determined using the rainflow counting method. Finally, the fatigue life of the carrier was predicted using the equivalent stress range by considering the effect of mean stress, and an S-N curve was drawn up using the GL guideline and the cumulative damage law.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.78-86
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• 1999

The improvement of the water level control is important since it will prevent the steam generator trip so that improve the reliability and credibility of operation system. In this paper, the closed loop system identification is performed which can be used for the system monitoring and prediction of the system response. The model also can be used for the prediction control. Irving model is used as a steam generator model. The plant is an open loop unstable and non-minimum phase system. Fuzzy controller stabilize the system and the stable controller stabilize the system and the stable closed loop system is identified using neural networks. The obtained neural network model is validated using the untrained input and output. The results of computer simulation show the obtained Neural Network model represents the closed loop system well.

• ETRI Journal
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• v.22 no.4
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• pp.32-39
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• 2000

In low earth orbit (LEO) satellite communication systems, more severe phase distortion due to Doppler shift is frequently detected in the received signal than in cases of geostationary earth orbit (GEO) satellite systems or terrestrial mobile systems. Therefore, an estimation of Doppler shift would be one of the most important factors to enhance performance of LEO satellite communication system. In this paper, a new adaptive Doppler compensation scheme using location information of a user terminal and satellite, as well as a weighting factor for the reduction of prediction error is proposed. The prediction performance of the proposed scheme is simulated in terms of the prediction accuracy and the cumulative density function of the prediction error, with considering the offset variation range of the initial input parameters in LEO satellite system. The simulation results showed that the proposed adaptive compensation algorithm has the better performance accuracy than Ali's method. From the simulation results, it is concluded the adaptive compensation algorithm is the most applicable method that can be applied to LEO satellite systems of a range of altitude between 1,000 km and 2,000 km for the general error tolerance level, M = 250 Hz.