• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.85-92
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• 2022

Currently, various researches on electric vehicle battery systems have been conducted from the viewpoint of safety and performance for SoC, SoH, etc. However, it is difficult to build a precise electrical model of a battery system based on the chemical reaction and SoC prediction. Experimental measurements and predictions of the battery SoC were usually performed using dynamometers. In this paper, we construct a simulation model of an electric vehicle system using Matlab Simulink, and confirm the response characteristics based on the vehicle test driving profiles. In addition, we show that it is possible to derive the correlation between the SoC, voltage, and current of the battery according to the driving time of the electric vehicle in conjunction with the BMS model.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.199-209
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• 1998

Consumers turn away their face from a vehicle that doesn't satisfy their sensibility and is designed without consideration of driver's performance. In terms of driver's performance, the manual motor processor carries out the primary interactions between driver and vehicle. Therefore, in this paper, time prediction model is studied that is an important part when drivers manipulate the manual control during driving. Experiments were executed for 20 subjects using two kinds of vehicles and regressed to Fitts' Law. The noise filtering method was suggested for the performance times of manipulating the manual control. Especially, it was shown that Fitts' Law derived by an approximation of Shannon's theorem can predict drivers performance time more appropriately than other methods.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.116-128
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• 2013

This paper focuses on development of a testbed for analysis of robot-terrain interaction on rough terrain and also, through one wheel driving experiments using this testbed, prediction of maximum velocity and acceleration of UGV. Firstly, from the review regarding previous researches for terrain modeling, the main variables for measurement are determined. A testbed is developed to measure main variables related to robot-terrain interaction. Experiments are performed on three kinds of rough terrains (grass, gravel, and sand) and traction-slip curves are obtained using the data of the drawbar pull and slip ratio. Traction-slip curves are used to predict driving performance of UGV on rough terrain. Maximum velocity and acceleration of UGVs are predicted by the simple kinematics and dynamics model of two kinds of 4-wheel mobile robots. And also, driving efficiency of UGVs is predicted to reduce energy consumption while traversing rough terrains.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.21-28
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• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3399-3419
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• 2020

In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.

• 전기의세계
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• v.21 no.3
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• pp.7-18
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• 1972

An analysis is made for the performance of twin stator single-phase induction machine having any movable asymmetrical angle of stator windings, with any symmetrical or asymmetrical magnetizing reactance and winding turn-ratio between two stators, provided that asymmetrical common squirrel cage rotor is utilized. This mechanism is a new type, which has the advantage of mading only not the performance prediction in applications as a single-phase electromagnetic driving mechanism but also the analysis prediction of single-phase induction motor with not in quadrature axis. The basis of the analyses are lead by Kron's primitive machine matrix and Morrill's double-revolving field concept. All the performances can be calculated from the test values and design details of the asymmetrical magnetizing reactance twin stator single-phase induction machine and verified by test. And its validity is still demonstrated to the pure twin stator single-phase induction machine.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1591-1601
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• 2013

In this study, It is conducted for novice drivers about driving behavior and psychological characteristics analysis to reduce traffic accident risk and provide the basic data of education program development. Therefore, this study classified by the category-specific characteristics and hazard prediction through survey of the novice driver and unpredictable behavior and psychological characteristics were studied. The novice and general characteristics and driving behavior with vehicle simulators, comparison and analysis of the novice driver traffic safety education basic research direction based on the statistical results. Prediction the results of this study, the Hazard of the driver, speeding, traffic violation, information providing omission, abrupt change, the number of accidents in all areas novice driver is high compared to the general driver. In addition, Novice driver showed a statistically significant level of Hazard compared to the general driver target novice drivers and the general ability to predict Hazard of violation, abrupt change, and a number of traffic accidents were omitted level of speeding and other information providing level drivers all showed similar results. Vehicle simulator. The experimental results showed that novice drivers compared to drivers poorly overall driving performance. It showed a notable difference in the number of collisions, especially novice drivers compared to drivers in complex road traffic conditions due to a lack of driving experience and learning ability are considered.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.541-547
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• 2015

In this study, the performance of a low-floor midsize bus under development is predicted through simulations. To predict the vehicle's acceleration, maximum speed, and uphill driving performance, a forward simulator which calculates the vehicle power is developed. Also we verify the forward simulator by comparing simulations and test result for benchmarking vehicle. To predict the fuel consumption, we use a backward simulator for a specified road cycle. However, to predict the fuel consumption using the backward simulation the engine fuel-consumption map is needed. The engine fuel-consumption map extracting data from a similar sized diesel engine is used by re-scaling the maximum torque. As a result, we simulate the vehicle's forward performance with a new engine. Further, we simulated the backward performance to optimize the fuel efficiency and gearshift timing.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.91-98
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• 2017

Background & Object: Basic daily activity screening tool such as the Modified Barthel Index (MBI) has been used commonly in rehabilitation clinic and community based rehabilitation setting. Previous studies have shown the significant relations between the level of daily activities and driving ability on stroke or elderly people. However, there is a lack of studies to investigate the usefulness of MBI on prediction of driving ability for stroke patient. This study was to predict driving abilities of stroke survivor using Korean version Modified Barthel Index (K-MBI). Methods: A sample of 48 patients with stroke in rehabilitation hospital was recruited. All participants were tested level of basic daily activities using K-MBI. The driving ability of participants was tested using virtual reality driving simulator. The predictive validity was calculated of the K-MBI among pass or fail group of driving simulator test using receiver operating characteristics curves. Results: The cut-off score of >86.5 on the K-MBI is proper sensitivity to predict on driving performance ability. Conclusion: This pilot result offers clinical reference to therapists and caregivers for reasoning on driving recommendation period during rehabilitation stage of stroke survivors. Further studies need to identify prediction using real on-road test in a large population group.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.97-106
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• 2011

Both an optimal design of the engine operating strategy and fuel economy prediction technique for a HEV under the vehicle driving condition are very crucial for the development of vehicle fuel economy performance. Thus, in this study, engine operating characteristics of PRIUS III were analyzed with vehicle running conditions and the correlations between vehicle tractive power and fuel consumption were introduced. As a result, fuel economy performance of PRIUS III with various test modes were predicted and verified. Errors of predicted fuel economy were between -5% and -1%.