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Junseong Park,Sung H. Han,Kyudong Park 대한인간공학회 2018 대한인간공학회 학술대회논문집 Vol.2018 No.5
Automated vehicles can perform many parts of driving tasks that used to be managed by a human driver these days. However, it is important to calibrate appropriate levels of trust to make drivers accept the technology. The aim of this study is to derive design factors for the automated vehicles to calibrate trust based on literature reviews. This study is a fundamental one in automated vehicles before their commercialization, and conducted literature reviews to find factors estimating trust and factors determining trust. As a result, a total of 12 trust estimating factors were found: deceptive, underhanded manner, suspicious, wary, harmful or injurious outcome, confident, security, integrity, dependable, reliable, trust, and familiar. and they were categorized into five representative groups: performance, experience, information, faults, and display. This study can be used to calibrate trust of automated vehicles.
A Case Study on Network Status Classification based on Latency Stability
( Junseong Kim ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.11
Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications` network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks` status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.
Optimal Guidance Law for Impact Angle and Acceleration Constraints with Time-Varying Gains
Junseong Kim,조성진 한국항공우주학회 2022 International Journal of Aeronautical and Space Sc Vol.23 No.3
In this paper, we establish an optimal guidance law for impact angle and acceleration constraints (OGL-IAAC). The optimal guidance law for impact angle (OGL) is widely used due to its energy optimality and analytic solutions. However, acceleration constraints may degrade the performance of the OGL owing to the significant guidance command at the initial and terminal time and hence saturated acceleration values may generate guidance errors. We introduce a new weighting function based on the guidance command closed-form solution of the OGL to address this problem. Then, we derive a new guidance law by using Schwarz’s inequality. The proposed guidance law generates time-varying gains to ensure that the guidance command is within the acceleration constraint. Moreover, the gains converge to the same values as those of the OGL when the time-to-go approaches zero. The proposed guidance law is demonstrated by simulations to investigate the performance and effect of varying coefficients of the weighting function.