• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Fashion & Textile Research Journal
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• v.24 no.2
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• pp.229-240
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• 2022

This study explores the effect of anthropomorphism on fashion chatbot reliability, mediated by perceived intelligence and cognitive evaluation. The moderating effects of individuals' need for human interaction between chatbot anthropomorphism and perceived intelligence, cognitive evaluation, and chatbot reliability are also explored. Participants, who were recruited through the online research firm, responded to questions after watching a video clip showing a conversation with a fashion chatbot on a mobile screen. The data were collected through Mturk, a crowdsourcing platform with an online research panel. All responses (N = 212) were analyzed using SPSS 26.0 for the descriptive statistics, frequency analysis, reliability analysis, exploratory factor analysis, and PROCESS procedure. The results demonstrate that chatbot anthropomorphism increases chatbot reliability, and this is mediated by chatbot intelligence. Although chatbot anthropomorphism increases cognitive evaluation, the effect of cognitive evaluation on chatbot reliability is not significant; thereby, the effect of chatbot anthropomorphism on chatbot reliability is not mediated by the cognitive evaluation. The direct effect of anthropomorphism on chatbot reliability is also moderated by individuals' need for human interaction. For participants with a high need for human interaction, chatbot anthropomorphism increases chatbot reliability; however, anthropomorphism does not significantly affect chatbot reliability for participants with a low need for human interaction. The study's findings contribute to expanding the literature on consumers' new technology acceptance by testing the antecedents affecting service reliability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.112-123
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• 2012

Different from general roads, intersections are the points where roads having different geometric structure and traffic operation system are met, and thereby they have complicated road structure and environmental factors. Various changes in driving patterns such as collision between vehicles approaching from roads adjacent to intersections, sudden stop of vehicles upon stop sign, quick start upon green lights kept increasing traffic accidents. It is known that traffic accidents are mainly derived from human factors. This study, in order to find out factors affecting drivers' behaviors within intersections, measured physiological responses such as brain wave, sight, driving speed, and so on by using state-of-the-art measuring device. As to concentration brain wave at individual intersections, it was found out that brain wave of testes was higher at main Arterial and accident-prone intersections compared with that of subsidiary Arterial. In addition, it was detected that drivers' visual activity was widely distributed at accident-prone intersections, meaning that it enhanced cautious driving from nearby vehicles. As to major factors causing drivers' workloads, factors from nearby vehicles such as deceleration, acceleration, lane change of nearby vehicles appeared as direct factors causing drivers' workloads, clarifying that these factors were closely related to causes of traffic accidents at intersections. Results of this study are expected to be used as basic data for evaluation of safety at intersections in consideration of physiological response of drivers.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.270-277
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• 2008

The development of a face robot basically targets very natural human-robot interaction (HRI), especially emotional interaction. So does a face robot introduced in this paper, named Buddy. Since Buddy was developed for a mobile service robot, it doesn't have a living-being like face such as human's or animal's, but a typically robot-like face with hard skin, which maybe suitable for mass production. Besides, its structure and mechanism should be simple and its production cost also should be low enough. This paper introduces the mechanisms and functions of mobile face robot named Buddy which can take on natural and precise facial expressions and make dynamic gestures driven by one laptop PC. Buddy also can perform lip-sync, eye-contact, face-tracking for lifelike interaction. By adopting a customized emotional reaction decision model, Buddy can create own personality, emotion and motive using various sensor data input. Based on this model, Buddy can interact probably with users and perform real-time learning using personality factors. The interaction performance of Buddy is successfully demonstrated by experiments and simulations.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1037-1045
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• 1996

In this paper, the parallel model for stereo camera is employed to find the heralding angle and the distance between a leading vehicle and the following vehicle, BART(Binocular Autonomous Research Team vehicle). Two TDNNs (Time Delay Neural Network) such as S-TDNN and A-TDNN are introduced to control BART. S-TDNN controls the speed of the following vehicle while A-TDNN controls the steering angle of BATR. A human drives BART to collect data which are used for training the said neural networks. The trained networks performed the vehicle tracking function satisfactorily under the same driving conditions performed by the human driver. The neural network approach has good portability which decreases costs and saves development time for the different types of vehicles.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.19-23
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• 2017

This paper presents a lateral driver model with neuromuscular system to evaluate the performance of electric power steering (EPS). Output of most previously developed driver models is steering angle. However, in order to evaluate EPS system, driver model which results in steering torque output is needed. The proposed lateral driver model mainly consists of 2 parts: desired steering angle calculation and conversion of steering angle into steering torque. Desired steering angle calculation part results in steering angle to track desired yaw rate for path tracking. Conversion of steering angle into torque is consideration with neuromuscular system. The proposed driver model is investigated via actual driving data. Compared to other algorithms, the proposed algorithm shows similar pattern of steering angle with human driver. The proposed driver can be utilized to efficiently evaluate EPS system in simulation level.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.743-746
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• 2011

An implementation for surface EMG network analysis and vertical control system of robotic arm is presented in this paper. The transmembranes are simulated by equivalent circuit and cable equation for propagation to be converted to circuit networks. The implementation is realized to be derived from the detecting EMG signal from 3 electrodes, and EMG transmembrane signals of human arm muscles are detected by several surface electrodes, high performance amplifier and filtering, converting analog to digital data and driving a servomotor for spontaneous robotic arm. The system is experimented by monitoring multiple steps vertical control angles corresponding to biceps muscle movement. The experimental results are that the vertical moving control level is measured to around 2 degrees and mean error ranges are lower 5%.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.381-386
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• 2000

Simple spring-damper-mass models have been widely used to understand whole-body vertical biodynamic response characteristics of the seated vehicle driver. However, most previous models have not considered about the non-rigid masses(wobbling masses). A simple mechanical model of seated human body developed in this study included the torso represented by a rigid and a wobbling mass. Within the 0.5-20Hz frequency range and for excitation amplitudes maintained below $5ms^{-2}$, this 4-degree-of-freedom driver model is proposed to satisfy the measured vertical vibration response characteristics defined from a synthesis of published data for subjects seated erect without backrest support. The parameters are identified by using the combinatorial optimization technique, simulated annealing method. The model response was found to be provided a closer agreement with the response characteristics than previously published models.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.196-196
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

• Proceedings of the KIEE Conference
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• 2000.11c
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• pp.452-454
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• 2000

In recent years, the Thin Film Transistor Liquid Crystal Display (TFT-LCD) is used in a variety of products as an interfacing device between human and them. Since TFT-LCDs have trend toward larger Panel sizes and higher spatial and/or gray-scale resolution, pixel charging characteristic is very important for the large panel size and high resolution TFT-LCD pixel characteristics. In this paper, both data line precharging method and line time extension (LiTEX) method is applied to Pixel Design Array Simulation Tool (PDAST) and the pixel charging characteristics of TFT-LCD array were simulated, which were compared with the results calculated by both PDAST In which the conventional device model of a-Si TFTs and gate step method is implemented.