• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.129.2-129
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• 2001

Visual effects are important cues for providing occupant s with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant´s posture, therefore, the total body motion must be considered in a graphic simulator. A real time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and acceleration. A multibody system analysis software, MADYMO, was used in the motion analysis of an adult male dummy in the seated position. Position data of the head were collected as inputs to the viewpoint movement. Based on these data, a back- propagation neural network was ...

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.27-43
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• 2020

Purpose - This study explores the impact of the real estate industry on related industries for the perspective of Chinese steel companies. Design/methodology/approach - The impact of housing prices on the 41 listed steel companies' performance was analyzed by using the panel data model. We used two kinds of housing price indexes that are set in the panel data models to estimate the range of the real estate market, driving the performance growth of steel listed companies. Moreover, the net profit of steel companies is used as the dependent variable. To test the stability of the model, ROA used as a dependent variable for the robustness test. Also, to avoid the time trend of housing prices, this paper selects the growth rate of housing prices as the primary research variable. After Fisher-type testings, there is no unit root problem in both independent and dependent variables. Findings - The results indicated that the rise in the housing price has a positive influence on the steel company performance. When the housing price increases by 1%, the net profit of steel enterprises will increase by 5 to 20 million yuan. Research implications or Originality - In this paper, empirical data at the micro-level and panel model are used to quantify China's real estate industry's driving effect on the iron and steel industry, providing evidence from the microdata level. It helps us to understand further the status and role of China's real estate industry in the economic structure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.511-514
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• 2011

Recently, devices such as smartphone and vehicle blackbox and EDR(Evern Data Recorder knowed automotive real-time control and driving data to use OBD-2(in-vehicle network). when devices receive vehicle driving data, communication way use each Wifi, Bluetooth. but if user and driver change device to use OBD-2 connect, the device differ communication network way. and driver buy and change OBD-2 connect. In this paper, to remedy one's shortcomings, there integrate Bluetooth and Wifi network module and design integrability hardware as any another device know vehicle real-time control and driving data with one integrability connect.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.619-624
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• 2006

This paper presents design of a vehicle stop-and-go cruise control strategy based on analyzed results of the manual driving data. Human drivers driving characteristics have been investigated using vehicle driving data obtained from 100 participants on low speed urban traffic ways. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver under low speed stop-and-go driving conditions. Vehicle following characteristics of the cruise controlled vehicle have been investigated using a validated vehicle simulator and real driving radar sensor data.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.2
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• pp.32-38
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• 2006

A vehicle adaptive cruise control strategy based on human drivers' driving characteristics has been investigated. Human drivers driving characteristics have been analyzed using vehicle test data obtained from 125 participants. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would reduce the workload of the human driver. Vehicle following characteristics of the cruise controlled vehicle have been compared to real-world driving radar sensor data of human drivers using a validated vehicle simulator. and compare nominal cruise control and adaptive cruise control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.278-287
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• 2022

With the development of technology, driving simulators have been used in various ways. In driving simulator experiments, scenario creation is essential to increase fidelity, achieve research aims, and provide an immersive experience to the driver. However, challenges remain when creating realistic scenarios, such as developing a database and the execution of scenarios in real-time. Therefore, to create realistic scenarios, it is necessary to acquire real-time data. This study intends to develop a method of acquiring real-time weather and traffic speed information for actual, specific roads. To this end, this study suggests the concatenator for dynamic data obtained from Arduino sensors and public open APIs. Field tests are then performed on actual roads to evaluate the performance of the proposed solution. Such results may give meaningful information for driving simulator studies and for creating realistic scenarios.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.359-364
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• 2012

Real-Time Traversability should be analyzed from the equiped sensors' data in real time for autonomous outdoor navigation. However, it is difficult to find out such traversability that considers the terrain roughness and the vehicle dynamics especially in case of skid type vehicle. The traversability based on real time dynamic analysis was proposed to solve such problem but in navigation with strait driving path. To adapt the method into the navigation with curved driving path, a path following controller should be incorporated into the dynamic model even though it cause the real time problem. In this paper, a dynamic model is proposed to solve the real time problem in the traversability analysis based on real time dynamic simualtion. The dynamic model contains the control dummy which is connected to the vehicle body with a universal joint to follow the curved path without controller. Simulation and experimental results on $6{\times}6$ articulated unmanned ground vehicle demonstrate the method's effectiveness and applicability into the traversability analysis on terrain with bumps.

• International Journal of Automotive Technology
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• v.5 no.2
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• pp.109-114
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• 2004

This paper presents a closed-loop evaluation of the Vehicle Stability Control (VSC) system using a vehicle simulator. Human driver-VSC interactions have been investigated under realistic operating conditions in the laboratory. Braking control inputs for vehicle stability enhancement have been directly derived from the sliding control law based on vehicle planar motion equations with differential braking. A driving simulator has been validated using actual vehicle driving test data. Real-time human-in-the loop simulation results in realistic driving situations have shown that the proposed controller reduces driving effort and enhances vehicle stability.