• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.51-58
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• 2008

Wireless sensor networks are used by various application areas to implement smart data processing and ubiquitous system. In the recent research of parking management system based on wireless sensor networks, adaptive sensing and efficient data processing are not considered. The effectiveness of implementing these distributed computing devices affects the performance of the applications in parking management. This paper proposes an adaptive sensing using fuzzy wireless sensor for the ubiquitous networks of parking management system. The fuzzy inference system is encoded in the sensor for efficient car presence detection. Moreover, a rule base adaptive module is proposed which wirelessly transmit the new values to each sensor for adapting the environment of car park area. The result of experiments shows that the fuzzy wireless sensor provides more throughputs and less time delays compared to a normal method of data gathering by wireless sensors.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2956-2958
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• 2000

This paper describes the design of a real time traffic data acquisition system using loop detector and piezzo sensor. Loop detector is the cheapest method to measure the speed and piezzo is used to detect the vehicle axle information. A ISA slot based I/O board is designed for data acquisition and PC process the raw traffic data and transfer the data to the host system. Simulation kit is designed with toy car kits. simulated loop detector and piezzo sensor. The data acquisition system collects up to 10 lane highway traffic data such as vehicle count. speed. length axle count. distance between the axles. The data is processed to generate traffic count, vehicle classification, which are to be used for ITS. The system architecture and simulation data is included and the system will be tested for field operation.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.148-156
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• 2003

Many car manufacturers have frequently adopted an aggressive inflator and a lower threshold speed for airbag deployment in order to meet an injury requirement for unbolted occupant at high speed crash test. Consequently, today's occupant safety restraint system has a weakness due to an airbag induced injury at low speed crash event. This paper proposes a new crash algorithm to improve the weakness by suppressing airbag deployment at low speed crash event in case of belted condition. The proposed algorithm consists of two major blocks-crash severity algorithm and deployment logic block. The first block decides crash severity with two levels by means of velocity and crash energy calculation from acceleration signal. The second block implemented by simple AND/OR logic combines the crash severity level and seat belt status information to generate firing commands for airbag and belt pretensioner. Furthermore, it can be extended to adopt additional sensor information from passenger presence detection sensor and safing sensor. A simulation using real crash data for a 1,800cc passenger vehicle has been conducted to verify the performance of proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.160-163
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• 1993

In this paper, design of an automatic grinding robot system for car brazing bead is introduced. Car roof and side panels are joined using brazing, and then the brazing bead is processed so that the bead is invisible after painting. Up to now the grinding process is accomplished manually. The difficulties in automation of the grinding process are induced by variation of position and shape of the bead and non-uniformity of the grinding area due to surface deformation. For each car, the grinding area including the brazing bead is sensed and then modeled using a 2-D optical sensor system. Using these model data, the position and the direction of discrete points on the car, body surface are obtained to produce grinding path for a 6 degrees of freedom grinding robot. During the process, it is necessary to sense the reaction forces continuously to prepare for the unexpected circumstances. In addition, to meet the line cycle time it is necessary to reduce the required time in sensing, signal processing, modeling, path planning and data transfer by utilizing real-time communication of the information. The key technique in the communication and integration of the complex information is obtaining in-field reliability. This automatic grinding robot system may be regarded as a jump in the intelligent robot processing technique.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.297-310
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• 2022

Road facilities such as CCTV poles have potential risk of collision accidents with a car. A collision detection algorithm installed in the facility allows the collision accident to be known remotely. Most collision detection algorithms are operated by simply focusing on whether a collision have occurred, because these methods are used to measure only acceleration data from a 3-axis sensor to detect collision. However, it is difficult to detect other detailed information such as malfunction of the sensor, collision direction and collision strength, because it is not known without witness the accident. Therefore, we proposed enhanced detection algorithm to get the collision direction, and the collision strength from the tilt of the facility after accident using a 9-axis sensor in this paper. In order to confirm the performance of the algorithm, an accuracy evaluation experiment was conducted according to the data measurement cycle and the invocation cycle to an detection algorithm. As a result, the proposed enhanced algorithm confirmed 100% accuracy for 50 weak collisions and 50 strong collisions at the 9-axis data measurement cycle of 10ms and the invocation cycle of 1,000ms. In conclusion, the algorithm proposed is expected to provide more reliable and detailed information than existing algorithm.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.73-80
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• 2007

In this paper, the characteristics of noise and vibration of high speed train is analyzed depending on its speed. The speed is a very important parameter because it can affect the interaction between the train and the environment as well as the characteristics of the train itself. To measure its characteristics, we analyzed the signals from microphones and accelerometers which were attached to the passenger car of the high speed train. The signals from each sensor were stored in the recorder, and then analyzed by using the signal processing program. The data from each sensor are analyzed with the spectrogram. From the spectrogram, we found some distinct characteristics of the passenger car. Also, the characteristics of the noise propagation were inferred from the spectrogram.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.109-120
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• 2017

It is important to estimate objectively in the driving test. Especially, the driving test is examined by totally driving ability, rule observation and situational judgement. For this, a grading automation system for driving test was presented by using GPS, sensor data and equipment operation informations. This system is composed of vehicle mounted module, automatic grading terminal, data controller, data storage and processing server. The vehicle mounted module gathters sensor data in the car. The terminal performs automatic grading using the received sensor data according the driving test criterion. To overcome the misposition of vehicle in the map due to GPS error, we proposed the automatic grading system by map matching method, path deviation and return algorithm. In the experimental results, it was possible to grade automatically, display the right position of the car, and return to the right path under 10 seconds when the vehicle was out of the shadow region of the GPS. This system can be also applied to the driving education.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.8
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• pp.2555-2560
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• 1996

Inspecting the dimensional accuracy of a car-body in assembly line is a very important process to assure high productivity. Now there exist two common inspecting methods in practice. One is to measure a sampled car-body with three dimensional measuring machine, and the other is to measure car-body with three dimensional measuring machine, and the other is to measure car-body in assembly line using many sensors fixed to a large jig frame. The formal method takes too long to inspect a sampled car-body of a same sort, and cannot therefore give an useful error trend for the whole production. On the other hand, the latter lacks flexibility and is very cost-intensive. By using industrial robots and sensors, an in-line Car-Body Measuring(CBM) system which ensured high flexiblity and sufficient accuracy was developed. This CBM cell operates in real production line and measures the check points by the non-contact type using camera and laser displacement sensor(LDS). This system can handle about 15 Measuring points within a cycle time of 40 seconds. A process computer controls whole process such as data acquisition file handling and data analysis. Robot arms changes in length due to ambient temperature fluctuation affecting the measuring accuracy. To compensate this error, a robot arm calibration process was developed.