• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.121-130
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• 2009

In this paper, Gaussian probability distribution model based multi-sensor data fusion algorithm is proposed for a vehicular location awareness system. Conventional vehicular location awareness systems are operated by GPS (Global Positioning System). However, the conventional system is not working in the indoor of building or urban area where the receiver is difficult to receive the signal from satellites. A method which is combined GPS and UWB (Ultra Wide-Band) has developed to improve this problem. However, vehicular is difficult to receive seamless location information since the measurement systems by both GPS and UWB convert the vehicle's movement information separately at each sensor. In this paper, normalized probability distribution model based Hybrid UWB/GPS is proposed by utilizing GPS location data and UWB sensor data. Therefore the proposed system provides information with seamless and location flexible properties. The proposed system tested by Ubisense and Asen GPS in the $12m{\times}8m$ outdoor environments. As a result, the proposed system has improved performance for accurateness and connection ability between devices to support various CNS (Car Navigation System).

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.21-29
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• 2017

For the efficient and stable operation of autonomous vehicles or advanced driver assistance systems being actively studied nowadays, it is important to determine the positions of the vehicle accurately and economically. A satellite based navigation system is mainly used for positioning, but it has a limitation in signal blockage areas. To overcome this limitation, sensor fusion methods including additional sensors such as an inertial navigation system have been mainly proposed but the high sensor cost has been a problem. In this work, we develop a vehicle position estimation algorithm using in-vehicle sensors and a low-cost imaging sensor without any expensive additional sensor. We determine the vehicle positions using the velocity and yaw-rate of a car from the in-vehicle sensors and the position and attitude of the camera based on the single photo resection process. For the evaluation, we built a prototype system, acquired test data using the system, and estimated the trajectory. The proposed algorithm shows the accuracy of about 40% higher than an in-vehicle sensor only method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.617-625
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• 2022

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.64-69
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• 2017

The rapid growth of algae occurs can induce the algae bloom when nutrients are supplied from anthropogenic sources such as fertilizer, animal waste or sewage in runoff the water currents or upwelling naturally. The algae blooms creates the human health problem in the environment as well as in the water resource managers including hypoxic dead zones and harmful toxins and pose challenges to water treatment systems. The algal blooms in the source water in water treatment systems affects the drinking water taste & odor while clogging or damaging filtration systems and putting a strain on the systems designed to remove algal toxins from the source water. This paper propose the emerging In-Situ self-diagnosable smart algae sensing device with wireless connectivity for smart remote monitoring and control. In this research, we developed the In-Site Algae diagnosable sensing device with wireless sensor network (WSN) connectivity with Optical Biological Sensor and environmental sensor to monitor the water treatment systems. The proposed system emulated in real-time on the water treatment plant and functional evaluation parameters are presented as part of the conceptual proof to the proposed research.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.315-328
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• 2024

Purpose: The purpose of this paper is to present a service construction plan using multiple complex sensor information to detect abnormal situations in urban life safety that are difficult to identify on CCTV. Method: This study selected service scenarios based on actual testbed data and analyzed service importance for local government control center operators, which are main users. Result: Service scenarios were selected as detection of day and night dynamic object, Detection of sudden temperature changes, and Detection of time-series temperature changes. As a result of AHP analysis, walking and mobility collision risk situation services and fire foreshadowing detection services leading to immediate major disasters were highly evaluated. Conclusion: This study is significant in proposing a plan to build an anomaly detection service that can be used in local governments based on real data. This study is significant in proposing a plan to build an anomaly detection service that can be used by local governments based on testbed data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1632-1652
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• 2011

This paper considers the object tracking problem in three dimensional (3-D) space when the azimuth and elevation of the object are available from the passive acoustic sensor. The particle filtering technique can be directly applied to estimate the 3-D object location, but we propose to decompose the 3-D particle filter into the three planes' particle filters, which are individually designed for the 2-D bearings-only tracking problems. 2-D bearing information is derived from the azimuth and elevation of the object to be used for the 2-D particle filter. Two estimates of three planes' particle filters are selected based on the characterization of the acoustic sensor operation in a noisy environment. The Cramer-Rao Lower Bound of the proposed 2-D particle filter-based algorithm is derived and compared against the algorithm that is based on the direct 3-D particle filter.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.43-52
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• 1998

In this paper, we have investigated the effects of geographic information on the performance of multiple ground target tracking system using multiple sensors. Geographic information is utilized in two cases: association and masking target measurement. Virtually no improvement is observed to the overall performance of tracking system when we applied mobility to the association procedure. Masking target measurement based on mobility produces desirable result that the number of false tracks is reduced. Since geographic information can be regarded as an additional sensor in sensor fusion paradigm, careful usage is required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1706-1725
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• 2024

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation. This network is a lightweight network with a small number of parameters for small image segmentation datasets. However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements. In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper. We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder. Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information. The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net. In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions. Finally, we validate our proposed MEDU-Net+ MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets. The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.