• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.177-184
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• 2016

This paper presents a strike velocity estimation using the recursive Bayesian filter that operates both correction and prediction models to probabilistically remove noises of sensors and accurately estimate the strike velocity during the real-time experiments. Four different types of bullets such as 5.56 mm M193, 7.62 mm M80, 5.45 mm 7N10 and 7.62 mm MSC were used to validate the proposed method. Compared to the existing method, the proposed method statistically results in higher stability of the strike velocity estimation as well as its reliability for the ballistic limit velocity computation.

• Proceedings of the KIEE Conference
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• 2000.11b
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• pp.375-377
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• 2000

In this paper, voltage controlled APF(Active Power Filter) is introduced to improve power factor and reduce harmonic generated from nonlinear load. The voltage controlled APF which is consisted of inverter and passive filter operates with nonlinear load simultaneously. According to the results of simulation, it is proved that the proposed system has the performance of improving power factor and reducing harmonics.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.97-105
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• 2016

Radiography image detector produces digital images by collecting the charges from the incident x-ray photons and converting it to the voltage signals and then the digital signals. The fixed-pattern noise from the nonuinform amplifier gains in the employed multiple readout circuits. In order to correct the nonuniform gains, a gain-correction technique which is based on the gain map is conventionally used. Since the photon noise remains in the designed gain map, the noise contaminates the gain-corrected images. In this paper, experimental observations are conducted for filtering the remained noise in the gain map, and a filter optimization algorithm is proposed to efficiently remove the noise. For acquired x-ray images from detectors, the filtered gain maps are evaluated and it is shown that optimization algorithm can improve the filtering performance even for relatively strong fixed-pattern noises, which cannot be removed by a simple filter.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.205-215
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• 2012

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.171-174
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• 2006

A large number of service providers in countries all over the world have established GNSS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. In current GNSS network processing, all the geometric related information such as ionospheric free carrier phase ambiguities from all stations and satellites, tropospheric effects, orbit errors, receiver and satellite clock errors are estimated in one centralized Kalman filter. Although this approach provides an optimal solution to the estimation problem, however, the processing time increases cubically with the number of reference stations in the network. Until now one single Personal Computer with Pentium 3.06 GHz CPU can only process data from a network consisting of no more than 50 stations in real time. In order to process data for larger networks in real time and to lower the computational load, a federated filter approach can be considered. The main benefit of this approach is that each local filter runs with reduced number of states and the computation time for the whole system increases only linearly with the number of local sensors, thus significantly reduces the computational load compared to the centralized filter approach. This paper presents the technical aspect and performance analysis of the federated filter approach. Test results show that for a network of 100 reference stations, with the centralized approach, the network processing including ionospheric modeling and network ambiguity fixing needs approximately 60 hours to process 24 hours network data in a 3.06 GHz computer, which means it is impossible to run this network in real time. With the federated filter approach, only less than 1 hour is needed, 66 times faster than the centralized filter approach. The availability and reliability of network processing remain at the same high level.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.165-173
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• 2014

This paper presents a fast and high-quality haze removal method by the modification of the conventional transmission estimation process. In the conventional haze removal method, the halo and blocking artifacts arises while estimating the transmission. In order to effectively reduce the artifacts, the proposed method employs the maximum filter after the calculation of the dark channel. Because of the reduction of the artifacts, the proposed method can simplify the transmission refinement process without sacrificing the quality of the results: this paper proposes to use the single-channel guided filter instead of the multi-channel guided filter. The experimental results demonstrate that the quality of the dehazed results by the proposed transmission correction process is improved and the haze removal speed is increased by up to 59.6%, when compared to the conventional ones.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.601-610
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• 2016

The light-emitting diode (LED) has been used in a variety of industrial fields and for general 0lighting purposes on account of its high efficiency, low power consumption and long lifespan. The LED is driven by direct current; therefore, an AC/DC converter is typically required for its use. An electrolytic capacitor is generally used for stabilizing DC voltage during use of the AC/DC converter. However, this capacitor has a short lifespan, which makes it a limiting factor in LED lighting. Furthermore, LED lighting requires a dimmable control to enable energy savings and fulfil a growing consumer demand. In this paper, the dimmable single-stage power factor correction (PFC) continuous conduction mode (CCM) flyback converter that employs no electrolytic capacitor is presented. The LC resonant filter is alternatively applied to reduce the 120[Hz] ripple on the output. And the optimum value of the LC resonant filter parameters considering both efficient and performance is analysed. Simulation and experimental results verify the satisfactory operation of the converter.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.129-142
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• 2016

In Wyner-Ziv coding, video signals are reconstructed by correcting side information generated by block-based motion estimation/compensation at the decoder. The correction is not always accurate due to the limited number of parity bits and early stopping of low-density parity check accumulate (LDPCA) decoding in distributed video coding, or due to the limited number of measurements in distributed compressive video sensing. The blocking artifacts caused by block-based processing are usually conspicuous in smooth areas and degrade the perceptual quality of the reconstructed video. Conventional deblocking filters try to remove the artifacts by treating both sides of the block boundary equally; however, coding errors generated by block-based processing are not necessarily the same on both sides of the block boundaries. Such a block-wise difference is exploited in this paper to improve deblocking for Wyner-Ziv frameworks by designing a filter where the deblocking strength at each block can be non-identical, depending on the reliability of the reconstructed pixels. Test results show that the proposed filter not only improves subjective quality by reducing the coding artifacts considerably, but also gains rate distortion performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.