• Nonlinear Functional Analysis and Applications
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• v.29 no.2
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• pp.307-332
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• 2024

Two inertial extragradient-type algorithms are introduced for solving convex pseudomonotone variational inequalities with fixed point problems, where the associated mapping for the fixed point is a 𝜌-demicontractive mapping. The algorithm employs variable step sizes that are updated at each iteration, based on certain previous iterates. One notable advantage of these algorithms is their ability to operate without prior knowledge of Lipschitz-type constants and without necessitating any line search procedures. The iterative sequence constructed demonstrates strong convergence to the common solution of the variational inequality and fixed point problem under standard assumptions. In-depth numerical applications are conducted to illustrate theoretical findings and to compare the proposed algorithms with existing approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1196-1206
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• 2013

In this paper, based on the information about navigation system of UAV with PTZ camera and 3D topography, algorithm able to show us in real-time UAV's geographical shooting location and automatically calculate superficial measure of the shooting area is proposed. And the method that can automatically estimate whether UAV is allowed to shoot a specific area is shown. In case of an UAV's shooting attempt at the specific area, obtainability of valid image depends on not only UAV's location but also information of 3D topography. As a result of the study, Ground Control Center will have real-time information about whether UAV can shoot the needed topography. Therefore, accurate remote flight control will be possible in real-time. Furthermore, the algorithm and the method of estimating shooting probability can be applied to pre-flight simulation and set of flight route.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.11-18
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• 2008

This study is implementation of attitude control system(ACS - Attitude Control System). for a multi legs robot. This study designs H/W of Inertial Measurement Unit (IMU) and attitude control algorithm S/W. Compare performance with Mtx and MTx in order to verify action performance of this system after implementation, and will verify a system integrated IMU of a multi-legs robot. ACS uses Gyro and an accelerometer and an earth magnetism sensor, and it is a system controlling a roll, pitch angle attitude of an object. Generally, low price MEMS is difficult to calculate a correct situation of an object as an error occurs severely the Inertial sensor. This study implements IMU in order to develop ACS as use MEMS, accelerometer, Gyro sensor and earth magnetism sensor. Design algorithm each a roll, pitch, yaw attitude guaranteeing regular performance, and do poling in a system as include an attitude calculation program in an IMU system implemented. Mixed output of Gyro and an accelerometer, and recompensed a roll, pitch angle, and loaded in this study on a target platform in order to implement the ACS which guaranteed performance more than a continuously regular level, and operated by real time, and did porting, and verified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.342-351
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• 2010

UAV (Unmanned Aerial Vehicle) have an INS(Inertial Navigation System) equipment and also have an electro-optical Equipment for mission. This paper proposes the vision based attitude estimation algorithm using Kalman Filter and Optical flow for UAV. Optical flow is acquired from the movie of camera which is equipped on UAV and UAV's attitude is measured from optical flow. In this paper, Kalman Filter has been used for the settlement of the low reliability and estimation of UAV's attitude. Algorithm verification was performed through experiments. The experiment has been used rate table and real flight video. Then, this paper shows the verification result of UAV's attitude estimation algorithm. When the rate table was tested, the error was in 2 degree and the tendency was similar with AHRS measurement states. However, on the experiment of real flight movie, maximum yaw error was 21 degree and Maximum pitch error was 7.8 degree.

• 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.

• Current Research on Agriculture and Life Sciences
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• v.32 no.3
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• pp.159-163
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• 2014

Agricultural unmanned helicopters have become a new paradigm for aerial application. Yet, such agricultural helicopters require easy and affordable attitude control systems. Therefore, this study presents an affordable attitude measurement system using a DCM (direction cosine matrix) algorithm that would be applied to agricultural unmanned helicopters. An IMU using a low-cost MEMS and an algorithm to estimate the attitude of the helicopter were applied in a gimbals structure to evaluate the accuracy of the attitude measurements. The estimation errors in the attitude were determined in comparison with the true angles determined by absolute position encoders. The DCM algorithm and sensors showed an accuracy of about 1.1% for the roll and pitch angle estimation. However, the accuracy of the yaw angle estimation at 3.7% was relatively larger. Such errors may be due to the magnetic field of the stepping motor and encoder system. Notwithstanding, since the intrinsic behavior of the agricultural helicopter remains steady, the determination of attitude would be reliable and practical.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.114-129
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• 1996

This paper describes the whole development phase for the underwater vehicle actuating system with high hydroload torque disturbance. This includes requirement analysis, system modeling, control algorithm design, real time implementation, test and performance evaluations. As for driving control algorithms, fuzzy logic, variable structure and PD(Proportional-Differential) algorithm were designed and implemented on board controller using a single chip microprocessor. Intel 8797. And test and performance evaluation is carried out both single test and wystem integration test. We could confirm the basic performance of actuating system through the single test and gereral developing work of any actuating systems was finished with a single performance test of actuating system without system integration test. But, we suggested that system integration test be needed. System integration test is carried out using G/C HILS(Guidance and Control Hardware-In-the -Loop Simulation) which is constituted flight motion simulator, load simulator, real time host computer and the related subsystems such as inertial navigation system, power supply system and Guidance and Control Computer etc.. The most important practical contribution of this paper is that full system characteristics such as minimal control effort, enhancement of guidance and autopilot performance by the actuating system using G/C HILS technique are investigated. Through full running G/C HILS, in spite of the passing to single tests, some control algorithm resulted in failure as to stability of full system and system time frame.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.279-286
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• 2021

The purpose of this study is to compare the predictive power of the Bagging and Boosting algorithm of ensemble method based on the motion information that occurs in woman handball matches and to analyze the availability of motion information. To this end, this study analyzed the predictive power of the result of 15 practice matches based on inertial motion by analyzing the predictive power of Random Forest and Adaboost algorithms. The results of the study are as follows. First, the prediction rate of the Random Forest algorithm was 66.9 ± 0.1%, and the prediction rate of the Adaboost algorithm was 65.6 ± 1.6%. Second, Random Forest predicted all of the winning results, but none of the losing results. On the other hand, the Adaboost algorithm shows 91.4% prediction of winning and 10.4% prediction of losing. Third, in the verification of the suitability of the algorithm, the Random Forest had no overfitting error, but Adaboost showed an overfitting error. Based on the results of this study, the availability of motion information is high when predicting sports events, and it was confirmed that the Random Forest algorithm was superior to the Adaboost algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.87-94
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• 2013

UKF (Unscented Kalman Filter) has been used in the nonlinear systems without initial accurate state estimates instead of EKF (Extended Kalman Filter) of the last decade because the UKF has robustness to the large initial estimation error. In the multirate integrated system such as INS (Inertial Navigation System)/GPS (Global Positioning System) integrated navigation system, however, it is difficult to implement the UKF based navigation algorithm in the mid-grade micro-processor due to the large computational burden. To overcome this problem, this paper proposes a MUKF (Modified UKF) that has a reduced computation burden using the basic idea that the change of the provability distribution for the state variables between measurement updates is small in the multi-rate INS/GPS integrated navigation filter. The performance of the proposed MUKF is verified by numerical simulations.