• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2118-2126
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• 2017

The large-scale power system blackouts have indicated that conventional protection relays that based on local signals cannot fit for modern power grids with complicated setting or heavily loaded-flow transfer. In order to accurately detect various faulted lines and improve the fault-tolerance of wide-area protection, a novel multi-section weighed fault matching and detecting algorithm is proposed. The real protection vector (RPV) and expected section protection vectors (ESPVs) for five fault sections are constructed respectively. The function of multi-section weighed fault matching is established to calculate the section fault matching degrees between RPV and five ESPVs. Then the fault degree of protected line based on five section fault degrees can be obtained. Two fault detecting criterions are given to support the higher accuracy rate of detecting fault. With the enumerating method, the simulation tests illustrate the correctness and fault-tolerance of proposed algorithm. It can reach the target of 100% accuracy rate under 5 bits error of wide-area protections. The influence factors of fault-tolerance are analyzed, which include the choosing of wide-area protections, as well as the topological structures of power grid and fault threshold.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Journal of KIISE
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• v.43 no.8
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• pp.848-856
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• 2016

Seeking and recognizing maritime targets are very important tasks for maritime safety. While searching for maritime targets using radar is possible, recognition is conducted without automatic identification system, radio communicator or visibility. If this recognition is not feasible, radar operator must tediously recognize maritime targets using movement features on radar base on know-how and experience. In this paper, to support the radar operator's mission of continuous observation, we propose an algorithm for automatic recognition of an unknown ship using movement features on radar and a method of detecting potential ship related accidents. We extract features from contact range, course and speed of four types of vessels and evaluate the recognition accuracy using SVM and suggest a method of detecting potential ship related accidents through the algorithm. Experimentally, the resulting recognition accuracy is found to be more than 90% and presents the possibility of detecting potential ship related accidents through the algorithm using information of MV Sewol. This method is an effective way to support operator's know-how and experience in various circumstances and assist in detecting potential ship related accidents.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.8
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• pp.29-36
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• 2008

An optical rotary encoder is easy to implement for automation system applications. In particular, the output of the encoder has a digital form pulse, which is also easy to be connected to a popular digital controller. By using an incremental encoder and a counting device, it is easy to measure angular displacement, as the number of the output pulses is proportional to the rotational displacement. This method can only detect the angular placement once a pulse signal comes out of the encoder. The angular displacement detection period is strongly subject to the change of the angular displacement in case of ultimate low velocity range. They have ultimate long detection period or cannot even detect angular displacement at near zero velocity. This paper proposes an algorithm for detecting angular displacement by using a dual encoder system with two encoders of normal resolution. The angular displacement detecting algorithm is able to keep detection period moderately at near zero velocity and even detect constant angular displacement within nominal period. It is useful for motion control applications in case of changing rotational direction at which there occurs zero velocity. In this paper, various experimental results are shown for the angular displacement detection algorithm.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.501-506
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• 2008

The classical snake algorithm has a problem in detecting the boundary of an object with deep concavities. While the GVF method can successfully detect boundary concavities, it consumes a lot of time computing the energy map. In this paper, we propose an algorithm to reduce the computation time and improve performance in detecting the boundary of an object with high concavity. We define the degree of complexity of object boundary as the local curvature. If the value of the local curvature is greater than a threshold value, new snake points are added. Simulation results on several different test images show that our method performs well in detecting object boundary and requires less computation time.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1350-1353
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• 2015

In this paper, we have researched about how to detect the fish robot objects in aquarium. We had used designed fish robots DOMI ver1.0, which had researched and developed for aquarium underwater robot. The model of the robot fish is analysis to maximize the momentum of the robot fish and the body of the robot is designed through the analysis of the biological fish swimming. We are planned to non-external equipment to find the position and manipulated the position using creating boundary to fish robot to detect the fish robot objects. Also, we focused the detecting fish robot in aquarium by using boundary algorithm. In order to the find the object boundary, it is filtering the video frame to picture frames and changing the RGB to gray. Then, applied the boundary algorithm stand of equations which operates the boundary for objects. We called these procedures is kind of image processing that can distinguish the objects and background in the captured video frames. It was confirmed that excellent performance in the field test such as filtering image, object detecting and boundary algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.9-18
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• 2004

This paper presents a vehicle detecting algorithm using microwave system signals. The Proposed algerian decides the breakpoint of signals using the likelihood criteria. The decided signals are segmented and simplified. The proposed searching algorithm uses the Euclid distance from the weighted signal data. We tested the proposed algorithm to compare with the segmentation which is a method using smoothing and edge detection. We confirm that the proposed algorithm is very useful for detecting vehicles by field test.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.173-179
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• 2000

Genetic algorithm(GA) is applied for detecting multiple outliers. GA is a heuristic optimization tool solving for near optimal solution. We compare the performance of GA and the other diagnostic measures commonly used for detecting outliers in regression model. The results show that GA seems to have better performance than the others for the detection of multiple outliers.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1014-1020
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• 1990

Iterative algorithms for detecting the collision of convex objects whose motion is characterized by a path in configuration space are described. They use as an essential substep the computation of the distance between the two objects. When the objects are polytopes in either two or three dimensional space, an algorithm is given which terminates in a finite number of iterations. It determines either that no collision occurs or the first collision point on the path. Extensive numerical experiments for practical problems show that the computational time is short and grows only linearly in the total number of vertices of the two polytopes.