• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.4
• /
• pp.358-365
• /
• 2017

In this paper, we propose an object recognition method using image information to improve the efficiency of visual servoingfor industrial manipulators in industry. This is an image-processing method for real-time responses to an abnormal situation or to external environment change in a work object by utilizing camera-image information of an industrial manipulator. The object recognition method proposed in this paper uses the Otsu method, a thresholding technique based on separation of the V channel containing color information and the S channel, in which it is easy to separate the background from the HSV channel in order to improve the recognition rate of the existing Harris Corner algorithm. Through this study, when the work object is not placed in the correct position due to external factors or from being twisted,the position is calculated and provided to the industrial manipulator.

• Journal of Korea Multimedia Society
• /
• v.5 no.4
• /
• pp.458-467
• /
• 2002

There has been proposed many simplification algorithms for effectively decreasing large-volumed polygonal surface data. These algorithms apply their own cost function for collapse to one of fundamental simplification unit, such as vertex, edge and triangle, and minimize the simplification error occurred in each simplification steps. Most of cost functions adopted in existing works use the error estimation method based on distance optimization. Unfortunately, it is hard to define the local characteristics of surface data using distance factor alone, which is basically scalar component. Therefore, the algorithms cannot preserve the characteristic features in surface areas with high curvature and, consequently, loss the detailed shape of original mesh in high simplification ratio. In this paper, we consider the vector component, such as surface orientation, as one of factors for cost function. The surface orientation is independent upon scalar component, distance value. This means that we can reconsider whether or not to preserve them as the amount of vector component, although they are elements with low scalar values. In addition, we develop a simplification algorithm based on half-edge collapse manner, which use the proposed cost function as the criterion for removing elements. In half-edge collapse, using one of endpoints in the edge represents a new vertex after collapse operation. The approach is memory efficient and effectively applicable to the rendering system requiring real-time transmission of large-volumed surface data.

• Journal of Navigation and Port Research
• /
• v.32 no.6
• /
• pp.459-464
• /
• 2008

In this paper, we present the model-based fuzzy controller for container cranes which effectively performs set-point tracking control of trolley and anti-swaying control under system parameter and disturbance changes. The first part of this paper focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear model of a container crane. In the second part, we present a design methodology of the model-based fuzzy controller. Sub-controllers are designed using LQ control theory for each subsystem in fuzzy model and then the proposed controller is performed with the combination of these sub-controllers by fuzzy IF-THEN rules. In the results of simulation, the fuzzy model showed almost similar dynamic characteristics compared to the outputs of the nonlinear container crane model. Also, the model-based fuzzy controller showed not only the fast settling time for the change in parameter and disturbance, but also stable and robust control performances without any steady-state error.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.11
• /
• pp.66-76
• /
• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• Journal of Korean Society of Transportation
• /
• v.23 no.2
• /
• pp.161-172
• /
• 2005

This study is to develop Bus Transit Route Analysis and Evaluation Model that can product the quantitative performance measures for Bus Transit Route Network Design. So far, in Korea, there are no so many models that evaluate a variety of other performance measures or service quality that are of concern to both the transit users and operator because of lower-level bus database system and transit route network analysis algorithm's limit. The BTRAEM in this research differ from the previous approach in that the BTRAEM employs a multiple path transit trip assignment model that explicitly considers the transfer and different travel time after boarding. And we develop input-output data structure and quantitative performance measure for the BTRAEM. In the numerical experimental applying BTRAEM to Mandl transit network, We got the meaningful results on performance measure of bus transit route network. In the future, we expect BTRAEM to give a good solution in real transit network.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_2
• /
• pp.819-829
• /
• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.885-901
• /
• 2021

This paper illustratesthe impact of the temporal gap between satellite images and targetsize in mesoscale atmospheric motion vector (AMV) algorithm. A test has been performed using GEO-KOMPSAT-2A (GK2A) rapid-scan data sets with a temporal gap varying between 2 and 10 minutes and a targetsize between 8×8 and 40×40. Resultsshow the variation of the number of AMVs produced, mean AMV speed, and validation scores as a function of temporal gap and target size. As a results, it was confirmed that the change in the number of vectors and the normalized root-mean squared vector difference (NRMSVD) became more pronounced when smaller targets are used. In addition, it was advantageous to use shorter temporal gap and smaller target size for the AMV calculation in the lower layer, where the average speed is low and the spatio-temporal scale of atmospheric phenomena is small. The temporal gap and the targetsize are closely related to the spatial and temporalscale of the atmospheric circulation to be observed with AMVs. Thus, selecting the target size and temporal gap for an optimum calculation of AMVsrequires considering them. This paper recommendsthat the optimized configuration to be used operationally for the near-real time analysis of mesoscale meteorological phenomena is 4-min temporal gap and 16×16 pixel target size, respectively.

• Journal of Internet Computing and Services
• /
• v.22 no.5
• /
• pp.17-26
• /
• 2021

This paper is about a physical layer frame security technique using OFB-style encryption/decryption with AES algorithms on Gigabit Ethernet network. We propose a data security technique at the physical layer that performs OFB-style encryption/decryption with AES algorithm with strong security strength when sending and receiving data over Gigabit Ethernet network. Generally, when operating Gigabit Ethernet network, there is no security features, but data security is required, additional devices that apply this technique can be installed to perform security functions. In the case of data transmission over Gigabit Ethernet network, the Ethernet frames conform to IEEE 802.3 specification, which includes several fields to ensure proper reception of data at the receiving node in addition to the data field. When encrypting, only the data field should be encrypted and transmitted in real time. In this paper, we show that only the data field of the IEEE802.3 frame is encrypted and transmitted on the sending node, and only the data field is decrypted to show the plain text on the receiving node, which shows that the encryption/decryption is carried out correctly. Therefore, additional installation of devices that apply this technique can increase the reliability of the system when security for data is required in Ethernet network operating without security features.

• Nuclear Engineering and Technology
• /
• v.53 no.8
• /
• pp.2534-2546
• /
• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• Journal of Broadcast Engineering
• /
• v.23 no.6
• /
• pp.855-865
• /
• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.