• Progress in Superconductivity
• /
• v.4 no.1
• /
• pp.74-79
• /
• 2002

High-speed probe made to test single flux quantum(SFQ) circuits was comprised of semi-rigid coaxial cables and microstrip lines. The impedance was set at 50 $\Omega$to carry high-speed signals without much loss. To do performance test of high-speed probe, we have attempted to fabricate a test chip which has a coplanar waveguide(CPW) structure. Electromagnetic simulation was done to optimize the dimension of CPW so that the CPW structure has an impedance of 50$\Omega$, matching in impedance with the probe. We also used the simulation to investigate the effect of the width of signal line and the gap between signal line and ground plane to the characteristics of CPW structure. We fabricated the CPW structure with a gold film deposited on Si wafer whose resistivity was above $1.5\times$10$_4$$\Omega$.cm. The magnitudes of S／sub 21／ of CPW at 6 ㎓ in simulations and in the actual measurements done with a network analyzer were: -0.1 ㏈ and -0.33 ㏈ (type A),-0.2 ㏈ and -0.48 ㏈ (type B), respectively. Using the test chip, we have successfully tested the performance of high-speed probe made for SFQ circuits. The probe showed the good performance overthe bandwidth of 10 ㎓.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.33 no.2
• /
• pp.113-119
• /
• 2020

The IoT-based sensor network is one of the methods that can be efficiently applied to maintain the facilities, such as bridges, at a low cost. In this study, based on LoRa LPWAN, one of the IoT communications, sensor board for cable tension monitoring, data acquisition board for constructing sensor network along with existing measurement sensors, are developed to create bridge structural health monitoring system. In addition, we designed and manufactured a smart sensor node for LoRa communication and established a sensor network for monitoring. Further, we constructed a test bed at the Yeonggwang Bridge to verify the performance of the system. The test bed verification results suggested that the LoRa LPWAN-based sensor network can be applied as one of the technologies for monitoring the bridge structure soundness; this is excellent in terms of data rate, accuracy, and economy.

• Journal of Advanced Navigation Technology
• /
• v.11 no.1
• /
• pp.79-86
• /
• 2007

In recent, one of the interesting research areas is about quality of network system. Therefore many research centers including ISO are preparing the measuring and evaluating method for network quality. This study will represent an evaluating model for network security based on checklist. In addition, we propose an measuring and evaluating method for network performance. The purpose of two studies is to present the evaluating procedure and method for measuring security of network on set workwill be identified and a measuring method and procedure will be proposed.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.4
• /
• pp.156-162
• /
• 2006

Transmission zones can be defined based on LMPs. Each zone consists of nodes with similar LMPs, and zonal price is determined by average nodal prices in each zone.[1] Network reduction is still important for the analysis of zonal systems under electricity market environments, even though the computing capability of computer system can deal with entire power systems. The Similarity Index is a good performance measure for the network reduction.[2] It can be applied to the network reduction between zones categorized by the nodal prices. This paper deals with a novel network reduction method between zones based on the similarity Index. Line admittances of reduced network were determined by using the least square method. The proposed method was verified by IEEE 39 bus test system.

• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.107-118
• /
• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.04a
• /
• pp.91-96
• /
• 2004

More recently, multiple reference based RTK GPS techniques(VRS : Virtual Reference System) are becoming increasingly important for many precise GPS applications in many countries to overcome the constrained distance limitations of standard RTK systems. The precision of the position solutions of the rover receiver is closely connected with that of the corresponding virtual reference points(VRPs). The objective of this paper is to investigate the accuracy and performance of the VRPs on the test network for static positioning in post processing mode. For this, some VRPs at the inside and outside of test network have made in post mode, and the overall analysis results were presented by comparing the solution for the VRPs from the existed GPS reference station with the true values of the coordinates used to produce the observation data. The results show the reasonable accuracies of VRPs in the network area by using the VRS concept in post mode.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2112-2115
• /
• 2003

In this paper, we describe a SSAS (stepping stones attack simulator) that is automatic tool for testing and evaluation in TCP connection traceback system. The SSAS can pass multiple hosts that are included with hacker, middle-path hosts and victim’s system. And SSAS can also attack through commands to exploit the victim’s system. Usually, hackers do not expose their real attack positions through compromising the middle-path hosts like stepping-stones. Namely, hackers perform the stepping stones attacks in Internet. The SSAS can be utilized by developments and tests of the various countermeasure techniques of hacking. Specially, in this paper, it is used to test the performance of TCP connection traceback system.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.6
• /
• pp.302-310
• /
• 2002

This paper presents an application of a improved branch exchange (IBE) algorithm with a tie branch power (TBP) flow equation to solve the Optimal Routing problem for operation of a radial Power system including power distribution system. The main objective of the Optimal Routing problem usually is to minimize the network real power loss and to improve the voltage profile in the network. The new BE algorithm adopts newly designed methods which are composed by decision method of maximum loss reduction and new index of loss exchange in loop network Thus, the proposed algorithm in this paper can search the optimal topological structures of distribution feeders by changing the open/closed states of the sectionalizing and tie switches. The proposed algorithm has been evaluated with the practical IEEE 32, 69 bus test systems and KEPCO 148 bus test system to show favorable performance gained.

• 한국신재생에너지학회:학술대회논문집
• /
• 2006.06a
• /
• pp.597-601
• /
• 2006

The advantages of solar energy are that it is renewable, infinite supplied and environmentally safe energy source. However, solar energy related products have the problems such as the limitation for installation, problems in maintenance and insufficient reliability which have been the barrier to consumers to satisfy the purchase need for solar heat related products. In this regard, in order to support the solar energy related companies the necessity for various technical information required for the commercialization of new products such as various performance test necessary for the certification of solar energy system, standardization for facilities and products, performance evaluation method and the measurement of performance is suggested. The purpose of this paper is to design the monitoring system for positive tests of system linked household hot water system using solar heat out of various solar energy systems, to establish an analysis and monitoring system for performance maintenance and operation technique, and to configure the centralized detection network by utilizing remote monitoring system. This research also aims at conducting monitoring for operation and performance evaluation in relation to database establishment and analytical evaluation and at describing the method of enhancing the reliability of solar energy system through the development of performance evaluation program.

• Journal of the Korean Society of Radiology
• /
• v.15 no.5
• /
• pp.613-620
• /
• 2021

The purpose of this study is to propose a convolutional neural network model that can classify normal and abnormal(cardiomegaly) in chest X-ray images. The training data and test data used in this paper were used by acquiring chest X-ray images of patients diagnosed with normal and abnormal(cardiomegaly). Using the proposed deep learning model, we classified normal and abnormal(cardiomegaly) images and verified the classification performance. When using the proposed model, the classification accuracy of normal and abnormal(cardiomegaly) was 99.88%. Validation of classification performance using normal images as test data showed 95%, 100%, 90%, and 96% in accuracy, precision, recall, and F1 score. Validation of classification performance using abnormal(cardiomegaly) images as test data showed 95%, 92%, 100%, and 96% in accuracy, precision, recall, and F1 score. Our classification results show that the proposed convolutional neural network model shows very good performance in feature extraction and classification of chest X-ray images. The convolutional neural network model proposed in this paper is expected to show useful results for disease classification of chest X-ray images, and further study of CNN models are needed focusing on the features of medical images.