• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.495-499
• /
• 2008

Last year UMTS (UMTS: Universal Mobile Telecommunication System) 3G service started and these days 3 its service subscribers sharply increased. Now totally we have been increasing 13 million subscribers, every month 0.8 million people join 3G Service. MSC (Mobile Switching Center) is most important equipment in 3G system, so we call it 'core' network. Higher capacity MSC required to accommodating 1 million subscribers. It is very important whether MSC can accommodate maximum subscriber or not. So systematic analysis, reliable test results are required. This article presents WCDMA MSC performance evaluation case. This would be some direction for designing and developing some communication equipment. This Case Study demonstrates our MSC system performance.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.51 no.8
• /
• pp.361-367
• /
• 2002

Many failures in high voltage equipment are preceded by partial discharge activity. In this paper deals with the application of the high frequency partial discharge measurement technique in motorette. HFPD measurement is very effective method to detect the PD occurred in motorette which is the called name of test specimen for accelerating test of stator winding[1] In this study, CT type HFPD sensor is used to detect the partial discharges and a measured HFPD pattern is analyzed by fractal mathematics. The neural network algorithm is used to pattern recognition and ageing diagnosis. As a result of this study, the fractal dimensions are increased along to applied voltage and HFPD pattern recognition using neural network shown excellent recognition rate. Also, the ageing diagnosis of motorette has been Possible.

• Nuclear Engineering and Technology
• /
• v.49 no.5
• /
• pp.989-995
• /
• 2017

Distributed Control System (DCS) communication networks, which use Fast Ethernet with redundant networks for the transmission of information, have been installed in digitalized nuclear power plants. Normally, failover tests are performed to verify the reliability of redundant networks during design and manufacturing phases; however, systematic integrity tests of DCS networks cannot be fully performed during these phases because all relevant equipment is not installed completely during these two phases. In additions, practical verification tests are insufficient, and there is a need to test the actual failover function of DCS redundant networks in the target environment. The purpose of this study is to verify that the failover functions works correctly in certain abnormal conditions during installation and commissioning phase and identify the influence of network failover on the entire DCS. To quantify the effects of network failover in the DCS, the packets (Protocol Data Units) must be collected and resource usage of the system has to be monitored and analyzed. This study introduces the use of a new methodology for verification of DCS network failover during the installation and commissioning phases. This study is expected to provide insight into verification methodology and the failover effects from DCS redundant networks. It also provides test results of network performance from DCS network failover in digitalized domestic nuclear power plants (NPPs).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1431-1445
• /
• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.4
• /
• pp.122-128
• /
• 2017

In order to simplify the wiring of construction heavy equipment, researches on PLC(Power Line Communication)-CAN(Controller Area Network) communication module are actively proceeding. Even if a PLC-CAN communication module is developed, a simulator capable of judging whether the PLC-CAN communication module is operating normally is needed. In this paper, we designed and developed a simulator that can measure the status of PLC-CAN communication module. We analyzed the characteristics of the power line communication frequency band by analyzing the characteristics of the power line and compared the noise characteristics with the passenger car in order to characterize the heavy equipment noise.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.2
• /
• pp.205-211
• /
• 2008

This paper describes the method aimed at establishing the equipment measuring the parameter of Cable Network by Best proper Cable Modem and PDA through RS232 interface. In this paper, we propose our creative experimental configuration and develope the Cable Modem (DOCSIS 2.0) superior more performing than the referred Cable Modem (DOCSIS 1.0, 1.1) and the measuring equipment of HFC network compositing of Cable Modem and PDA through RS232 interface. This equipment analyzes Upstream (U/S) and Downstream (D/S) Signal quality and generates the signal of Upstream by CW signal. The paper also provides the experimental results to check the Best Proper Cable Modem and the displayed screen for parameters for SNR, BER and the demodulated IQ diagram of 256QAM through LCD of PDA. Henceforth, it'll be possible to support a variety of the functions for E-mail, Internet, the speed test of WEB connection and the transmission of the measured result real time by PDA.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.230-235
• /
• 2008

Recently, LG chemical corporation developed new material called HYPERIER, which has an excellent barrier characteristic. It has many layers which are made of nano-composite within LDPE(Low-Density Poly Ethylene). In order to guarantee the quality of the final product from the production line, a certain test equipment is required to investigate the existence of layers inside the HYPERIER. In this work, ultrasonic sensor based test equipment for investigating the existence of inner layers is proposed. However, it is a tedious job for human operators to check the existence by just looking at the resounding waveform from ultrasonic sensor. Therefore, to enhance the performance of the ultrasonic test equipment, Fast Fourier Transform(FFT) and Principle Components Analysis(PCA) and Back-Propagation Neural Network(BPNN) are utilized which is used for classification of Quality. To verily the feasibility of the proposed scheme, some experiments are executed.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.5
• /
• pp.713-718
• /
• 2014

DC urban railway power system consists of DC power network and AC power network. The DC power network supplies electric power to railway vehicles and the AC power network supplies electric power to station electric equipment. Recently, because of power consumption reduction and peak load shaving, intelligent measurement of regenerative energy and renewable energy adapted on DC urban railway is required. For this reason, DC smart metering system for DC power network shall be developed. Therefore, in this paper, DC voltage sensor, current sensor, and DC smart meter were developed and evaluated by performance test. DC voltage sensor was developed for measuring standard voltage range of DC urban railway, and DC current sensor was developed as hall effect split core type in order to install in existing system. DC smart meter possesses function of general intelligent electric power meter, such as measuring electricity and wireless communication etc. And, DC voltage sensor showed average 0.17% of measuring error for 2,000V/50mA, and current sensor showed average 0.21% of measuring error for ${\pm}2,000V/{\pm}4V$ in performance test. Also DC smart meter showed maximum 0.92% of measuring error for output of voltage sensor and current sensor. In similar environment for real DC power network, measuring error rate was under 0.5%. In conclusion, accuracy of DC smart metering system was confirmed by performance test, and more detailed performance will be verified by further real operation DC urban railway line test.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.239-240
• /
• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.5
• /
• pp.1482-1488
• /
• 2014

The reliability evaluation of the electrical distribution networks (EDN) requires sufficient consideration of the effects of planned outages. The planned outages of the EDN can be divided, by outage models and their effects on the reliability into two major categories: by equipment and by feeder. After studying the characteristics of different categories of planned outages, this paper expands the classification of load points by outage time from 4 types to 7 types and defines corresponding reliability parameters for the different types. By using the section algorithm, this paper proposes a reliability evaluation technique of EDN considering equipment random failures and two categories of planned outages. The proposed technique has been applied to the RBTS-BUS6 test system and some practical EDNs in China. The study results demonstrate that the proposed technique is of higher practical value and can be used for evaluating the reliability performance of EDN more efficiently considering the planned outages.