• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.436-439
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• 2011

As mobile digital devices have come into more widespread use, the demand for mobile storage devices have been increasing rapidly and most of digital cameras and camcorders are using SD memory cards. The SD memory card are generally employing a form of copying data into a personal computer after storing user data based on flash memory. The current paper proposes the SD memory card of being capable of storing photograph and image data through network rather than using a method of storing data in flash memory. By delivering data and memory address values obtained through SD Slave IP to network server without sending them to flash memory, one can store data necessary to be stored in a computer's SD memory in real time in a safe and convenient way.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.150-159
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• 2000

In-process diagnosis of the cutting state is essential for the automation of manufacturing systems. Especially when the cutting process becomes unstable it induces self-exited vibrations a frequent case of poor tool life rough surface finish damage to the workpiece and the machine tool itself and excessive down time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time monitoring and controlling chatter. This paper describes the detection method of chatter vibration using cutting force in turning process. In order to detect a chatter vibra-tion the dynamic fluctuation of radial force is analyzed since this components is sensitive to the chatter. The envelope sig-nal of radial force has been calculated by the use of FIR Hilbert transformer and it was useful to classify the chatter signal from the dynamically unstable circumstances. It was found that the mode and the mode width were closely correlated with the chatter amplitude was well. Finally back propagation(BP) neural network have been applied to the pattern recognition for the classification of chatter signal in various cutting conditions. The validity of this systed was confirmed by the experiments under the various cutting conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1741-1747
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• 2010

The development of overload management systems for distribution transformers offers new opportunities for improving the reliability of distribution systems. It allows network planners to optimize the system resource utilization and investment cost. Such an improvement in the flexibility of the distribution network is only possible if the operator has more accurate knowledge of the realtime conditions of distribution transformers. In this paper, we present an improved overload decision system for distribution transformers using realtime monitoring data. Our study can be categorized into two parts: (a) improvement in the criteria for judging the overload conditions of distribution transformers and (b) development of an overload evaluation system using realtime monitoring data. In order to determine the overload criteria, overload experiments are performed on sample transformers; the results of these experiments are used to define the relationship between the transformer overload and the increase in the top-oil temperature. To verify the accuracy of the experimental results, field tests are performed using specially manufactured transformers, the loads and top-oil temperatures of which can be measured. For arriving at online overload decisions, we propose methods whereby the measured load curve can be converted into an overload characteristic curve and the overload time can be calculated for any load condition. The developed system is able to evaluate the overload for individual distribution transformers and calculate the losses using realtime monitoring data.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.199-204
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• 2008

Sensor nodes have various energy and computational constraints because of their inexpensive nature and ad-hoc method of deployment. Considerable research has been focused at overcoming these deficiencies through faster media accessing, more energy efficient routing, localization algorithms and system design. Our research attempts to provide a method of improvement MAC performance in these issues. We show that traditional carrier-sense multiple access(CSMA) protocols like IEEE 802.11 do not handle the first constraint adequately, and do not take advantage of the second property, leading to degraded latency and throughput as the network scales in size, We present more efficient method of a medium access for real-time wireless sensor networks. Proposed MAC protocol is a randomized CSMA protocol, but unlike previous legacy protocols, does not use a time-varying contention window from which a node randomly picks a transmission slot. To reduce the latency for the delivery of event reports, it carefully decides a fixed-size contention window, non-uniform probability distribution of transmitting in each slot within the window. We show that it can offer up to several times latency reduction compared to legacy of IEEE 802.11 as the size of the sensor network scales up to 256 nodes using widely used simulator ns-2. We, finally show that proposed MAC scheme comes close to meeting bounds on the best latency achievable by a decentralized CSMA-based MAC protocol for real-time wireless sensor networks which is sensitive to latency.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.835-843
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• 2017

Humans have often been used gesture to communicate with each other. The communication between computer and person was also not different. To interact with a computer, we command with gesture, keyboard, mouse and extra devices. Especially, the gesture is very useful in many environments such as gaming and VR(Virtual Reality), which requires high specification and rendering time. In this paper, we propose a gesture recognition method based on CNN model to apply to gaming and real-time applications. Deep learning for gesture recognition is processed in a separated server and the preprocessing for data acquisition is done a client PC. The experimental results show that the proposed method is in accuracy higher than the conventional method in game environment.

• Journal of the Korea Society of Computer and Information
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• v.7 no.3
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• pp.115-121
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• 2002

This paper discussed to design system to overcome time, place and service limitation in the case of extracting traffic on active service in the ATM network. There are several specification in this system : for a remote control function, a real-time traffic extraction from ATM link on service without affect in-service, and an O&M(Operating and Maintenance) cost down effect. This paper include the requirement, module structure, operating characteristics and command for the developing function. This product installed at the KT's telephone office. And we have tested the stability, reliability and functionality. As the result, it was verified that this system commercially is abel to use without especial problem. Hereafter, we are improving module structure for the cost down.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.199-208
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• 2022

The world health organization classified the emerging coronavirus (known as Covid-19) as a pandemic after confirming the extent of spread and scale. As a matter of fact, outbreaks of similar scale or even worse have been witnessed throughout history. Thus, the development of prevention strategies exists to protect against such calamaties. One of the widely proven measures that controls the spread of any contagious diseases is social distancing. As a result, this paper will demonstrate the concept of an application "Be Aware" on enabling the implementation of this preventive measure. In particular "Be aware" evaluates the extent of congestion in public places using current time data. The proposed project will use Global Positioning System (GPS), and Application Programming Interface (API), to ensure information accuracy, and the API use Crowdsourcing to collect Real-Time Data (RTD) from the selected places. One line

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2106-2111
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-trans formed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.