• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.486-491
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• 2000

The dicing process cuts a semiconductor wafer to lengthwise and crosswise direction by using a rotating circular diamond blade. But inferior goods are made under the influence of several parameters in dicing such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using neural network in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, five features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision, back-propagation neural network is adopted to classify the dicing process into normal and abnormal dicing, and normal and damaged blade. Experiments have been performed for GaAs semiconductor wafer in the case of normal/abnormal dicing and normal/damaged blade. Based upon observation of the experimental results, the proposed scheme shown has a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 6.5%.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.3
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• pp.65-70
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• 2003

In this paper, an adapted TWC model and its application to the monitoring algorithm are proposed. As TWCs have the different characteristics, the model has to be corrected to diagnose more accurately. In the TWC model oxygen storage and release rate model are adapted to the installed TWC to whose characteristics related. The model learns from the downstream $O_2$ sensor output during the vehicle's operation. From the results, the model is adapted to the Installed TWC's characteristics. using this model, the monitoring algorithm can diagnose the no more accurately. Finally the algorithm is validated with simulations using the data logged from a retail car.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.277-278
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• 2017

Recently, many industrial instruments face the problem of protocol compatibility with the external monitoring and control system. This paper implemented a protocol conversion algorithm for industrial monitoring about communication Data. This algorithm is supported programmable method by user. we experimented the protocol conversion by communication gateway module using implemented algorithm, and obtained good results.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.333-335
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• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.263-264
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• 2007

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.

• Smart Structures and Systems
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• v.20 no.2
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.73-78
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• 2016

In wireless sensor networks, sensors have capabilities of sensing and wireless communication, computing power and collect data such as sound, movement, vibration. Sensors need to communicate wirelessly to send their sensing data to other sensors or the base station and so they are vulnerable to many attacks like garbage packet injection that cannot be prevented by using traditional cryptographic mechanisms. To defend against such attacks, a behavior-based attack detection is used in which some specialized monitoring nodes overhear the communications of their neighbors(normal nodes) to detect illegitimate behaviors. It is desirable that the total sensing area of normal nodes covered by monitoring nodes is as large as possible. The previous researches have focused on selecting the monitoring nodes so as to maximize the number of normal nodes(node coverage), which does not guarantee that the area sensed by the selected normal nodes is maximized. In this study, we have developed an algorithm for selecting the monitoring nodes needed to cover the maximum sensing area. We also have compared experimentally the covered sensing areas computed by our algorithm and the node coverage algorithm.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.791-799
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• 2010

In this paper, integrated navigation algorithm is designed for land transport sector which is needed high accuracy and monitoring program is developed for lane departure warning. High accuracy position information which is possible lane separation is needed for lane departure warning, so position detection algorithm based GPS/DR which combine GPS with dead reckoning is proposed. For the verification of the designed integrated navigation algorithm, we drived to acquire data and showed post-processing experiment results with monitoring program. Vehicle driving movie and aerial photograph in monitoring program is designed to show lane keeping and lane separation.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.50_51
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• 2009

This paper describes the novel real-time embeded Dynamic System Monitor(KDSM) for dynamic device modeling and disturbace monitoring. The KDSM uses the variable resampling technique together with DFT algorithm so that it overcomes the shortcomings of the existing DFT algorithm at the big deviation of network frequency. The suggested algorithm is implemented by using the NI-PXI system, and verified by applying to the generator testing.