• 한국CDE학회논문집
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• 제4권4호
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• pp.295-311
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• 1999

An increasing attention is paid to STEP-NC, the next generation CNC controller interfacing STEP-compatible data. In this paper, we first propose an Architecture for the STEP-NC (called FBCC: Feature Baled CNC Controller) accepting feature code compatible with STEP-data, and executing NC motion feature by feature while monitoring the execution status. The main thrust of the paper has been to develop an automatic on-line tool path generation and modification scheme for milling operation. The tool path it generated iota each feature by decomposing into a finite number of primitive features. The key function in the new scheme is haw to accommodate unexpected execution results. In our scheme, the too1 path plinker is designed to have a tracing capability iota the tool path execution so that a new path can be generated from the point where the operation is stopped. An illustrative example is included to show the capability of the developed algorithm.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.703-704
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• 2013

• Nuclear Engineering and Technology
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• 제49권7호
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• Journal of Information Processing Systems
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• 제5권2호
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• pp.77-86
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• 2009

In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling and feature extraction from bio-potentials. Such a system could increase the quality of life and significantly lower healthcare costs for everyone in general, and for the elderly and those with disabilities in particular.

• 한국정밀공학회지
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• 제11권1호
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2005년도 춘계학술대회
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• pp.388-391
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• 2005

In this paper, Cell voltage monitoring method is studied for fault detection of PEMFC(Proton Exchange Membrane Fuel Cell) for FCEV(fuel cell electric vehicle). To measuring several hundred of cells in fuel cell stack, The demanded feature of hardware and software is studied and several types are analysed. Finally, $3.26\%$ maximum measuring error is acquired and verified experimentally.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.43-52
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• 2024

Health Monitoring System is a sophisticating technology and another way to the normal/regular management of the health of the patient. This Health Monitoring Mobile Application is a contribution from our side to the public and to the overall health industry in Pakistan. With the help of Health mobile application, the users will be able to store their medical records, prescriptions and retrieve them later. The users can store and keep track of their vital readings (heart rate, blood pressure, fasting glucose, random glucose). The mobile application also shows hospitals that are nearby in case the user wants to avail of any medical help. An important feature of the application is the symptoms-based disease prediction, the user selects the symptoms which he has and then the application will name certain diseases that match those symptoms based on relevant algorithms. The major advances and issues have been discussed, and as well as potential tasks to health monitoring will be identified and evaluated.

• Advanced Composite Materials
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• 제16권2호
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• pp.135-149
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• 2007

In the automobile industry, structural health monitoring of fiber reinforced polymer composite parts is a widespread need for maintenance before breakdown of the functional elements or a complete vehicle. High performance sensors are generally used in many of the structural health monitoring operations. Within this study, a carbon fiber sewing thread has been used as a low cost laminate failure sensing element. The experimentation plan was set up according to the electrical conductance and flexibility of carbon fiber threads, advantages of preforming operations, and sewing mechanisms. The influence of the single thread damages by changing the electrical resistance and monitoring the impact location by using carbon thread sensors has been performed. Innovative utilization of relatively cost-effective carbon threads for monitoring the delamination of metallic inserts from the basic composite laminate structure is a highlighting feature of this study.