• 한국정밀공학회지
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• 제12권1호
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• pp.15-21
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• 1995

Automatic monitoring of cutting process is one of the most important technologies for increasing the stability and the reliability of unmanned manufacturing system. In this study, the methods which use laser displacement signals and banded energy method are proposed to monitor chatter vibration in the turning process. From this method, the monitoring system of the chatter vibration was developed and investigated its practical possibility. As a result, it is shown by experiments that the chatter vibration can be detected accurately, and it can be widely used in most turning processes.

• 성인간호학회지
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• 제15권2호
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• pp.183-192
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• 2003

Purpose: This study was performed to validate the linkage between nursing diagnoses and nursing interventions by identifying performance and importance of nursing interventions linked to five NANDA nursing diagnoses. Method: Data was collected from 153 staff and head nurses working in 4 hospitals in K city. The results were analyzed using mean, SD and spearman correlation for ranking correlation. Result: The most importantly considered interventions were Medication Administration (IV) for pain, Pain Management for Constipation, Intravenous (IV) Insertion for Diarrhea, treatment, Vital Sign Monitoring for Hyperthermia, and Vital Sign Monitoring for Infection risk. The most frequently performed interventions was Medication Administration (IV) for Pain, Fluid Management for Constipation, Intravenous (IV) Insertion for Diarrhea, Vital Sign Monitoring for Hyperthermia, and Vital Sign Monitoring for Infection: Risk for. The rank correlations between importance and performance were highest in Diarrhea and lowest in Constipation. Conclusion: The above findings can be used to develop a nursing information system which can be used to facilitate documenting the nursing process, and a nursing information system developed by this research process will ultimately contribute to identifying nurses contribution to patient health.

• 한국정밀공학회지
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• 제10권2호
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• pp.66-75
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• 1993

This paper proposes a monitoring method for nondestructive and in-process measurement of the case depth in LASER surface heat treatment process. The method is essentially an eddy-current method, and measures sensing coil's electrical impedance which varies with the changes of the material microstructure due to hardening. To investigate te validity of the proposed method a series of experiments were performed for various hardning depths. The results show that the relationship between the eddy- current sensor output and the changes in case depth is almost linear. This indicates that the eddy-current measuring method can be used as one of the possible monitoring method for mesauring the hardened depth in LASER heat treatment processes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.73.1-73
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• 2001

To utilize the uranium resources contained in the spent nuclear fuel generated from the nuclear power plants, the remote handling and dismantling technology is required. The dismantling process of the sport fuel is the most common process involved in the spent fuel recycling, the rod consolidation and the disposal processes. Since the machine used in the dismantling process are located and operated in isolated space, so called a hot cell, the reliability of machines is very important. To enhance the reliability of the process, in this research, the graphical monitoring system is developed for the fuel dismantling process. The graphic model of each machine is composed of many parts and every parts of the graphic model are given their own kinematics. Using the kinematics and simulating the graphic model in the virtual environment, the validity of the conceptual design can be verified before ...

• 한국소음진동공학회논문집
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• 제26권6_spc호
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 대한산업공학회지
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• 제39권4호
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• 한국광학회:학술대회논문집
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• 한국광학회 2008년도 하계학술발표회 논문집
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• pp.207-208
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• 2008

In this study, we present the error compensation effect of optical monitoring methods for apply of narrow band pass filter and IR-cutoff filter. Then we show the optimization process for apply of optical monitoring methods.

• 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.

• Smart Structures and Systems
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• 제8권1호
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.