• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.2
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• pp.182-192
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• 2014

Objectives: The aim of this study is to analyze the current status of possession of measurement and analytical instruments at work environment monitoring organizations and their relationships with human resources, including the number of professional engineers and evaluation scores resulting from evaluation programs in 2012. Materials: Data for measurement and analytical instruments were gathered by inspectors who had been assigned by the Korea Occupational Safety and Health Agency(KOSHA) and the Ministry of Employment and Labor(MoEL) during the evaluation program for 2012. Data for 11 monitoring instruments and nice pieces of analytical equipment were collected from 103 organizations. Additional data such as the type of service provides and the number of professional engineers employed were also recorded by the inspectors. Evaluation scores could be acquired from KOSHA. Results: Comprehensive Occupational Health Service Providers showed good operation quality, while University or Hospital Subsidiary and Work Environment Monitoring Organizations recorded relatively poor results. Evaluation scores correlated well with the possession of measurement instruments and human resources for each organization. High yields provided by professional engineers also showed statistically-associated contributions to evaluation scores and monitoring instrument possession. Compared with monitoring instruments, the amount of analytical equipment had little positive impact on organizations' competence. Conclusions: The evaluation results for domestic monitoring organizations revealed that human resources, possession of instruments, and the quality of employees were critical factors for operating the corporations. Each organization should give considerable effort to improving their ability to strengtheninternal quality, resulting in high-yield production for workers and employers by providing improved workplace monitoring services.

• Food Science and Biotechnology
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• v.14 no.6
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• pp.836-840
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• 2005

Extraction characteristics of Bonus species of Brassica oleracea var. capita and functional properties of corresponding extract were monitored by response surface methodology (RSM). Maximum extraction yield of 44.07% was obtained at ratio of solvent to sample of 27.94 mL/g, ethanol concentration of 24.35%, and extraction temperature of $55.21^{\circ}C$. At ratio of solvent to sample, ethanol concentration, and extraction temperature of 21.11 mL/g, 58.53%, and $68.83^{\circ}C$, respectively, maximum electron-donating ability was 48.44%. Maximum inhibitory effect on tyrosinase was 68.94% at ratio of solvent to sample, ethanol concentration, and extraction temperature of 24.08 mL/g, 10.49%, and $78.71^{\circ}C$, respectively. Superoxide dismutase (SOD) showed maximum pseudo-activity of 24.78% at ratio of solvent to sample of 22.66 mL/g, ethanol concentration of 45.69%, and extraction temperature of $93.81^{\circ}C$. Based on superimposition of four-dimensional RSM with respect to extraction yield, electron-donating ability, and pseudo-activity of SOD, optimum ranges of extraction conditions were ratio of solvent to sample of 20-30 mL/g, ethanol concentration of 35-65%, and extraction temperature of $50-80^{\circ}C$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.623-629
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• 2012

During the construction of crossing engineering one of the important measures to ensure the safety of subway operation is the implementation of deformation surveying to the existing subway tunnel. Guangzhou new subway line 2 engineering which crosses the existing tunnel is taken as the background. How to achieve intelligent and automatic deformation surveying forecast during the subway tunnel construction process is studied. Because large amount of surveying data exists in the subway construction, deformation analysis is difficult and prediction has low accuracy, a subway intelligent deformation prediction model based on the PBIL and support vector machine is proposed. The PBIL algorithm is used to optimize the exact key parameters combination of support vector machine though probability analysis and thereby the predictive ability of the model deformation is greatly improved. Through applications on the Guangzhou subway across deformation surveying deformation engineering the prediction method's predictive ability has high accuracy and the method has high practicality. It can support effective solution to the implementation of the comprehensive and accurate surveying and early warning under subway operation conditions with the environmental interference and complex deformation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.788-796
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• 2001

VR(Virtual reality) technologies have given engineers the ability to design, test, and evaluate engineering systems in a virtual environment. The virtual plant is the highlight of the application of the VR technology to plant engineering. Plant design, maintenance, control, management, operation are integrated in the virtual plant. The VR monitoring system including the concept of the virtual plant is developed to replace a current control room that has number of gages and warning lamps in two-dimensional panels which shows the operating status of a plant. The operating status of the plant is displayed in the VR monitoring system through the realistic computer graphics. Sophisticated, realistic and prompt control becomes possible. The VR monitoring system consists of advanced visualization, walk-through simulation and navigation. In the virtual environment, a user can navigate and interact with each component of a plant. In addition, the user can access the information by just clicking interesting component. The VR monitoring system is operated with various modules, such as (1) virtual plant constructed with Graphic Management System (GMS), (2) Touch & Tell System, and (3) Equipment DB System of Part. In order to confirm the usefulness of the VR monitoring system, a pilot gas plant which is currently being used for plant operator training is taken as application. The end of the paper gives an outlook on the future work and a brief conclusion.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.3
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• pp.301-308
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• 2011

International Maritime Organization (IMO) proposed the Energy Efficiency Operation Indicator (EEOI) in 2005 and the Energy Efficiency Design Index (EEDI) in 2008 so as to address emission concern and regulation. Likewise, Ship Energy Efficiency Management Plan (SEEMP) and Greenhouse Gas (GHG) monitoring and management are also becoming an issue lately. This paper introduces the energy efficiency design index (operation indicator) monitoring system (EDiMS) software can continuously monitor $CO_2$, $NO_x$, $SO_x$, and PM values emitted from ship. The accurate inventory of ships GHG can be obtained from base of emission result during the engine shop test trial and the actual monitoring of shaft power and ship speed. In addition, the ability to store all exhaust emission and engine operation data can be applied as the useful tool of the inventory work of air pollution and ship energy management plan for the mitigation or reduction of ship emissions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.625-631
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• 2002

In the previous study, a polishing robot system was developed to automate the polishing process and to cope with the shortage of skilled workers. This polishing robot system has several advantages: reduced time for setting polishing work, decreased labor costs, effective operation, continuous polishing work without an operator, improved machine accuracy, and the ability to polish a free curved surface die. However, still the problem remains that a worker must stay to monitor the polishing process in the poor working conditions for a long time. Nowadays some advanced manufacturing companies need to find a way to check the performance of their production equipments and plants from remote sites. Thus, this study constructed the communication network and developed the monitoring programs (a servo program and a client program) to operate the polishing robot from remote sites. Using these programs, workers are able to monitor and control the polishing robot on the web page, in any place where internet service is possible. To guarantee a stable operation in spite of a variable computer operating environment, the monitoring system is implemented in Java. The experimental results showed that the developed monitoring programs provided a stable communication.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.801-806
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• 2004

The pilot study was conducted to (i) investigate the ability of various membrane integrity monitoring methods to detect changes in membrane integrity during operation, and (ii) determine the impact of membrane damage on microbial removal by microfiltration. Two variations of air pressure hold tests were investigated for direct integrity monitoring: pressure decay (PD) and diffusive air flow (DAF) tests which are most commonly used integrity tests for microfiltration (MF) membranes. Both PD and DAF tests were sensitive enough to detect one damaged fiber out of 66,000 under field operaing conditions. Indirect integrity monitoring such as turbidity and particle counting, however, responded poorly to defects in membrane systems. Microbial challenge study was performed using both new and deliberately damaged membranes, as well as varying the state of fouling of the membrane. This study demonstrated that MF membrane with nominal pore size $0.2{\mu}m$ was capable of removing various pathogens including coliform, spore, and cryptosporidium, at the level required by drinking water regulations, even when high operating pressures were applied. A sharp decrease in average log reduction value (LRV) was observed when one fiber was damaged, emphasizing the importance of membrane integrity in control of microbial contamination.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.15-22
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• 2011

U-Healthcare service is a real-time service using the vital signs which are continuously transferred from monitoring sensors attached to mobile patients under the wireless network environments. It should monitor the health condition of mobile patients everywhere at any time. In this paper, we have improved two features of the three layered mobile patient monitoring system with load balancing ability. First, the simulation process has been improved by allowing the number of related nodes to be changed. Secondly, we have modified S node to which queue is added to reduce the loss rate of collecting data from patients during the delay of S node process. And the data from the patient with high priority can be transferred to the server immediately through the filtering function. Furthermore, we have solved the problem of redundancy in sharing information among S nodes by differentiating process time to each S node. By performing a DEVS Java-based system simulation, we have verified the efficiency of this improved system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.