• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1473-1483
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• 2014

A public restroom is different from a household toilet in terms of location and a large number of sharing users. In addition, public restroom is usually messy and filthy. Recently, public toilet tends to be clearly managed than before, but it still has hygienic and clear problems. In this paper, we propose a machine-to-machine (M2M)-based public restroom management system to solve these problems, in which the system with a wireless communication device sends the status information of the toilet, such as blockage or trouble detected by a sensor, to the manager of the restroom at a remote location. In particular, we have developed a prototype management system for public restroom taking into account several system requirements, and verified the basic operations and performance of the management system. With the application of the system to public facilities, it will furnish users with more pleasant environments by restroom administrators who can respond effectively to the troubled toilet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2396-2402
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• 2015

Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Fire Science and Engineering
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• v.30 no.4
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• pp.20-26
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• 2016

When the fire takes place in Nuclear Powr Plants(NPPs), the reactor should achieve and maintain safe shut-down conditions and minimize the radioactive material released to the environment. The U.S. Nuclear Regulatory Commission (NRC) has issued numerous generic communications related to fire protection over the past 20 years, after it issued its requirements in the Fire Protection Rule set forth in Title 10, Section 50.48 of the Code of Federal Regulations (10 CFR 50.48) and Appendix R to the 10 CFR 50. The and Nuclear Energy Institute (NEI) has developed a Methodology for Risk Informed Fire Safe-Shutdown Analysis, which is related to the Deterministic Method for Multiple Spurious Operations solutions. The aim of this study was to identify, achieve, and maintain Post-Fire Safe-Shutdown of the Main Control Room (MCR) of the CANDU reactor, even though one train of the multiple Safety Structures, Systems, and Components (SCCs) fail by the technical specification and analysis method.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.169-176
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• 2010

Nonstationary power quality(PQ) signals which the Sag, Swell, Impulsive Transients, and Harmonics make sometimes the operations of the industrial power electronics equipment, speed and motion controller, plant process control systems in the undesired environments. So, this PQ problem might be critical issues between power suppliers and consumers. Therefore, We have studied the PQ recognition system in order to acquire, analyze, and recognize the PQ signals using the software, i.e, MATLAB, Simulink, and CCS, and the hardware. i.e., TMS320C6713DSK(TI), The algorithms of the PQ recognition system in the Wavelet transforms and Backpropagation algorithms of the neural networks. Also, in order to verify the real-time performances of the PQ recognition system under the environments of software and hardware systems, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances of average 99%.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1043-1051
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• 2008

In polyolefin processes melt index (MI) is the most important controlled variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a lot of MI estimation and correlation methods have been proposed. In this work a new dynamic MI estimation scheme is developed based on system identification techniques. The empirical MI estimation equation proposed in the present study is derived from the $1^{st}$-order dynamic models. Effectiveness of the present estimation scheme was illustrated by numerical simulations based on plant operation data including grade change operations in high density polyethylene (HDPE) processes. From the comparisons with other estimation methods it was found that the proposed estimation scheme showed better performance in MI predictions. Using the model predictive control method based on the present dynamic MI estimation model, MI values are estimated and compared with those of MI setpoints. From the numerical simulation of the proposed control system, it was found that significant reduction of transition time and the amount of off-spec during grade changes were achieved.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.362-371
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• 2008

Generally, a PLM education program in university consists of lectures of theory, software lab and software development raining as an advanced subject. Most industries want more than these, such as practical problem solving capabilities, teamwork skills and engineering communications including human relationship, rhetoric, technical writing, presentation and etc. Problem-Based Learning is a problem-stimulated and student-centered teaming method, and an innovative education strategy for collaborative and self-directed learning by applying real world problems. Education paradigm changes from "teaching" to "learning" accomplished by team working, and students are encouraged to develop, present, explain and defense their ideas, suggestions or solutions of a problem, and the "cooperative teaming" proceeds spontaneously during team operations. Co-operative education program is an into-grated academic model and a structured educational program combining classroom learning with productive work experience in a field related to a student's academic or career goals. Based on the partnership between academic institutions and industries, students are engaged in real and productive "work" in the industry, in contrast with merely observing. In this paper, PBL with Co-op program is suggested as an effective approach for PLM education, and we made and operated a PBL-based education course with industry co-op program. The Co-op education in industry accompanied with the PBL course in university can improve practical problem solving capabilities of students, including modeling and management of P3R(Product, Process, resource and Plant) using commercial PLM software tools. By the result, we found this to be an effective strategy for helping students, professors and industries succeed in engineering education, especially PLM area.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.5
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• pp.587-596
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• 2016

This study attempts to draw factors for an analysis of the operation effect of a rechlorination facility and autodrain equipment for residual chlorine equalization by installing and operating a rechlorination facility and autodrain equipment in P City and analyzing the practical evaluation method and operation effect. For this purpose, this study selected three indicators for an analysis of the effectiveness of residual chlorine equalization and conducted a comparative analysis before and after the implementation of the residual chlorine equalization. As a result of estimation, (1) the reduction of the residual chlorine concentration range from a water treatment plant to the pipe end was 16.0%; (2) the total reduction of chlorination input was 18.0%; and (3) the reduction of the generation of disinfection by-products was 19.5%. In addition, this achieved enough residual chlorine equalization in the supply process and shows that it could successfully achieve the economic feasibility of investment in equipment and the reduction of the generation of disinfection by-products. Like this, it is judged that the three indicators suggested in this study will be used sufficiently as indicators of an analysis of the effectiveness of residual chlorine equalization according to the operations of the rechlorination facility and autodrain equipment.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.59-67
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• 2009

A study on fugitive dusts was conducted at Saemangeum reclaimed land located in Gim-je area. The monitoring result on the fugitive and suspended dust was significantly affected by the farming activities (harvesting and land cultivation) and vehicles passing nearby the measuring points. The concentration of the fugitive dust generated from the reclaimed land was reduced remarkably by the effect of halophyte present on the ground and the tide embankment. Comparing the data collected in 2006 and 2007, the concentrations of TSP and PM10 decreased by 47.4% and 29.5%, respectively. After harvesting operations at paddy field, TSP increased by 22% while PM10 increased by 54%. The concentration of a Cl- which is a representative ion of sea-salt decreased to about 35% in 2007 compared with 2006. This represents that the inside area change and plant covering rate affected on the decrease of fugitive dust. The correlation analysis for the compounds of topsoil at each measuring point shows that near the coastline is more comparable. The canopy of halophyte in the source area also increased which reduced the fugitive dust remarkably. The dust distribution measured by dust spectrometer at the same point shows that most particles are $0.5{\mu}m$ to 2um size but not greater than $5{\mu}m$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.551-553
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• 2016

Since air compressor, as an essential equipment used in the factory and plant operations, accounts for around 20% of the total domestic electricity consumption, a real time sensor data monitoring based analysis for electricity consumption reduction is important. In particular, flow rates and pressures of these monitored variables has a direct correlation with the power consumption. This paper proposes a method to identify if the measurement error of the flow rate sensor comes from the sensor measurement limit through bivariate classification analysis of the flow rate and power using the EM (Expectation and Maximization) Algorithm and show how to enable more accurate analysis by the correlation between the flow rate and power on the right-censored data.