• Korean Journal of Hydrosciences
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• v.7
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• pp.9-19
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• 1996

The purpose of this study is to investigate the influence of the position of lake upon groundwater fluxes on a lake watershed, and to provide for the monitoring network design to survey the exchange relations between groundwater and lake water. Three kinds of hypothetical flow through lakes, which are located at the upper, middle, and lower portion of a watershed were considered. Groundwater flow for each case was numercally simulated under three-dimensional steady state conditions. The exchange rates of the groundwater, the amounts of recharge and discharge, and groundwater fluxes between lake and groundwater in a watershed system with a lake were clarified.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.63-68
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• 2016

Environmental downtime produces a significant cost to organizations and makes them unable to do business because what happens in the data center affects everyone. In addition, the amount of electrical energy consumed by data centers increases with the amount of computing power installed. Installation of physical Information Technology and facilities related to environmental concerns, such as monitoring temperature, humidity, power, flood, smoke, air flow, and room entry, is the most proactive way to reduce the unnecessary costs of expensive hardware replacement or unplanned downtime and decrease energy consumed by servers. In this paper, we present remote system for monitoring datacenter implementing using open-source hardware platforms; Arduino, Raspberry Pi, and the Gobetwino. The sensed data displayed through Arduino are transferred using Gobetwino to the nearest host server such as temperature, humidity and distance every time an object hitting another object or a person coming in entrance. The raspberry Pi records the sensed data at the remote location. The objective of collecting temperature and humidity data allows monitoring of the server's health and getting alerts if things start to go wrong. When the temperature hits $50^{\circ}C$, the supervisor at remote headquarters would get a SMS, and then they would take appropriate actions to reduce electrical costs and preserve functionality of servers in data centers.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Transactions on Electrical and Electronic Materials
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• v.14 no.2
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• pp.71-77
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• 2013

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.69-76
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• 2008

Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.229-235
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• 2011

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.

• Membrane and Water Treatment
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• v.10 no.1
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• pp.99-104
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• 2019

In Korea, nonpoint pollutants have a significant effect on rivers' water quality, and they are discharged in very different ways depending on rainfall events. Therefore, preparing an optimal countermeasure against nonpoint pollutants requires much monitoring. The present study was conducted to help prepare a method for installing an automatic nonpoint pollutant measurement system for the cost-effective monitoring of the effect of nonpoint pollutants on rivers. In the present study, monitoring was performed at six sites of a river passing through an urban area with a basin area of $454.3km^2$. The results showed that monitoring could be performed for a relatively long time interval in the upstream and downstream regions, which are mainly comprised of forests, regardless of the rainfall amount. On the contrary, in the urban region, the monitoring had to be performed at a relatively short time interval each time when the rainfall intensity changed. This was because the flow rate was significantly dependent on the rainfall's intensity. The appropriate sites for installing an automatic measurement system were found to be a site before entering the urban region, a site after passing through the urban region, and the end of a river where the effects of nonpoint pollutant sources can be well-decided. The analysis also showed that the monitoring time should be longer for the rainfall events of a higher rainfall class and for the sites closer to the river end. This is because the rainfall runoff has a longer effect on the river. However, the effect of nonpoint pollutant sources was not significantly different between the upstream and the downstream in the cases of rainfall events over 100 mm.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.208-214
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• 2010

This study aimed at the introduction of desktop method for assessment of environmental flows developed by International Water Management Institute (IWMI) recently and its application to Geum river basin. This scheme simulated the influence on aquatic ecosystem caused by watershed development and in turn the decrease of water quantity keeping the river's own flow regime. It was found to be as very effective method although it had simple structure. Flow duration curves for different environmental classes at Sutong and Gongjoo sites were estimated according to the natural conditional scenario of Geum river basin and the results were relatively compared well with the previous studies. The behaviors of monthly average runoff time series of both sites showed the level of A class. The results of this study would provide the fundamental data to establish the future plans of monitoring or management for aquatic ecosystem of Geum river basin.