• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.156-162
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• 2005

Particularly in research related to seawater intrusion the change of fluid electrical conductivity is one of major concerns, and effective monitoring can help to optimize a water pumping performance in coastal areas. Special considerations should be given to the mounting of sensors at proper depth during the monitoring design since the vertical distribution of fluid electrical conductivity is sensitive to the characteristics of seawater intrusion zone. This tells us the multi-channel electrical conductivity monitoring is of paramount consequence. It, however, is a rare event when this approach becomes routinely available in that commonly used commercial stand-alone type sensors are very expensive and inadequate for a long term monitoring of electrical conductivity or water level due to their restricted storage and difficulty of real-time control. For this reason, we have developed a real-time monitoring system that could meet these requirements. This system is user friendly, cost-effective, and easy to control measurement parameters - sampling interval, acquisition range, and others. And this devised system has been utilized for the electrical conductivity monitoring in boreholes, Yeonggwang-gun, Korea. Monitoring has been consecutively executed for 24 hours, and the responses of electrical conductivity at some channels have been regularly increased or decreased while pumping up water. It, with well logging data implemented before/after pumping water, verifies that electrical conductivity changes in the specified depths originate from fluid movements through sand layer or permeable fractured rock. Eventually, the multi-channel electrical conductivity monitoring system makes an effective key to secure groundwater resources in coastal areas.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.155-160
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• 2019

In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.86-92
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• 2022

Recently, the bio-healthcare market is enlarging worldwide due to various reasons such as the COVID-19 pandemic. Among them, biometric measurement and analysis technology are expected to bring about future technological innovation and socio-economic ripple effect. Existing systems require a large-capacity battery to drive signal processing, wireless transmission part, and an operating system in the process. However, due to the limitation of the battery capacity, it causes a spatio-temporal limitation on the use of the device. This limitation can act as a cause for the disconnection of data required for the user's health care monitoring, so it is one of the major obstacles of the health care device. In this study, we report the concept of a standalone healthcare monitoring module, which is based on both triboelectric effects and electromagnetic effects, by converting biomechanical energy into suitable electric energy. The proposed system can be operated independently without an external power source. In particular, the wireless foot pressure measurement monitoring system, which is rationally designed triboelectric sensor (TES), can recognize the user's walking habits through foot pressure measurement. By applying the triboelectric effects to the contact-separation behavior that occurs during walking, an effective foot pressure sensor was made, the performance of the sensor was verified through an electrical output signal according to the pressure, and its dynamic behavior is measured through a signal processing circuit using a capacitor. In addition, the biomechanical energy dissipated during walking is harvested as electrical energy by using the electromagnetic induction effect to be used as a power source for wireless transmission and signal processing. Therefore, the proposed system has a great potential to reduce the inconvenience of charging caused by limited battery capacity and to overcome the problem of data disconnection.