• Journal of Multimedia Information System
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• v.8 no.2
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• pp.143-146
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• 2021

This work presents an automated transistor biasing scheme for analog integrated circuits. In order to effectively bias the transistor at a desired operating point, the proposed method uses a linearized transistor circuit model along with the curve fitted expressions obtained from the pre-simulated I-V characteristics of the actual transistor. As a result, the transistor size that leads to the desired operating point can be easily determined without heavily relying on the circuit simulator, which will lead to significant design time reduction. Furthermore, the proposed method is applied to an actual amplifier circuit where the design time based on the proposed biasing method showed 10× faster than the conventional design approach using the circuit simulator.

• Current Photovoltaic Research
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• v.10 no.3
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• pp.77-83
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• 2022

Renewable energy is receiving attention again as a way to realize carbon neutrality to overcome the climate change crisis. Among renewable energy sources, the installation of Photovoltaic is continuously increasing, and as of 2020, the global cumulative installation amount is about 590 GW and the domestic cumulative installation amount is about 17 GW. Accordingly, O&M technology that can analyze the power generation and fault diagnose about PV plants the is required. In this paper, a study was conducted to diagnose fault using I-V curves of PV strings and deep learning. In order to collect the fault I-V curves for learning in the deep learning, faults were simulated. It is partial shade and voltage mismatch, and I-V curves were measured on a sunny day. A two-step data pre-processing technique was applied to minimize variations depending on PV string capacity, irradiance, and PV module temperature, and this was used for learning and validation of deep learning. From the results of the study, it was confirmed that the PV fault diagnosis using I-V curves and deep learning is possible.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.87-97
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• 2016

First of all, Fe or/and Mn immobilized granular activated carbons (Fe-GAC, Mn-GAC, (Fe, Mn)-GAC) were synthesized and tested to remove arsenate (As(V)). The results in batch test indicated that Fe-GAC removed As(V) effectively, even though the surface area of Fe-GAC was reduced largely. Moreover, adsorption isotherm test indicated that the experimental data fit well with Langmuir model and the maximum adsorption capacity ($q_{max}$) of Fe-GAC for As(V) was $3.49mg\;g^{-1}$, which was higher than GAC ($2.24mg\;g^{-1}$). In column test, the simulated water, which consisted of As(V), Fe(III), Mn(II) and Ca(II) in tap water, was used. Fe-GAC column with 1 hr of pre-washing time treated As(V) effectively while GAC column removed Fe(III) better than Fe-GAC column. Moreover, the increasing pre-washing time from 1 to 9 hour in Fe-GAC column enhanced Fe(III) removal with little negative impact of As(V) removal. Mostly, the column filled with Fe-GAC and GAC (i.e. the mass ratio of Fe-GAC:GAC = 2:8) showed the higher treatability of both As(V) and Fe(III), even it operated with 1 hr pre-washing time.