• Journal of Korean Society of Environmental Engineers
• /
• v.38 no.8
• /
• pp.411-419
• /
• 2016

The objective of this study was to evaluate the application possibility of tube type electrolysis module system using recirculation process through removal organic matters and nitrogen in the pigment wastewater. The tube type electrolysis module consisted of a inner rod anode and an outer tube cathode. Material used for anode was titanium electroplated with $RuO_2$. Stainless steel was used for cathode. It was observed that the pollutant removal efficiency was increased according to the decrease of flowrate and increase of current density. When the retention time in tube type electrolysis module system was 180 min, chlorate concentration was 382.4~519.6 mg/L. The chlorate production was one of the major factors in electrochemical oxidation of tube type electrolysis module system using recirculation process used in this research. The pollutant removal efficiencies from the bench scale tube type electrolysis module system using recirculation operated under the electric charge of $4,500C/dm^2$ showed the $COD_{Mn}$ 89.6%, $COD_{Cr}$ 67.8%, T-N 96.8%, and Color 74.2%, respectively and energy consumption was $5.18kWh/m^3$.

• Proceedings of the KIPE Conference
• /
• 2013.11a
• /
• pp.60-62
• /
• 2013

기본적으로 많은 인자들에 의해 영향을 받는 배터리 시스템은 일반적으로 사용 용도에 따라 단위 배터리 셀을 직렬 또는 병렬로 연결하여 하나의 배터리 모듈을 형성하고 있다. 다수개의 단위 배터리 셀을 연결하여 하나의 배터리 모듈로 사용하는 경우, 개별 셀의 활물질 및 전해액의 미소 변동, 충방전 사이클 차이, 온도의 영향에 따라 배터리 특성이 다르게 나타난다. 이러한 특성으로 인해 충전 및 방전이 진행됨에 따라 셀간의 전압 불균형 현상이 발생하고 이로 인해 배터리 수명은 급격하게 감소하여 배터리 교체와 같은 경제적 손실을 초래한다. 본 논문에서는 배터리의 성능과 안전성을 확보하기 위해 배터리 밸런싱에 관한 연구를 수행하였다. 기존의 수동적 밸런싱의 단점을 보완한 능동적 밸런싱을 사용하였고 제안한 회로의 기능 및 성능의 검증을 위해 배터리 관리 장치 보드를 설계 및 제작하여 시험한 결과 개선된 기능이 원활히 수행됨을 확인하였다.

• Proceedings of the KIPE Conference
• /
• 2014.07a
• /
• pp.453-454
• /
• 2014

태양광 발전용 AC-모듈에서 발생하는 전력 디커플링을 위해 사용되는 대용량 전해 커패시터는 시스템의 신뢰성을 감소시키므로 최근 보조회로를 이용한 디커플링 기법이 연구되고 있다. 하지만 디커플링 회로의 제어기 오차와 시스템의 효율이 고려될 경우, 태양광 패널에 전력 맥동이 존재하게 되고 이는 인버터 출력 전류를 왜곡시키는 원인이 된다. 본 논문에서는 제어기 오차와 시스템 효율을 고려하여 발생하는 전력 맥동에 대해 분석하고, 이를 저감시키기 위한 전류 왜곡 보상 기법을 제안하였다. 이를 PSIM 시뮬레이션을 통해 그 타당성을 검증하였다.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.5
• /
• pp.259-266
• /
• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.2
• /
• pp.28-36
• /
• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.