• BMB Reports
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• 제50권6호
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• pp.323-328
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• 2017

Lysophosphatidylcholine (LPC) is a major phospholipid component of oxidized low-density lipoprotein (ox-LDL) and is implicated in its atherogenic activity. This study investigated the effects of LPC on cell viability, intracellular calcium homeostasis, and the protective mechanisms of chlorogenic acid (CGA) in human umbilical vein endothelial cells (HUVECs). LPC increased intracellular calcium ($[Ca^{2+}]_i$) by releasing $Ca^{2+}$ from intracellular stores and via $Ca^{2+}$ influx through store-operated channels (SOCs). LPC also increased the generation of reactive oxygen species (ROS) and decreased cell viability. The mRNA expression of Transient receptor potential canonical (TRPC) channel 1 was increased significantly by LPC treatment and suppressed by CGA. CGA inhibited LPC-induced $Ca^{2+}$ influx and ROS generation, and restored cell viability. These results suggested that CGA inhibits SOC-mediated $Ca^{2+}$ influx and ROS generation by attenuating TRPC1 expression in LPC-treated HUVECs. Therefore, CGA might protect endothelial cells against LPC injury, thereby inhibiting atherosclerosis.

• 한국자동차공학회논문집
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• 제17권1호
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• pp.130-136
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• 2009

A lumped parameter model of Li-ion battery in hybrid electric vehicle(HEV) is constructed and system parameters are identified by using recursive least square estimation for different C-rates, SOCs and temperatures. The system characteristics of pole and zero in frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of Li-ion battery indicates highly dependant of temperatures. The system pole and internal resistance changes 6.6 and 18 times at $-20^{\circ}C$, comparing with those at $25^{\circ}C$, respectively. These results will be utilized on constructing model-based state observer or an on-line identification and an adaptation of the model parameters in battery management systems for hybrid electric vehicle applications.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2018년도 전력전자학술대회
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• pp.156-158
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• 2018

The inconsistencies between paralleled battery cells are becoming more considerable issue in high capacity battery applications like electric vehicles. Due to differences in state-of-charge (SOC) and internal resistance within individual cells in parallel, charging or discharging current is not appropriately balanced to each cell in terms of SOC, which may shorten the lifetime or sometimes cause safety issues. In this paper, an intelligent cell-balancing algorithm is proposed to overcome the inconsistency issue especially for paralleled battery cells. In this scheme, SOC information collected in the sub-BMS module is sent to the main-BMS module, where the number of parallel cells to be connected to DC bus is continuously updated based on the suggested SOC comparison rule. To verify the method, operation of the algorithm on 4 paralleled battery cells are simulated on Matlab/Simulink. The simulation result shows that the SOCs of paralleled cells are evenly redistributed. It is expected that the proposed algorithm provides high reliable and prolong the life cycle and working capacity of the battery pack.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 추계학술대회
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• 한국통신학회논문지
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• 제35권9A호
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• pp.868-873
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• 2010

H.264/AVC 비디오 압축 표준은 DMB, 디지털 TV 및 각종 차세대 방송, 통신 및 가전 분야에 채택 되어 왔고, 최근 감시카메라용 DVR 분야에서도 사실상의 표준으로 자리 잡아가고 있다. PC 기반 DVR의 경우 PC와의 데이터 전송 채널은 통상적으로 PCI 버스를 이용하는 반면, SOC용으로 사용되는 H.264/AVC 코덱은 대개 AMBA 버스를 기반으로 하여 호스트 인터페이스가 수행된다. 본 논문에서는 AHB 버스를 시스템 버스로 이용하는 H.264/AVC 코덱을 효과적으로 PCI 버스로 연결해 주기 위한 인터페이스 모듈 설계 및 실험 결과를 제시하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 전력전자학술대회 논문집
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• pp.191-192
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• 2014

Multiple output converters (MOCs) are widely used for applications which require various levels of the output voltages due to their benefits in cost, volume, and efficiency. However, most of the MOCs developed so far can regulate only one output tightly and require as many secondary windings in the transformer as the number of the outputs. In this paper, a novel Time Division Multiple Control (TDMC) method to regulate all the outputs in high precision is proposed and applied for the multiple output battery charger based on the phase shift full bridge topology to charge a multiple number of batteries at one time. The proposed converter can charge three different kinds of batteries or same kind of batteries in different state of charges (SOCs) by using constant current/constant voltage (CC/CV) charge mode independently. At the same time it can provide an even degree of tight regulation for each output to satisfy the strict ripple requirement of the battery. The validity and feasibility of the proposed method are verified through the experiments.

• 전력전자학회논문지
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• 제24권5호
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• pp.365-371
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• 2019

This study shows the feasibility of the parameter of the 1st RC parallel equivalent circuit as a factor of the heat generation of lithium-ion cell. The internal resistance of a lithium-ion cell consists of ohmic and polarization resistances. The internal resistances at various SOCs of the lithium-ion cell are obtained via an electrical characteristic test. The internal resistance is inversely obtained through the amount of heat generated during the experiment. By comparing the resistances obtained using the two methods, the summation of ohmic and polarization resistances is identified as the heating factor of lithium-ion battery. Finally, the amounts of heat generated from the 2C, 3C, and 4C-rate discharge experiments and the COMSOL multiphysics simulation using the summation of ohmic and polarization resistances as the heating parameter are compared. The comparison shows the feasibility of the electrical parameters of the 1st RC parallel equivalent circuit as the heating factor.

• 전력전자학회논문지
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• 제26권1호
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Biomolecules & Therapeutics
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• 제17권4호
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• pp.370-378
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• 2009

N-myc downstream-regulated gene 2 (NDRG2) has recently been found to be a tumor suppressor gene. Although it has been reported that NDRG2 expression in breast cancer cells decreases cell proliferation by inhibiting STAT3 activation via SOCS1 induction, the molecular mechanism of chemotherapeutic agent-induced apoptosis is not well known. To elucidate the effect of NDRG2 on the apoptotic pathway induced by doxorubicin, we established stable cell lines expressing NDRG2 and investigated the effect of NDRG2 expression on the doxorubicin-induced apoptosis. While STAT3 activation was remarkably inhibited by NDRG2 overexpression, the expression level of p21 was increased by NDRG2 expression. We confirmed that NDRG2-expressing cells treated with doxorubicin suppressed STAT3 activation and upregulated p21 expression. NDRG2 expression considerably enhanced TUNEL positive apoptotic cells, poly-ADP ribose polymerase (PARP) cleavage, release of cytochrome c to cytosol, and caspase-3 activity in doxorubicin-induced apoptosis. Bid expression in a resting state and after treatment with doxorubicin increased in MDA-MB-231-NDRG2 cells compared to MDA-MB-231-mock cells. Meanwhile, Bcl-$x_L$ expression decreased in MDA-MB-231-NDRG2 cells compared to MDA-MB-231-mock cells in a resting state and in doxorubicin-treated cells. Collectively, these data suggest that suppression of STAT3 activation by NDRG2 influences the sensitivity to doxorubicin-induced apoptosis of breast cancer cells and this may provide a potential therapeutic benefit to overcome the resistance against doxorubicin in breast cancer.