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Dang Hoang Long,In-Kag Hwang,Sang-Wan Ryu 한국물리학회 2009 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.55 No.1
A light-emitting diode (LED) with a bottom photonic crystal (PC), where the PC was fabricated below the active layer, was theoretically analyzed to obtain its light extraction efficiency. A finitedifference time-domain (FDTD) method was used for the simulation of LEDs with top and bottom PCs, and their light extraction efficiencies were compared. The bottom PC was shown to be effective in increasing the extraction efficiency for a small distance from the PC to the active layer. This superior extraction efficiency was attributed to the large confinement of light between the top surface of the LED and the PC. For the analyzed designs, we theoretically estimated the enhancement of light extraction for various lattice constants, PC thicknesses, ratios of the hole radius to the lattice constant, and distances from the PC to the active layer. The optimized parameters were obtained for the highest light extraction efficiency. A light-emitting diode (LED) with a bottom photonic crystal (PC), where the PC was fabricated below the active layer, was theoretically analyzed to obtain its light extraction efficiency. A finitedifference time-domain (FDTD) method was used for the simulation of LEDs with top and bottom PCs, and their light extraction efficiencies were compared. The bottom PC was shown to be effective in increasing the extraction efficiency for a small distance from the PC to the active layer. This superior extraction efficiency was attributed to the large confinement of light between the top surface of the LED and the PC. For the analyzed designs, we theoretically estimated the enhancement of light extraction for various lattice constants, PC thicknesses, ratios of the hole radius to the lattice constant, and distances from the PC to the active layer. The optimized parameters were obtained for the highest light extraction efficiency.
Hoang-Long Dang,Sangshin Kwak(곽상신) 대한전기학회 2023 전기학회논문지 Vol.72 No.6
Metalized polymer-film capacitors have acquired a distinctive position among a variety of capacitor types due to their self-healing ability. Therefore, these devices are appropriate for critical power applications that demand reliability and durability. Nevertheless, as converters are increasingly being used for transmissions in networks, it is essential to improve stability to ensure the safety of system operations. Therefore, it is necessary to have a monitoring process that enables predictive maintenance to evaluate the health status and ensure the stability of electrical systems. However, the research in this field concentrates on electrolytic capacitors; and the characteristics of electrolytic and film capacitors differ. Hence, the need for further investigation into film capacitors is evident. This research proposes a condition monitoring approach that employs frequency signal analysis to assess the health status of capacitors in a three-phase AC-DC converter. The capacitor current is subjected to discrete wavelet transform and normalized by various indices, which serve as the input for learning algorithms. In addition, for comparison, capacitor voltage, output current, and output voltage are investigated using the discrete wavelet transform and fast Fourier transform. In this study, various indexes including root-mean-squared value, variance, average, and median, are utilized as inputs for artificial intelligent models to investigate factors affecting film capacitors. Eight learning algorithms are implemented to monitor the health status of film capacitors. The results show that utilizing the discrete wavelet transform combined with indexes for capacitor current yields a high accuracy of approximately 99.85%. These findings offer valuable insights into monitoring film capacitors using advanced techniques, and are anticipated to be informative for practical applications of film capacitor monitoring.
Analysis and Diagnosis Scheme of Parallel Arc Failure in DC Power Lines
Dang Hoang-Long,Kim Jae-Chang,Kwak Sangshin,Choi Seungdeog 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
The arc phenomena usually relate to the undesired ejection of two electric conductors. The emission power discharge from an arc event might wrath the electrical lines and cause a fire. Numerous studies were proposed to detect arc events and isolate them in time. The DC arc faults are sorted into two common types: series and parallel arcs. Due to corrupting contamination or insulation, a parallel arc occurs between two electrical wiring. The parallel arc currents of the system can be considerably amplified compared with the series type. In this research, the characteristic behaviors of the system in both time and frequency domains are studied during DC parallel arc failures, and arc energy was also discussed and analyzed. The unique behaviors are adopted to identify parallel arcs in different conditions. Sorting electrical arcs that are beneficial and trustworthy for the judicial procedure and deciding the protection schemes. The analyzed process is based on different domains of load current, source current, and arc voltage.
Dang Hoang-Long,Park Hye-Jin,Kwak Sangshin,Choi Seungdeog 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4
Capacitors are essential parts of power converters since the cost, size, and performance of converters are mainly dependent on them. Nevertheless, the capacitor is the most degeneration device among all converter parts owing to its aging failures and little lifetime. Thus, the monitoring process is an essential route for valuing health status and gives predictive maintenance to ensure steadiness in electric converter. The equivalent series resistance and the capacitance are commonly indexes employed for estimating the condition grade of capacitors. In this research, six artificial intelligence (AI) algorithms are adopted to estimate the aluminum capacitor (Al-Cap) parameters in the single-phase inverter system. Various circuit signals, such as load voltage and current, capacitor voltage and current, are examined by utilizing the discrete wavelet transform (DWT) analysis and the combinations of fast Fourier transform with various filters. The considered signals are handled as AI model’s inputs to guesstimate the health status of the Al-cap. In addition, the root-mean-square value is employed as an index to compare the accuracy with the analyzed signals. Furthermore, several indicators are mixed to acquire the best recipes for capacitor health evaluation.
Design Optimization of Photonic Crystal Structure for Improved Light Extraction of GaN LED
Dang Hoang Long,In-Kag Hwang,Sang-Wan Ryu IEEE 2009 IEEE journal of selected topics in quantum electro Vol.15 No.4
<P>We performed a theoretical analysis on improved light extraction efficiency of LEDs with photonic crystals (PCs). The light propagation and extraction of PC LEDs were simulated using the finite-difference time-domain method for various PC LED structures. LEDs with both top and bottom PCs and PC LEDs grown on patterned substrates were considered to maximize light extraction efficiency. The design parameters of the PC were varied, and optimized values were obtained. A disordered PC was simulated, and we showed that the light extraction efficiency of a disordered PC was nearly equivalent to that of an ordered PC with the same pattern periodicity. This result revealed that the increased light extraction of PC LEDs was mainly due to scattering. Moreover, by comparing the enhancement of PC LEDs with different shapes of air holes, we showed that only the density of holes and the area occupied by holes play important roles in light extraction. The shapes of the holes have no strong effect on the enhancement of light extraction.</P>
Detection Algorithms of Parallel Arc Fault on AC Power Lines Based on Deep Learning Techniques
Park Chang-Ju,Dang Hoang-Long,Kwak Sangshin,Choi Seungdeog 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
Several studies on arc fault detection have been recently conducted. The arc fault is detected by analyzing the frequency- and time-domain current characteristics of the arc in the AC parallel arc fault. In this study, the focus was on detecting AC arc faults using artifi cial intelligence concepts. The detection performance was analyzed by comparing diff erent combinations of input feature parameters and neural networks. In particular, the performances of the input parameters were compared and analyzed, including the frequency average, instantaneous frequency, entropy, fast Fourier transform and the maximum slip diff erence combination, and the FFT and frequency average combination. Diff erent combinations of parameters and neural network structures were applied to the respective parallel AC enclosed case and unenclosed case, and the performances were compared. It was determined that the combinations of two input parameters should be applied to achieve high performance in both enclosed and unenclosed cases. In addition, the detection rate with respect to the amount of training data was analyzed. The combination of two input parameters improves the robustness and reliability of arc fault detection.
Deep learning-based series AC arc detection algorithms
Park, Chang-Ju,Dang, Hoang-Long,Kwak, Sangshin,Choi, Seungdeog The Korean Institute of Power Electronics 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.10
Various studies on arc detection methods are described. Series AC arc is detected based on the characteristics extracted from arc voltage, frequency, and time domain of the current. Methods of arc detection using artificial intelligence have been studied previously. In the present study, the performance of multiple methods is analyzed by comparing different input parameters and artificial neural networks. In addition to the input parameters presented in the literature, the performance is compared and analyzed using the following parameters: zero-crossing period, frequency average, instantaneous frequency, entropy, combination of fast Fourier transform (FFT) and maximum slip difference, and combination of FFT and frequency average. These parameters and different neural networks are studied in the bounded and unbounded case, and the performance is compared. For different combinations of neural networks and input parameters, another research question is to identify the input parameters to be used if the number of training data is limited. Moreover, this study investigates the change in detection rate depending on the number of training samples. As a result, the minimum dataset size required to obtain the final detection rate is identified.
Synthesis and Anti-osteoporosis Potential of Two New Indirubin-3`-oxime Derivatives
( Nguyen Manh Cuong ),( Bui Huu Tai ),( Dang Hoang Hoan ),( Pham Quoc Long ),( Eun Mi Choi ),( Young Ho Kim ) 한국응용생명화학회 2010 Applied Biological Chemistry (Appl Biol Chem) Vol.53 No.1
Two new indirubin-3`-oxime derivatives, indirubin-3`-[O-(3-bromoprop-1-yl)-oxime] (2) and indirubin-3`-[O-(methoxycarbonylmethyl)-oxime] (3) were synthesized. Their structures were confirmed by ESI-MS and NMR spectroscopic method. Both of them (5 μg/mL) significantly caused a elevation of cell growth, alkaline phosphate activity, and mineralization in osteoblastic MC3T3-E1 cells (p<0.05).