Search

The deployment rate of distributed energy resources (DER) based on renewable energy has recently been increasing worldwide. Direct current (DC) power distribution has been proposed as an efficient approach for operating digital loads and DC-based renewable energy. DC distribution systems with DERs, however, are not commonly used in the real world. From the viewpoint of distribution system operators, it is important to identify the operation status of DERs for effective grid operation. In this article, a novel monitoring methodology for low-voltage DC (LVDC) distribution systems with DERs is proposed based on frequency-domain analyses. A deep-learning technology is applied to model the frequency characteristics of individual DERs. A case study considering two approaches was conducted using a photovoltaic generator, wind turbine, diesel generator, and energy-storage system installed in an LVDC testbed operated by KEPCO in Gochang, Jeolla-do, Korea. In the first approach, monitoring is performed with sensors installed near individual DERs. In the second approach, monitoring is performed with a single sensor in the distribution line, and the signal is disaggregated to identify the status of the individual DERs. The results show that the proposed methodology tracks the status of DERs with an accuracy of 98% and 95%, respectively, demonstrating the validity of the proposed methodologies.

The dissemination of distributed resources presents a new paradigm for the power industry. Especially solar power is creating a new chapter for trading electricity as a new source. Solar power sources and load resources are each uncertain by their size and time factors. This paper discusses the uncertainties due to the accuracy of the forecast by forecasting PV power source and by forecasting load resource based multi-scale and multi-time. To do this, artificial neural networks are used to forecast these resources at a different time interval and the amounts by aggregation. Uncertainty from the forecasting results is discussed.

In this study, an LED emotional lighting algorithm optimized to the situation was implemented using fuzzy inference for users in exercise and resting situations in indoor environment. The fuzzy theory was used instead of the conventional simple color temperature control in order to control the colors and color temperatures of LED light sources in line with user environment under complex conditions. An LED emotional lighting system based on fuzzy theory was designed through a combination of colors according to the color and temperature of emotional language based on the user behavior. As a result, the color and color temperature can give a good effect on user`s emotions for an emotional lighting that is effective for resting and exercise.

In order to improve spectrum sensitivity of photodiode for detection of the laser at 850 nm ~ 1000 nm of near-infrared wavelength band, this study has produced silicon-based fast film PIN photodiode and analyzed electrical and optical properties. The manufactured device is packaged in TO-18 type. The electrical properties of the dark currents both Anode 1 and Anode 2 have valued of approximately 0.055 nA for 5 V reverse bias, while the capacitance showed 19.5 pF at frequency range of 1 kHz and about 19.8 pF at the range of 200 kHz for 0 V. In addition, the rising time of output signal was verified to have fast response time of about 30 ns for 10 V. For the optical properties, the best spectrum sensitivity was 0.66 A/W for 880 nm, while it was relatively excellent value of 0.45 A/W for 1,000 nm. 키워드 : 핀 포토다이오드, 광검출기, 실리콘, 분광감응도, TO 패키지 Key word : PIN Photodiode, Photodetector, Silicon(Si), Spectral responsivity, TO packageJournal of the Korea Institute of Information andCommunication Engineering

In order to improve spectrum sensitivity of photodiode for detection of the laser wavelength at 850 nm ∼1000 nm of near-infrared band, this study has produced silicon-based photodiode whose area is 5,000 μm x 2,000 μm, and the thickness is 280 μm. It was packed by the TO-5 type. The electrical properties of the dark currents have valued of approximately 0.1 nA for 5 V reverse bias, while the capacitance showed 32.5 pF at frequency range of 1 kHz and about 32.4 pF at the range of 200 kHz for 0 V. In addition, the rising time of output signal was as fast response as 20.92 ns for 10V. For the optical properties, the best spectrum sensitivity was 0.57 A/W for 890 nm, while it was relatively excellent value of 0.37 A/W for 1,000 nm. Over all, there were good spectrum sensitivity for this diode over the range of 870 ∼ 920 nm.

There are increasing interests in COB (Chip On Board) that densely arranged many LED chips on one board in order to solve the heat issue. There are many problems being on the rise: the lifespan decreases as the temperature of LED devices increases; Red Shift phenomenon, in which wave length of spectral line moves from original wave length to long wave length, occurs; and optical power decreases as  increases. In order to resolve such problems, this study selected the optimum thickness and length of Fin, planned the second Heat sink that is optimum for COB LED with 15W, and analyzed thermal mode by Solid Works Flow Simulation through 15W COB packaging with the planned Heat sink. 15W COB down-light Heat sink that is produced based on this analysis was utilized to analyze thermal mode through contact thermometer and electrical properties through Kelthley 2430.

As the demand of the LED for lighting that emits light by p-n junction is increasing, studies on heatproof plate technology is being conducted to minimize the temperature of the LED lighting. As for the temperature of the LED devices, their light emitting efficiency decreases and the maximum lifespan drops down to 1/5. Therefore there are heat dissipation studies going on to minimize the heat. For LED heat dissipation, aluminum heat sink plates are mostly used. For this paper, we designed heat sink that fits residential 20W COB LED Down Light; packaged the heat sink and 20W COB and analyzed and evaluated the thermal properties through a Solidworks flow simulation. We are planning to design the optimal heat sink plate to solve the thermal agglomeration considering TIM(Thermal Interface material). Key words : Heat sink, Solidworks Simulation, LED, Junction Temperature, Optimum Design 1. I NTRODUCTION Due to the increased efficiency and decreased price of the current Light emitting diode (LED) semiconductor, the use of lighting fixtures is growing. The LED lighting fixtures developed for study has been commercialized solving the heat dissipation problem, but as the social demand for high capacity increases, the single heat capacity of semiconductor element is getting larger and various ways to solve heat problems have been proposed. When converting electrical energy into light energy, heat is generated. Such heat and LED’s heat dissipation are important factors to consider when improving the operation of LED and its reliability [1-2]. In general, high power Chip on Board (COB) LED lighting uses the heat sink made of aluminum with insulation area expansion pins installed in it as a cool system dissipating heat on PCB. [3-4]. In order to develop a cooling system, the target amount of dissipation, the environment and possible temperature must be set; and the prediction of heat transfer coefficient and the design of the cooling system should be made[5-6]. In this paper, we will design a heat sink plate for 20W COB LED down light and look at the thermal properties through a Solidworks Flow simulation.

In this paper, we propose the genetic design of fuzzy neural networks with multi-output based on interval type-2 fuzzy set (IT2FSFNNm) for pattern recognition. IT2FSFNNm is the networks of combination between the fuzzy neural networks (FNNs) and interval type-2 fuzzy set with uncertainty. The premise part of the networks is composed of the fuzzy partition of respective input spaces and the consequence part of the networks is represented by polynomial functions with interval set. We also consider real-coded genetic algorithms to estimate the optimal values of the parameters of IT2FSFNNm. The numerical experimentation is used for evaluating the proposed networks for pattern recognition.

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

In this paper, we introduce the fuzzy neural network based on the individual input space to design the pattern recognizer. The proposed networks configure the network by individually dividing each input space. The premise part of the networks is independently composed of the fuzzy partition of individual input spaces and the consequence part of the networks is represented by polynomial functions. The learning of fuzzy neural networks is realized by adjusting connection weights of the neurons in the consequent part of the fuzzy rules and it follows a back-propagation algorithm. In addition, in order to optimize the parameters of the proposed network, we use real-coded genetic algorithms. Finally, we design the optimized pattern recognizer using the experimental data for pattern recognition.

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

This paper is implemented simple visible light communication system by combining the communications through LED lighting. It uses a variable PD sensor to the receiver and 1~12 the LED light-emitting device to the transmitter to realize LED visible light communication. Initial distance value of the developed transmission and receiver is more than 0~1.5m for LED visible light communication, the overall system transmission rate is organized with thousands kbps variability visible light media transmission system. It was measured the performance experiment during lens wearing or not wearing in order to increase the overall efficiency of the LED module, configures the LED and PD to existing PC module for the performance analysis of the implemented research, experiments the maximum communication distance of the transmitter/receiver according to LED count and the transmission rate, check about application methods and the possibility.

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

To prevent the electrical accidents in the laboratory, we identify problems for periodic inspections of the electric field and develop a fuzzy inference system that can be practically applied to check items. Focusing on electrical safety in the lab environment, we draw check items that can be applied in common and develop a standard checklist that is consistent with the laboratory electrical safety and the periodic inspections. Using the standard checklist we select the items that may contain a linguistic ambiguity and define the membership functions for these items. We also have a safety rating defined by the membership function. Using these fuzzy variables we form the fuzzy rules in the form of `If-Then` and develop a fuzzy inference system through the fuzzy engine. From this, electrical accidents could be prevented in advance continuously by managing the intelligent and efficient inspection and electrical safety to prevent the electrical accidents in the laboratory.