http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Power Plant Pump Process Model Error Minimization Technique for Improving Simulator Fidelity
SooYong Yun,Kwan-Woong Gwak,Seung-Hyun Byun,Deockho Kim,Jaeyong Cho,Man-su Shin,ChangHyun Kim 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The power plant simulator is used to simulate the actual operation of a power plant, and for training and control verification. The simulator is built with the design data during the plant construction. The fidelity is low in the initial stage of operation because power plant modifications, deterioration, and logic corrections are not sufficiently reflected in a timely manner. To solve this problem, we propose a model error minimization technique for a pump process, one of the fundamental elements of the plant process, to improve fidelity. Model accuracy is improved by adjusting the parameters of the pump rated speed and characteristic curve based on the actual operating data. Performance is verified using actual operation data of the boiler feedwater pump in the circulating fluidized bed power plant.
Biologically Inspired Sensing Strategy using Spatial Gradients
( Sooyong Lee ) 한국센서학회 2020 센서학회지 Vol.29 No.3
To find food, homes, and mates, some animals have adapted special sensing capabilities. Rather than using a passive method, they discharge a signal and then extract the necessary information from the response. More importantly, they use the slope of the detected signal to find the destination of an object. In this paper, similar strategy is mathematically formulated. A perturbation and correlationbased gradient estimation method is developed and used as a sensing strategy. This method allows us to adaptively sense an object in a given environment effectively. The proposed strategy is based on the use of gradient values; rather than instantaneous measurements. Considering the gradient value, the sampling frequency is planned adaptively, i.e., sparse sampling is performed in slowly varying regions, while dense sampling is conducted in rapidly changing regions. Using a temperature sensor, the proposed strategy is verified and its effectiveness is demonstrated.
Performance Trade-Offs in Using NVRAM Write Buffer for Flash Memory-Based Storage Devices
Sooyong Kang,Sungmin Park,Hoyoung Jung,Hyoki Shim,Jaehyuk Cha IEEE 2009 IEEE Transactions on Computers Vol.58 No.6
<P>While NAND flash memory is used in a variety of end-user devices, it has a few disadvantages, such as asymmetric speed of read and write operations, inability to in-place updates, among others. To overcome these problems, various flash-aware strategies have been suggested in terms of buffer cache, file system, FTL, and others. Also, the recent development of next-generation nonvolatile memory types such as MRAM, FeRAM, and PRAM provide higher commercial value to non-volatile RAM (NVRAM). At today's prices, however, they are not yet cost-effective. In this paper, we suggest the utilization of small-sized, next-generation NVRAM as a write buffer to improve the .overall performance of NAND flash memory-based storage systems. We propose various block-based NVRAM write buffer management policies and evaluate the performance improvement of NAND flash memory-based storage systems under each policy. Also, we propose a novel write buffer-aware flash translation layer algorithm, optimistic FTL, which is designed to harmonize well with NVRAM write buffers. Simulation results show that the proposed buffer management policies outperform the traditional page-based LRU algorithm and the proposed optimistic FTL outperforms previous log block-based FTL algorithms, such as BAST and FAST.</P>
Design of a Fast Learning Classifier for Sleep Apnea Database based on Fuzzy SVM
SooYong Lee,Erdenebayar Urtnasan,Kyoung-Joung Lee 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.3
In this paper, we compared the performance of support vector machine (SVM) and fuzzy SVM (FSVM) for reduction of learning time when classifying large-scale time series data into two classes. The fast learning time of the pattern classifier for large time series data is very useful in decision support systems. Considering the high interest in healthcare, including big data analysis, it is necessary to design a pattern classifier with a fast learning capability. We used large-scale time series data of 32 patients with sleep apnea (SA) for this study. The experiment was conducted by extending the parameter n, of the fuzzy membership function of FSVM, from 1 to 500. The result shows that the shortest learning time of FSVM is 3 s for radial base function (RBF), 17 s for a polynomial, and 35 s for a linear kernel, where the parameter n of the fuzzy membership function is n = 2, n = 433, and n = 4, respectively. The maximum classification hit rate of FSVM is 93.23%, and the learning time is significantly faster than conventional SVM. Therefore, FSVM can be used as a good classifier for the large-scale time series SA database.
Influence of Physical Load on the Stability of Organic Solar Cells with Polymer
Sooyong Lee,Hwajeong Kim,Youngkyoo Kim 한국태양광발전학회 2016 Current Photovoltaic Research Vol.4 No.2
We report the effect of physical load on the stability of organic solar cells under physical loads. The active layers in organic solar cells were fabricated with bulk heterojunction films (BHJ) films of poly (3-hexylthiophene) and phenyl-C61-butyric methyl ester. The loading time was varied up to 60 s by keeping the physical load constant. Results showed that the open circuit voltage was not nfluenced by the physical load but other solar cell parameters were sensitive to the loading time. The fill factor was very slightly increased at 15 s, while short circuit current density was well kept for 30 s. The power conversion efficiency was reasonably maintained for 45 s but became significantly decreased by the continuous loading for 60 s.
Development of Smart Healthcare Scheduling Monitoring System for Elderly Health Care
Sooyong Cho,Sang Hyun Lee 한국인터넷방송통신학회 2018 International Journal of Internet, Broadcasting an Vol.10 No.2
Health care has attracted a lot of attention, recently due to an increase in life expectancy and interest in health. Various biometric data of the user are collected by using the air pressure sensor, gyro sensor, acceleration sensor, and heart rate sensor to perform the Smart Health Care Activity Tracker function. Basically, smartphone application is made and tested for biometric data collection, but the Arduino platform and bio-signal measurement sensor are used to confirm the accuracy of the measured value of the smartphone. Use the Google Maps API to set user goals and provide guidance on the location of the user and the points the user wants. Also, the basic configuration of the main UI is composed of the screen of the camera, and it is possible for the user to confirm the forward while using the application, so that accident prevention is possible.