http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Anh Duy Duong Le (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models
Duong Minh Bui,Phuc Duy Le,Tien Minh Cao,Hung Nguyen,Trang Thi Pham,Duy Anh Pham 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5
Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifi cally, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-fi ltering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confi dence levels in order to fi lter-out the noise/outliers for accuracy improvement of diff erent short-term load forecasting models. The proposed statistical data-fi ltering method is also compared to other existing data-fi ltering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artifi cial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-fi ltering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTMCNN, and ARIMA models with the statistical data-fi ltering method can all outperform those with the existing data-fi ltering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended.
-
Nguyen Duy Dat,Ton That Loc,Mai Thuan Trieu,Dong Thanh Nguyen,Khuong Quoc Nguyen,My Linh Nguyen,Anh Duy Duong Le,Hai Nguyen Tran 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.4
Two new composites from synthetic clay-like materials and carbon spheres were developed. Layered doubledhydroxides (LDH) were synthesized from the coprecipitation of Mg2+ and Al3+ ions. Spherical hydrochar (SH) wasprepared from pure glucose through hydrothermal carbonization at 190 oC. The composite LDH–SH was synthesizedthrough a simple hydrothermal method of the mixture of LDH and SH. Another composite, LDO-SB, was directly preparedthrough the carbonization of LDH-SH at 500 oC. Under such high temperature, LDH was converted to layereddoubled oxides (LDO), and SH was transferred to spherical biochar (SB). Those materials were characterized by chemicalstability, surface morphology and element composition, crystal structure, surface functional group, and texturalcharacteristic. They were applied for removing cationic dye (methylene blue; MB) and anionic dye (Congo red; CR)under different pH solutions. Three adsorption components—kinetics, isotherm, and thermodynamics—were conductedunder batch experimenters. Results demonstrated that the LDH or LDO particles were assembled on the surfaceof SH or SB, respectively. The surface area, total pore volume, and average pore width of LDH–SH and LDO-SBwere 58.5 and 198m2/g, 0.319 and 0.440 cm3/g, and 21.8 and 8.89 nm, respectively. The maximum adsorption capacityof the materials, calculated from the Langmuir model, at 30 oC for CR and MB dyes was 1589 and 78.6mg/g (LDOSB)and 499 and 226mg/g (LDH-SH), respectively. The composites exhibited a higher affinity to anionic than cationicdyes, which resulted from the great contribution of the clay-like materials. Therefore, they can serve as a promisingcomposite for the decolorization of wastewater.
-
-
Ha-Linh Quach,Thai Quang Pham,Ngoc-Anh Hoang,Dinh Cong Phung,Viet-Cuong Nguyen,Son Hong Le,Thanh Cong Le,Dang Hai Le,Anh Duc Dang,Duong Nhu Tran,Nghia Duy Ngu,Florian Vogt,Cong-Khanh Nguyen 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.4
Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33–0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07–0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23–2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20–76.70) or unverified (OR = 5.03; 95% CI, 1.66–15.24). Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online “infodemics” to inform public health responses.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
