Research on non-intrusive load disaggregation method based on multi-model combination

2021 ◽  
Vol 200 ◽  
pp. 107472
Author(s):  
Yi Guo ◽  
Xuejun Xiong ◽  
Qi Fu ◽  
Liang Xu ◽  
Shi Jing
Keyword(s):  
Model Combination ◽  
Load Disaggregation

scholarly journals Anticancer effects of the combined Thai noni juice ethanolic extracts and 5-fluorouracil against cholangiocarcinoma cells in vitro and in vivo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeerati Prompipak ◽  
Thanaset Senawong ◽  
Banchob Sripa ◽  
Albert J. Ketterman ◽  
Suppawit Utaiwat ◽  
...  
Keyword(s):  
Nude Mice ◽  
Combination Treatment ◽  
Synergistic Effects ◽  
Single Agent ◽  
Xenograft Model ◽  
Ethanolic Extract ◽  
Model Combination ◽  
Mouse Xenograft Model ◽  
Ethanolic Extracts

AbstractApplication of 5-fluorouracil (5-FU) in cholangiocarcinoma (CCA) is limited by adverse side effects and chemoresistance. Therefore, the combination therapy of 5-FU with other substances, especially natural products may provide a new strategy for CCA treatment. The aim of this study was to evaluate the combination effects of 5-FU and two ethanolic extracts of Thai noni juice (TNJ) products on CCA cell lines and nude mice xenografts. The results of antiproliferative assay showed the combination treatment of 5-FU and each TNJ ethanolic extract exerted more cytotoxicity on CCA cells than either single agent treatment. Synergistic effects of drug combinations can enable the dose reduction of 5-FU. The mechanism underlying a combination treatment was apoptosis induction through an activation of p53 and Bax proteins. In the nude mouse xenograft model, combination treatments of 5-FU with each TNJ ethanolic extract suppressed the growth of CCA cells implanted mice more than single agent treatments with no effects on mouse body weight, kidney, and spleen. Moreover, low doses of TNJ ethanolic extracts reduced the hepatotoxicity of 5-FU in nude mice. Taken together, these data suggested that the ethanolic extracts of TNJ products can enhance the anti-CCA effect and reduce toxicity of 5-FU.

scholarly journals The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients

2021 ◽  
Vol 7 (2) ◽  
pp. 17
Author(s):  
Michael Baine ◽  
Justin Burr ◽  
Qian Du ◽  
Chi Zhang ◽  
Xiaoying Liang ◽  
...  
Keyword(s):  
Clinical Features ◽  
Dna Methyltransferase ◽  
Predictive Ability ◽  
Post Treatment ◽  
Model Combination ◽  
Methylguanine Dna Methyltransferase ◽  
Mri Scans ◽  
Area Under Roc Curve ◽  
True Progression ◽  
Potential Use

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.

scholarly journals Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

2020 ◽  
Vol 10 (24) ◽  
pp. 9132
Author(s):  
Liguo Weng ◽  
Xiaodong Zhang ◽  
Junhao Qian ◽  
Min Xia ◽  
Yiqing Xu ◽  
...  
Keyword(s):  
Smart Grids ◽  
Recognition Rate ◽  
Low Frequency ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Residual Network ◽  
Multi Scale ◽  
Energy Disaggregation ◽  
Benchmark Datasets ◽  
Load Disaggregation

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.

scholarly journals ỨNG DỤNG DỮ LIỆU MƯA CHIRPS VÀ MÔ HÌNH THỦY VĂN HEC-HMS MÔ PHỎNG DÒNG CHẢY LŨ Ở LƯU VỰC SÔNG LẠI GIANG

2021 ◽  
Vol 5 (1) ◽  
pp. 2252-2261
Author(s):  
Ngô Anh Tú ◽  
Phan Thái Lê ◽  
Nguyễn Hữu Xuân ◽  
Trần Văn Bình
Keyword(s):  
Economic Development ◽  
River Basin ◽  
Hydrological Model ◽  
Flow Data ◽  
Model Combination ◽  
The North ◽  
Socio Economic Development ◽  
Flood Peaks ◽  
Simulation Results ◽  
Flood Flow

Bài báo xác định lưu lượng dòng chảy theo thời đoạn dựa vào mô hình HEC-HMS, số liệu mưa từ ảnh vệ tinh CHIRPS của NASA và Hệ thống thông tin địa lý (GIS) trong mô phỏng dòng chảy lũ tháng 12 năm 2016 tại lưu vực sông Lại Giang, lưu vực lớn thứ hai của tỉnh Bình Định (sau lưu vực sông Kôn) và có vai trò quan trọng về phát triển kinh tế-xã hội ở phía Bắc của tỉnh. Kết quả mô phỏng dòng chảy lũ rất đáng tin cậy, lưu lượng dòng chảy lũ đạt đỉnh 2542,6 m3/s tương ứng với với tần suất lũ 5%. Chỉ số kiểm định mô hình NSE với giá trị là 0,93; hệ số R2 đạt 0,78 sai số PBIAS khoảng 24% và sai số đỉnh lũ PEC = 52,01.  ABSTRACT The paper aimed to introduce the application of the HEC-HMS hydrological model combination with the CHIRPS (Climate Hazards Group Infrared Precipitation with Station) and GIS to restore flood flow data in the Lai Giang river basin in 2016. The Lai Giang river basin is the second largest basin of Binh Dinh province (after the Kon river basin), it plays an important role in socio-economic development in the North of Binh Dinh province. The simulation results of flood peaks reached 2542,6 m3.s-1 (P=5%). Model test indices such as NSE = 0.93, the correlation coefficient reached 0,78; the percentage of PBIAS error was about 24%, and peak error (PEC) was 52,01.

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