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Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods 认领 引用 被引量：4
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作者 Daixuan Zhou Yujin Liu +2 位作者 Xu Wang Fuxing Wang Yan Jia 《Energy Engineering》 EI 2024年第12期3573-3616,共44页
With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject t... With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject to a variety of factors,it brings great challenges to the stable operation and dispatch of the power grid.Therefore,accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy.Currently,the short-term prediction of PV power has received extensive attention and research,but the accuracy and precision of the prediction have to be further improved.Therefore,this paper reviews the PV power prediction methods from five aspects:influencing factors,evaluation indexes,prediction status,difficulties and future trends.Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification ofmodel features,statistical methods,artificial intelligence methods,and combinedmethods of prediction.Finally,the development trend ofPVpower generation prediction technology and possible future research directions are envisioned. 展开更多
关键词 Photovoltaic power generation power prediction artificial intelligence algorithm
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Clinically applicable artificial intelligence algorithm for the diagnosis,evaluation,and monitoring of acute retinal necrosis 认领 引用 被引量：2
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作者 Lei FENG Daizhan ZHOU +5 位作者 Chenqi LUO Junhui SHEN Wenzhe WANG Yifei LU Jian WU Ke YAO 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2021年第6期504-511,共8页
The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)alg... The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)algorithms in these areas of clinical research has not been reported previously.The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs.A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected,and the U-Net method was used to construct the AI algorithm.Thereby,a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time.This algorithm had an area under the receiver operating curve of 0.92,with 86%sensitivity and 88%specificity in the detection of retinal necrosis.For the purpose of retinal necrosis evaluation,necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444,P<0.0001)and therapeutic response of ARN(R2=0.999,P<0.0001).Therefore,our AI algorithm has a potential application in the clinical aided diagnosis of ARN,evaluation of ARN severity,and treatment response monitoring. 展开更多
关键词 Acute retinal necrosis(ARN) Artificial intelligence(AI)algorithm Clinical application
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Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation 认领 引用 被引量：15
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作者 Zeng-Lei He Jun-Bin Zhou +5 位作者 Zhi-Kun Liu Si-Yi Dong Yun-Tao Zhang Tian Shen Shu-Sen Zheng Xiao Xu 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2021年第3期222-231,共10页
Background: Acute kidney injury(AKI) is a common complication after liver transplantation(LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help... Background: Acute kidney injury(AKI) is a common complication after liver transplantation(LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. Methods: A total of 493 patients with donation after cardiac death LT(DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes(KDIGO). The clinical data of patients with AKI(AKI group) and without AKI(non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve(AUC). Results: The incidence of AKI was 35.7%(176/493) during the follow-up period. Compared with the nonAKI group, the AKI group showed a remarkably lower survival rate( P<0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval(CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models( P<0.001). Conclusions: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT. 展开更多
关键词 Artificial intelligence algorithm Random forest Acute kidney injury Liver transplantation
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A review of closed-loop reservoir management 认领 引用 被引量：7
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作者 Jian Hou Kang Zhou +2 位作者 Xian-Song Zhang Xiao-Dong Kang Hai Xie 《Petroleum Science》 SCIE CAS CSCD 2015年第1期114-128,共15页
The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. T... The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization. 展开更多
关键词 Closed-loop reservoir management Automatic history matching Reservoir production optimization Gradient-based algorithm Gradient-free algorithm Artificial intelligence algorithm
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An experiment evaluating how the tiny mass eccentricities in spinstabilized projectiles affect the position of impact points 认领 引用 被引量：4
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作者 Chuan-lin Chen Hui Xu +2 位作者 Chen-lei Huang Zhong-xin Li Zhi-lin Wu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2021年第3期948-960,共13页
This study investigates and quantifies some possible sources affecting the position of impact points of small caliber spin-stabilized projectiles(such as 12.7 mm bullets).A comparative experiment utilizing the control... This study investigates and quantifies some possible sources affecting the position of impact points of small caliber spin-stabilized projectiles(such as 12.7 mm bullets).A comparative experiment utilizing the control variable method was designed to figure out the influence of tiny eccentric centroids on the projectiles.The study critically analyzes data obtained from characteristic parameter measurements and precision trials.It also combines Sobol’s algorithm with an artificial intelligence algorithmdAdaptive Neuro-Fuzzy Inference Systems(ANFIS)ein order to conduct global sensitivity analysis and determine which parameters were most influential.The results indicate that the impact points of projectiles with an entry angle of 0°deflected to the left to that of projectiles with an entry angle of 90°.The difference of the mean coordinates of impact points was about 12.61 cm at a target range of 200 m.Variance analysis indicated that the entry angleei.e.the initial position of mass eccentricityehad a notable influence.After global sensitivity analysis,the significance of the effect of mass eccentricity was confirmed again and the most influential factors were determined to be the axial moment and transverse moment of inertia(Izz Iyy),the mass of a projectile(m),the distance between nose and center of mass along the symmetry axis for a projectile(Lm),and the eccentric distance of the centroid(Lr).The results imply that the control scheme by means of modifying mass center(moving mass or mass eccentricity)is promising for designing small-caliber spin-stabilized projectiles. 展开更多
关键词 Tiny mass eccentricity Small-caliber projectile Bullet Artificial intelligence algorithm Global sensitivity analyses Precision trials ANFIS Sobol’s algorithm
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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds 认领 引用
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作者 Mengdie Fan Chenhui Sang +7 位作者 Hua Li Yue Wei Bin Zhang Yang Xing Jing Zhang Jie Yin Wei An Bing Shao 《Research》 SCIE EI CSCD 2025年第4期591-602,共12页
Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications.Traditional retention time approaches heavily rely on the use of standard compound... Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications.Traditional retention time approaches heavily rely on the use of standard compounds,which is limited by the speed of synthesis and manufacture of standard products,and is time-consuming and labor-intensive.Recently,machine learning and artificial intelligence algorithms have been applied to retention time prediction,which show unparalleled advantages over traditional experimental methods.However,existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets,sparsity of valid data,and lack of classification in datasets,resulting in poor generalization capability and accuracy.In this study,a dataset for 10,905 compounds was constructed including their retention times.Next,an innovative classification system was implemented,classifying 10,905 compounds into a 3-tier hierarchy across 141 classes,based on functional group weighting.Then,data augmentation was performed within each category using simplified molecular input line entry system(SMILES)enumeration combined with structural similarity expansion.Finally,by training the optimal quantitative structure-retention relationship(QSRR)models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period,a novel and universal high-throughput retention time prediction model was established.The results demonstrate that this model achieves an R2 of 0.98 and an average prediction error of 23 s,outperforming currently published models.This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants,data mining,nontargeted screening,etc. 展开更多
关键词 artificial intelligence algorithms retention time standard productsand liquid chromatographic retention times artificial intelligence standard compoundswhich traditional experimen machine learning
Deep Learning Methods Using Imagery from a Smartphone for Recognizing Sorghum Panicles and Counting Grains at a Plant Level 认领 引用 被引量：1
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作者 Gustavo N.Santiago Pedro H.Cisdeli Magalhaes +3 位作者 Ana J.P.Carcedo Lucia Marziotte Laura Mayor Ignacio A.Ciampitti 《Plant Phenomics》 SCIE EI CSCD 2024年第4期1047-1057,共11页
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops.Specifically for sorghum(Sorghum bicolor L.),rapid plant-level yield estimation is hi... High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops.Specifically for sorghum(Sorghum bicolor L.),rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle.In this context,the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time.Therefore,this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.A preharvest benchmark dataset was collected at field scale(2023 season,Kansas,USA),with 648 images of sorghum panicles retrieved via smartphone device,and grain number counted.Each sorghum panicle image was manually labeled,and the images were augmented.Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation,with an average precision of 75%and 89%,respectively.For the grain number,3 models were trained:MCNN(multiscale convolutional neural network),TCNN-Seed(two-column CNN-Seed),and Sorghum-Net(developed in this study).The Sorghum-Net model showed a mean absolute percentage error of 17%,surpassing the other models.Lastly,a simple equation was presented to relate the count from the model(using images from only one side of the panicle)to the field-derived observed number of grains per sorghum panicle.The resulting framework obtained an estimation of grain number with a 17%error.The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level. 展开更多
关键词 traditional field phenotyping artificial intelligence algorithms sorghum panicle detection smartphone capture images computer vision improve sorghu characterizing number grains field trait characterization
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