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A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

ENERGIES 2024年第7期17卷 1627页

作者： Famoso, Fabio Oliveri, Ludovica Maria Brusca, Sebastian Chiacchio, Ferdinando Univ Messina Dept Engn I-98166 Messina Italy Univ Catania Dept Elect Elect & Comp Engn I-95125 Catania Italy

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

关键词： cluster analysis artificial intelligence algorithms Reliability Block Diagrams wind energy wind farm production estimation artificial neural network

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Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

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MATERIALS 2023年第8期16卷 3220-3220页

作者： Saldana, Manuel Galvez, Edelmira Navarra, Alessandro Toro, Norman Cisternas, Luis A. Univ Arturo Prat Fac Engn & Architecture Iquique 1110939 Chile Univ Antofagasta Dept Ingn Quim & Proc Minerales Antofagasta 1270300 Chile Univ Catolica Norte Dept Met & Min Engn Ave Angamos 0610 Antofagasta 1270709 Chile McGill Univ Dept Min & Mat Engn 3610 Univ St Montreal PQ H3A 0C5 Canada

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

关键词： SAG mill comminution processes artificial intelligence algorithms modeling optimization mineral processing

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Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms

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AGRICULTURAL WATER MANAGEMENT 2023年 280卷

作者： Di Nunno, Fabio Granata, Francesco Univ Cassino & Southern Lazio Dept Civil & Mech Engn DICEM Via Biasio 43 I-03043 Cassino Frosinone Italy

In years of increasing impact of climate change effects, a reliable characterization of the spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant improvement in water resource management, especially as regards irrigation activities. Sicily, an insular region of Southern Italy, has exceptionally valuable agricultural production and high irrigation needs. In this study, the ETo reference evapotranspiration in Sicily was first evaluated on the basis of historical and future climate parameters, referring for future values to two climate scenarios characterized by different Representative Concentration Pathways: RCP 4.5 and RCP 8.5. Then, the Hierarchical algorithm was used to divide Sicily into three homogeneous regions, each characterized by specific ETo features. In addition, some Machine Learning (ML) algorithms were used to develop forecasting models based on only historical data. Support Vector Regression (SVR) was used to predict the future values of Tmin and Tmax, while an ensemble model based on Multilayer Perceptron (MLP) and M5P Regression Tree was developed for the ETo forecasting. Predictions made with the ensemble MLP-M5P model were compared with the ETo computed for the RCP 4.5 and RCP 8.5 future climate scenarios. During the forecast period, from 2001 to 2091, evapotranspiration increases were observed for all three clusters. For cluster C1, along the coast, percentage increases of 7.52%, 14.64% and 10.78%, were computed for RCP 4.5, RCP 8.5, and MLP-M5P, respectively, while, for cluster C3, in the inland, percentage increases were higher and equal to 8.12%, 16.71%, and 14.98%, respectively. The ensemble MLP-M5P model led to intermediate trends between RCP 4.5 and RCP 8.5, showing a high correlation with the latter (R2 between 0.93 and 0.98). The developed approach, based on both clustering and forecasting algorithms, provided a comprehensive analysis of the reference evapotranspiration, with the detection of the different ho

关键词： Climate change artificial intelligence algorithms Clustering Ensemble models Mediterranean climate Irrigation planning

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A mixing algorithm of ACO and ABC for solving path planning of mobile robot

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Li, Guangxin Liu, Chao Wu, Lei Xiao, Wensheng China Univ Petr East China Natl Engn Lab Offshore Geophys & Explorat Equipmen Qingdao 266580 Peoples R China China Univ Petr East China Sch Elect & Mech Engn Qingdao 266580 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore

Path planning is involved in many applications such as trajectory planning, mobile robotics, pipeline layout, etc. Researchers use artificial intelligence algorithms to solve path planning efficiently, among which the ant colony algorithm (ACO) is one of the common intelligent algorithms to solve path planning problems. However, the traditional ACO has defects such as low early search efficiency and easy to fall into local optimum, while the artificial bee colony algorithm (ABC) has high search efficiency. Therefore, an improved ant colony optimization artificial bee colony algorithm (IACO-IABC) is proposed in this study. IACO-IABC contains three mechanisms. First, the heuristic mechanism with directional information for the ACO is improved to enhance the efficiency of steering towards the target direction. Secondly, the novel neighborhood search mechanism of the employed bee and the onlooker bee in the ABC is presented to enhance the exploitation of optimal solutions. Then, the path optimization mechanism is introduced further to reduce the number of turn times in the planned path. To verify the performance of the IACO-IABC, a series of experiments are conducted with 10 different maps. The experiments compare nine variants of ACO and eight commonly used intelligent search algorithms, and the results show the advantages of the IACO-IABC in reducing the number of turn times and path lengths and enhancing the convergence speed of the algorithm. Compared to the best results of other algorithms, the average improvement percentages of the proposed algorithm in terms of the path turn times are 375%, 258.33%, 483.33%, 186.67%, 166.77% and 255.33%, further demonstrating the ability of IACO-IABC to obtain high-quality path planning result.

关键词： Path planning Mobile robot artificial intelligence algorithms Ant colony optimization artificial bee colony

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How Clinicians Perceive artificial intelligence-Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

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JOURNAL OF MEDICAL INTERNET RESEARCH 2021年第12期23卷 e33540页

作者： Hah, Hyeyoung Goldin, Deana Shevit Florida Int Univ Coll Business Informat Syst & Business Analyt 11200 SW 8th St Miami FL 33199 USA Florida Int Univ Nicole Wertheim Coll Nursing & Hlth Sci Miami FL 33199 USA

Background: With the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making. Objective: This study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care. Methods: This study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques. Results: A total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (beta=-0.421, P=.02). Instead, clinicians' diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms. Conclusions: This study elucidated clinicians' current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis.

关键词： artificial intelligence algorithms AI diagnostic capability virtual care multilevel modeling human-AI teaming natural language understanding

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Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors

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LABORATORY INVESTIGATION 2023年第12期103卷 100257页

作者： Vazzano, Jennifer Johansson, Dorota Hu, Kun Euren, Kristian Elfwing, Stefan Parwani, Anil Zhou, Ming Ohio State Univ Wexner Med Ctr Dept Radiol Columbus OH 43210 USA Inify Labs AB Stockholm Sweden Tufts Univ Tufts Med Ctr Sch Med Dept Pathol Boston MA 02111 USA

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histo-pathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity

关键词： artificial intelligence algorithms digital pathology preanalytical factors prostate cancer whole slide imaging

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Stem Taper Estimation Using artificial Neural Networks for Nothofagus Trees in Natural Forest

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FORESTS 2022年第12期13卷 2143页

作者： Sandoval, Simon Acuna, Eduardo Univ Concepcion Fac Ciencias Forestales Dept Manejo Bosques & Medio Ambiente Concepcion 4070386 Chile Univ Concepcion Fac Ciencias Forestales Lab Anal & Modelamiento Geoinformac Concepcion 4070386 Chile

The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak's model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.

关键词： machine learning artificial intelligence algorithms stem profile modeling volume estimate

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intelligence decision mechanism for prediction of compressive strength of self-compaction green concrete via neural network

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JOURNAL OF CLEANER PRODUCTION 2022年 340卷

作者： Jiang, Haidong Liu, Guoliang Alyami, Hashem Alharbi, Abdullah Jameel, Mohammed Khadimallah, Mohamed Amine Guizhou Inst Technol Sch Resources & Environm Engn Guiyang 550003 Guizhou Peoples R China Tongji Univ Dept Geotech Engn Minist Educ Key Lab Geotech & Underground Engn Shanghai 200092 Peoples R China Changshu Inst Technol Sch Text Garment & Design Changshu 215500 Jiangsu Peoples R China Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 At Taif 21944 Saudi Arabia Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 At Taif 21944 Saudi Arabia King Khalid Univ Coll Engn Dept Civil Engn Abha Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Civil Engn Dept Al Kharj 16273 Saudi Arabia Univ Carthage Polytech Sch Tunisia Lab Syst & Appl Mech Tunis Tunisia

Civil engineering has a specific position for different forms of concrete as a century-old material. One of the most popular materials for human consumption is made from this substance. In recent years, solutions have been presented for the manufacturing of concrete, making concrete environmentally friendly and also making current waste useable as additives for concrete. In this research, the mechanical features of self-compacting green concrete (SCGC) comprising of compressive, tensile and flexural strength have been studied while diverse amounts of calcium carbide waste (CCW) and rice husk ash (RHA) were tested as partial cement replacements at 3, 7 and 28 days of the curing age. Then, according to available experimental results, artificial intelligence algorithms including Emotional Neural Network-chaotic particle swarm optimization (EANN-CPSO), First-principles molecular-dynamics (FPMD), and conventional Linear Regression (LR) model were applied for predicting the mechanical features of self-compacting concrete (SCC) while adding up to 10% RHA and up to 20% CCW in the SCGC blends. It was reported that the addition of CCW decreases the workability of SCGC mixes and raises the compressive strength (CS) at 28 days for SCGC mixes including 10% of RHA and without CCW compared to control mixes. Also, in both the testing and training phases, the minimum R-2 value FPMD, EANN-CPSO and LR models is about 0.904. It was found that the hybrid models of EANN-CPSO-S3, FPMD -S3 and LR-S3 show the most accurate performance with R-2 = 0.997 and 0.970 for EANN-CPSO-S3, R-2 = 0.967 and 0.954 for FPMD -S3 and R-2 = 0.934 and 0.929 for LR-S3 in both phases. The result shows that additional models might increase the performance of the model, such as new algorithms, hybrid models and optimization approaches.

关键词： artificial intelligence algorithms Neural network Rice husk ash Calcium carbide waste Compressive strength Self-compacting green concrete

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Microalgae conversion to alternative energy, operating environment and economic footprint: An influential approach towards energy conversion, and management

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ENERGY CONVERSION AND MANAGEMENT 2022年 269卷

作者： Goswami, Rahul Kumar Agrawal, Komal Upadhyaya, Hari M. Gupta, Vijai Kumar Verma, Pradeep Cent Univ Rajasthan Dept Microbiol Bioproc & Bioenergy Lab BPBEL Ajmer 305817 Rajasthan India London South Bank Univ Sch Engn 103 Borough Rd London SE1 0AA England Scotlands Rural Coll SRUC Ctr Safe & Improved Food Kings BldgWest Mains Rd Edinburgh EH9 3JG Scotland Scotlands Rural Coll SRUC Biorefining & Adv Mat Res Ctr Kings BldgWest Mains Rd Edinburgh EH9 3JG Scotland Lovely Profess Univ Sch Bio Engn & Biosci Dept Microbiol Phagwara 144411 Punjab India Scotlands Rural Coll SRUC Ctr Safe & Improved Food Kings Bldg West Mains Rd Edinburgh EH9 3JG Scotland Scotlands Rural Coll SRUC Biorefining & Adv Mat Res Ctr Kings BldgWest Mains Rd Edinburgh EH9 3JG Scotland

Microalgae (MA) biorefinery is a vital platform for the conversion of biomass into biofuels, biomaterials, and bioactive substances and it can remediate the pollutant from the environment. Moreover, its commercialization can enable a jump start towards resolving energy and economic crisis. However, the major bottleneck in bio-refinery includes (i) low biomass and biomolecules production, (ii) high cost, (iii) less economic viability, (iv) high energy requirement, and (v) environmental impacts of the process. Furthermore, the lack of advanced models or algorithms for process designing and limited genetic and molecular knowledge is a key bottleneck. Thus, there is an urgent need for novel integrated microalgal bioenergy conversion methods that provide eco-nomic and environmental balance. Moreover, advanced molecular knowledge in the microalgal research pipeline can be developed that improves the biosynthesis of biomolecules. Furthermore, novel technological development is required that automatically controls and monitors the microalgal biorefinery. Thus, the current review de-scribes a comparative analysis of the gaps from previous studies. Subsequently, it highlights advanced cutting -edge cultivation and molecular and genetic approaches for enhancing biomass and biomolecules. It critically evaluates the potential of artificial intelligence (AI) algorithms and IoTs-based sensors used in microalgal cultivation, system optimization pipeline, and other aspects. Finally, it aims to provide a conceptual scenario and integrated mechanisms of multiple biofuel production with co-products formation concerning zero-carbon and waste footprint to maintain a circular-bioeconomy. Additionally, it also offers elaboration on recent life-cycle -assessment (LCA), techno-economic analysis (TEA) of recent microalgal biofuels biorefinery, and algal bio-fuels' current and future market trends towards sustainability key challenges and opportunities. This review will contribute critically t

关键词： Microalgae biorefinery Biomass to Biofuels Co -products artificial intelligence algorithms Life cycle assessment Techno-economic analysis

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Method for automatic railway track surface defect classification and evaluation using a laser-based 3D model

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IET IMAGE PROCESSING 2020年第12期14卷 2701-2710页

作者： Ye, Jiaqi Stewart, Edward Zhang, Dingcheng Chen, Qianyu Roberts, Clive Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B15 2TT W Midlands England

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

关键词： flaw detection image classification artificial intelligence rails mechanical engineering computing railways maintenance engineering automatic optical inspection railway engineering feature extraction pattern classification wheels rail measurement data automatic railway track surface defect classification laser-based 3D model physical surface defects wheels laser-based optical inspection technologies 2D profile measurement rail maintenance artificial intelligence algorithms geometrical feature extraction multiclass classifiers

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