检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps

引用

JOURNAL OF BIOMEDICAL OPTICS 2021年第1期26卷 015001-015001页

作者： Lai, Lily L. Blakely, Andrew Invernizzi, Marta Lin, James Kidambi, Trilokesh Melstrom, Kurt A. Yu, Kevin Lu, Thomas City Hope Natl Med Ctr Dept Surg Duarte CA 91010 USA NCI Dept Surg Natl Inst Hlth Campus Bethesda MD 20892 USA City Hope Natl Med Ctr Div Gastroenterol Duarte CA USA Jet Prop Lab Pasadena CA USA

Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： artificial intelligence algorithms deep learning polyp discrimination colorectal cancer narrow-band imaging color channel separation

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Reactive Power Optimization of Power System based on Improved Particle Swarm Optimization

Reactive Power Optimization of Power System based on Improve...

引用

2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies(第四届电力设施管制、重建及能源技术国际会议 DRPT 2011)

作者： Hao Wang Huilan Jiang Ke Xu Guodong Li Key Laboratory of Power System Simulation and Control of Ministry of Education Tianjin University Ti Tianjin Electric Power Corporation Technology Center Tianjin 300384China

reactive power optimization is a nonlinear,multivariable,multi-constrained programming problem,which makes the optimization process *** this paper,based on the characteristics of reactive power optimization,a mathematical model of reactive power optimization,including comprehensive consideration of the practical constraints and reactive power regulation means for optimization,is *** particle swarm optimization (PSO) has been studied,and the method based on improved particle swarm optimization for reactive power is going to be taken in this *** for the IEEE 14-bus system proves that the improved PSO algorithm used in this paper for reactive power optimization is *** algorithm is simple,convergent and of high quality for optimization,and thus suitable for solving reactive power optimization problems,with some application prospect.

关键词： Power System:Reactive Power Optimization artificial intelligence algorithms Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

Constructing a Sorting Machine for Degenerative Cerebropathia

Constructing a Sorting Machine for Degenerative Cerebropathi...

引用

International Conference on Advanced Communication Technology

作者： Seohyun Moon Bogeum Choi Jiwoo An Taeseon Yoon Natural Science Hankuk Academy of Foreign Studies Faculty Hankuk Academy of Foreign Studies

ISBN: (纸本)9781509048922

Dementia with Lewy bodies and Parkinson's Disease has the common cause: mutation of gene CYP2D6. Thus, it is hard to distinguish those two diseases. In order to classify them clearly, construction of sorting machine is required. Various algorithms including Apriori, Decision tree, Support Vector Machine, K-means and Neural Network was used in the construction of sorting machine. Two kinds of sorting machines were constructed: one classifying whether the patient is under Dementia with lewy bodies or Parkinson's Disease, the other classifying whether the gene is normal or mutation has undergone.

关键词： Parkinson's Disease Dementia with Lewy bodies Classification Machine Function Construction artificial intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：