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Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

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GISCIENCE & REMOTE SENSING 2022年第1期59卷 2217-2246页

作者： Ma, Changhui Xie, Yaowen Duan, Si-Bo Qin, Wenhua Guo, Zecheng Xi, Guilin Zhang, Xueyuan Bie, Qiang Duan, Hanming He, Lei Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr Beijing 100081 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China Neijiang Normal Univ Sch Geog & Resource Sci Neijiang 641100 Peoples R China Lanzhou Univ Coll Earth & Environm Sci South Tianshui Rd Lanzhou 730000 Peoples R China

Due to the limitations of spatial quantification methods, the spatio-temporal patterns of grassland utilization intensity (GUI) in the Selinco watershed (SLCW), the core region of ecological security on the Qinghai-Tibetan Plateau, is unclear under multiple utilization modes. This paper quantified GUI by constructing the association between the potential and actual Enhanced Vegetation Index (EVI) of grasslands in terms of interannual variability. To obtain an accurate spatio-temporal dataset of potential EVI, the following two components were considered on. Firstly, the temporal lag effects of each raw climate factor were investigated to determine the optimal climate variables affecting vegetation productivity. Secondly, four machine learning (ML) algorithms, including an artificial neural network, random forest, support vector machine, and gradient boosting regression tree combined with the Bayesian model average, were used to construct grassland potential EVI models involving EVI, grassland type, and environmental factors (topography, soil, raw climate, and bioclimatic). Meanwhile, to maximize the performance of ML models, variable selection, variable transformation, and hyperparameter optimization were systematically implemented, where the hyperparameter optimization algorithms employ the grid search algorithm, Bayesian optimization, genetic algorithm, and particle swarm optimization. Then, the spatio-temporal dataset of GUI in the SLCW from 2001 to 2019 was established by using the above quantification method based on multisource remote sensing and artificial intelligence algorithms. The analysis of spatio-temporal variation in GUI showed that the implementation of ecological restoration projects leads to a significant and rapid decline in the overall GUI of the SLCW after 2010 (declining by 4.8%), which is more obvious in the non-nature reserve (declining by 9.3%). In the Qiangtang Nature Reserve within the SLCW, although the GUI shows a declining trend after 2

关键词： Grassland utilization intensity spatial quantification method artificial intelligence algorithms spatio-temporal patterns Qinghai-Tibetan Plateau nature reserve

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Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第8期83卷 314-314页

作者： Wang, Yaxu Liu, Bin Wang, Jiwen Meng, Qingyang Liu, Zhengyu Shandong Univ Inst Geotech & Underground Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Peoples R China

Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method.

关键词： Tunnel boring machine Laser-induced breakdown spectroscopy Quartz content artificial intelligence algorithms

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Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 55卷

作者： Wang, Ying Shandong Inst Commerce & Technol Coll Digital Mkt Ind Jinan 250103 Shandong Peoples R China

As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.

关键词： artificial intelligence algorithms Electric vehicles Energy management Operational efficiency

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Application of artificial intelligence algorithms in Ultrasound Diagnosis of Thyroid Diseases

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Procedia Computer Science 2025年 261卷 1129-1135页

作者： Xiaoyan Deng Chengwu Liu School of Clinical Medicine Guangzhou Health Science College Guangzhou 510450 China School of Public Health and Health Professions Guangzhou Health Science College Guangzhou 510450 China

Objective: To explore the application effect of artificial intelligence algorithms in ultrasound diagnosis of thyroid diseases. Method: Select a certain number of suspected thyroid disease patients and use conventional ultrasound diagnostic methods and ultrasound diagnostic methods combined with artificial intelligence algorithms for detection and diagnosis. Compare the accuracy, specificity, sensitivity, and diagnostic time of two diagnostic methods. The results showed that artificial intelligence algorithm assisted diagnosis was superior to conventional ultrasound diagnosis in terms of accuracy, specificity, and sensitivity, while significantly shortening the diagnosis time. Conclusion: artificial intelligence algorithms can effectively improve the accuracy and efficiency of ultrasound diagnosis of thyroid diseases, which is of great significance for improving the diagnostic effectiveness of thyroid diseases.

关键词： artificial intelligence algorithms Ultrasonic diagnosis Thyroid disease

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English-Speaking Teaching System with Natural Language Processing Technology Based on artificial intelligence algorithms 10

English-Speaking Teaching System with Natural Language Proce...

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10th International Conference on Information and Education Technology (ICIET)

作者： Shi, Jing Ni, Li Su, Zhihao Gannan Normal Univ Sci & Technol Coll Ganzhou Peoples R China Guangzhou Coll Appl Sci & Technol Guangzhou Peoples R China Guangzhou Coll Appl Sci & Technol Comp Sch Guangzhou Peoples R China

ISBN: (纸本)9781665480482

English speaking is a very important skill of English ability for students. At the same time, English speaking learning needs a demanding environment. Because English teachers have heavy teaching tasks and limited time. This research developed an English-speaking teaching system with natural language processing technology based on artificial intelligence algorithms, which can solve this problem very well. The system engages the most advanced natural language processing technology based on artificial intelligence algorithms to correct and feedback students' oral problems in a timely, fast, and targeted manner, which is beneficial to improve students' speaking learning efficiency and interest. This system proposes an improved Bidirectional Encoder Representations from Transformers model, which is simply called BERT and based on Transformer's bidirectional encoding representation. The accuracy of this algorithm for spoken English recognition is significantly higher than other classical algorithms. This system has been tested and evaluated in some universities, and the learning efficiency and satisfaction of students is significantly higher than other oral English learning systems.

关键词： English education formatting natural language processing technology artificial intelligence algorithms

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Prediction of the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms in a big database of beams generated by 3D nonlinear finite element analysis

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COMPUTERS AND CONCRETE 2021年第6期28卷 433-447页

作者： Markou, George Bakas, Nikolaos P. Univ Pretoria Civil Engn Dept Struct Div Hatfield Campus ZA-0028 Pretoria South Africa RDC Informat Res & Dev Dept 2 Irous St Athens 10442 Attica Greece

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

关键词： artificial intelligence algorithms beams without stirrups design formulae machine learning reinforced concrete shear strength prediction

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Transformation Path of artificial intelligence algorithms and Intelligent Engineering Management

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Procedia Computer Science 2025年 261卷 520-528页

作者： Huaizhu Wang Shandong Polytechnic College Jining 272067 Shandong China

In response to the low accuracy problem faced by the current engineering management field in project schedule management and other aspects, this article introduces the research on artificial intelligence algorithms and intelligent transformation paths, aiming to optimize project management processes and improve efficiency. Firstly, the core issues of project progress, resource allocation, and risk control in engineering management can be systematically identified. Then, this article designs an intelligent decision support system based on machine learning and deep learning, constructs a multidimensional prediction model, and achieves project key milestone prediction, resource scheduling optimization, and potential risk analysis. Finally, an intelligent management framework that adapts to projects of different scales is introduced. Through data collection, real-time analysis, and reinforcement learning dynamic decision support mechanisms, iterative optimization of resource allocation and risk control is achieved. At time points 3, 4, 6, and 9, the probability of actual risk events occurring increases, and the prediction accuracy of the Bayesian network model significantly improves. Its predictions at these time points almost completely match the actual situation (the predicted values are the same as the actual values). The combination of artificial intelligence algorithms and intelligent transformation paths provides new development opportunities for the field of engineering management. By gradually building an intelligent management system, not only can the management level of engineering projects be effectively improved, but also the optimal allocation of resources, precise control of risks, and efficient achievement of project goals can be achieved.

关键词： artificial intelligence algorithms engineering management intelligent transformation path resource scheduling optimization

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ETHICAL EVALUATION AND OPTIMIZATION OF artificial intelligence algorithms BASED ON SELF SUPERVISED LEARNING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3594-3602页

作者： Deng, Ruoyu Zhao, Yang Chengdu Technol Univ Sch Marxism Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

Active learning solves the problem of requiring a large amount of manpower and resources due to the large size of training samples. The core problem is how to select valuable samples to reduce annotation costs. Using neural networks as classifiers, most methods choose samples with large amounts of information without considering the issue of information redundancy between the selected samples. Through the study of redundancy issues, the author proposes a sample selection optimization method to reduce information redundancy. Using uncertainty methods to select samples with high information content to form a candidate sample set, and using latent variable vectors calculated in the network to represent sample information, the cosine distance between candidate samples is calculated using this vector to select subsets with large interval distance and low information redundancy. Compared with several uncertainty methods in the Mnist, Fashion mnist, and Cifar-10 datasets, this method reduces labeled samples by a minimum of 11% with the same sample accuracy. The higher the dimensionality of the feature vector calculated by CNN, the more candidate samples it contains, and the more information it contains. After being improved by self supervised learning algorithms, the effect becomes more significant. The more candidate samples are selected, the stronger the information redundancy. The better the performance of self supervised learning algorithms.

关键词： artificial intelligence algorithms Information redundancy Cosine distance Uncertainty method

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Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening

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SLAS TECHNOLOGY 2024年第6期29卷 100218页

作者： Guo, Jian Xue, Yu Shanghai Pudong Hosp Dept Gen Surg Shanghai 201300 Peoples R China

In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct indepth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.

关键词： Magnetic resonance imaging Imaging images artificial intelligence algorithms Cancer screening

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Investigation on the coordinated operation control technology of distributed new energy grid connection using artificial intelligence algorithms

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Lu, Yonglin Sun, Jinyong Hu, Jinying Chen, Rui Liao, Yifa Shang, Jingrui Yunnan Power Grid Co Ltd Dehong Power Supply Bur Dehong Yunnan Peoples R China

New energy power generation has strong randomness and volatility. Especially in the case of a high proportion of NE (network security) power generation, its sudden random power generation in a short period of time will seriously affect the stable operation of the power grid. Therefore, this paper proposes BP neural network algorithm to study the distributed NE grid-connected cooperative operation control technology. First of all, this paper studies the artificial intelligence algorithm in detail and applies it to the coordinated operation control of distributed NE grid-connected;then, based on the status quo of renewable energy PG (power generation), this paper establishes a suitable wind speed time series model, and thus proposes an optimization model based on a rolling scheduling optimization algorithm. The experimental results show that the average running time of the rolling scheduling optimization algorithm is maintained at about 0.2 s, which can effectively realize online operation. In addition, through rolling adjustment, the error between the total output curve of the unit and the actual total output curve of the unit can be significantly reduced. The research shows that the rolling scheduling optimization algorithm has a good optimization effect, can promote the coordinated development of wind farms and power systems, and increase the capacity of power systems.

关键词： distributed new energy coordinated operation control technology artificial intelligence algorithms coordinated development

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