检索结果-内蒙古大学图书馆

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Pandey, Ritesh Upadhya, Maneesh Singh, Manvendra Galgotias University Greater Noida India

ISBN: (纸本)9798350383522

Accurate rainfall forecasts are important throughout Agriculture, Water Resources, and Disaster preparation. In this example, we use two complex devices study design, Logistic regression and Random Forest classifier, to predict rainfall in Australia. The data set source for this study came from Kagle, which provides a complete summary of climate changes over time. The aim of our research is to develop a robust prediction model concerning possible precipitation types Australian territories. Logistic regression provides a baseline model, while the Random Forest Classifier provides an ensemble based the technique improves accuracy. We look for the content engineering and hyperparameter tuning to improve the model performance. The results of this study are important for decision makers and stakeholders, as accurate rainfall forecasts can provide to strengthen resource management, risk reduction and sustainability planning in different areas. The results of our models is that it will contribute to a better understanding of Australian rainfall Patterns, therefore helps in effective decision making and preparation. © 2024 IEEE.

关键词： Anaconda Jupyter Logistic regression Rainfall prediction Random Forest Classifier regression algorithm

来源：评论

学校读者我要写书评

暂无评论

Practical Batch Quality Trend Analysis Based on regression algorithms

Practical Batch Quality Trend Analysis Based on Regression A...

引用

International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Wu, Ming Ye, Long Han, Huijie Tong, Jun Wang, Zhenguo China Tobacco Guizhou Ind Co Ltd Guizhou Peoples R China

ISBN: (纸本)9781665469012

Batch production enables the manufacture of things in large quantities in a flexible manner while lowering manufacturing costs and improving quality. In modern industry, prediction and monitoring of quality trends are critical for batch production quality management. Correspondingly this study utilizes linear regression and exponential regression algorithms to anticipate the future values of industrial data, as well as employs trigonometric function regression methods to compute and construct periodic curves. Numerical results show that with appropriately selected parameters, our proposed methods can accurately predict batch production quality and give early warning of production problems.

关键词： trend analysis batch production quality control regression algorithm

来源：评论

学校读者我要写书评

暂无评论

Geospatial modeling of climate change indices at Mexico City using machine learning regression

引用

TRANSFORMING GOVERNMENT- PEOPLE PROCESS AND POLICY 2024年第3期18卷 353-367页

作者： Saldana-Perez, Magdalena Guzman, Giovanni Palma-Preciado, Carolina Arguelles-Cruz, Amadeo Moreno-Ibarra, Marco Inst Politecn Nacl Ctr Invest Comp Mexico City Mexico

PurposeClimate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico ***/methodology/approachIn this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics *** paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R2 score of 0.99, in contrast to Support Vector regression, which yielded an R2 score of ***/valueThe full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

关键词： Climate indices regression algorithm Supervised machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting the thermal structural behaviour of steel pallet rack connections using machine learning

引用

ENGINEERING STRUCTURES 2025年 322卷

作者： Shah, S. N. R. Umer, Qasim Pathan, Nazia Mehran Univ Engn & Technol Dept Civil Engn SZAB Campus Khairpur Mirs 66020 Pakistan CUI Vehari Campus Dept Comp Sci Vehari Pakistan Queen Magaret Univ Ctr Hlth Act & Rehabil Res CHEARR Edinburgh Scotland

Unlike traditional bolted or welded steel connections, the beam-to-column connection in steel pallet racks is established using a unique beam end connector with punched tabs, which is welded to the end of the pallet beam. This design leads to complex structural behaviour, particularly at elevated temperatures due to reduced steel properties, thermal expansion, and fire-induced failure modes. Design standards recommend experimental testing for each connector type, as a general analytical model is lacking. However, such testing is often costly and time-consuming. Recent advancements in machine learning offer a promising alternative to traditional testing methods in civil engineering. This study explores the application of machine learning techniques to predict the moment-rotation behaviour of steel pallet rack beam-to-column connections under elevated temperatures. The methods investigated include Linear regression, Ridge regression, Ridge Cross-Validation regression, Logistic regression, Least Shrinkage regression, Least Absolute Shrinkage and Selection Operator with Cross-Validation regression, and Localised Linear Support regression. The dataset comprised 63 experimental tests, varying in column thickness, beam depth, and number of tabs in the beam end connector, conducted at temperatures ranging from 25 degrees C to 700 degrees C. Model evaluation employed metrics such as mean-square error, Pearson correlation coefficient, and coefficient of determination. Results indicate that linear regression effectively predicts moment-rotation behaviour, closely aligning with experimental data. While other machine learning techniques performed well in moment prediction, their accuracy in rotation forecasting was limited. This research enhances the accuracy of structural behavior predictions and offers a cost-effective alternative to physical testing for steel pallet rack designers. Future research recommendations are provided based on these findings.

关键词： Steel pallet rack Boltless beam-to-column connection Elevated temperature Moment-rotation law regression algorithm Machine learning

来源：评论

学校读者我要写书评

暂无评论

Carbon stock inversion study of a carbon peaking pilot urban combining machine learning and Landsat images

引用

ECOLOGICAL INDICATORS 2024年 159卷

作者： Yang, Kui Zhou, Peng Wu, Jingdong Yao, Qian Yang, Zenan Wang, Xiaoxuan Wen, Youyue Henan Polytech Univ Sch Surveying & Land Informat Engn Jiaozuo 454003 Peoples R China South China Inst Environm Sci Minist Ecol & Environm Guangzhou 510535 Peoples R China Natl Key Lab Urban Ecol Environm Simulat & Protect Guangzhou 510535 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Environm Protect Key Lab Satellite Remote Se Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing Peoples R China

Global warming is a significant challenge, and carbon stocks in terrestrial ecosystems are crucial for reducing the greenhouse effect and increasing sinks. A study was conducted in Zhengzhou City from 2000 to 2020 using Landsat image spectral reflectance to analyze changes in carbon stock. Environmental variables such as surface moisture, salinity, vegetation index, brightness, and soil texture were constructed. Multiple linear regression (MLR), support vector machine regression (SVR), random forest regression (RFR), and long short-term memory (LSTM) models were used to invert the carbon stock. The results showed that the NDCS index, constructed using Landsat's blue band and NIR band, was the best inversion variable for carbon stock, with the clay index (CI) playing a primary role. The LSTM algorithm had the best fitting effect on carbon stock, with an R2 of 0.84 and RMSE of 3.56. The carbon stock in Zhengzhou City decreased by 13.93% between 2000 and 2020, possibly due to the large-scale reduction of arable land. Future land -use planning should focus on protecting arable land, optimizing land -use patterns, and enhancing the ecosystem's carbon sequestration capacity.

关键词： Spectral reflectance Auxiliary environment variable regression algorithm Carbon stock Correlation analysis

来源：评论

学校读者我要写书评

暂无评论

Efficacy of machine learning algorithm in estimating oxyhydrogen gas generation system: Electrolyte concentration and current influence on sustainable energy production

引用

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 191卷 2292-2302页

作者： Subramanian, Balaji Sridharan, Naveen Venkatesh Vaithiyanathan, Sugumaran Gurusamy, Manikandaraja Varuvel, Edwin Geo Tsai, Pei-Chien Ponnusamy, Vinoth Kumar SRM Inst Sci & Technol Fac Engn & Technol Dept Mech Engn Kattankulathur 603203 India Vellore Inst Technol Sch Mech Engn SMEC Chennai Campus Chennai 600127 India Lulea Univ Technol Div Operat & Maintenance Engn Lulea Sweden Istinye Univ Fac Engn & Nat Sci Dept Mech Engn Istanbul Turkiye Saveetha Inst Med & Tech Sci Inst Bioinformat Saveetha Sch Engn Dept Computat Biol Chennai 602105 Tamil Nadu India Kaohsiung Med Univ Dept Med & Appl Chem Kaohsiung 807 Taiwan Kaohsiung Med Univ KMU Res Ctr Precis Environm Med Kaohsiung 807 Taiwan Kaohsiung Med Univ Hosp KMUH Dept Med Res Kaohsiung 807 Taiwan Natl Sun Yat Sen Univ NSYSU Dept Chem Kaohsiung 804 Taiwan

Hydrogen gas has emerged as a promising and ecologically friendly substitute for fossil fuels, providing a longterm energy alternative. This research study applies machine learning regression models for estimating the volume of HHO gas generated using two distinct electrolytes with varying concentrations, namely sodium hydroxide (NaOH) and potassium hydroxide (KOH). The efficiency of the HHO gas production system hinges on various factors, such as the type of electrolyte and electrode, distance between electrodes, applied current and voltage. This investigation employed a comprehensive array of 29 multiple-regression algorithms to predict gas production. Notably, the input data for these algorithms were directly fed from the collected experimental data without any preprocessing. The input variables include the electrolyte concentration, current, and voltage, while the output is the estimated production of HHO gas. The findings of this study revealed that the multilayer perceptron (MLP) regression algorithm yielded the highest correlation coefficients, registering values of 0.9945 for NaOH and 0.9942 for KOH, respectively. Root relative squared error, relative absolute error, root mean squared error, mean fundamental error, and correlation coefficient were the metrics used to determine the model performance in predicting the volume of HHO gas produced. Furthermore, the experimental analysis demonstrated a direct relationship between the concentration of the electrolyte solution and the current with the rate of HHO gas production. Notably, the study identified that operating the cell at 40 A with KOH resulted in the maximum productivity of 0.9 litres per minute (LPM) of HHO gas, representing a 16 % increase compared to NaOH. These findings underscore the potential of machine learning regression models in optimizing hydrogen production and highlight the advantages of KOH as an electrolyte in this context.

关键词： Hydrogen production regression algorithm Alternating model tree Machine learning

来源：评论

学校读者我要写书评

暂无评论

Driving Fatigue Monitoring via Kernel Sparse Representation regression With GMC Penalty

引用

IEEE SENSORS JOURNAL 2022年第16期22卷 16164-16177页

作者： Zhang, Xuan Wang, Dixin Wu, Hongtong Lei, Chang Zhong, Jitao Peng, Hong Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Lanzhou Univ Minist Educ Key Lab Special Funct Mat & Struct Design Lanzhou 730000 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci Shanghai 200031 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Biol Sci Shanghai 200031 Peoples R China Beijing Inst Technol Sch Med Technol Brain Hlth Engn Lab Beijing 100081 Peoples R China

Automatic monitoring technology for fatigued driving can greatly reduce traffic accidents caused by drivers due to decreased attention. The use of monitoring algorithms based on physiological signal sensors is of great significance for objective monitoring. In this paper, a novel algorithm with excellent performance and strong generalization ability, kernel sparse representation regression based on generalized minimax-concave (GMC-KSRR), is proposed to identify fatigue physiological signals. Specifically, in the proposed algorithm, the training samples of physiological features are mapped in reproducing kernel Hilbert space (RKHS) for linear separability. Then, in RKHS, the sparse coefficients of the test samples are obtained by generalized minimax-concave (GMC) penalty-based sparse representation (SR). Different from the traditional l(1)-norm, the GMC penalty we used does not underestimate the high-amplitude component of sparsity coefficients, resulting in a more accurate regression result. Finally, the regression decision is performed in the label subspace according to the obtained sparse coefficients. Meanwhile, a multimodality algorithm based on GMC-KSRR has also been proposed that weighs each modality according to quality for robust fatigue monitoring. Eventually, competitive experimental results on the well-known SEED-VIG dataset against the state-of-the-art methods demonstrate the efficacy of the proposed algorithms in identifying fatigue physiological signals, indicating its powerful application potential in driving fatigue monitoring.

关键词： Fatigue driving monitoring regression algorithm sparse representation (SR) GMC penalty robust recognition

来源：评论

学校读者我要写书评

暂无评论

Perception-Based Prediction for Efficient Kinesthetic Coding

引用

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2530-2534页

作者： Xu, Yiwen Huang, Qingfeng Zheng, Quanfei Fang, Ying Zhao, Tiesong Fuzhou Univ Fujian Key Lab Intelligent Proc & Wireless Transmi Fuzhou 350025 Peoples R China Fuzhou Univ Coll Zhicheng Fuzhou 350025 Peoples R China

Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity.

关键词： Encoding Haptic interfaces Prediction algorithms Force Signal processing algorithms Mathematical models Linear regression Data compression haptics kinesthetic coding regression algorithm

来源：评论

学校读者我要写书评

暂无评论

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

引用

CEREBRAL CORTEX 2024年第4期34卷 bhae121-bhae121页

作者： Zhang, Ning Chen, Shuning Jiang, Keying Ge, Wei Im, Hohjin Guan, Shunping Li, Zixi Wei, Chuqiao Wang, Pinchun Zhu, Ye Zhao, Guang Liu, Liqing Chen, Chunhui Chang, Huibin Wang, Qiang Tianjin Normal Univ Sch Math Sci Tianjin 300387 Peoples R China Tianjin Normal Univ Fac Psychol Tianjin 300387 Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res Beijing 100875 Peoples R China Hefei Normal Univ Key Lab Philosophy & Social Sci Anhui Prov Adolesc Hefei 230061 Peoples R China

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

关键词： machine learning individual prediction regression algorithm gray matter volume

来源：评论

学校读者我要写书评

暂无评论

Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development

引用

SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Paneru, Biplov Mainali, Durga Prasad Paneru, Bishwash Sapkota, Sanjog Chhetri Pokhara Univ Nepal Engn Coll Dept Elect & Commun Engn Bhaktapur Nepal Delft Univ Technol Dept Mech Maritime & Mat Engn Delft Netherlands Hague Univ Appl Sci Dept Technol Innovat & Soc The Hague Netherlands Tribhuvan Univ Inst Engn Dept Appl Sci & Chem Engn Pulchowk Campus Lalitpur Nepal Nepal Res & Collaborat Ctr Kathmandu Nepal

The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each of these was assessed based on its R-squared score, which is an important statistical measure in indicating the variance proportion yielded by the model. In contrast, the Random Forest model significantly improved, with an R-squared value of 0.83, thereby doing an excellent job in capturing nuances of the data. Only surpassed by the Gradient Boosting model at an astonishing R-squared score of 0.87, it is this excellent score that underlines its capability to predict the outcome quite accurately by modeling complex interrelations. In other words, gradient boosting outran the rest and provided the most robust results concerning drivers of students' performance. It also underlines how important choosing a good model is in educational analytics in order to increase the accuracy of the predictions. The use of these models in the proposed EV Battery Charging Cycle Predictor App results in accurate predictions to aid schedule maintenance and energy-related decisions. This research brings light to the future of advanced machine learning methods in enhancing the battery efficiencies of EVs and the development of electric mobility technologies. It is possible that the future work will imply the additional inclusion of real data and the integration of the application to general energy systems.

关键词： Electric vehicles (EVs) Charging cycles Battery management system (BMS) regression algorithm Tkinter application

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：