期刊文献+
共找到5篇文章
< 1 >
每页显示 20 50 100
Energy and Emission Reduction Potential for Bank ATM Units in India
1
作者 hemant kumar singh Ravi Prakash Karunesh kumar Shukla 《Open Journal of Energy Efficiency》 2016年第4期107-120,共15页
With the growing economy of India, banking sector growth has led to installation of thousands of Automatic Teller Machines (ATMs) throughout the country. ATMs provide 24 × 7 services as well as operate at low-tem... With the growing economy of India, banking sector growth has led to installation of thousands of Automatic Teller Machines (ATMs) throughout the country. ATMs provide 24 × 7 services as well as operate at low-temperature ranges of cooling, hence have high operating energy costs. Insulating an ATM’s envelope is not a prevalent technique in India. In the present study, an effort has been made to determine the optimum insulation thickness for three different insulation materials for the typical ATM envelope in four different climatic zones of India. Life cycle savings and payback periods for various insulation materials are also evaluated. Further, these optimally insulated ATM envelopes can be integrated with grid connected rooftop solar PV systems. The energy saving and emissions reduction potential due to these two interventions have been estimated on the national basis. Altogether in the four selected climate zones, energy saving of 17% - 30% provides the annual economic benefit of Indian National Rupees (Rs.) 3570 million with annual carbon reduction potential of about 0.60 million tCO<sub>2</sub>. From this study, it is observed that properly insulated ATMs integrated with rooftop solar PV systems, can significantly reduce the energy costs as well as carbon emissions in India’s context. 展开更多
关键词 Life Cycle Cost Optimum Insulation Thickness Life Cycle Saving Solar PV Payback Period Carbon Emissions
下载PDF
Characterizing large-scale weak interlayer shear zones using conditional random field theory 被引量:1
2
作者 Gang Han Chuanqing Zhang +5 位作者 hemant kumar singh Rongfei Liu Guan Chen Shuling Huang Hui Zhou Yuting Zhang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第10期2611-2625,共15页
The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,com... The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697. 展开更多
关键词 Interlayer shear weakness zone Baihetan hydropower station Conditional random field Kriging interpolation technique Activation analysis
下载PDF
A Classification Algorithm to Improve the Design of Websites 被引量:1
3
作者 hemant kumar singh Brijendra singh 《Journal of Software Engineering and Applications》 2012年第7期492-499,共8页
In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vas... In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website. 展开更多
关键词 WEB USAGE MINING WEB PERSONALIZATION RECOMMENDER Systems Classification Algorithms
下载PDF
Spray Prediction Model for Aonla Rust Disease Using Machine Learning
4
作者 hemant kumar singh Bhanu Pratap +4 位作者 S.K.Maheshwari Ayushi Gupta Anuradha Chug Amit Prakash singh Dinesh singh 《Journal of Agricultural Science and Technology(B)》 2023年第1期1-12,共12页
Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are prese... Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop. 展开更多
关键词 Aonla Internet of Things machine learning plant disease RUST spray prediction.
下载PDF
Impact of Climate Change on Diseases of Crops and Their Management-A Review
5
作者 Manish kumar Maurya Vikash kumar Yadav +3 位作者 Sumant Pratap singh Rajender Jatoth hemant kumar singh Dinesh singh 《Journal of Agricultural Science and Technology(B)》 2022年第1期1-15,共15页
Change in global climate is primarily due to rising concentrations of greenhouse gases in the atmosphere that is mostly caused by human activities.The important factors affecting the occurrence and spread of the plant... Change in global climate is primarily due to rising concentrations of greenhouse gases in the atmosphere that is mostly caused by human activities.The important factors affecting the occurrence and spread of the plant diseases are temperature,moisture,light,and CO_(2) concentration.These factors cause physiological changes in plants that result in increase in intensity of crop diseases.Climate change causes a significant impact on germination,reproduction,sporulation and spore dispersal of pathogens.Climate change affects all life stages of the pathogen as well as its host to cause impact on host-pathogen interaction which facilitates the emergence of new races of the pathogen ultimately breakdowns the host resistance.It also affects the microbial community in the soil which is beneficial to the plants in various aspects.The minor diseases become major ones due to alteration in climatic parameters thus posing a threat to the food security. 展开更多
关键词 Climate change greenhouse gases temperature elevated CO_(2) PATHOGENS SPORULATION
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部