John Deere柴油机的电子控制

用于John Deere400系列7.6L和500系列10.1L柴油机的电控系统已研制成功,在对道路、非道路及固定作业用的发动机的控制方面,这些独具特色的微机控制系统的性能优于机械控制系统。其功能包括:限时提高功率、不带歧管传感器的瞬态排烟控制、开关选择扭矩曲线、调速、特殊油门选择、自适应油门校正、温度补偿及出厂编程。

Fertilizer-Induced Advances in Corn Growth Stage and Quantitative Definitions of Nitrogen Deficiencies

Evidence that nitrogen (N) fertilization tends to accelerate maturation as well as increase rates of growth has received little attention when diagnosing N deficiencies in corn (Zea mays L.).Such a tendency could be a potential source of errors when the diagnosis is solely based on comparing plants with different rates of growth.Whether N fertilization could accelerate rates of growth and maturation was tested in a field study with 12 paired plots representing relatively large variability in soil properties and landscape positions.The plots were located under conditions where preplant N fertilization reduced or avoided temporary N shortages for some plants but did not reduce for other plants early in the season.We measured corn heights to the youngest leaf collar,stages of growth and chlorophyll meter readings (CMRs). The added N advanced growth stages as well as increased corn heights and CMRs at any given time.Fertilization effects on corn heights,growth stages and ear weights were statistically significant (P<0.05) despite substantial variability associated with landscape.Reductions in growth due to a temporary shortage of N within a growth stage might be partially offset by longer periods of growth within that stage to physiological maturity.Temporary shortages of N,therefore,may produce symptoms of N deficiency in situations where subsequent additions of N should not be expected to increase yields.Recognition of these two somewhat different effects (i.e.,increase growth rates and advance growth stages) on corn growth could help to define N deficiency more precisely and to improve the accuracy of diagnosing N status in production agriculture.

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recommendation systems. The main areas which play major roles are social networking, digital marketing, online shopping and E-commerce. Recommender system consists of several techniques for recommendations. Here we used the well known approach named as Collaborative filtering (CF). There are two types of problems mainly available with collaborative filtering. They are complete cold start (CCS) problem and incomplete cold start (ICS) problem. The authors proposed three novel methods such as collaborative filtering, and artificial neural networks and at last support vector machine to resolve CCS as well ICS problems. Based on the specific deep neural network SADE we can be able to remove the characteristics of products. By using sequential active of users and product characteristics we have the capability to adapt the cold start product ratings with the applications of the state of the art CF model, time SVD++. The proposed system consists of Netflix rating dataset which is used to perform the baseline techniques for rating prediction of cold start items. The calculation of two proposed recommendation techniques is compared on ICS items, and it is proved that it will be adaptable method. The proposed method can be able to transfer the products since cold start transfers to non-cold start status. Artificial Neural Network (ANN) is employed here to extract the item content features. One of the user preferences such as temporal dynamics is used to obtain the contented characteristics into predictions to overcome those problems. For the process of classification we have used linear support vector machine classifiers to receive the better performance when compared with the earlier methods.

Multiple Architectural Approach for Urban Development Using Wearable IoT Devices: A Combined Machine Learning Approach

Machine Learning becomes a part of our life in recent days and everything we do in interlinked with machine learning. As a technocrat, we tried to implement machine learning with Internet of Things (IoT) for better implementation of technology in organizations for security. We designed an sample architecture which will carry the burden of safeguarding the organizational data with IoT using machine learning with an effective manner and in this case we were proposing utilization of cloud computing for better understanding of data storage and retrieval process. Machine learning is used for the prediction models based on which we need to perform high level analysis of data and using IoT we promote authorization mechanism based on which we recognize the appropriate recipient of data and cloud for managing the data services with the three-tier architecture. We present the architecture we are proposing for better utilization of machine learning and IoT with cloud architecture.

Interpolation of Generalized Functions Using Artificial Neural Networks

In this paper we employ artificial neural networks for predictive approximation of generalized functions having crucial applications in different areas of science including mechanical and chemical engineering, signal processing, information transfer, telecommunications, finance, etc. Results of numerical analysis are discussed. It is shown that the known Gibb’s phenomenon does not occur.