浅谈小型水稻联合收割机在我国南方的应用

我国南方丘陵、山区较多,存在着田块小、坡度大、田块形状不规则以及田间道路窄等特点,大中型水稻联合收割机很难在此开展作业。小型水稻联合收割机因其体积小、质量轻、操作简易、成本底等,经在丘陵、山区等多处现场演示,特别适合丘陵、山区的小田块田间作业,是农民买得起,用得起的农用机械,近年来得到了南方农民的青睐,在我国南方具有广阔的应用前景。     

经济型多功能微耕机使用及维护的技术要点

IZ-41祥乐牌微耕机按照人机工程原理进行了轻量化、紧凑化、标准化等优化设计,具有操作简便、易维修的优点。但市场上使用时还对其正确的维护、使用还是存在较多误区。文章在分析这些问题及误区的基础上,论述了经济型多功能微耕机的使用操作、维护保养、故障查排的技术要点和注意事项。     

IZ-41型祥乐牌经济型多功能微耕机的设计

通过分析祥乐牌经济型多功能微耕机的设计思路、整机结构及原理、机构设计、多功能配置、经济型部件设计、试验结果等内容,论述了该机型的经济性和多功能的技术基础与效果。     

前旋后驱微型耕作机日常维护的技术要求

1WGQ5祥乐牌前旋后驱微耕机经过多年设计,在一般单箱体微耕机基础上结合人的耕作使用性能做了专业化、标准化整合设计,具有操作轻便,安全性能高和劳动力大幅度降低的设计优势。然而市场上使用不少农户朋友对其正确的维护、使用还是存在较多的误区,文章在分析这些误区的基础上,论述了前旋后驱微耕机的使用操作、维护保养、故障排查的技术要点和注意事项。     

江汉平原农业景观格局及生物多样性研究

通过野外调查、航片解译和农户调查,应用地理信息系统,对不同时期（1995、1977、1984、1993）亚热带江汉平原农业景观格局变化以及与社会经济变革和农作系统多样性的相互关系、不同类型景观单元的生物多样性,进行了案例研究。指出,景观格局变化受大尺度因素和小尺度因素相互作用影响;庭院中树篱和林地以及农田边界可能是保持生物多样性最重要的景观单元,稳定这些景观单元可能是景观生态及农作系统持续性管理最重的组成部分。     

江汉平原农业景观格局及生物多样性研究——以两个村为例

通过野外调查、航片解译和农户调查,应用地理信息系统,对不同时期(1955、1977、1984、1993)亚热带江汉平原农业景观格局变化以及与社会经济变革和农作系统多样性的相互关系、不同类型景观单元的生物多样性,进行了案例研究.指出,景观格局变化受大尺度因素和小尺度因素相互作用影响;庭院中树篱和林地以及农田边界可能是保持生物多样性最重要的景观单元,稳定这些景观单元可能是景观生态及农作系统持续性管理最重的组成部分.     

帕卢斯之美

帕卢斯位于美国华盛顿东部,这里是无数摄影爱好者口口相传的秘密天堂。帕卢斯的地形非常特别,浅丘陵、山脉以及山中盆地和小块平原交错并存。其中,此起彼伏、曲线柔美的浅丘陵最具特色。     

低压线路防洪自动隔离控制系统的研发与应用

<正>低压线路防洪自动隔离控制系统,采用自动控制原理中最基本的一种控制系统——开环控制系统,由水位控制器、电动操作机构和通讯装置等重要部件组成。其起到的作用主要是:在汛期水位超过警戒水位时,能够自动断开被水浸的低压线路,达到隔离故障线路的目的,既能保证人身和设备安全,又能提高防洪工作效率。     

电子天平的检定工作探讨

电子天平是计量测量仪器中最常用的仪器,其外形体积小操作简单,精准度高等优点,在生活应用领域使用广泛,在科学研究领域、商业领域中应用更加普及。测量精准度高,它是利用电磁力的平衡重力并快速清晰的显示出该质量的重量或尺度。电子天平内部,安置了称重的传感器。由于精密的电子部件,如使用一段时期以后,会造成测量的精度标准性不准,有误差等现象。所以,要定期对内部以及外部的的部件进行检查检测,以确保精准度的精准性能,使使用期限延长。本文主要讨论电子天平计在计量的检测中,应经常检查检测该仪器的性能以及精准度。     

互补者领导力与部件领导力对企业绩效的影响——基于生态系统视角

基于创新生态视角,提出了互补者领导力以及部件领导力的概念,探究了其对企业创新绩效以及经济绩效的影响,并利用广东、江苏、浙江三个省份的589家新三板上市公司数据进行了验证。结果表明:增强互补者领导力和部件领导力可以有效促进企业经济绩效和创新绩效的提升,其中互补者领导力起到的作用相对较大。同时也发现,研发投入正向调节了部件领导力与企业创新绩效之间的关系,政府政策支持对互补者领导力与企业经济绩效之间的关系没有起到显著的调节作用。     

国内组合机床行业现状分析

随着现代化工业技术的快速发展,组合机床的研究已经成为当今机器制遣界的一个重要方向,在现代工业运用中,大多数机器的设计和制造都是用机床大批量完成的。现代大型工业技术的飞速发展,降低了组合机床的实现成本,软件支持机制也使得实现变得更为简单,因此,研究组合机床的行业现状具有十分重要的现实意义。     

网络环境下信息资源共建共享模式研究

以人机系统工程为视角，构建网络环境下信息资源共建共享系统工程模式。它突出了人、机、环境的协调建设与最佳结合的思想。最后从重视人、机、环境综合管理，推进信息资源共享整合技术研究等4个方面提出了信息资源共建共享的主要措施。     

云计算环境下基于负载平衡的数字图书馆虚拟机集群资源调度策略

为了解决云计算环境下,数字图书馆云数据中心虚拟机集群资源负载不平衡的问题,本文提出了一种云计算环境下,基于负载平衡的数字图书馆虚拟机集群资源调度策略。该策略可减少云计算环境下数字图书馆虚拟机的迁移数量,并改善云数字图书馆的用户服务质量。     

一种多元非线性回归模型的发音评测技术

目前,在计算机辅助语言发音评测系统中大部分采用简单的线性回归模型进行机器分与人工评分之间的数据融合,该模型要求机器的评分特征与人的主观评分呈近似的线性关系,而实际呈现非线性。对此,文章引入多元非线性回归模型对分数进行映射融合,使其更符合人的主观评分。同时,在算法优化问题上,用改进步长下的高斯-牛顿算法来求解,大大提高了收敛精度。实验表明,该算法在普通话口语评测中使系统性能得到明显提升。     

交互式汉—英机器翻译的理论基础

理想的机器翻译系统应该是全自动高质量的批处理式系统,在目前的计算语言学发展水平下,计算机还无法彻底解决自然语言的错综复杂现象,达到全自动高质量的翻译。本文认为人机交互的方法是最自然的手段,用户易学易会,具有广阔的发展前景。文章详细地分析归纳汉—英机器翻译的歧义问题,总结了现有的解决歧义的手段,提出了用人机对话解决歧义问题的交互式汉—英机器翻译的思想,并从语言学的角度提供了论据。基于这一思想设计的CEMT—Ⅱ汉英机器翻译系统模型已基本完成。     

云计算环境下基于QOS保障的数字图书馆虚拟机资源分配策略

针对云计算环境下数字图书馆虚拟化资源分配的QOS保障问题,本文提出了一种云计算环境下基于QOS保障的数字图书馆虚拟机资源分配策略。该策略可以提高云计算环境下数字图书馆虚拟机的可靠性,有助于保持云环境负载均衡,并能提高虚拟化资源的利用效率。     

机械制造企业的生产能力及能力计划研究

本文从技术的角度出发，分别研究了宏观和微观两种情况下，生产过程中各因素对生产能力的贡献，以及它们综合作用的效果，进而形成生产能力的定义，明确了提高生产能力必须处理的两个关键问题，最后用基于优先等级的方法来解决CIMS下能力需求计划的制定问题。     

Towards a big data framework for analyzing social media content

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.     

What is it about humanity that we can’t give away to intelligent machines? A European perspective

One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.     

Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.