Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm

A grating eddy current displacement sensor （GECDS） can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions. The parameters optimization of the sensor is essential for economic and efficient production. This paper proposes a method to combine an artificial neural network （ANN） and a genetic algorithm （GA） for the sensor parameters optimization. A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS, and then a GA is used in the optimization process to determine the design parameter values, resulting in a desired minimal nonlinearity error of about 0.11%. The calculated nonlinearity error is 0.25%. These results show that the proposed method performs well for the parameters optimization of the GECDS.     

Containment control for second-order nonlinear multi-agent systems with aperiodically intermittent position measurements

In some real systems, the intermittent communications and the inaccurate velocity measurements are usually inevitable. To overcome these two communication limitations, this article aims at investigating the containment control problem for a class of second-order multi-agent systems with inherent nonlinear dynamics and aperiodically intermittent position measurements. Under the case that the velocity information is unavailable, a distributed filter is introduced for each second-order follower. Based on the distributed filter, a novel intermittent containment control protocol without velocity measurements is designed. Some sufficient conditions are derived under the common assumption that only relative position measurements between the neighbouring agents are utilized intermittently, and these conditions ensure that the second-order nonlinear multi-agent systems can achieve containment control. Furthermore, some simpler containment conditions are obtained for multi-agent systems with double-integrator dynamics under aperiodically intermittent communications. Finally, numerical simulations are provided to verify the effectiveness of the theoretical results.     

Big data governance of personal health information and challenges to contextual integrity

Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness.     

中国大陆经济综合竞争力区域差异分析

本文以中国大陆31个省级行政区为研究对象,选取反映地区经济综合竞争力的17项重要指标,通过对原始数据的采集处理,运用人工神经网络（ANN）中的自组织映射网络（SOFM）及统计分析软件SPSS中的两种系统聚类分析方法对地区经济综合竞争力区域差异进行分类研究,最终将31个省、市、自治区的经济综合竞争力强弱划分为4类,即经济综合竞争力最强、经济综合竞争力较强、经济综合竞争力一般及经济综合竞争力较弱4类地区。研究表明：我国经济综合竞争力区域差异显著,主要表现为东部和中、西部及沿海和内地之间的差异。研究也表明运用人工神经网络和系统聚类分析方法进行分类研究可以相互检验分类结果,易于发现问题,提高分类的准确性,是一条具有发展和应用前景的途径。     

A new design method based on artificial bee colony algorithm for digital IIR filters

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.     

基于神经网络的语音识别鲁棒性研究

语音识别技术已经取得令人鼓舞的成就,市场上也出现了许多相对成熟的语音识别产品,但是大部分语音识别系统仍局限于特定的环境,距离真正的实用化还相差很远。本文以提高语音识别系统的鲁棒性为目标,进行了相关的实验和研究。     

世界森林资源保护及中国林业发展对策分析

根据联合国粮农组织的最新统计结果介绍了世界森林面积、森林拥有量最大的国家的情况和造成森林面积减少的主要原因;论述了世界各国为解决天然林日益减少的问题所采取的发展人工林的共同战略,把大力发展人工林作为21解决世纪对于环境和木材需求的根本措施。林业政策的修订和林业管理机构的改革是许多发展中国家和经济体制转轨国家正在或计划中的重要举措。文章还论述了我国的森林资源现状、林业所面临的问题及发展对策,作者建议采取,建立国家生态林补偿机制和森林生态效益补偿机制、建立保护森林资源、改善生态环境的多元化投资机制、调整林业政策、建立森林资源与环境综合核算体系等措施。     

人工智能技术在视频应用中的发展——以中央广播电视总台应用为例

介绍人工智能的发展历程及在视频领域的主要发展方向,并以中央广播电视总台的5G+4K+AI的电视制作的研究和实践为例,阐释人工智能的应用场景和前景。     

Artificial intelligence for sustainability: Challenges,opportunities, and a research agenda

Artificial intelligence (AI) will transform business practices and industries and has the potential to address major societal problems, including sustainability. Degradation of the natural environment and the climate crisis are exceedingly complex phenomena requiring the most advanced and innovative solutions. Aiming to spur groundbreaking research and practical solutions of AI for environmental sustainability, we argue that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities. The true value of AI will not be in how it enables society to reduce its energy, water, and land use intensities, but rather, at a higher level, how it facilitates and fosters environmental governance. A comprehensive review of the literature indicates that research regarding AI for sustainability is challenged by (1) overreliance on historical data in machine learning models, (2) uncertain human behavioral responses to AI-based interventions, (3) increased cybersecurity risks, (4) adverse impacts of AI applications, and (5) difficulties in measuring effects of intervention strategies. The review indicates that future studies of AI for sustainability should incorporate (1) multilevel views, (2) systems dynamics approaches, (3) design thinking, (4) psychological and sociological considerations, and (5) economic value considerations to show how AI can deliver immediate solutions without introducing long-term threats to environmental sustainability.     

国内图书情报领域人工智能研究演化分析

利用CiteSpace软件,梳理CNKI中相关文献,探究人工智能在国内图书情报领域的发文力量及主题演进、研究热点及未来趋势,并进行对比及可视化。结果表明相关研究数量持续增加,但尚未成熟,具有发文力量分散、主题联系紧密且交叉等特点。人工智能在国内图书情报领域的8类研究主题中,智慧图书馆、智能数据服务、知识工程等是研究热点及趋势。机器学习、知识图谱、语义网络等人工智能技术推动国内图书情报领域的发展。