高压开关的自动化与智能化控制器

将电力系统中的微机保护、测量和多种自动化功能就地分散到高压开关设备上,形成一个有机整体,应用分布式结构、现场总线技术和智能化技术,结合高压开关运行参数的在线检测技术与故障诊断专家系统,构成新型的分体式“智能化高压开关”,以改进和取代进口自动重合器。     

数据开采技术及应用研究

本文讨论数据开采的定义、任务、适用算法、实现过程及其若干应用问题,并就我国ＫＤＤ技术发展阐明了观点     

差压变送器的现状和进展

】简要地介绍了差压变送器的市场情况、应用领域、主要生产厂商及其各自所采用的技术原理,重点介绍了差压变送器的发展动向、智能差压变送器的特点及其代表性品种的主要特征。     

基于知识图谱的网络安全漏洞智能检测系统设计

网络安全漏洞智能检测需要依赖大量的真实数据来进行分析,冗余数据与异常数据的存在会导致检测准确性下降;为保障网络系统稳定运行,提出基于知识图谱的网络安全漏洞智能检测系统设计研究;从结构、逻辑模型以及运行模式3个方面设计网络安全漏洞检测器,实现网络安全漏洞智能检测系统硬件设计;系统软件设计通过网络爬虫采集安全漏洞数据,去除冗余数据与异常数据,根据属性信息识别安全漏洞实体,获取安全漏洞属性信息关系,以此为基础,定义安全漏洞知识图谱表示形式,设计安全漏洞知识图谱结构,从而实现安全漏洞知识图谱的构建与可视化;以上述网络设计结果为依据构建网络安全漏洞智能检测整体架构,制定网络安全漏洞智能检测具体流程,从而获取最终网络安全漏洞智能检测结果;实验结果表明,在不同实验工况背景条件下,设计系统应用后的网络安全漏洞漏检率最小值为1.23%,网络安全漏洞检测F1值最大值为9.50,网络安全漏洞检测响应时间最小值为1 ms,证实了设计系统的安全漏洞检测性能更佳。     

Graphene oxide-sulfated lanthanum oxy-carbonate nanocomposite as an adsorbent for the removal of malachite green from water samples with application of statistical optimization and machine learning computations

Sulfated lanthanum oxy-carbonate nanorods (S-La₂O₂(CO₃) NRs) was synthesized by the reverse micelle microemulsion method and then used to modify graphene oxide nanosheets to synthesize of graphene oxide-sulfated lanthanum oxy-carbonate (GO-S-La₂O₂(CO₃)) nanocomposite. By characterization of S-La₂O₂(CO₃) NRs and GO-S-La₂O₂(CO₃) nanocomposite by the Fourier Transform-Infrared (FT-IR) Spectrophotometry, Field Emission-Scanning Electron Microscopy (FE-SEM), Energy-dispersive X-ray spectroscopy (EDS), Transmission Electron Microscopy (TEM) and X-ray diffraction analysis (XRD), GO-S-La₂O₂(CO₃) was used for treatment of malachite green (MG). To find the optimum removal percentage (RP), influencing parameters were investigated by the response surface methodology based on central composite design (RSM-CCD). Adsorption mechanism was evaluated by Dubinin–Radushkevich (D-R), Langmuir, Temkin, Freundlich (two parameter equations) and Sips (Three parameter equations) isotherms and based on the results the adsorption of MG into the GO-S-La₂O₂(CO₃) nanocomposite obeyed by the Freundlich isotherm with the maximum adsorption capacity of 555.5 mg g^?1. Also, the results of kinetic analysis show that the adsorption of MG onto the GO-S-La₂O₂(CO₃) nanocomposite followed by the pseudo second order kinetic model. For estimation of adsorption behavior, different machine learning techniques are used and based on the results; ANFIS model has the confidential operation because of fuzzy procedure and flexibility of data mining in distributed adsorption data.     

CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge

Generating explanations within a local and model-agnostic explanation scenario for text classification is often accompanied by a local approximation task. In order to create a local neighborhood for a document, whose classification shall be explained, sampling techniques are used that most often treat the according features at least semantically independent from each other. Hence, contextual as well as semantic information is lost and therefore cannot be used to update a human’s mental model within the according explanation task. In case of dependent features, such explanation techniques are prone to extrapolation to feature areas with low data density, therefore causing misleading interpretations. Additionally, the ”the whole is greater than the sum of its parts” phenomenon is disregarded when using explanations that treat the according words independently from each other. In this paper, an architecture named CaSE is proposed that either uses Semantic Feature Arrangements or Semantic Interrogations to overcome these drawbacks. Combined with a modified version of Local interpretable model-agnostic explanations (LIME), a state of the art local explanation framework, it is capable of generating meaningful and coherent explanations. The approach utilizes contextual and semantic knowledge from unsupervised topic models in order to enable realistic and semantic sampling and based on that generate understandable explanations for any text classifier. The key concepts of CaSE that are deemed essential for providing humans with high quality explanations are derived from findings of psychology. In a nutshell, CaSE shall enable Semantic Alignment between humans and machines and thus further improve the basis for Interactive Machine Learning. An extensive experimental validation of CaSE is conducted, showing its effectiveness by generating reliable and meaningful explanations whose elements are made of contextually coherent words and therefore are suitable to update human mental models in an appropriate way. In the course of a quantitative analysis, the proposed architecture is evaluated w.r.t. a consistency property and to Local Fidelity of the resulting explanation models. According to that, CaSE generates more realistic explanation models leading to higher Local Fidelity compared to LIME.     

ABB智能差压变送器在锅炉汽包水位测量中的应用

介绍了采用ABB智能差压变送器测量锅炉汽包水位的方法,包括导压管路的敷设、变送器的安装、量程设定、零点迁移等安装和调试技巧。     

Multi-source brain computing with systematic fusion for smart health

With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social–cyber–physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion.     

数控机床智能化的主要技术特征

智能化作为数控机床最重要的发展趋势,其开发、运用正日益受到机床生产厂商和机床使用者的重视。数控机床的智能化不仅关系到所加工产品质量的优劣和效率的高低;更为重要的是,这一目标的实现是网络制造、敏捷制造、虚拟制造、并行工程等先进生产方式实施的基础。重点介绍了数控机床智能化的主要技术特征。