Enterprise intelligent manufacturing data analysis technology based on big data analysis

The rise of big data has deeply influenced various industries, especially the intelligent manufacturing of enterprises. However, traditional data analysis methods are difficult to adapt to the storage and analysis of sea volume data in intelligent production. To address this issue, a method relying on big data analysis and cluster analysis is proposed to design data analysis techniques for enterprise intelligent manufacturing. The proposed improved algorithm is subjected to performance testing. The accuracy of this algorithm is 97%, which exceeds the comparison algorithm. The error is 6% and the running time is 5 s, both of which are below the comparison algorithm. The effectiveness of the enterprise intelligent manufacturing data analysis technology is tested. The experimental group completes orders in 4.1 weeks, 5.2 weeks, 3 weeks, 3.4 weeks, and 4.9 weeks, respectively, shorter than the control group. The product qualification rates for the experimental group are 92%, 93%, 95%, 92%, and 92%, respectively, which exceed the control group. In summary, the proposed enterprise intelligent manufacturing data analysis technology relying on big data and cluster analysis can better utilize data resources and information technology, improving the production efficiency and competitiveness of enterprises. It is hope that this research result can provide useful guidance and reference for the application and development of intelligent manufacturing data analysis technology in enterprises

Incorporating the knowledge management cycle in e-business

In e-business, knowledge can be extracted from the recorded information by intelligent data analysis and then utilised in the business transaction. E-knowledge is a foundation for e-business. E-business can be supported by an intelligent information system that provides intelligent business process support and advanced support of the e-knowledge management cycle. Knowledge is stored as knowledge models that can be updated in the e-knowledge management cycle. As illustrated in examples, the e-knowledge cycle aids in the business decision taking, production management, and costs management

Behavior-Based Outlier Detection for Network Access Control Systems

Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices connected to an enterprise network. These abnormal devices can be detected using outlier detection techniques. Existing outlier detection techniques focus on specific application domains such as fraud, event or system health monitoring. In this paper, we review attacks on Bring Your Own Device (BYOD) enterprise networks as well as existing clustering-based outlier detection algorithms along with their limitations. Importantly, existing techniques can detect outliers, but cannot detect where or which device is causing the abnormal behaviour. We develop a novel behaviour-based outlier detection technique which detects abnormal behaviour according to a device type profile. Based on data analysis with K-means clustering, we build device type profiles using Clustering-based Multivariate Gaussian Outlier Score (CMGOS) and filter out abnormal devices from the device type profile. The experimental results show the applicability of our approach as we can obtain a device type profile for five dell-netbooks, three iPads, two iPhone 3G, two iPhones 4G and Nokia Phones and detect outlying devices within the device type profile

Information standards to support application and enterprise interoperability for the smart grid

Copyright @ 2012 IEEE.Current changes in the European electricity industry are driven by regulatory directives to reduce greenhouse gas emissions, at the same time as replacing aged infrastructure and maintaining energy security. There is a wide acceptance of the requirement for smarter grids to support such changes and accommodate variable injections from renewable energy sources. However the design templates are still emerging to manage the level of information required to meet challenges such as balancing, planning and market dynamics under this new paradigm. While secure and scalable cloud computing architectures may contribute to supporting the informatics challenges of the smart grid, this paper focuses on the essential need for business alignment with standardised information models such as the IEC Common Information Model (CIM), to leverage data value and control system interoperability. In this paper we present details of use cases being considered by National Grid, the GB transmission system operator for information interoperability in pan-network system management and planning.This study is financially supported by the National Grid, UK

Computational and Biological Analogies for Understanding Fine-Tuned Parameters in Physics

In this philosophical paper, we explore computational and biological analogies to address the fine-tuning problem in cosmology. We first clarify what it means for physical constants or initial conditions to be fine-tuned. We review important distinctions such as the dimensionless and dimensional physical constants, and the classification of constants proposed by Levy-Leblond. Then we explore how two great analogies, computational and biological, can give new insights into our problem. This paper includes a preliminary study to examine the two analogies. Importantly, analogies are both useful and fundamental cognitive tools, but can also be misused or misinterpreted. The idea that our universe might be modelled as a computational entity is analysed, and we discuss the distinction between physical laws and initial conditions using algorithmic information theory. Smolin introduced the theory of "Cosmological Natural Selection" with a biological analogy in mind. We examine an extension of this analogy involving intelligent life. We discuss if and how this extension could be legitimated. Keywords: origin of the universe, fine-tuning, physical constants, initial conditions, computational universe, biological universe, role of intelligent life, cosmological natural selection, cosmological artificial selection, artificial cosmogenesis.Comment: 25 pages, Foundations of Science, in pres

Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.Comment: 6 page

Is Ambient Intelligence a truly Human-Centric Paradigm in Industry? Current Research and Application Scenario

The use of pervasive networked devices is nowadays a reality in the service sector. It impacts almost all aspects of our daily lives, although most times we are not aware of its influence. This is a fundamental characteristic of the concept of Ambient Intelligence (AmI). Ambient Intelligence aims to change the form of human-computer interaction, focusing on the user needs so they can interact in a more seamless way, with emphasis on greater user-friendliness. The idea of recognizing people and their context situation is not new and has been successfully applied with limitations, for instance, in the health and military sectors. However its appearance in the manufacturing industry has been elusive. Could the concept of AmI turn the current shop floor into a truly human centric environment enabling comprehensive reaction to human presence and action? In this article an AmI scenario is presented and detailed with applications in human’s integrity and safety.Ambient Intelligence, networks, human-computer interaction