Risk management for safety operation utilizing virtual reality simulation supported by intelligent HAZOP analysis

Ensuring safe operability and minimizing risk is the key component to prevent negative impact in all industries dealing with toxic, reactive, flammable and explosive materials. HAZOP (Hazard and Operability), a preliminary and systematic approach for identifying hazards has been unquestionably successful in reducing incident of hazards by mitigating the consequence of major accident in the industrial process facilities. However, laborious work, time and cost are the shortcoming in performing and maintaining HAZOP analysis. Many research works on HAZOP automation are available, yet the traditional approach is still widely used by plant operators. The traditional method only covers parts and aspects of a specific plant type rather than generalizing to fit many plant types. In HAZOP analysis of chemical process industries (CPI), process analysis can be divided into two groups - defined or routine process, which roughly occupies 60-80% and predefined or non routine process, which occupies 20-60% of HAZOP analysis. Thus leading towards the significance of having safety information as update and accessible as possible. In recent years, computer hardware capable of developing and running virtual reality model has become more affordable for middle and small scale CPI. Consequently, virtual reality has been proposed as a technological breakthrough that holds the power to facilitate analysis. The ability to visualize complex and dynamic systems involving personnel, equipment and layouts during any real operation is a potential advantage of such an approach. With virtual reality supporting HAZOP, analysis which often solely relied on expert imaginative thinking in simulating hazard conditions, will aid understanding, memory retention and create a more interactive analysis experience. In focusing assessment for safety operator and safety decision maker, we present a web-based HAZOP analysis management system (HMS) to help HAZOP team and related individuals to perform revision, tracking and even complete HAZOP analysis without management bureaucracy. Besides, depending solely on expert imaginative thinking of scenario using P&ID, this work will develop a dynamic visual model which brings to the user a different view of consequent and subsequent to an accident and will further enable three dimensional analyses of effects. This approach will prevent ‘miss looks’ due to ‘paper-based’ view. We also present Virtual HAZOP Training system, a risk-managing virtual training concept supported by intelligent HAZOP proposed to eliminate analysis redundancies and bring static ‘paper-based’ analysis to more dynamic and interactive virtual analysis simulation. However, the efficiency of VR simulator depends on the scenario accuracy to the real world that can be simulated. We introduce the system’s artificial intelligent engine responsible for retrieving the most accurate and highest possibility ‘to-happen’ scenario case. A fuzzy – CBR method enables the engine to classify and use real past scenarios combined with suitable parameters in creating a defined scenario. This method resolves issues in balancing between computational complexity and knowledge elicitation Reactor section in a vacuum gas oil hydrodesulphurization (VGO HDS) process is used as the case study to illustrate the performance of the proposed system. The wide usages of HDS unit in the petroleum refining industry play important roles in chemical plant incidents happening worldwide. HAZOP analysis management system in average manages to reduce more than half the time required in performing HAZOP analysis compares to traditional method. With the proposed system, operator is able to optimally use safety information in HMS to prevent common and repetitive mistakes. Virtual process and accident simulator available in virtual HAZOP training system help to improve safety operator estimate overall impact towards equipment, operator and environment during process 20-35% better. This system is expected to be the main foundation for Virtual Reality simulator research in analyzing accident caused by human factor. Asides providing better and healthier working environment, negative profitability impact which influence not only the company that runs it but also the world economy due to byproduct shortage, can be avoided

Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions

This study reviews and analyses the research landscape for intrusion detection systems (IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the gap in this pivotal research area. The focus is on articles related to the keywords ‘deep learning’, ‘intrusion’ and ‘attack’ and their variations in four major databases, namely Web of Science, ScienceDirect, Scopus and the Institute of Electrical and Electronics Engineers’ Xplore. These databases are sufficiently broad to cover the technical literature. The dataset comprises 68 articles. The largest proportion (72.06%; 49/68) relates to articles that develop an approach for evaluating or identifying intrusion detection techniques using the DL approach. The second largest proportion (22.06%; 15/68) relates to studying/applying articles to the DL area, IDSs or other related issues. The third largest proportion (5.88%; 4/68) discusses frameworks/models for running or adopting IDSs. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology’s utility, authors’ recommendations and substantial analysis. Then, a result analysis mapping for new directions is discussed. Three phases are designed to meet the demands of detecting distributed denial-of-service attacks with a high accuracy rate. This study provides an extensive resource background for researchers who are interested in IDSs based on DL

The potential use of service-oriented infrastructure framework to enable transparent vertical scalability of cloud computing infrastructure

Cloud computing technology has become familiar to most Internet users. Subsequently, there has been an increased growth in the use of cloud computing, including Infrastructure as a Service (IaaS). To ensure that IaaS can easily meet the growing demand, IaaS providers usually increase the capacity of their facilities in a vertical IaaS increase capability and the capacity for local IaaS amenities such as increasing the number of servers, storage and network bandwidth. However, at the same time, horizontal scalability is sometimes not enough and requires additional strategies to ensure that the large number of IaaS service requests can be met. Therefore, strategies requiring horizontal scalability are more complex than the vertical scalability strategies because they involve the interaction of more than one facility at different service centers. To reduce the complexity of the implementation of the horizontal scalability of the IaaS infrastructures, the use of a technology service oriented infrastructure is recommended to ensure that the interaction between two or more different service centers can be done more simply and easily even though it is likely to involve a wide range of communication technologies and different cloud computing management. This is because the service oriented infrastructure acts as a middle man that translates and processes interactions and protocols of different cloud computing infrastructures without the modification of the complex to ensure horizontal scalability can be run easily and smoothly. This paper presents the potential of using a service-oriented infrastructure framework to enable transparent vertical scalability of cloud computing infrastructures by adapting three projects in this research: SLA@SOI consortium, Open Cloud Computing Interface (OCCI), and OpenStack