Evaluation of a fuzzy-expert system for fault diagnosis in power systems

A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)

BUILDING TRUST FOR SERVICE ASSESSMENT IN INTERNET-ENABLED COLLABORATIVE PRODUCT DESIGN & REALIZATION ENVIRONMENTS

Reducing costs, increasing speed and leveraging the intelligence of partners involved during product design processes are important benefits of Internet-enabled collaborative product design and realization environments. The options for cost-effective product design, re-design or improvement are at their peak during the early stages of the design process and designers can collaborate with suppliers, manufacturers and other relevant contributors to acquire a better understanding of associated costs and product viability. Collaboration is by no means a new paradigm. However, companies have found distrust of collaborative partners to be the most intractable obstacle to collaborative commerce and Internet-enabled business especially in intellectual property environments, which handle propriety data on a constant basis. This problem is also reinforced in collaborative environments that are distributed in nature. Thus trust is the main driver or enabler of successful collaborative efforts or transactions in Internet-enabled product design environments. Focus is on analyzing the problem of ¡®trust for services¡¯ in distributed collaborative service provider assessment and selection, concentrating on characteristics specific to electronic product design (e-Design) environments. Current tools for such collaborative partner/provider assessment are inadequate or non-existent and researching network, user, communication and service trust problems, which hinder the growth and acceptance of true collaboration in product design, can foster new frontiers in manufacturing, business and technology. Trust and its associated issues within the context of a secure Internet-enabled product design & realization platform is a multifaceted and complex problem, which demands a strategic approach crossing disciplinary boundaries. A Design Environment Trust Service (DETS) framework is proposed to incorporate trust for services in product design environments based on client specified (or default) criteria. This involves the analysis of validated network (objective) data and non-network (subjective) data and the use of Multi Criteria Decision Making (MCDM) methodology for the selection of the most efficient service provision alternative through the minimization of distance from a specified ideal point and interpreted as a Dynamic (Design) Trust Index (DTI) or rank. Hence, the service requestor is provided with a quantifiable degree of belief to mitigate information asymmetry and enable knowledgeable decision-making regarding trustworthy service provision in a distributed environment

Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

Being susceptible to climate change means being unable to cope with the adverse effects of climate change especially droughts and a likelihood of experiencing harm due to its occurrence. The study sought to evaluate the effects of exposure to Climate related shocks on the livelihoods of the smallholder farmers with the intent of formulating appropriate policies to enable them cope with its impacts. A descriptive survey research design was used. Stratified random sampling was employed to select 390 households. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The results revealed that the final value of the negative consequences of drought was 0.35.The study also established a single index as 0.45 for exposure for the entire study period of 35 years for Kinakomba Ward .The study showed that exposure was statistically significant at (0.000066). The study further revealed that the periods between occurrence of extreme droughts were reducing and at the same time that droughts were moving from being severe to being extreme within shorter periods of time leaving smallholder farmers who depend on rain fed agriculture with high exposures and risks as well as experiencing longer hunger periods with severe implications on their food and nutritional security for the vast populations in the study area. The Study concluded that the exposure to drought of the smallholder farmers in Kinakomba Ward is significantly related to their farming livelihood systems. This study recommends that the County Government in partnership with the National Government and other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic and vicious droughts’ impacts that have led to serious irreversible harm to humans and livestock in the area. Keywords: Exposure, Drought, Mitigation, Adaptation, Food security DOI: 10.7176/FSQM/94-07 Publication date: February 29th 202

Development of procedures for land use assessment at the regional scale

Multi-criteria land evaluation is an important process required for sustainable resource management. During the process of land evaluation, various factors related to land and corresponding resources need to be addressed. Availability of simple, ready to use procedures is particularly valuable for land evaluation. In this thesis approaches and tools aimed at the evaluation of land use change processes and land suitability for rural tourism, as well as sensitivity analysis procedure for land evaluation models are presented

Cognitive radio networks : quality of service considerations and enhancements

The explosive growth of wireless and mobile networks, such as the Internet of Things and 5G, has led to a massive number of devices that primarily use wireless channels within a limited range of the radio frequency spectrum (RFS). The use of RFS is heavily regulated, both nationally and internationally, and is divided into licensed and unlicensed bands. While many of the licensed wireless bands are underutilised, useable unlicensed bands are usually overcrowded, making the efficient use of RFS one of the critical challenges faced by future wireless communication technologies. The cognitive radio (CR) concept is proposed as a promising solution for the underutilisation of useful RFS bands. Fundamentally, CR technology is based on determining the unoccupied licensed RFS bands, called spectrum white spaces or holes, and accessing them to achieve better RFS utilisation and transmission propagation. The holes are the frequencies unused by the licensed user, or primary user (PU). Based on spectrum sensing, a CR node, or secondary user (SU), senses the surrounding spectrum periodically to detect any potential PU transmission in the current channel and to identify the available spectrum holes. Under current RFS regulations, SUs may use spectrum holes as long as their transmissions do not interfere with those of the PU. However, effective spectrum sensing can introduce overheads to a CR node operation. Such overheads affect the quality of service (QoS) of the running applications. Reducing the sensing impact on the QoS is one of the key challenges to adopting CR technology, and more studies of QoS issues related to implementing CR features are needed. This thesis aims to address these QoS issues in CR while considered the enhancement of RFS utilisation. This study concentrates on the spectrum sensing function, among other CR functions, because of its major impact on QoS and spectrum utilisation. Several spectrum sensing methods are reviewed to identify potential research gaps in analysing and addressing related QoS implications. It has been found that none of the well-known sensing techniques is suitable for all the diverse QoS requirements and RFS conditions: in fact, higher accuracy sensing methods cause a significant QoS degradation, as illustrated by several simulations in this work. For instance, QoS degradation caused by high-accuracy sensing has not yet been addressed in the IEEE 802.11e QoS mechanism used in the proposed CR standard, IEEE 802.11af (or White-Fi). This study finds that most of the strategies proposed to conduct sensing are based on a fixed sensing method that is not adaptable to the changeable nature of QoS requirements. In contrast, this work confirms the necessity of using various sensing techniques and parameters during a CR node operation for better performance

An Efficient Method to Enhance Health Care Big Data Security in Cloud Computing Using the Combination of Euclidean Neural Network And K-Medoids Based Twin Fish Cipher Cryptographic Algorithm

Big data is a phrase that refers to the large volumes of digital data that are being generated as a consequence of technology improvements in the health care industry, e-commerce, and research, among other fields. It is impossible to analyze Big Data using typical analytic tools since traditional data storage systems do not have the capacity to deal with such a large volume of data. Cloud computing has made it more easier for people to store and process data remotely in recent years. By distributing large data sets over a network of cloudlets, cloud computing can address the challenges of managing, storing, and analyzing this new breed of data It's possible for private data to be leaked when it is kept in the cloud, as users have no control over it. This paper proposes a framework for a secure data storage by using the K-medoids-based twin fish cipher cryptographic algorithm. We first normalize the data using the Filter splash Z normalization and then apply the Euclidean neural network to compute similarity, which ensures data correctness and reduces computational cost. As a result, the suggested encryption strategy is used to encrypt and decode the outsourced data, thereby protecting private information from being exposed. The whole experiment was conducted using health data from a large metropolis from the Kaggle database. Using the recommended encryption method, users will be able to maintain their privacy while saving time and money by storing their large amounts of data on the cloud