Human activity in covered urban space a case study of Petaling street, Kuala Lumpur, Malaysia

Streets are channels of movement and a symbolic representation of local tradition and culture. In South East Asian cities like Kuala Lumpur, Malaysia, they are avenues for socialization just as what plazas are in Europe. They are publicly acknowledged joints for entertainment of peer groups, family members, and guests alike. The physical quality of a street determines the human activities it could support. The changing identity of Petaling Street activity character with the introduction of a permanent cover for the traditional market altered its original activity pattern. It is therefore imperative to evaluate the impact of the new physical environment on human activities within it. The behavioural analysis of the covered street was conducted to appraise its ability to support or restrict human behaviour. User activities of the environment were observed systematically to establish behaviour pattern of the street. Behavioural mapping, behaviour categorisation and behaviour analysis were carried out to identify the functional characteristics associated with covered streets. The research findings show that there is a very strong functional characteristic of the covered street which is consistent with theories of environmental quality enhances human activity. They play a very important role in determining the user of the street, their activities and behavioural patterns. It also realized that the built environment could be to be manipulated to control human behaviour. The research identified that elements such as pedestrian comfort and adjacent land uses appear to influence the level of use within the pedestrian malls. This research will enhance the understanding of design decisions at different scales, such as the introduction of covered streets in Malaysian traditional markets like Petaling Street

Knowledge discovery in distance relay event report: a comparative data-mining strategy of rough set theory with decision tree

A protective relay performance analysis is only feasible when the hypothesis of expected relay operation characteristics as decision rules is established as the knowledge base. This has been meticulously accomplished by soliciting the relay knowledge domain from protection experts who are usually constrained by their experience and expertise. Manually analyzing an event report is also cumbersome due to the tremendous amount of data to be perused. This paper addresses these issues by intelligently divulging the knowledge hidden in the relay recorded event report using a data-mining strategy based on rough set theory and a rule-quality measure under supervised learning to discover the relay decision algorithm and association rule. The high prediction accuracy rate and the close-to-unity areas under ROC curve value of the relay operating characteristic curve of the discovered relay decision algorithm verifies its generalized ability to predict trip status in an expert system of relay performance analysis. The relay association rule that was subsequently discovered by using the rule-quality analysis had also been verified as being a reliable hypothesis of the relay operation characteristics. This hypothesis helps the protection engineers understand the behavior of the distance relay. These rules would then be compared with and validated by benchmarking decision-tree-based data-mining analysis

Discovering decision algorithm from a distance relay event report

In this study rough-set-based data mining strategy was formulated to discover distance relay decision algorithm from its resident event report. This derived algorithm, aptly known as relay CD-prediction rules, can later be used as a knowledge base in support of a protection system analysis expert system to predict, validate or even diagnose future unknown relay events. Nowadays protection engineers are suffering from very complex implementations of protection system analysis due to massive quantities of data coming from diverse points of intelligent electronic devices. In helping the protection engineers deal with this overwhelming data, this study relied merely on digital protective relay’s recorded event report because, among other intelligent electronic devices, digital protective relay sufficiently provided virtually most attributes needed for data mining process in knowledge discovery in database. The method of discovering the distance relay decision algorithm essentially involved formulating rough set discernibility matrix and function from relay event report, finding reducts of pertinent attributes using genetic algorithm and finally generating relay prediction rules. The classification accuracy and the area under the ROC curve measurements provided an acceptable evaluation of the fact that the discovered relay decision algorithm

Making implicit knowledge of distance protective relay operations and fault characteristics explicit via rough set based discernibility relationship

This paper discusses the novel application of the discernibility concept inherent in rough set theory in making explicit of the implicit knowledge of distance protective relay operations and fault characteristics that are hidden away in the recorded relay event report. A rough-set-based data mining strategy is formulated to analyze the relay trip assertion, impedance element activation, and fault characteristics of distance relay decision system. Using rough set theory, the uncertainty and vagueness in the relay event report can be resolved using the concepts of discernibility, elementary sets and set approximations. Nowadays protection engineers are suffering from very complex implementations of protection system analysis due to massive quantities of data coming from diverse points of intelligent electronic devices (IEDs such as digital protective relays, digital fault recorders, SCADA's remote terminal units, sequence of event recorders, circuit breakers, fault locators and IEDs specially used for variety of monitoring and control applications). To help the protection engineers come to term with the crucial necessity and benefit of protection system analysis without the arduous dealing of overwhelming data, using recorded data resident in digital protective relays alone in an automated approach called knowledge discovery in database (KDD) is certainly of an immense help in their protection operation analysis tasks. Digital protective relay, instead of a host of other intelligent electronic devices, is the only device for analysis in this work because it sufficiently provides virtually most attributes needed for data mining process in KDD. Unlike some artificial intelligence aproaches like artificial nueral network and decision tree in which the data mining analysis is "population-based" and single since it is common to the entire population of training data set, the rough set approach adopts an "individually-event-based" paradigm in which detailed time tracking analysis of relay operation has been successfully performed

Proceedings of First Conference for Engineering Sciences and Technology: Vol. 2

This volume contains contributed articles of Track 4, Track 5 & Track 6, presented in the conference CEST-2018, organized by Faculty of Engineering Garaboulli, and Faculty of Engineering, Al-khoms, Elmergib University (Libya) on 25-27 September 2018. Track 4: Industrial, Structural Technologies and Science Material Track 5: Engineering Systems and Sustainable Development Track 6: Engineering Management Other articles of Track 1, 2 & 3 have been published in volume 1 of the proceedings at this lin