Case-based reasoning system for prediction of fuel consumption by haulage trucks in open-pit mines

The shovel-truck system is commonly used in open-pit mining operations. Truck haulage cost constitutes about 26% of open-pit mining costs as the trucks are mostly powered by diesel whose cost is escalating annually. Therefore, reducing fuel consumption could lead to a significant decrease in overall mining costs. Various methods have been proposed to improve fuel efficiency in open-pit mines. Case-based reasoning (CBR) can be used to estimate fuel consumption by haulage trucks. In this work, CBR methods namely case-based reasoning using forward sequential selection (CBR-FSS), traditional CBR, and Naïve techniques were used to predict fuel consumption by trucks operating at Orapa Mine. The results show that the CBR method can be used to predict fuel consumption by trucks in open-pit mines; the predicted values of fuel consumption using the CBR-FSS technique gave much lower absolute residual values, higher standardised accuracy values, and effect sizes than those of other prediction techniques on all the datasets used. The system will enable mine planners to know the fuel consumed per trip and allow them to take mitigation measures on trucks with high fuel consumption

Modified Timed Efficient Stream Loss-tolerant Authentication to Secure Power Line Communication

This paper investigates the feasibility of Timed Efficient Stream Loss-tolerant Authentica- tion to serve security needs of Power Line Communication (PLC) system. PLC network has been identified as the ideal choice to function as the last mile network, deliver load management messages to smart meters. However, there is need to address the security concerns for load management messages delivered over power line communications. The ubiquitous nature of the power line communication infrastructure exposes load management systems (LMS) deployed over it to a security risk. Ordinarily, PLC network does not em- ploy any security measures on which the smart meters and data concentrators can depend on. Therefore, the need to provide a secure mechanism for communication of load man- agement system messages over a PLC network. In LMS, source authentication is of highest priority because we need to respond only to messages from an authenticated source. This is achieved by investigating suitable robust authentication protocols. In this paper we present modifications to Timed Efficient Stream Loss-tolerant Authentication for secure authentica- tion to secure messages for load management over PLC. We demonstrate that PLC can be used to securely and effectively deliver Load Management messages to smart meters, with minimal overhead.

Systematic literature survey: applications of LoRa communication

LoRa is a communication scheme that is part of the low power wide are network (LPWAN) technology using ISM bands. It has seen extensive documentation and use in research and industry due to its long coverage ranges of up-to 20Km or more with less than 14dB transmit power. Moreover, some applications report theoretical battery lives of upto 10years for field deployed modules utilising the scheme in WSN applications. Additionally, the scheme is very resilient to losses from noise, as well bursts of interference through its FEC. Our objective is to systematically review the empirical evidence of the use-cases of LoRa in rural landscapes, metrics and the relevant validation schemes. In addition the research is evaluated based on (i) mathematical function of the scheme (bandwidth use, spreading factor, symbol rate, chip rate and nominal bit rate) (ii) use-cases (iii) test-beds, metrics of evaluation and (iv) validation methods. A systematic literature review of published, refereed primary studies on LoRa applications was conducted. Using articles from 2010-2019. We identified 21 relevant primary studies. These reported a range of different assessments of LoRa. 10 out of 21 reported on novel use cases. As an actionable conclusion, the authors conclude that more work is needed in terms of field testing, as no articles could be found on performance/deployment in Botswana or South Africa despite the existence of LoRa networks in both countries. Thus researchers in the region can research propagation models performance, energy efficiency of the scheme and MAC layer as well as the channel access challenges for the region

An investigation of feature weighting algorithms and validation techniques using blind analysis for analogy-based estimation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContext: Software effort estimation is a very important component of the software development life cycle. It underpins activities such as planning, maintenance and bidding. Therefore, it has triggered much research over the past four decades, including many machine learning approaches. One popular approach, that has the benefit of accessible reasoning, is analogy-based estimation. Machine learning including analogy is known to significantly benefit from feature selection/weighting. Unfortunately feature weighting search is an NP hard problem, therefore computationally very demanding, if not intractable. Objective: Therefore, one objective of this research is to develop an effi cient and effective feature weighting algorithm for estimation by analogy. However, a major challenge for the effort estimation research community is that experimental results tend to be contradictory and also lack reliability. This has been paralleled by a recent awareness of how bias can impact research results. This is a contributory reason why software effort estimation is still an open problem. Consequently the second objective is to investigate research methods that might lead to more reliable results and focus on blinding methods to reduce researcher bias. Method: In order to build on the most promising feature weighting algorithms I conduct a systematic literature review. From this I develop a novel and e fficient feature weighting algorithm. This is experimentally evaluated, comparing three feature weighting approaches with a na ive benchmark using 2 industrial data sets. Using these experiments, I explore blind analysis as a technique to reduce bias. Results: The systematic literature review conducted identified 19 relevant primary studies. Results from the meta-analysis of selected studies using a one-sample sign test (p = 0.0003) shows a positive effect - to feature weighting in general compared with ordinary analogy-based estimation (ABE), that is, feature weighting is a worthwhile technique to improve ABE. Nevertheless the results remain imperfect so there is still much scope for improvement. My experience shows that blinding can be a relatively straightforward procedure. I also highlight various statistical analysis decisions which ought not be guided by the hunt for statistical significance and show that results can be inverted merely through a seemingly inconsequential statistical nicety. After analysing results from 483 software projects from two separate industrial data sets, I conclude that the proposed technique improves accuracy over the standard feature subset selection (FSS) and traditional case-based reasoning (CBR) when using pseudo time-series validation. Interestingly, there is no strong evidence for superior performance of the new technique when traditional validation techniques (jackknifing) are used but is more effi cient. Conclusion: There are two main findings: (i) Feature weighting techniques are promising for software effort estimation but they need to be tailored for target case for their potential to be adequately exploited. Despite the research findings showing that assuming weights differ in different parts of the instance space ('local' regions) may improve effort estimation results - majority of studies in software effort estimation (SEE) do not take this into consideration. This represents an improvement on other methods that do not take this into consideration. (ii) Whilst there are minor challenges and some limits to the degree of blinding possible, blind analysis is a very practical and an easy-to-implement method that supports more objective analysis of experimental results. Therefore I argue that blind analysis should be the norm for analysing software engineering experiments.Botswana International University of Science & Technology (BIUST)

Finnish Software Effort Dataset

<p>Cases number 167 and 286 have a worksup (effort) equal to 0. Studies such as,</p> <p>B. Sigweni and M. Shepperd. Using Blind Analysis for Software Engineering Experiments. In <em>The 19th</em> <em>International Conference on Evaluation and</em> <em>Assessment in Software Engineerin</em>g, ACM, 2015. http://dx.doi.org/10.1145/2745802.2745832, excluded these two cases</p> <p>The column identifiers are as follows:</p> <p> </p> <p>Project_tech_ID = Project code<br>YK = Customer code<br>Project_name = Project identifier<br>Business_names = Business sector of the customer<br>Protype_names = Development type<br>Hardware_names = Hardware/platform type<br>Duration = Duration of the project in months<br>Size_ep99_proj = Total size of the project software in Experience 2.0 FP's<br>Worksup = Total effort of the supplier<br>SituCoeff = Situation coefficient multiplier<br>T01 = Involvement of the customer representatives<br>T02 = Performance and availability of the development environment<br>T03 = Availability of IT staff<br>T04 = Number of stakeholders<br>T05 = Pressure on schedule<br>T06 = Impact of standards<br>T07 = Impact of methods<br>T08 = Impact of tools<br>T09 = Level of change management<br>T10 = Maturity of software development process<br>T11 = Logical complexity of software<br>T12 = Size of database based on number of entities<br>T13 = Number of interfaces to other software<br>T14 = Quality requirements of software<br>T15 = Efficiency requirements of software<br>T16 = Training and installation/platform requirements<br>T17 = Analysis skills of staff<br>T18 = Application knowledge of staff<br>T19 = Tool skills of staff<br>T20 = Experience of project management<br>T21 = Team skills of the project team<br>InpTot = Total number of input functions<br>InpFP = Size of inputs in Experience 2.0 fp's<br>InqTot = Total number of inquiry functions<br>InqFP = Size of inquiries in Experience 2.0 fp's<br>OutTot = Total number of output functions<br>OutFP = Size of outputs in Experience 2.0 fp's<br>IntTot = Total number of interface functions<br>IntFP = Size of interfaces in Experience 2.0 fp's<br>EntTot = Total number of entities<br>EntFP = Size of entities in Experience 2.0 fp's<br>AlgTot = Total number of algorithmic functions<br>AlgFP = Size of algorithms in Experience 2.0 fp's<br>AllTot = Total number of all types of functions<br>AllFP_ep20 = Application size in Experience 2.0 fp's</p> <p> </p> <p> </p

Remote patient monitoring systems: Applications, architecture, and challenges

Research in Remote Patient Monitoring Systems (RPMS) is considered to be one of the most crucial fields since it deals with human lives. The rise in usage of RPMS has increased since the emergence of the pandemic. Even though there is a rise in these systems, there are some challenges, such as mobility, heterogeneous networks, standardization of RPMSs, automation, and Quality of Service (QoS). Our discussion focuses on RPMS systems for physiological parameter monitoring in the areas of their applications, architecture, and challenges. Thus, an in-depth review of RPMS and the analysis of these data are performed in order to understand where the current RPMS literature stands. The literature shows that research in these RPMS is concentrated on two or more of the following areas: applications, architecture, methodologies, and their performance. It appears that prior to 2020, researchers focused on nearly all aspects of RPMS until the pandemic. Then there was a shift in RPMS research to focus more on the applications and architectures of these systems. As a result, more companies are developing mobile RPMS. In this paper, we present a detailed of various existing RPMS with areas of focus on their application, architecture, technology applied, and challenges faced. We further provided a comparative and statistical analysis of the existing literature, and, finally, an overview of Quality of Service (QoS) as one challenge of RPMS is provided. The surveyed QoS requirements based on traffic type, data quality, device quality and network metrics are provided, with the aim of providing the current trend for researchers and industries to adapt to the best approach in the design of quality-aware RMPS. We then conclude the work by providing future work, which, when adopted, will brighten the future of RPMS deployment