/pdf/how-to-conduct-surveys-a-step-by-step-guide-4za2dab8jy.pdf

How to Conduct Surveys: A Step-by-Step Guide

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

/pdf/application-of-big-data-and-machine-learning-in-smart-grid-1taystle2u.pdf

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1006&context=electricalpub

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resiliency.

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

This paper proposes the use of a novel algorithm for smart-direct load control (S-DLC) and load shedding to minimize power outages in sudden grid load changes and reduce the peak-to-average ratio. The algorithm utilizes forecasting, shedding, and S-DLC. It also uses the Internet of Things and stream analytics to provide real-time load control, and generates a daily schedule for customers’ equipped with intelligent electronic devices based on their demands, thermal comfort, and the forecasted load model. The demand response techniques are utilized for real-time load control and optimization. To test the algorithm, a simulation system was developed, which takes into account 100 customers owning randomly selected appliances. The results indicated that load shedding using autoregressive integrated moving average time-series prediction model, and applying S-DLC and Internet of Things can significantly reduce customers’ power outage.

Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management

Digital game-based learning (DGBL) has been perceived as an engaging teaching approach to foster students' learning and motivation. There are different opinions about the potential benefits of gaming on students' academic achievements, motivation, and skills in science courses due to the lack of empirical evidence and mixed results. To address this issue, this paper provides a review of relevant literature from 2006 to 2017 to examine the effects of using educational computer games in teaching science at the elementary education level. This paper employed a multidimensional framework to classify learning outcomes from studies of DGBL applications in the area of elementary science education. The findings of this review show a promising potential of DGBL, particularly in the area of content understanding. However, the findings of the review also suggest that there is a need to provide additional research in order to gain a more comprehensive picture of the educational effectiveness of DGBL. Hence, researchers are advised to conduct more randomized controlled trials (RCTs), various learning modes (e.g., collaborative and individual), and comparisons of DGBL to traditional methods of teaching. Furthermore, the researchers are highly encouraged to examine the effectiveness of DGBL applications in other areas, such as problem-solving and critical thinking. The findings of this review can benefit educational computer game designers, educators, and practitioners in the area of science education, particularly at the elementary level.

/pdf/effects-of-digital-game-based-learning-on-elementary-science-vyp0av1e47.pdf

Effects of Digital Game-Based Learning on Elementary Science Learning: A Systematic Review

The precision of software project estimation such as project cost estimation, project quality estimation and project risk analysis are important issues in software project management. The ability to accurately estimate software development costs is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle. The estimates are often the least accurate, because very little detail is known about the project and the product at the beginning. The need for reliable and accurate cost predictions in software engineering is an ongoing challenge for software engineers. In this paper a novel neural network Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Proposing a new software cost estimation model based on artificial neural networks

Review of Agile Methodologies in Software Development

Often, software managers have to monitor and manage many projects concurrently. Unfortunately, some projects were completed successfully but some were not completed on time, over budget or being cancelled. Some of the reasons of this project failure are: lack of user involvement, lack of planning, incomplete requirements, lack of resources, incorrect cost estimation, just to name a few. There are many project planning and scheduling techniques to manage and help to ensure project success. Some of these techniques, however, may not be suitable for specific types of projects and thus, cause projects to fail. This paper discusses the issues involved in project success and failure, and presents the feedback of 50 undergraduate students who undertook a team project of the project management course.

Siew Hock Ow

Papers

Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management

Effects of Digital Game-Based Learning on Elementary Science Learning: A Systematic Review

Proposing a new software cost estimation model based on artificial neural networks

Review of Agile Methodologies in Software Development

Project Management Practices: The Criteria for Success or Failure