The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

Sleep, Wakefulness, Dreams and Memory

Sleep-wakefulness cycle mechanisms shown in central neural activity change

A Review of the Teaching and Learning on Power Electronics Course

—In this review, we describe various kinds of problem and solution related teaching and learning on power electronics course all around the world. The method was used the study of literature on journal articles and proceedings published by reputable international organizations. Thirtynine papers were obtained using Boolean operators, according to the specified criteria. The results of the problems generally established that student learning motivation was low, teaching approaches that are still teacher-centered, the scope of the curriculum extends, and the physical limitations of laboratory equipment. The solutions offered are very diverse ranging from models, strategies, methods and learning techniques supported by information and communication technology

A study of energy-related occupancy activities in a sample of monitored domestic buildings in the UK

Domestic energy use is determined by multiple non-technological factors, such as the occupants’ lifestyle and activities, which can even offset the effect from energy-efficiency technologies. Acquiring the actual occupancy data relating to energy use in a uniform format to generate comparable and representative information is challenging. Projects that seek to address this issue, such as the Retrofit for the Future and Building Performance Evaluation programmes of the Technology Strategy Board in the UK, usually require major investment. Long-term monitoring and longitudinal observation are two major features in these major investment projects. The former approach refers to the frequent measurement of indoor / outdoor environments and energy use conducted over at least two heating seasons, in line with the whole-house carbon and energy monitoring protocol of the Energy Saving Trust (2011). The latter approach, longitudinal observation, refers to observations conducted on the same group of individuals over an extended study period of years or decades to examine changes over time (Bryman, 2012). The majority of existing households and associated stakeholders that could potentially benefit from the investigation of energy-related occupancy activities cannot feasibly be involved in projects requiring major investment

Washington University Record, April 23, 1998

https://digitalcommons.wustl.edu/record/1792/thumbnail.jp

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

A Classroom Based Assessment in a High School Social Studies Classroom

The purpose of this study was to investigate whether the use of brain-based pre-writing strategies will improve students’ abilities to support claims, with evidence, on a state-mandated, classroom-based, assessment. Specifically, the research evaluated the working hypothesis that using brain-based, pre-writing activity in the non-fiction, expository writing process will assist students in their performances, as assessed by the Office of the Superintendent of Public Instruction for the state-approved You and the Economy CBA CBA Rubric. By using brain-based strategies as a pre-writing activity in the non-fiction, explanatory, secondary social studies writing process, I hypothesized that those students would demonstrate logical use of claims and evidence in their typed essays. The research questions were answered through an action-research data cycle. This research is guided by two overarching research questions: 1. As brain-based learning strategies are being implemented in real time, what is the nature of the process of using brain-based interventions? In documenting the brain-based interventions, what decision-making factors are considered when designing the unit of instruction? 2. What changes—if any—are demonstrated in student writing performances on a Classroom Based Assessment, when brain-based learning strategies are implemented over the span of the research cycle? These research questions were answered through a study design involving a cycle of instruction, culminating in an explanatory writing sample. The results of the CBA-related to claims and evidence outlined in EALRs 2.2.1 and 5.2.2, instructional practices to implement brain-based pre-writing strategies will be implemented. Using brain principles to increase visual, auditory and kinesthetic contact with the concepts presented may improve students’ abilities to make claims and provide proper evidence for those claims, as measured by the Office of the Superintendent of Public Instruction for the State approved You and the Economy Class Based Assessment (CBA) Rubric. The process of my decision making, as well as student writing, was examined to evaluate the effect of brain-based pre-writing strategies, which students use to complete the CBA