A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper

Coreset selection can accelerate quantum machine learning models with provable generalization

Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges. Nonetheless, the training efficiency challenge poses a limitation on both QNNs and quantum kernels, curbing their efficacy when applied to extensive datasets. To confront this concern, we present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels by distilling a judicious subset from the original training dataset. Furthermore, we analyze the generalization error bounds of QNNs and quantum kernels when trained on such coresets, unveiling the comparable performance with those training on the complete original dataset. Through systematic numerical simulations, we illuminate the potential of coreset selection in expediting tasks encompassing synthetic data classification, identification of quantum correlations, and quantum compiling. Our work offers a useful way to improve diverse quantum machine learning models with a theoretical guarantee while reducing the training cost.Comment: 25 pages, 7 figure

Exploring Machine Learning to Improve Procurement and Purchasing Processes

Machine learning is an area of artificial intelligence that enables systems to improve their per-formance by learning from data without being purposefully programmed for the task. Learning occurs by training algorithms to identify correlations and patterns in large amounts of data, which can be then utilized to make predictions and conclusions. Machine learning has grown in popularity in recent years for a variety of commercial applications, and the purchasing process is no exception. The continuously improving computing power and data management capabili-ties of computers have enabled more sophisticated machine learning applications, which has also expanded the research work around machine learning. However, when looking at studies on the development of purchasing and purchasing processes using machine learning applica-tions, the number of publications is limited, especially for studies that have used empirical data from interviews. Thus, the aim of this research was to provide a current overview of the opportunities and po-tential challenges of implementing machine learning applications in procurement and purchas-ing processes. In addition, interviews were conducted with the aim of finding out the reasons that are preventing efficient procurement and purchasing processes, the work tasks that inter-viewees would most like to see assist from information systems, and whether machine learn-ing could offer help with perceived problems. The research methods used in this thesis were both theoretical and empirical. The theoretical part consisted of an introduction to different aspects of machine learning and procurement and purchasing processes, using available aca-demic and industry material as sources. The aim was to keep the source material as up to date as possible. Interviews were conducted with one company, involving eight participants in total. Based on the results of the research, machine learning applications that provide assist with pricing, cost analysis and forecasting of material requirements were seen especially useful. Challenges were perceived, in particular due to the poor quality of the used data, the large amount of data and the traceability of the data. Based on the interviews the low level of au-tomatization of processes, data reliability problems, forecasting material requirements, and pricing and analysing of materials were seen as challenges. The results suggested that machine learning can be used to improve purchasing and procurement processes, and the empirical research supports this conclusion.Koneoppimisella tarkoitetaan tekoälyn osa-aluetta, joka mahdollistaa järjestelmien suorituskyvyn parantamisen oppimalla datasta ilman, että sitä on tarkoituksenmukaisesti ohjelmoitu kyseisestä tehtävää varten. Oppiminen tapahtuu kouluttamalla algoritmeja tunnistamaan suurista tietomääristä korrelaatioita ja malleja, joiden perusteella pystytään luoman ennusteita sekä tekemään johtopäätöksiä. Koneoppiminen on kasvattanut viime vuosina suosiotaan erilaisten kaupallisten sovelluskohteiden muodossa, eikä myöskään hankinta- ja ostoprosessit ole tässä asissa poikkeus. Jatkuvasti parantuva tietokoneiden laskentakyky ja tiedonhallinta ovat mahdollistaneet entistä kehittyneempiä koneoppimista hyödyntäviä sovelluksia, mikä on myös laajentanut koneoppimisen ympärillä tapahtuvaa tutkimustyötä. Kuitenkin kun tarkastellaan tutkimuksia, joissa käsitellään hankinta - ja ostoprosessien kehittämistä koneoppimisen avulla on julkaisumäärä rajallista, erityisesti sellaisten tutkimusten osalta, joissa on hyödynnetty kokemusperäistä, haastatteluista saatua tietoa. Täten tämän työn tavoitteena oli tarjota ajankohtainen katsaus koneoppimista hyödyntävien sovellusten tarjoamista mahdollisuuksista ja potentiaalisista haasteista niitä hankinta ja osto prosseihin käyttöönotettaessa. Lisäksi suoritettiin haastatteluita, joiden tavoitteena oli saada selville syyt, jotka ovat esteinä tehokkaan hankinnan- ja ostoprosessien tapahtumiselle sekä mihin työtehtäviin haastateltavat toivoisivat erityisesti apua tietojärjestelmien kautta ja voisiko koneoppiminen tarjota apua koettuihin ongelmiin. Työn tutkimusmetodit olivat teoreettisia sekä empiirisiä. Teoreettinen osio koostui koneoppimisen sekä hankinnta- ja ostoprosessien eri osa-aluiden esittelystä, joiden lähdemateriaalina hyödynnettin saatavilla olevia akateemisia sekä koneoppimisen ja hankinnan ja oston alojen julkaisuja. Lähdemateriaali pyrittiin pitämään mahdollisimman ajankohtaisena. Haastattelut suoritettiin yhden yrityksen kanssa, johon otti osaa kahdeksan henkilöä. Tutkimuksen tulosten perusteella koneoppimisen sovellukset, jotka auttavat hinnoittelussa, kustannusten analysoinnissa sekä materiaalitarpeiden ennustamisessa nähtiin erityisen hyödyllisinä. Haasteina koettin ongelmat, jotka johtuivat erityisesti käytetyn datan heikosta laadusta, datan suuresta määrästä sekä datan jäljitettävyydestä. Haastatteluiden perusteella haasteina koettiin prosessien vähäinen automatisointi, datan luotettavuussongelmat, materiaalitarpeiden ennustaminen sekä materiaalien hinnoitteluun ja analysointiin liittyvät haasteet. Tuloksista voitiin tulkita, että hankinnan ja oston prosesseja voidaan kehittää koneoppimisen avulla, ja empiirinen tutkimusosio myös tukee tätä johtopäätöstä

Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations

Materials management is a vital process in the delivery of construction facilities. Studies by the Construction Industry Institute (CII) have demonstrated that materials and installed equipment can constitute 40– 70% of the total construction hard cost and affect 80% of the project schedule. Despite its significance, most of the construction industry sectors are suffering from poor material management processes including inaccurate warehouse records, over-ordering and large surpluses of material at project completion, poor site storage practices, running out of materials, late deliveries, double-handling of components, out-of-specification material, and out of sequence deliveries which all result in low productivity, delay in construction and cost overruns. Inefficient material management can be attributed to the complex, unstructured, and dynamic nature of the construction industry, which has not been considered in a large number of studies available in this field. The literature reveals that available computer-based materials management systems focus on (1) integration of the materials management functions, and (2) application of Automated Data Collection (ADC) technologies to collect materials localization and tracking data for their computerized materials management systems. Moreover in studies that focused on applying ADC technologies in construction materials management, positioning and tracking critical resources in construction sites, and identifying unique materials received at the job site are the main applications of their used technologies. Even though, various studies have improved materials management processes copiously in the construction industry, the benefits of considering the dynamic nature of construction (in terms of near real-time progress monitoring using state of the art technologies and techniques) and its integration with a dynamic materials management system have been left out. So, in contrast with other studies, this research presents a construction materials management framework capable of considering the dynamic nature of construction projects. It includes a vital component to monitor project progress in near real-time to estimate the installation and consumption of materials. This framework consists of three models: “preconstruction model,” “construction model,” and “data analysis and reporting model.” This framework enables (1) generation of optimized material delivery schedules based on Material Requirement Planning (MRP) and minimum total cost, (2) issuance of material Purchase Orders (POs) according to optimized delivery schedules, (3) tracking the status of POs (Expediting methods), (4) collection and assessment of material data as it arrives on site, (5) considering the inherent dynamics of construction operations by monitoring project progress to update project schedule and estimate near real-time consumption of materials and eventually (6) updating MRP and optimized delivery schedule frequently throughout the construction phase. An optimized material delivery schedule and an optimized purchase schedule with the least cost are generated by the preconstruction model to avoid consequences of early/late purchasing and excess/inadequate purchasing. Accurate assessment of project progress and estimation of installed or consumed materials are essential for an effective construction material management system. The construction model focuses on the collection of near real-time site data using ADC technologies. Project progress is visualized from two different perspectives, comparing as-built with as-planned and comparing various as-built status captured on consecutive points of time. Due to the recent improvements in digital photography and webcams, which made this technology more cost-effective and practical for monitoring project progress, digital imaging (including 360° images) is selected and applied for project progress monitoring in the construction (data acquisition) model. In the last model, which is the data analysis and reporting model, Deep Learning (DL) and image processing algorithms are proposed to visualize and detect actual progress in terms of built elements in near real-time. In contrast with the other studies in which conventional computer vision algorithms are often used to monitor projects progress, in this research, a deep Convolutional Auto-Encoder (CAE) and Mask Region-based Convolutional Neural Network (R-CNN) are utilized to facilitate vision-based indoor and outdoor progress monitoring of construction operations. The updated project schedule based on the actual progress is the output of this model, and it is used as the primary input for the developed material management framework to update MRP, optimized material delivery, and purchase schedules, respectively. Applicability of the models in the developed material management framework has been tested through laboratory and field experiments. The results demonstrated the accuracy and capabilities of the developed models in the framework