IoT Masters Programs

В данной работе обсуждаются фундаментальные вопросы разработки программ магистратуры в области Интернета вещей (Internet of Things — IoT). Мы кратко сравниваем предложения Сколтеха и Стэнфорда и утверждаем, что наиболее гибкое решение достигается посредством вводного блока и четырех параллельных потоков учебных курсов: обработка сигналов и управление, обучение машин и искусственный интеллект (ИИ), программирование и схемотехника платформ с применением микроконтроллеров, и, наконец, сети и кибербезопасность. Вводный блок предполагается оснастить достаточным количеством предметов по выбору, чтобы поступающие выпускники бакалавриата из областей прикладной математики, информационных технологий и электроники/телекоммуникаций могли приобрести необходимые знания для освоения потоковых курсов. Мы утверждаем, что еще одним необходимым отличием программы IoT должен явиться междисциплинарный групповой дипломный проект значительного объема, также основанный на потоковых курсах. This paper discusses the fundamentals of postgraduate curriculum development for the area of the Internet of Things (IoT). We provide a brief contrasting analysis of Skoltech and Stanford Masters programs and argue that the most flexible way forward is via the introduction of a leveling-off, elective introductory stage, and four parallel course streams: signal processing and control; Artificial Intelligence (AI), and machine learning; microcontroller systems design; and networks and cyber security. The leveling-off stage is meant to provide sufficient electives for graduates of applied math, Information Technologies (IT), or electronics/telecom degrees to learn the necessary fundamentals for the stream modules. We argue that another distinguishing feature of an IoT masters program is a large project drawing on the stream modules and requiring a multidisciplinary, team development effort.

Schlumberger’s Drilling ECG Goes to the Heart of the Matter To Diagnose Abnormal Conditions in Real Time

The trading of technologies is nothing new for the upstream oil and gas and medical communities. The connection makes sense, particularly since both disciplines rely heavily on applied math for diagnoses. It should come as no surprise, then, that a popular paper at the 2021 Virtual SPE/IADC International Drilling Conference proposed a medically inspired approach to prevent catastrophic drilling system failures brought on by downhole shocks (SPE/IADC 204098). The paper describes a “drilling electrocardiogram” that diagnoses “arrhythmic drilling” similarly to how medical electrocardiograms diagnose dangerous vibration anomalies in heart patients. The approach classifies shock wave-forms acquired at 31,250 hertz (Hz) downhole. The shock signals are treated as drilling electrocardiograms (D-ECG) that are processed using clustering algorithms and merged with drilling incidents to identify in real time an arrhythmic signature pattern that can lead to catastrophic failures. A Revelation Justo Matheus, senior control engineer for Schlumberger and lead author of the paper, said that in studying field incidents in which rotary steerable system (RSS) bottomhole assemblies (BHA) had been severely damaged by shocks, the signature patterns reminded him of ECGs (Fig. 1). This led him to medical libraries, which in turn led to a revelation—that throughout the 3 decades that downhole vibration measurements while drilling (MWD) have been studied, only the analysis of the amplitude and root mean square (rms) values of shocks had been the focus. No one had considered frequency for diagnosing downhole shocks. It was this revelation that drove the concept of the D-ECG based on shock waveforms acquired at high frequency in real time to prevent failures of the BHA. What Is “Normal” and What Is Not? Drilling-generated shocks and vibrations affect rate of penetration, directional control, and wellbore quality, making them among the main causes of failures in drilling. “Shocks are present almost all the time,” said Matheus. “The challenge is in knowing which are normal and which are not.” RSS are equipped with measurement devices such as magnetometers, accelerometers, and shock and vibration sensors that obtain statistical information from which whirl, bit bounce, and stick/slip severity are inferred. Often, however, the derived statistics are not sufficient to distinguish between normal drilling vs. abnormal drilling for a location in the wellbore. Recent electronic advances enabled the development of high-resolution drilling dynamic data recorders, extending the sampling frequency from traditional 100 Hz to 1,600 Hz. However, most of these devices are for data recording only. There is no real-time communication with surface and no capability to inform about drilling conditions downhole.

Fraud Detection in Health Insurance Claims using Machine Learning Algorithms

Fraud can be spread broadly and it is extremely costly to the therapeutic protection framework. Unscrupulous protection might be a case created to cover up or twist information that is intended to deliver social insurance edges. Cheats might be of the numerous sorts and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the wellbeing segment. The commitment of this case misrepresentation discovery is Associate in nursing trial study on extortion recognizable proof and exploitative examples. Along these lines, to identify the misrepresentation information handling procedures are utilized. For the most part essential based oddities are implemented exploitation applied math call rules and k-means, rule based mining and affiliation rule bolstered appropriation calculations are applied. Through these abnormalities the extortion in certifiable information is recognized. Be that as it may, there might be a great deal of progress done by exploitation various information handling procedures. In this way the arranged methodology has been assessed basing on the protection information and furthermore the trial results from our methodology are efficient in human services misrepresentation. Other self-advancing misrepresentation location ways can likewise be applied on this protection information

Application of SPSS Programme in the Field of Social Science Research

SPSS is a combined package of software which is a set of statistical package for the social science research. The primary application of this program is to explore the technical data which have relevance with social science. These data can be utilised for survey research, market analyzation etc. The benefits of the package area unit its relative simple use, its familiarity to several applied math consultants and its practicality. Statistics is that the body of mathematical techniques or processes for gathering, describing, organizing and decoding numerical information. With the use of SPSS, the researcher can properly analyse the activities and views of people in a analytical method. Presently, various educational institution, medical Sciences institutions, academicians, import-export organizations etc have been using SPSS for fulfilling different objectives. The researcher can easily understand the demand for a product in the market with the help of statistical analyzation which can change their strategy. Primarily, SPSS is a useful package which is designed in such a way to handle the large quantum of variables within a short period of time by using different technical commands to produce a set of suitable outputs. All forms of statistical analysis can be easily taken up in the full package software which can convert the quantitative data into qualitative analysis. The primary purpose of this proposed work is to explore the utilization and significance of SPSS in the field of social science.

The g-Drazin inverse involving power commutativity

Let A be a complex Banach algebra. An element a ? A has g-Drazin inverse if there exists b ? A such that b = bab, ab = ba, a-a2b ? A qnil. Let a, b ? Ad. If a3b = ba, b3a = ab, and a2adb = aadba, we prove that a + b ? Ad if and only if 1 + adb ? Ad. We present explicit formula for (a + b)d under certain perturbations. These extend the main results of Wang, Zhou and Chen (Filomat, 30(2016), 1185-1193) and Liu, Xu and Yu (Applied Math. Comput., 216(2010), 3652-3661).

“Analyzing the Effect with Integrated Technique for Feature Selection and Software Defect Prediction”

software defect prediction (sdp) technique was projected to designate testing assets sanely, decide the testing want of assorted modules of the software system, and improve programming quality. By utilizing the implications of sdp, programming specialists will fruitfully pass judgment on it that software system modules area unit sure to be blemished, the conceivable range of imperfections in a very module or different information known with software system defects before testing the software system [1]. Existing sdp studies may be divided into four types: (1) classification, (2) regression, (3) mining association rules, (4) ranking. The primary aim of the primary class is to classification of the software system entities like functions, classes, files, etc into completely different levels of severity with the assistance of various applied math techniques like supply regression [2] and discriminant analysis [3] and techniques of machine learning like svm [4] and ann [5]. The second kind aims to assess the amount of imperfections within the components of the software system by victimisation completely different ways, for instance, genetic programming, and support vector regression [6]. The third category utilizes association rule mining approaches, for instance, relative affiliation rule [7], and also the cba2 algorithmic rule, to mine the affiliation between the errors of programming components and programming measurements. The fourth category contemplates to rank the product of the software system as per the amount of errors in components or specifically streamlining the performance of ranking, i.e., faults share average (fpa) as indicated by existing studies of sdp [8]. Sdp distinguishes the modules that area unit imperfect and it needs a large scope of testing. Early recognizable proof of a blunder prompts viable allotment of assets, decreases the time and value of developing software system of high-quality. Hence, associate degree sdp model assumes a vital job in comprehending, assessing and rising the character of a product framework. Consequently, predicting deformity is incredibly basic within the field of reliableness and quality of software system. Predicting the defects is almost a unique analysis space of programming quality planning. By covering key indicators, forms of info to be assembled and also the role of sdp in software system quality, the connection among predictor and defect may be established