Teaching and Learning of Mathematics and its Applications: First Results from a  Comparative Empirical Study in England and Germany

Blum, Werner; Burghes, David; Green, Nigel; Kaiser, Gabriele

doi:10.1093/teamat/11.3.112

Cited by 7 publications

(8 citation statements)

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“…With enough hidden units, an FFNN with at least two layers can approximate an arbitrary mapping from a finite input space to a finite output space [121,122,123]. However, for an FFNN, finding the optimum set of weights w is an NP-complete problem [124]. To train the model, there is a variety range of learning methods such as stochastic gradient descent, adaptive delta, adaptive gradient, adaptive moment estimation, Nesterov's accelerated gradient and RM-Sprob.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

Machine learning for internet of things data analysis: a survey

Mahdavinejad

Rezvan

Barekatain

et al. 2018

Digital Communications and Networks

895

378

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Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25-50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a

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Section: Feed Forward Neural Networkmentioning

confidence: 99%

Machine learning for internet of things data analysis: a survey

Mahdavinejad

Rezvan

Barekatain

et al. 2018

Digital Communications and Networks

895

378

View full text Add to dashboard Cite

show abstract

“…Unfortunately, they require a large amount of computation for their training (based on network complexity). Finding the optimum set of weights in cases of multiple hidden layer structures is a NP-complete problem [27]. Therefore, an alternative for more than one hidden layer MLP networks was proposed (deep neural network), in which layers have their functions, i.e., analyses of higher-level features based on the low-level features of previous layers [17,20].…”

Section: Related Workmentioning

confidence: 99%

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

Jancarczyk

Bernaś

Boczar

2019

Sensors

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This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.

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“…A drawback of this approach is that the training procedure requires a large amount of data and computation power to obtain a high model accuracy. Moreover, finding the optimum set of weights in the case of a multiple hidden layer structure is an NP-complete problem [35]. Thus, a substitute for classic multiple layer perceptron networks was proposed (deep neural network), in which the network is divided into layers with specific functions [28,32].…”

Section: State-of-the-artmentioning

confidence: 99%

“…Thus, a substitute for classic multiple layer perceptron networks was proposed (deep neural network), in which the network is divided into layers with specific functions [28,32]. Significant results using deep neural networks have led them to be the most commonly employed classifiers in machine learning [35,36].…”

Section: State-of-the-artmentioning

confidence: 99%

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

Jancarczyk

Bernaś

Boczar

2020

Sensors

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The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

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Teaching and Learning of Mathematics and its Applications: First Results from a Comparative Empirical Study in England and Germany

Abstract: We will also describe the ongoing research taking place in the next phases of the project.

Cited by 7 publications

References 0 publications

Machine learning for internet of things data analysis: a survey

Machine learning for internet of things data analysis: a survey

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

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