Special issue on methodologies of training data processing professionals and advanced end-users

1990 ◽  
Vol 6 (1-2) ◽  
pp. 1-2
Author(s):  
Ben Zion Barta ◽  
Lauri Fontell ◽  
Patrick Raymont
Keyword(s):  
Data Processing ◽  
End Users ◽  
Training Data ◽  
Special Issue

scholarly journals Editorial for Special Issue “Virtual Reality and Its Application in Cultural Heritage”

2021 ◽  
Vol 11 (4) ◽  
pp. 1530
Author(s):  
Christos Fidas ◽  
Stella Sylaiou
Keyword(s):  
Virtual Reality ◽  
Cultural Heritage ◽  
End Users ◽  
Special Issue

Recent advancements in Virtual Reality (VR) technologies provide new opportunities for Cultural Heritage (CH) organizations to attract, engage, and support end-users more efficiently and effectively [...]

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood
Keyword(s):  
Supervised Learning ◽  
Monitoring System ◽  
End Users ◽  
Training Data ◽  
Smart Device ◽  
Energy Monitoring ◽  
Load Monitoring ◽  
Benchmark Datasets ◽  
Smart Home Environment ◽  
Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

scholarly journals On Learning Interpreted Languages with Recurrent Models

2022 ◽  
pp. 1-13
Author(s):  
Denis Paperno
Keyword(s):  
Natural Language ◽  
Data Processing ◽  
Syntactic Structure ◽  
Neural Nets ◽  
Training Data ◽  
Sequential Data ◽  
Extensive Training ◽  
Formal Syntax ◽  
Compositional Interpretation

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

Attenuation of random noise using denoising convolutional neural networks

2019 ◽  
Vol 7 (3) ◽  
pp. SE269-SE280
Author(s):  
Xu Si ◽  
Yijun Yuan ◽  
Tinghua Si ◽  
Shiwen Gao
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Random Noise ◽  
Training Data ◽  
Prediction Errors ◽  
Network Learning ◽  
Deep Network ◽  
Residual Image ◽  
And Training

Random noise often contaminates seismic data and reduces its signal-to-noise ratio. Therefore, the removal of random noise has been an essential step in seismic data processing. The [Formula: see text]-[Formula: see text] predictive filtering method is one of the most widely used methods in suppressing random noise. However, when the subsurface structure becomes complex, this method suffers from higher prediction errors owing to the large number of different dip components that need to be predicted. Here, we used a denoising convolutional neural network (DnCNN) algorithm to attenuate random noise in seismic data. This method does not assume the linearity and stationarity of the signal in the conventional [Formula: see text]-[Formula: see text] domain prediction technique, and it involves creating a set of training data that are obtained by data processing, feeding the neural network with the training data obtained, and deep network learning and training. During deep network learning and training, the activation function and batch normalization are used to solve the gradient vanishing and gradient explosion problems, and the residual learning technique is used to improve the calculation precision, respectively. After finishing deep network learning and training, the network will have the ability to separate the residual image from the seismic data with noise. Then, clean images can be obtained by subtracting the residual image from the raw data with noise. Tests on the synthetic and real data demonstrate that the DnCNN algorithm is very effective for random noise attenuation in seismic data.

Special Issue on Education Based on Practical Exercise on Sensing and Control

2019 ◽  
Vol 31 (3) ◽  
pp. 375-375
Author(s):  
Shoichiro Fujisawa ◽  
Kazuo Kawada ◽  
Yoshihiro Ohnishi
Keyword(s):  
High School ◽  
Elementary School ◽  
Big Data ◽  
Data Processing ◽  
Engineering Education ◽  
Fiscal Year ◽  
Special Issue ◽  
Control Engineering ◽  
And Control ◽  
Measurement And Control

Control engineering and sensing engineering improve productivity and save resources and energy in industry, and they are also deeply related to the solving greater societal, economic, and environmental problems. Control engineering and sensing engineering have become dynamic forces that enrich various phases of life through interdisciplinary or cross-sectional study. Furthermore, in recent years, due to the development of information technology, as symbolized by terms such as “big data” or “AI,” “sensing and control at a higher level” has become possible, premised by big data processing that is faster by orders of magnitude than conventional data processing. All this has increased the importance of control engineering and sensing engineering. In response to the development of the fields of control engineering and sensing engineering associated with the advance of the “information society,” education in these fields has also needed to be enhanced. On the national scale, the Ministry of Education, Culture, Sports, Science and Technology will introduce Japanese elementary school computational thinking education into elementary school in fiscal year 2020, and the new Courses of Study for High School Information Education in fiscal year 2022. At the same time, individual companies, educational institutions, etc. have also been experimenting with various forms of education in control engineering and sensing engineering. During these changing times, the most advanced studies related to the development of instruction and evaluation methods for educational materials on control engineering, sensing engineering, and control technology have been collected, and the present special issue was planned. This special issue is a collection of practical papers related to measurement and control education, including one paper on Model-Based Development education in a company and eight papers on education in an educational institution. These eight papers include two on education using a robot contest in a university, one on introducing measurement and control engineering education in a national institute of technology college, three on introducing it in a junior high school, and two on introducing it in an elementary school. We hope that this special issue serves to support the readers’ future efforts in control engineering and sensing engineering education, and we thank the authors and reviewers of the papers.

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