No abstract

Sleep staging aims to gather biological signals during sleep, and categorize them by sleep stages: waking (W), non-REM-1 (N1), non-REM-2 (N2), non-REM-3 (N3), and REM (R). These stages are distributed irregularly, and their number varies with sleep quality. These features adversely affect the performance of automatic sleep staging systems. This paper adopts Siamese neural networks (SNNs) to solve the problem. During the network design, seven distance measurement methods, namely, Euclidean, Manhattan, Jaccard, Cosine, Canberra, Bray-Curtis, and Kullback Leibler divergence (KLD), were compared, revealing that Bray-Curtis (83.52%) and Cosine (84.94%) methods boast the best classification performance. The results of our approach are promising compared to traditional methods.

Sleep staging is the process of acquiring biological signals during sleep and marking them according to the stages of sleep. The procedure is performed by an experienced physician and takes more time. When this process is automated, the processing load will be reduced and the time required to identify disease will also be reduced. In this paper, 8 different transform methods for automatic sleep-staging based on convolutional neural networks (CNNs) were compared to classify sleep stages using single-channel electroencephalogram (EEG) signals. Five different labels were used to stage the sleep. These are Wake (W), Non Rapid Eye Movement (NonREM)-1 (N1), NonREM-2 (N2), NonREM-3 (N3), and REM (R). The classifications were done end-to-end without any hand-crafted features, ie without requiring any feature engineering. Time-Frequency components obtained by Short Time Fourier Transform, Discrete Wavelet Transform, Discrete Cosine Transform, Hilbert-Huang Transform, Discrete Gabor Transform, Fast Walsh-Hadamard Transform, Choi-Williams Distribution, and Wigner-Willie Distribution were classified with a supervised deep convolutional neural network to perform sleep staging. The discrete Cosine Transform-CNN method (DCT-CNN) showed the highest performance among the methods suggested in this paper with an F1 score of 89% and a value of 0.86 kappa. The findings of this study revealed that the transformation techniques utilized for the most accurate representation of input data are far superior to traditional approaches based on manual feature extraction, which acquires time, frequency, or nonlinear characteristics. The results of this article are expected to be useful to researchers in the development of low-cost, and easily portable devices.

Technology rapidly advances on a daily basis and the resulting changes can provide numerousbenefits for manufacturing methods and machines. Manufacturers who are able to swiftly embrace thesedevelopments can increase their manufacturing output, thereby boosting profitability and gainingcompetitive advantages over their rivals. However, the cost savings which result from new innovationscan vary, depending on the manufacturing model. Consequently, manufacturers need to conduct accurateanalyses for appropriate manufacturing methods in order to ensure that new changes are cost-effective.Nowadays, the use of industrial automation systems is gaining popularity as a method of increasingprofitability for mass production, and these systems utilize control systems, such as industrial robots andprogrammable logic controllers. The use of these elements in the manufacturing process not onlyprovides quality and flexible production methods, which are indispensable considerations, but alsoconserves human effort. The aim of this study was to minimize the cost of a factory-installed industrialautomation system, which produced globe valves with side couplings, through the combined use ofindustrial robots and programmable logic controllers. While calculating returns from the installed system,the differential evolution algorithm was used to predict future unit prices of electricity, and it wasdetermined that the cost of investment would be recovered after a maximum of 2.5 years and that currentyearly production would increase fourfold.

Biological signals that occur during sleep are recorded and classified by specialists. This process is called sleep staging. However, this is a very long and laborious process. Therefore, automatic sleep staging systems are needed. Nevertheless, automatic sleep staging studies to date have not provided satisfactory performance. The main reasons for this are inter-channel interference, electrode disconnection, and noise. In this paper, a new method (eye method) based on the Euclidean distance measurement method has been developed to solve the electrode disconnection or non-contact problem. This method was applied to three different datasets and detected all electrode disconnections with 100% accuracy. Thanks to this advanced method are aimed to increase the success of automatic sleep staging systems to be designed in the future.