Search

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. [ABSTRACT FROM AUTHOR]

Summary: Currently, wireless sensor networks (WSNs) have been widely developed in different domains. Generally, sensors work independently, so it is relatively easier for a WSN to add new nodes. Often, a WSN is constrained to low energy consumption. However, data encryption/decryption not only takes a long time but also consumes many network resources. Due to limited battery power and CPU capability, a sensor's encryption algorithm should not be complicated. However, a simple security algorithm will downgrade the system's security level. Hence, designing a secure wireless transmission system with high encryption/decryption performance, particularly for 5G networks, is an urgent task. Therefore, in this study, we propose an efficient and secure approach, named the Symmetric Key Matrix and Rectangular Frame Scheme (SKeMRF, for short), which involves security mechanisms to speed up encryption/decryption operations for a WSN. Further, the SKeMRF integrates global Symmetric Key Matrix (SKM, for short) and other security mechanisms to ensure the security of transmitted data and enable the system to effectively resist the forgery KDS attack, thus greatly enhancing security of the whole system. Theoretical analysis and simulation show that the SKeMRF is an efficient and highly secure WSN compared with those state‐of‐the‐art schemes. [ABSTRACT FROM AUTHOR]

In recent years, by merit of convenient and unique features, bio-authentication techniques have been applied to identify and authenticate a person based on his/her spoken words and/or sentences. Among these techniques, speaker recognition/identification is the most convenient one, providing a secure and strong authentication solution viable for a wide range of applications. In this paper, to safeguard real-world objects, like buildings, we develop a speaker identification system named mel frequency cepstral coefficients (MFCC)-based speaker identification system for access control (MSIAC for short), which identifies a speaker U by first collecting U's voice signals and converting the signals to frequency domain. An MFCC-based human auditory filtering model is utilized to adjust the energy levels of different frequencies as U's voice quantified features. Next, a Gaussian mixture model is employed to represent the distribution of the logarithmic features as U's specific acoustic model. When a person, eg, x, would like to access a real-world object protected by the MSIAC, x's acoustic model is compared with known-people's acoustic models. Based on the identification result, the MSIAC will determine whether the access will be accepted or denied. [ABSTRACT FROM AUTHOR]