No abstract

With the strengthening of international environmental regulations, many studies on the integrated electric propulsion systems applicable to eco-friendly ship are being conducted. However, few studies have been performed to establish a guide line for the overall pure electric propulsion ship design. Therefore, this paper introduces the comprehensive design of DC shipboard power system for pure electric propulsion ship based on battery energy storage system (BESS). To design and configure the pure electric propulsion ship, 2 MW propulsion car ferry was assumed and adopted to be the target vessel in this paper. In order to design the overall system, a series of design processes, such as the decision of the ship operation profile, BESS capacity selection, configuration of the power conversion systems for propulsion, battery charging/discharging procedures, classification of system operation modes, and analysis of the efficiency, were considered. The proposed efficient design and analysis of the pure electric propulsion ship was qualitatively and quantitatively validated by MATLAB Simulink tool. The methodology presented in this paper can help design real ships before the system commissioning.

With the spread of the modern media industry, harmful genre contents are indiscriminately disseminated to teenagers. The password identification method used to block sensational and violent genre content has become a problem that teenagers can easily steal. Therefore, a user identification method with less risk of theft and hacking is required. The surface EMG (sEMG) signal, which is an electrical signal generated inside the body and has individual features, is being studied as a next-generation user identification method. sEMG involves measuring an individual’s unique muscular strength activated over time as digital signals, thus giving it the advantage of generating different signal patterns. However, it is difficult to constantly and repeatedly acquire each motion signal and the number of repetitions for each motion is insufficient, thus there is a limit to improving user identification accuracy. In this paper, we propose a user identification system that solves the problem of insufficient data by applying the matching pursuit that enables signal generation to the sEMG signal from which the resting signal has been removed and improves classification accuracy by extracting STFT-based time–frequency features. As a result of the experiment, the user identification accuracy of the sEMG spectrogram with the resting state signal removed was 85.4%. In addition, when the training data were increased through data generation, the accuracy was improved, showing a user identification accuracy of 96.1%. Improved user recognition accuracy was confirmed when the training data of the sEMG signal from which the resting signal was removed were increased and multidimensional features including time–frequency were used.

No abstract

No abstract