This paper presents an open-switch fault detection method for a hybrid active neutral-point clamped (HANPC) inverter based on deep learning technology. The HANPC inverter generates a three-level output voltage with four silicon switches and two silicon carbide switches per phase. The probability of open fault in switching devices increases because of the large number of switches of the entire power converter. The open-switch fault causes distortion of output currents. A convolution neural network (CNN) comprising several convolution layers and fully connected layers is used to extract features of distorted currents. A CNN network was trained using three-phase current information to determine the location of the open-switch fault. Our proposed CNN model can accurately detect approximately 99.6% of open-switch faults without requiring additional circuitry and regardless of the current level. The feasibility and effectiveness of the proposed method are verified by experimental results.

This letter analyzes the effect of the carrier frequency offset on orthogonal frequency division multiplexing (OFDM) systems for multipath fading channels. A simple approximate expression for the average signal-to-noise ratio (SNR) is derived. This approximate expression is shown to be an upper bound of the average SNR for flat fading channels and an exact expression for the AWGN channel. The approximate average SNR expression is validated using Monte Carlo simulation for both flat fading channels and frequency-selective fading channels

In order to meet the requirements of 4G mobile networks targeted by the cellular layer of IMT-advanced, next generation mobile WiMAX devices based on IEEE 802.16m will incorporate sophisticated signal processing, seamless handover functionalities between heterogeneous technologies and advanced mobility mechanisms. This survey provides a description of key projected features of the physical (PHY) and medium access control (MAC) layers of 802.16m, as a major candidate for providing aggregate rates at the range of Gbps to high-speed mobile users. Moreover, a new unified method for simulation modeling, namely the evaluation methodology (EVM), introduced in 802.16m, is also presented.

This paper proposes and analyzes receiver schemes for multiple-input multiple-output (MIMO) systems with hybrid automatic-repeat-request (HARQ). It is shown by means of analysis as well as computer simulations that the proposed receiver schemes have optimal decoding performance in the sense that all the relevant information is fully used. Also complexity-wise, the proposed receiver schemes are found beneficial when designing MIMO systems, because the same decoding architecture can be re-used regardless of the number of retransmissions.

Smart factories should be able to respond to catastrophic situations proactively, such as recalls caused by production line disruptions and equipment failures. Therefore, the necessity for predictive maintenance technology, such as fault detection or diagnosis of equipment has increased in recent years. In particular, predicting the faults of collaborative robots is becoming increasingly crucial because smart factories pursue efficient collaboration between humans and devices. However, collaborative robots have the characteristic of executing programmable motions designed by an operator, rather than performing fixed tasks. If existing fault diagnosis methods are applied to non–fixed programmable motions, problems arise in terms of setting absolute criteria for fault analysis, interpreting the meanings of detected values, and fault tracking or fault cause analysis. Therefore, we propose a method of programmable motion-fault detection by analyzing motion residuals to solve the three problems mentioned above. The proposed method can expand the fault diagnostic range of collaborative robots.