The catalysts were prepared from pseudoboehmite mixed with dilute nitric acid and calcined at different temperatures. The vapour-phase reaction of furan and hydrogen sulfide was performed in a fixed-bed flow in the presence of catalyst. The catalysts were characterized by XRD, N 2 adsorption, FT-IR techniques. The Al 2 O 3 calcined at 550°C has large surface areas which resulted in high yield of thiophene under the conditions: at atmosphere, reaction temperature 500°C, the ratio of H 2 S to Furan about 10 (mol) and LHSV 0.2 h -1 . The reaction mechanism was proposed for the synthesis of thiophene from furan and hydrogen sulfide over Al 2 O 3 .

In this study, the frequency characteristics of series DC arcs are analyzed according to the types of frequency fluctuations caused by inverters in photovoltaic (PV) systems. These frequency fluctuation types used in analysis include centralized frequency fluctuations by three-phase inverter, spread frequency fluctuations by three-phase inverter, and centralized frequency fluctuations by single-phase inverter. To collect arc current data, the frequency fluctuations are generated by inverters in the arc-generating circuit, designed by referring to UL1699B, and the arcs are generated by separating the arc rods of the arc generator. The frequency analysis of the arc current data, collected using an oscilloscope, is conducted using MATLAB. From the results of the frequency characteristics analysis, it is confirmed that the frequencies in the range from 5 to 40 kHz increase after arc generation regardless of the type of frequency fluctuation. In addition, the smaller the current, the greater the increase in frequencies between 5 and 40 kHz after arc generation. Further, in case of arc currents with centralized frequency fluctuations, for larger switching frequencies, the 5 to 40 kHz components increase to a greater extent after arcing.

The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current-average value, median value, variance value, RMS value, and distance of the maximum and minimum values-were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

This paper proposes a DC series arc detection algorithm in a photovoltaic (PV) system using an adaptive moving average (AMA). The proposed algorithm uses two moving averages of which is the average of 5 kHz to 40 kHz frequency band. One is which is the moving average highly affected by recent . The other is which is the moving average heavily affected by past . There is a little difference between and before arcing because is approximately constant. However, this difference increases when the arc occurs because slowly follows . This difference is used as an arc detection indicator (ADI) in this study. Additionally, AMA is proposed to avoid nuisance tripping in the normal transient state. The proposed method determines the arc occurrence using the relative magnitudes of the two moving averages. Therefore, it is less affected by the shape of the frequency fluctuations caused by the load inverter. Hence, the proposed algorithm is effective in the centralized and spread-type of frequency fluctuations. These results were verified through an arc detection test and nuisance tripping test using arc experimental data and MATLAB.

Audio super resolution aims to predict the missing high resolution components of the low resolution audio signals. While audio in nature is continuous signal, current approaches treat it as discrete data (i.e., input is defined on discrete time domain), and consider the super resolution over fixed scale factor (i.e., it is required to train a new neural network to change output resolution). To obtain a continuous representation of audio and enable super resolution for arbitrary scale factor, we propose a method of neural implicit representation, coined Local Implicit representation for Super resolution of Arbitrary scale (LISA). Our method locally parameterizes a chunk of audio as a function of continuous time, and represents each chunk with the local latent codes of neighboring chunks so that the function can extrapolate the signal at any time coordinate, i.e., infinite resolution. To learn a continuous representation for audio, we design a self-supervised learning strategy to practice super resolution tasks up to the original resolution by stochastic selection. Our numerical evaluation shows that LISA outperforms the previous fixed-scale methods with a fraction of parameters, but also is capable of arbitrary scale super resolution even beyond the resolution of training data.

Federated Learning (FL) is a distributed learning framework, in which the local data never leaves clients' devices to preserve privacy, and the server trains models on the data via accessing only the gradients of those local data. Without further privacy mechanisms such as differential privacy, this leaves the system vulnerable against an attacker who inverts those gradients to reveal clients' sensitive data. However, a gradient is often insufficient to reconstruct the user data without any prior knowledge. By exploiting a generative model pretrained on the data distribution, we demonstrate that data privacy can be easily breached. Further, when such prior knowledge is unavailable, we investigate the possibility of learning the prior from a sequence of gradients seen in the process of FL training. We experimentally show that the prior in a form of generative model is learnable from iterative interactions in FL. Our findings strongly suggest that additional mechanisms are necessary to prevent privacy leakage in FL.

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