Search Results (3)

Exchange rate volatility has emerged to be one of the most critical determinants of price stability for countries operating in multicurrency systems with their own currency in the basket of currencies. This study empirically examined the impact of exchange rates (official and parallel market rates) on inflation in Zimbabwe during the multicurrency system for the period 2014 to 2024, together with comparing the impacts of the official and parallel market exchange rates on inflation. Time series and monthly data were used to examine the short and long run impact of exchange rates on inflation in an ARDL estimation framework. Findings revealed a short run and long run positive relationship between both the official and parallel market exchange rates and inflation, with the parallel market exchange rate being the most significant variable. Other control variables used, such as domestic productivity, have a highly significant negative impact on inflation through the official and parallel exchange rate models in both the short and the long run. Money supply, real interest rate, trade balance, foreign prices, foreign output, stock market prices and foreign currency reserves have varied impacts through either the official or parallel market exchange rate models. Policy recommendations include a contractionary Monetary and expansionary Fiscal policy mix that will result in exchange rate appreciation and stability, productivity growth, trade surplus, growth in reserves, and ultimately low prices. The exchange rate policy recommended in this study is to shelve discard the local currency in the multicurrency system until industrial capacity utilization exceeds 50% to add the local currency to the basket of currencies and 75% for mono-local currency (de-dollarization). Full article

The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method’s strong capability in fusing current features and detecting anomalies. Full article

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used. Full article