The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Pedestrian detection has attracted great research attention in video surveillance, traffic statistics, and especially in autonomous driving. To date, almost all pedestrian detection solutions are derived from conventional framed-based image sensors with limited reaction speed and high data redundancy. Dynamic vision sensor (DVS), which is inspired by biological retinas, efficiently captures the visual information with sparse, asynchronous events rather than dense, synchronous frames. It can eliminate redundant data transmission and avoid motion blur or data leakage in high-speed imaging applications. However, it is usually impractical to directly apply the event streams to conventional object detection algorithms. For this issue, we first propose a novel event-to-frame conversion method by integrating the inherent characteristics of events more efficiently. Moreover, we design an improved feature extraction network that can reuse intermediate features to further reduce the computational effort. We evaluate the performance of our proposed method on a custom dataset containing multiple real-world pedestrian scenes. The results indicate that our proposed method raised its pedestrian detection accuracy by about 5.6–10.8%, and its detection speed is nearly 20% faster than previously reported methods. Furthermore, it can achieve a processing speed of about 26 FPS and an AP of 87.43% when implanted on a single CPU so that it fully meets the requirement of real-time detection.

This paper presents a compact vector quantizer based on the self-organizing map (SOM), which can fulfill the data compression task for high-speed image sequence. In this vector quantizer, we solve the most severe computational demands in the codebook learning mode and the image encoding mode by a reconfigurable complete-binary-adder-tree (RCBAT), where the arithmetic units are thoroughly reused. In this way, the hardware efficiency of our proposed vector quantizer is greatly improved. In addition, by distributing the codebook into the multi-parallel processing sub-blocks, our design obtains a high compression speed successfully. Furthermore, a mechanism of partial vector-component storage (PVCS) is adopted to make the compression ratio adjustable. Finally, the proposed vector quantizer has been implemented on the field programmable gate array (FPGA). The experimental results indicate that it respectively achieves a compression speed of 500 frames/s and a million connections per second (MCPS) of 28,494 (compression ratio is 64) when working at 79.8 MHz. Besides, compared with the previous scheme, our proposed quantizer achieves a reduction of 8% in hardware usage and an increase of 33% in compression speed. This means the proposed quantizer is hardware-efficient and can be used for high-speed image compression.

The Long Short-Term Memory (LSTM) model has been applied in recent years to handle time series data in multiple application domains, such as speech recognition and financial prediction. While the LSTM prediction model has shown promise in anomaly detection in previous research, uncorrelated features can lead to unsatisfactory analysis result and can complicate the prediction model due to the curse of dimensionality. This paper proposes a novel method of clustering and predicting multidimensional aircraft time series. The purpose is to detect anomalies in flight vibration in the form of high dimensional data series, which are collected by dozens of sensors during test flights of large aircraft. The new method is based on calculating the Spearman’s rank correlation coefficient between two series, and on a hierarchical clustering method to cluster related time series. Monotonically similar series are gathered together and each cluster of series is trained to predict independently. Thus series which are uncorrelated or of low relevance do not influence each other in the LSTM prediction model. The experimental results on COMAC’s (Commercial Aircraft Corporation of China Ltd) C919 flight test data show that our method of combining clustering and LSTM model significantly reduces the root mean square error of predicted results.