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Extracting the flight trajectory of the shuttlecock in a single turn in badminton games is important for automated sports analytics. This study proposes a novel method to extract shots in badminton games from a monocular camera. First, TrackNet, a deep neural network designed for tracking small objects, is used to extract the flight trajectory of the shuttlecock. Second, the YOLOv7 model is used to identify whether the player is swinging. As both TrackNet and YOLOv7 may have detection misses and false detections, this study proposes a shot refinement algorithm to obtain the correct hitting moment. By doing so, we can extract shots in rallies and classify the type of shots. Our proposed method achieves an accuracy of 89.7%, a recall rate of 91.3%, and an F1 rate of 90.5% in 69 matches, with 1582 rallies of the Badminton World Federation (BWF) match videos. This is a significant improvement compared to the use of TrackNet alone, which yields 58.8% accuracy, 93.6% recall, and 72.3% F1 score. Furthermore, the accuracy of shot type classification at three different thresholds is 72.1%, 65.4%, and 54.1%. These results are superior to those of TrackNet, demonstrating that our method effectively recognizes different shot types. The experimental results demonstrate the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

Conventional deep neural networks face challenges in handling the increasing amount of information in real-world scenarios where it is impractical to gather all the training data at once. Incremental learning, also known as continual learning, provides a solution for lightweight and sustainable learning with neural networks. However, incremental learning encounters issues such as "catastrophic forgetting" and the "stability–plasticity dilemma". To address these challenges, this study proposes a two-stage training method. In the first stage, dual knowledge distillation is introduced, including feature map-based and response-based knowledge distillation. This approach prevents the model from excessively favoring new tasks during training, thus addressing catastrophic forgetting. In the second stage, an out-of-distribution dataset is incorporated to calculate the discrepancy loss between multiple classifiers. By maximizing the discrepancy loss and minimizing the cross-entropy loss, the model improves the classification accuracy of new tasks. The proposed method is evaluated using the CIFAR100 and RESISC45 benchmark datasets, comparing it to existing approaches. Experimental results demonstrate an overall accuracy improvement of 6.9% and a reduction of 5.1% in the forgetting rate after adding nine consecutive tasks. These findings indicate that the proposed method effectively mitigates catastrophic forgetting and provides a viable solution for image classification in natural and remote sensing images. [ABSTRACT FROM AUTHOR]

Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone that can be applied to multi-task models to increase the accuracy and processing speed. The SCSM model uses a new hierarchical spatial compression, employing the spatiotemporal fusion method, consisting of a spatial domain and a temporal domain. The SCSM model maintains the independence of each frame in the spatial domain for feature extraction and fuses the spatiotemporal features in the temporal domain. The architecture can be adapted to different frame rate requirements due to its high scalability. It is suitable to serve as a backbone for multi-task video feature extraction or industrial applications with its low prediction and training costs. According to the experimental results, SCSM has a low number of parameters and low computational complexity, making it highly scalable with strong transfer learning capabilities. The model achieves video action recognition accuracy comparable to state-of-the-art models with a smaller parameter size and fewer computational requirements. [ABSTRACT FROM AUTHOR]

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system's feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments. [ABSTRACT FROM AUTHOR]

In this work, we propose a two-stage architecture to perform image inpainting from coarse to fine. The framework extracts advantages from different designs in the literature and integrates them into the inpainting network. We apply region normalization to generate coarse blur results with the correct structure. Then, contextual attention is applied to utilize the texture information of background regions to generate the final result. Although using region normalization can improve the performance and quality of the network, it often results in visible color shifts. To solve this problem, we introduce perceptual color distance in the loss function. In quantitative comparison experiments, the proposed method is superior to the existing similar methods in Inception Score, Fréchet Inception Distance, and perceptual color distance. In qualitative comparison experiments, the proposed method can effectively resolve the problem of color shifts. [ABSTRACT FROM AUTHOR]

This paper presents a framework that can automatically analyze the images and comments in user-uploaded location databases. The proposed framework integrates image processing and natural language processing techniques to perform scene classification, data cleaning, and comment summarization so that the cluttered information in user-uploaded databases can be presented in an organized way to users. For scene classification, RGB image features, segmentation features, and the features of discriminative objects are fused with an attention module to improve classification accuracy. For data cleaning, incorrect images are detected using a multilevel feature extractor and a multiresolution distance calculation scheme. Finally, a comment summarization scheme is proposed to overcome the problems of unstructured sentences and the improper usage of punctuation marks, which are commonly found in customer reviews. To validate the proposed framework, a system that can classify and organize scenes and comments for hotels is implemented and evaluated. Comparisons with existing related studies are also performed. The experimental results validate the effectiveness and superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

In this article, we propose a pose transfer framework that can deal with large body motion to generate dance videos. To solve the problem of body shape deformation from large movements, a multiple scale region extractor (MSRE) is proposed. The features of each body region can be extracted from multiple layers of the encoder according to the body key points and passed through shortcuts to the decoder to reduce the spatial information loss. We add a region style loss calculated by the style representations of the body regions to the loss function to improve the quality of the generated images. In addition, the concept of learnable region normalization is integrated into the proposed framework to prevent introducing undesired mean and variance shifts by the corrupted regions during normalization. The experiments have shown that the proposed system can significantly improve the pose generation results compared with existing methods, especially when there are large body movements in the dancing poses. [ABSTRACT FROM AUTHOR]

In this paper, a framework based on generative adversarial networks is proposed to perform nature-scenery generation according to descriptions from the users. The desired place, time and seasons of the generated scenes can be specified with the help of text-to-image generation techniques. The framework improves and modifies the architecture of a generative adversarial network with attention models by adding the imagination models. The proposed attentional and imaginative generative network uses the hidden layer information to initialize the memory cell of the recurrent neural network to produce the desired photos. A data set containing different categories of scenery images is established to train the proposed system. The experiments validate that the proposed method is able to increase the quality and diversity of the generated images compared to the existing method. A possible application of road image generation for data augmentation is also demonstrated in the experimental results. [ABSTRACT FROM AUTHOR]

In this article, we propose a system for automatically classifying and cleaning location images in large-scale image databases uploaded by arbitrary users. Detecting incorrect scenes uploaded by users and maintaining the correctness of the database through automatic data cleaning are essential because human inspection is not feasible for verifying massive amounts of data. In this study, we compared different feature extractors using deep convolutional neural networks trained using big data. We designed a multilevel extractor to improve feature extraction. Moreover, a detector based on multiresolution dissimilarity calculation was designed to overcome the issue of large intraclass distances and successfully identify incorrect scenes. The proposed system was validated using a highly challenging dataset with 138,000 images collected from Google Places. The experiments show that the multilevel extractor and the detector based on multiresolution dissimilarity calculation can improve the accuracy in identifying incorrect scenes and achieve satisfying data cleaning results. [ABSTRACT FROM AUTHOR]

Accidental fall is the most prominent factor that causes the accidental death of elder people due to their slow body reaction. Automatic fall detection technology integrated in a health-care system can assist human monitoring the occurrence of fall, especially in dusky environments. In this paper, a novel fall detection system focusing mainly on dusky environments is proposed. In dusky environments, the silhouette images of human bodies extracted from conventional CCD cameras are usually imperfect due to the abrupt change of illumination. Thus, our work adopts a thermal imager to detect human bodies. The proposed approach adopts a coarse-to-fine strategy. Firstly, the downward optical flow features are extracted from the thermal images to identify fall-like actions in the coarse stage. The horizontal projection of motion history images (MHI) extracted from fall-like actions are then designed to verify the incident by the proposed nearest neighbor feature line embedding (NNFLE) in the fine stage. Experimental results demonstrate that the proposed method can distinguish the fall incidents with high accuracy even in dusky environments and overlapping situations. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent highway surveillance system that performs self-diagnosis and detects conditions when the camera is seriously tampered by raindrops at night. The system also provides solutions to analyze the traffic flow under the challenging nighttime raindrop-tampered conditions. To deal with the challenging scenes, we extract effective features via salient region detection and block segmentation. The extracted features are used to train a support vector machine to achieve self-diagnosis. For traffic flow analysis, we use the extracted features in the region of interest and construct a regression model to get an estimated vehicle count for each frame. The vehicle counts in consecutive frames form a vehicle count sequence. We propose a mapping model to acquire the desired per-minute traffic flow from the vehicle count sequence. The model utilizes state transfer likelihoods and takes into account the length of the segmented vehicle count sequence. The experiments on highly challenging data sets have demonstrated that the proposed system can effectively estimate the traffic flow for raindrop-tampered highway surveillance cameras at night. [ABSTRACT FROM PUBLISHER]

A GaN-based light-emitting diode (LED) grown on a nanocomb-shaped patterned sapphire substrate (PSS) is fabricated and studied. Nanocomb-shaped patterns are transferred on a sapphire substrate using a well-ordered anodized aluminum oxide (AAO) thin film as a mask for the inductively coupled plasma etching process. This well-ordered AAO thin film with a high aspect ratio is grown on a sapphire substrate by an oxalic acid-based electrochemical system and a three-step anodization. The strain state generated during epitaxial growth could be effectively alleviated by the use of nanocomb-shaped PSS. The treading dislocation density could be reduced. Thus, the enhanced crystalline quality is obtained. In addition, due to the presence of photonic crystal-like air buffer layer, part of reflected photons upward the top side could be scattered by this layer. Therefore, more photons could be extracted outside. Experimentally, at 20 mA, as compared with a conventional LED grown on a planar sapphire substrate, the studied LED grown on a nanocomb-shaped PSS shows 53.8% and 43.7% enhancements in light output power and external quantum efficiency as well as a reduced leakage current. [ABSTRACT FROM AUTHOR]

This paper proposes a vision-based fingertip handwriting recognition system to provide an alternative to input devices. Traditional handwriting recognition systems are limited because they require a specific or expensive input device, such as pen, tablet, or touch panel. Recently, cameras have gradually become standard components in many computer-based products. Therefore, a fingertip and camera combination provides a flexible and convenient input device. The proposed system combines fingertip detection, trajectory feature extraction, and character recognition. First, fingertip moving trajectories are tracked and recoded. The proposed cyclic chain code histograms are then obtained from the trajectories and used as features in the following recognition process. An improved radial basis function (RBF) neural network is used to recognize handwritten characters. Experimental results show that the proposed novel input system is feasible and effective. This study also presents possible applications for camera input devices. [ABSTRACT FROM AUTHOR]

While the use of deep neural networks is popular for predicting students' learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed. [ABSTRACT FROM AUTHOR]

Some physical factors including initial pH of medium, cultivation temperature and shaking speed as well as reuse affecting the production of cholesterol oxidase (CholOx) in reactors containing calcium alginate-immobilized cells of Rhodococcus equi No. 23 were investigated. Results revealed that the free cells showed the maximum CholOx in the culture with an initial pH of 5.0, while culture inoculated with immobilized cells exhibited a broad pH range, 6.0–9.0, for maximum CholOx production. The immobilized and free cells produced the maximum CholOx in the culture incubated at 30 and 25°C, respectively. The CholOx production decreased upon increasing the cultivation temperature. Increasing CholOx activity was also noted for both immobilized and free cells of R. equi No. 23 in the culture with increasing shaking speed. Under the optimal culture conditions, that were established, a higher maximum CholOx production of 0.94 unit/ml was found for immobilized R. equi No. 23 compared to that of 0.84 unit/ml for free cells after 48 h of cultivation. Furthermore, no gel leakage was noted after re-use of the calcium alginate-immobilized R. equi No. 23 for seven consecutive 48 h batch culture. The CholOx production in the seventh cycle was about 60.4% of that obtained in the first cycle. [ABSTRACT FROM AUTHOR]

Learning about artificial intelligence (AI) has become one of the most discussed topics in the field of education. However, it has become an equally important learning approach in contemporary education to propose a "general education" agenda that conveys instructional messages about AI basics and ethics, especially for those students without an engineering background. The current study proposes a situated learning design for education on this topic. Through a three-week lesson session and accompanying learning activities, the participants undertook hands-on tasks relating to AI. They were also afforded the opportunity to learn about the current attributes of AI and how these may apply to understanding AI-related ethical issues or problems in daily life. A pre- and post-test design was used to compare the learning effects with respect to different aspects of AI (e.g., AI understanding, cross-domain teamwork, AI attitudes, and AI ethics) among the participants. The study found a positive correlation among all the factors, as well as a strong link between AI understanding and attitudes on the one hand and AI ethics on the other. The implications of these findings are discussed, and suggestions are made for possible future revisions to current instructional design and for future research. [ABSTRACT FROM AUTHOR]

An interesting approach to improving the light extraction efficiency of high-power GaN-based light-emitting diodes (LEDs) by the use of a 3-D-photonic crystal (3-D-PhC) backside reflector is studied. A 3-D-PhC backside reflector is formed by coating a self-assembled SiO2 nanosphere monolayer between the hybrid reflector and backside of a sapphire substrate. The 3-D-PhC structure is used to enhance light scattering. At 350 mA, as compared with a conventional LED (with a hybrid reflector while only without the 3-D-PhC structure), the studied device exhibits 23.6% enhancement in the light output power without the degradation of electrical properties. Therefore, the performance of high-power GaN-based LEDs could be further improved by using a 3-D-PhC backside reflector. [ABSTRACT FROM AUTHOR]

Photovoltaics modules are usually installed with a tilt angle to improve performance and to avoid water or dust accumulation. However, measured irradiance data on inclined surfaces are rarely available, since installing pyranometers with various tilt angles induces high costs. Estimating inclined irradiance of arbitrary orientations and tilt angles is important because the installation orientations and tilt angles might be different at different sites. The goal of this work is to propose a unified transfer model to obtain inclined solar irradiance of arbitrary tilt angles and orientations. Artificial neural networks (ANN) were utilized to construct the transfer model to estimate the differences between the horizontal irradiance and the inclined irradiance. Compared to ANNs that estimate the inclined irradiance directly, the experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy. Moreover, the trained model can successfully estimate inclined irradiance with tilt angles and orientations not included in the training data. [ABSTRACT FROM AUTHOR]