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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]

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]

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]

In this work, a day-ahead to week-ahead solar irradiance prediction mechanism based on convolutional Long Short-Term Memory (LSTM) model is proposed. The system takes hourly irradiance data from several days previous to the prediction day as the input. Then, features are extracted from the input data using one dimensional convolutional filters. The extracted features from different days are concatenated and serve as the input of the LSTM network. The output of the LSTM is further concatenated with selected original data to emphasize its importance and enhance the prediction results. Afterwards, a fully connected layer is used to produce the final prediction output. The proposed framework can be trained using a relatively small amount of training data within the duration of only two months. Therefore, it has the advantage of being applicable in the initial deployment phase when the amount of training data is limited. The proposed system has been validated using a highly challenging dataset collected in Taiwan with tropical and subtropical marine island climate. [Display omitted] • A convolutional long short-term memory framework is designed for solar irradiance prediction. • Weather observation and forecast information are used in the training and prediction process. • The proposed framework can be trained with relatively small amount of data. • The proposed convolutional LSTM framework outperforms related works. [ABSTRACT FROM AUTHOR]

This paper proposes a framework to automatically conduct cloud classification on all-sky images and perform short-term solar irradiance prediction according to the classification results. The all-sky images are divided into blocks to deal with the mixed cloud type conditions. Local texture patterns and statistical texture features are extracted from the image blocks for cloud classification. Different cloud types with various heights, thickness, and opacity have different impact on the variation of solar irradiance. Therefore, several regression models are trained to capture the characteristics of irradiance changes under different cloud types. The current classified cloud type is used to select a corresponding prediction model. Such design substantially increases the prediction accuracy. The experimental results verify the effectiveness of the proposed framework. Both the proposed cloud classification method and irradiance prediction mechanism outperform existing works. Adding local texture patterns in the feature vector enhance the classification performance. Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%–10%. Utilizing multiple prediction models according cloud types could lower both the mean absolute error and the root mean squared error on short-term irradiance prediction. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an accurate short-term solar irradiance prediction scheme via support vector regression. Utilizing clearness index conversion and appropriate features, the support vector regression models are able to output satisfying prediction results. The prediction results are further improved by the proposed ramp-down event forecasting and solar irradiance refinement procedures. With the help of all-sky image analysis, two separated regression models are constructed based on the cloud obstruction conditions near the solar disk. With bi-model prediction, the behavior of the changing irradiance can be captured more accurately. Moreover, if a ramp-down event is forecasted, the predicted irradiance is corrected based on the cloud cover ratio in the area near the sun. The experiments have shown that the proposed method can effectively improve the prediction accuracy on a highly challenging dataset. [Copyright &y& Elsevier]

A uranium(IV) phosphate, Na10U2P6O24, was synthesized under hydrothermal conditions at 570 °C and 160 MPa and structurally characterized by single-crystal X-ray diffraction. The valence state of uranium was established by UV-vis and U 4f X-ray photoelectron spectroscopy. The powder sample has a second-harmonic-generation signal, confirming the absence of a center of symmetry in the structure. The structure contains UO8 snub-disphenoidal polyhedra that are linked to monophosphate tetrahedra by vertex and edge sharing such that a three-dimensional framework with intersecting 12-sided circular and rectangular channels is formed. All 10 sodium sites are situated inside the channels and are fully occupied. This is the first uranium(IV) phosphate synthesized under high-temperature, high-pressure hydrothermal conditions. The isotypic cerium(IV) phosphate, Na10Ce2P6O24, was also synthesized under the same hydrothermal conditions. It is the first structurally characterized Ce(IV) phosphate with a P/Ce ratio of 3. Crystal data of Na10U2P6O24: orthorhombic, P212121 (No. 19), a = 6.9289(3) Å, b = 16.1850(7) Å, c = 18.7285(7) Å, V = 2100.3(2) ų, Z = 4, R1 = 0.0304, and wR2 = 0.0522. Crystal data of Na10Ce2P6O24: orthorhombic, P212121 (No. 19), a = 6.9375(14) Å, b = 16.215(3) Å, c = 18.765(4) Å, V = 2111.0(7) ų, Z = 4, R1 = 0.0202, and wR2 = 0.0529. [ABSTRACT FROM AUTHOR]

Highlights: [•] Moving skin regions and shadows are detected. [•] Skin and shadow probabilities are integrated into the mixture models. [•] Learning rates are adjusted by the skin and shadow probabilities. [•] Mandarin Phonetic Symbol (MPS) combination recognition algorithm is designed. [•] Single fingertip virtual keyboard system is implemented. [ABSTRACT FROM AUTHOR]

A series of cobalt(III) complexes of the potent DNA minor groove alkylator (1-(chloromethyl)-5-hydroxy-1H-pyrrolo[3,2-f]quinolin-3(2H)-yl)(5,6,7-trimethoxy-1H-indol-2-yl)methanone (3; seco-CPyI-TMI), with cyclam or cyclen auxiliary ligands (L3 and L5) containing a cross-bridging ethylene (CH2CH2) group or the N,N'-dimethyl derivatives of these (L4 and L6), was prepared. Two 8-quinolinato (2) model complexes of these, [Co(L3)(2)](ClO4)2 and [Co(L6)(2)](ClO4)2, and the aquated derivative [Co(L6)(H2O) 2](OTf)3 were characterized by X-ray crystallography. Electrochemistry of the 8-quinolinato model complexes showed that the Co(III)/(II) reduction potential was lowered relative to the unsubstituted cyclen ligand. Evaluation of the cytotoxicity of the racemic seco-CPyI cobalt complexes in vitro showed considerable attenuation of their cytotoxicity relative to the free alkylator and marked hypoxic selectivity, especially [Co(L3)(3)]2+ (9), which was 81-212-fold more potent under hypoxia than 20% oxygen in a panel of 10 human tumor cell lines. However, 9 did not elicit significant killing of hypoxic cells in HT29 tumor xenografts, suggesting possible pharmacological limitations in vivo. [ABSTRACT FROM AUTHOR]

A salt-inclusion mixed-valence uranium(V,Vl) silicate, [Na9F2][(UvO2)- (UviO2)2(Si2O7], was synthesized under hydrothermal conditions at 585 °C and 160 MPa and structurally characterized by powder and single-crystal X-ray diffraction (XRD). The valence states of uranium were established by U 4f X-ray photoelectron spectroscopy (XPS). The structure contains two-dimensional (2D) sheets of uranyl disilicate with the composition [U02Si2O7], which are connected by U(l)v06 tetragonal bipyramids to form thick layers. The Na+ cations are located at sites in the intralayer and interlayer regions. In addition to Na+ cations, the interlayer region also contains F- anions such that infinite chains with the formula FNa1/1Na4/2 are formed. The same type of chain was observed in K2SnO3. The title compound is not only the first example of salt-inclusion metal silicate synthesized under high-temperature, high-pressure hydrothermal conditions, as well as the first salt-inclusion mixed-valence uranium silicate, but it is also the first mixed-valence uranium(V,Vl) silicate in the literature. Crystal data: [Na9F2][(UvO2)(Uvi02)2(Si207)2], tridinic, Pi (No. 2), a = 5.789(1) A, b = 7.423(2) A, c = 12.092(2) Å, α =90.75(3)°, β = 96.09(3)°, γ = 90.90(3)°, V = 516.5(2) ų, Z = 1, R1 = 0.0241, and wR 2 = 0.0612. [ABSTRACT FROM AUTHOR]

Abstract: Most content-based image/video retrieval systems use histogram matching method to compute the similarity between two histograms. The matching of two images can be accomplished by matching their corresponding histograms. A good image/video retrieval system requires two factors: fast response time and high accuracy. A fast search algorithm called MRSA was proposed previously by applying a multi-resolution structure to gain speed-up and to have the same retrieval accuracy as the exhaustive search algorithm. In this paper, we improve the retrieving speed of MRSA while maintaining the global retrieval accuracy. The retrieving speed is improved by using the non-uniform quantization method to obtain lower resolution histograms and the non-uniform quantization method is proven to be able to reduce the number of comparisons at lower resolution levels. Furthermore, we not only extend the multi-resolution concept from uniform quantization to non-uniform quantization but also employ another similarity measurement, χ 2 distance, to construct the multi-resolution structure. Due to the thresholding mechanism, the proposed non-uniform quantization based method relieves the over-smooth problem suffering from downsampling. Hence, our method will reduce noticeable unnecessary comparisons at low resolution levels than MRSA by selecting a proper quantization table. The employing of additional similarity measurement and different quantization criterion increases the flexibility and the efficiency of the algorithm. Experiments demonstrate the validity and efficiency of our algorithm in some typical image/video retrieval applications. [Copyright &y& Elsevier]

In this work, a framework that can automatically create cartoon images with low computation resources and small training datasets is proposed. The proposed system performs region segmentation and learns a region relationship tree from each learning image. The segmented regions are clustered automatically with an enhanced clustering mechanism with no prior knowledge of number of clusters. According to the topology represented by region relationship tree and clustering results, the regions are reassembled to create new images. A swarm intelligence optimization procedure is designed to coordinate the regions to the optimized sizes and positions in the created image. Rigid deformation using moving least squares is performed on the regions to generate more variety for created images. Compared with methods based on Generative Adversarial Networks, the proposed framework can create better images with limited computation resources and a very small amount of training samples. [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]

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]

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]