The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching electrical power, helps design a PV-integrated energy management system, enhances security grid operation. However, due to randomness solar energy, output PV system will fluctuate, which affect safe operation grid. To solve this problem, high-precision hybrid prediction model based on variational quantum circuit (VQC) long memory (LSTM) network is developed predict irradiance 1 hour in...

In this paper, we contend that the objective of representation learning is to compress and transform distribution data, say sets tokens, towards a mixture low-dimensional Gaussian distributions supported on incoherent subspaces. The quality final can be measured by unified function called sparse rate reduction. From perspective, popular deep networks such as transformers naturally viewed realizing iterative schemes optimize incrementally. Particularly, show standard transformer block derived...

This article develops a series of improved Z-source dc–dc converters to realize additional voltage pumping and high power density through two propositions. Proposition 1 positive- negative-connected (IPZSC INZSC), using the same number components as in existing converter. Based on 1, 2 proposes four novel embedded (EZSCs) by placing source specifically designed position, which realizes lower stresses across capacitors. Various technical aspects proposed EZSCs, including operations, losses,...

DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned often enable state-of-the-art performance for downstream tasks, such as image classification segmentation. However, they employ many empirically motivated design choices their training pipelines highly complex unstable -- hyperparameters need be carefully tuned ensure that the do not collapse which poses considerable difficulty improving them or...

Taguchi methods and gray relational analysis are used to determine optimal processing conditions for the selective laser melting (SLM) of 18Ni‐300 maraging steel. The following SLM process parameters selected optimization: power ( P ), scanning speed v hatch spacing h ). Statistical indicates that parameter configuration no. 5 (275 W, 700 mm s −1 , 0.08 mm) as most suitable simultaneous improvement tensile strength, elongation (or ductility), hardness, impact toughness. order influence is...

Transformer-like models for vision tasks have recently proven effective a wide range of downstream applications such as segmentation and detection. Previous works shown that properties emerge in transformers (ViTs) trained using self-supervised methods DINO, but not those on supervised classification tasks. In this study, we probe whether emerges transformer-based solely result intricate learning mechanisms, or if the same emergence can be achieved under much broader conditions through...

It is difficult for users to understand the complex cloud product information selection. Using this recommend satisfactory products a challenge. Previous studies focused on similar of and while neglecting relevance; therefore, they could not create recommendation approaches that account functional dependencies among products. To overcome challenge, study proposes set model based hierarchical knowledge graph (KG) with pre-post correlation functionality. There are two main contributions:...

An electric spring (ES) can well maintain the balance between supply and demand to compensate for intermittent nature of small-scale renewable energy resources (RES). Despite its popularity, second generation ES (ES-2) is deemed have a few practical problems. The most conspicuous one requirement accurate dead-time control in circuit avoid bridge shoot-through problem, which necessitated by series-connection multiple voltage sources and/or converters realize wide range. This however could...

Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the as foundations for downstream tasks. These are empirically designed; such, they usually not interpretable, their structured, and designs potentially redundant. White-box networks, in which each layer explicitly identifies transforms structures data, present a promising alternative. However, existing white-box architectures...

As various types of crime continue to threaten public safety and economic development, predicting the occurrence multiple crimes becomes increasingly vital for effective prevention measures. Although extensive efforts have been made, most them overlook heterogeneity different categories fail address issue imbalanced spatial distribution. In this work, we propose a Spatial-Temporal Mixture-of-Graph-Experts (ST-MoGE) framework collective multiple-type prediction. To enhance model's ability...

The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety tasks. However, operators often impose significant computational burden, with complexity scaling quadratically number tokens. In this work, we propose novel whose scales linearly We derive our network architecture by extending prior work has shown that style naturally arises "white-box" design, where each layer designed to implement...

Considering the high surface adsorption capacity in cryogenic condition, we can achieve etch rate, good bottom circularity and less risk of chamber arcing for high-aspect-ratio etch, by using C1 gases lower power. However, strategy will cause top layer blowing out sharp front. We here explore a specific gas, CF3I, to deposit on side-wall. It significantly decrease bowing while results reduced rate mask. By tuning source bias power, further enlarge front mitigate negative effect CF3I.

Ethyl levulinate (EL) is a key biomass-derived compounds due to its socio-economic benefits for the synthesis of commodity chemicals. Herein, we proposed an efficient one-step bamboo conversion EL in ethanol, and novel stepwise fractionation purify lignocellulose degradation products. A proton acid, high catalytic efficiency, yielded 26.65% 120 min at 200 °C. The productions ethyl glucoside 5-ethoxymethylfurfural were analyzed terms by-products formation. To best our knowledge, there no...

In this paper, we contend that a natural objective of representation learning is to compress and transform the distribution data, say sets tokens, towards low-dimensional Gaussian mixture supported on incoherent subspaces. The goodness such can be evaluated by principled measure, called sparse rate reduction, simultaneously maximizes intrinsic information gain extrinsic sparsity learned representation. From perspective, popular deep network architectures, including transformers, viewed as...

This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing closed-loop transcription between the and corresponding set subspaces, known as linear discriminative representation, low-dimensional feature space. method simpler than existing approaches learning, more efficient terms size, storage, computation: it requires only single, fixed-capacity autoencoding network with space...