The solar power forecasting task plays a vital role in the field of photovoltaic (PV) power generation. To account for the complex correlations between PV power, meteorological data, and equipment data, a novel model called TimeRelationNet is proposed in this study. The TimeRelationNet model leverages a graph neural network to learn the relationship matrix among different features and construct a network of feature relationships. It combines the cross-attention mechanism with a two-dimensional modeling method for time series data. This enables the model to effectively capture the intricate temporal variations in PV power, transforming one-dimensional temporal sequences into two-dimensional tensors based on multiple periods. The model utilizes multi-scale convolution operations to extract PV data features from within and between different time periods more effectively and comprehensively. Additionally, the transformer is used to process the original data, and the transformed data is subjected to cross-attention mechanism, adaptive aggregation, and attention-based weighted fusion. The performance of the TimeRelationNet model is evaluated using data from a specific power station in Liaoning Province. Comparative analysis with three other forecasting models - Transformer, ESTformer, and LightTS - reveals that the TimeRelationNet model achieves the best predictive results, with a Mean Squared Error (MSE) of 0.089±0.002 and a mean absolute error (MAE) of 0.225±0.002.
Mechanical manufacturing process knowledge exhibits complexity, detail-oriented nature, experiential characteristics, high learning cost, and continuous evolution, posing challenges in its management, utilization, and transmission. Enterprises currently face low knowledge reuse rates and high knowledge management costs. To address these issues, we propose a novel approach utilizing deep learning models to extract entities and relationships from unstructured knowledge, constructing a process knowledge graph. With deep learning's powerful comprehension abilities, we automatically extract critical information, organizing it into a structured knowledge graph consisting of 12 entities and 12 relationships. The process knowledge graph enables convenient knowledge transmission and sharing, increasing reuse rates and reducing redundant learning. It serves as an effective tool for process formulation, enhancing efficiency and enterprise benefits. Additionally, it facilitates prompt identification of suitable process parameters, accurate prediction of manufacturing issues, and timely adjustments. Moreover, the process knowledge graph fosters innovation by identifying optimization solutions and improving product quality and cost reduction. This approach offers significant competitive advantages and commercial value to the manufacturing industry.
With the rapid development of the manufacturing industry, the design of manufacturing processes has become increasingly complex, and there is a growing demand from companies and users for efficient process recommendation systems. However, existing research on process recommendation often overlooks the importance of user intent. In reality, user intent often consists of multiple facets, each with different emphases, collectively driving users to choose different processes. For example, an intent to meet tight deadlines may favor less time-consuming processes, while an intent for safety requirements may prefer more mature processes. Disregarding the notion of multiple intents from users would confine the modeling of interactions between users and processes. Therefore, to enhance the efficiency and quality of process design, this paper proposes an intent-aware manufacturing process recommendation algorithm. This algorithm combines process knowledge activity analysis with intent awareness and establishes a mapping between process knowledge and process knowledge graph through graph representation learning and attention mechanisms, enabling perception of user intent and corresponding process knowledge recommendation. Through empirical validation and application, this paper demonstrates the effectiveness and practicality of the algorithm. Experimental results show that the intent-aware process knowledge recommendation algorithm significantly improves the efficiency and accuracy of process design, providing strong support for the development of the manufacturing industry.
Spiral polishing is a traditional process of computer-controlled optical surfacing. However, the additional polishing
amount is great and the center polishing amount is difficult to control. At first, a simplified mathematics model is
presented for magnetorheological finishing, which indicates that the center polishing amount and additional polishing
amount are proportional to the length and peak value of magnetorheological finishing influence function, and are
inversely proportional to pitch and rotation rate of spiral track, and the center polishing amount is much bigger than
average polishing amount. Secondly, the relationships of "tool feed way and center polishing amount", "spiral pitch and
calculation accuracy of influence matrix for dwell time function solution", "spiral pitch and center polishing amount"
and "peak removal rate, dimensions of removal function and center removal amount" are studied by numerical
computation by Archimedes spiral path. It shows that the center polishing amount is much bigger in feed stage than that
in backhaul stage when the head of influence function is towards workpiece edge in feeding; and the bigger pitch, the
bigger calculation error of influence matrix elements; and the bigger pitch, the smaller center polishing amount, and the
smaller peak removal rate and dimensions of removal function, the smaller center removal amount. At last, the polishing
results are given, which indicates that the center polishing amount is acceptable with a suitable polishing amount rate of
feed stage and backhaul stage, and with a suitable spiral pitch during magnetorheological finishing procedure by spiral
motion way.
An optical system with double beam interference system was designed to measure the movement of nano-particles in fluid with laser speckle technology. In order to investigate the influence of the scattering light generated from liquid surface on speckle patterns, the liquid surface contour was dynamically detected by WYKO during nano-particle movement. The result demonstrates that the fluctuation of fluid surface is slight. Meanwhile, in order to further analyze this effect, a piece of ground glass was employed. The process of ground glass to be polished to optical glass was studied and tested continuously using WYKO and laser speckle technology. And then, the speckle patterns generated from kerosene fluid surface were studied.
By contrast, the results show that, the fluid surface is transparent to the measurement wavelength. The influence of the surface of fluid can be neglected. Furthermore, the conclusion illustrates that laser speckle technique is an effective and reliable method to study the movement of nano-granular in fluid.
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