Intelligent Filling Method of Power Grid Working Ticket Based on Historical Ticket Knowledge Base

The traditional power grid ticket filling method has a large workload, low efficiency, and cannot achieve comprehensive and effective reference of historical tickets. This paper proposes a method of intelligent filling in a power grid working ticket based on a historical ticket knowledge base. Firstly, the historical ticket data are preprocessed, then the historical ticket information is mined by the association rule algorithm, and the method of establishing the historical ticket knowledge base is proposed. Based on the improved word bag model, an intelligent grid work ticket filling model is established based on the historical ticket knowledge base, and the correctness of the method is verified by an example. The results show that the accuracy of the proposed method is at least 18% higher than that of the traditional model, and the matching efficiency is 50% higher than the evaluation results of the three models based on semantic expressions. The method enables the identification and extraction of similar and associated work tickets, improves the efficiency of filling work tickets for power grids, and promotes the intelligence of the safety procedures for power grid operations.

Research on the Supply Chain Problems and Optimization of Live Streaming E-commerce

The acceleration of the pace of mobile Internet construction and the outbreak of the COVID-19 epidemic have created new opportunities for the development of the live broadcasting economy. More and more businesses provide consumers with more visual and personalized services through live broadcasting. While developing the rapid development of live streaming e-commerce, it also puts forward new requirements for the whole supply chain. The optimization of the supply chain can comprehensively improve the operation efficiency of enterprises in all links. This article combines the meaning and basic structure of the live streaming e-commerce supply chain combined with the concept of live streaming e-commerce and supply chain. Compared with the traditional supply chain, the live streaming e-commerce supply chain has high information interaction efficiency, supply and demand matching efficiency and service efficiency. The article believes that the supply chain facing supply and undertaking ability, response speed and low coordination and sharing of supply chain should build reverse integrated supply chain; strengthen supply chain information sharing through digital technology, ensure production stability, improve the service quality of all links of the supply chain, and enhance competitiveness.

ГАУССОВ ПУЧОК КАК МОДЕЛЬ ИСТОЧНИКА ИОНОВ

Для расчета статистического аксептанса КФМ использовался траекторный метод. Функция плотности вероятности захваченных фазовых точек предназначена для определения матриц вторых моментов. Элементы этих матриц описывают эллипсы захвата на X и Y фазовых плоскостях. Мерой согласования Гауссова пучка и аксептанса квадруполя служат площади эллипсов. При постоянных параметрах Гауссова пучка ионов эффективность согласования слабо уменьшается с увеличением разрешающей способности. Полученные данные будут полезны при проектировании современных источников ионов. To calculate the statistical QMF acceptance, an ion trajectory method has been used. The probability density functions of accepted points allow fitting the matrix of the second moments. The elements of these matrices describe the acceptance ellipses on phase X and Y planes. The measure of the coupling Gaussian beam and quadrupole acceptance is ellipse area. Colored distributions of the input and output coordinates and velocities are presented, in which the initial phases are marked with different colors. It was found that with increasing resolution, the statistical acceptance ellipses are nested into each other. At constant parameters of the input Gaussian beam, the matching efficiency weakly decreases with resolution. The obtained data will be useful for creation a new modern ion sources.

Performance Comparison of Feature Generation Algorithms for Mosaic Photoacoustic Microscopy

Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

EHEFT-R: multi-objective task scheduling scheme in cloud computing

In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

Efficiency of photo identification of inguinal color patterns of Pithecopus gonzagai (Anura: Phyllomedusidae) from northeastern Brazil

Animal monitoring research involving mark-recapture techniques increasingly requires non-invasive methods of individual identification. The photographic identification method (PIM) is an excellent tool for this purpose and has been applied successfully to many taxa. However, the utility of PIM is a function of species-specific features that are judged suitable for a given target-species. Herein, the suitability of inguinal color patterns for photo identification of individuals of Pithecopus gonzagai are evaluated by comparing two widely used computer-assisted photographic matching programs (I³S and Wild.ID). Both programs accurately identified more than 70% of individuals in the top 20 potential matching photographs. Wild.ID was slightly better than I³S in matching efficiency and has a faster processing time. Thus, PIM is useful to identify individual P. gonzagai; however, before implementing the technique in animal-monitoring studies of other taxa, one must evaluate the suitability PIM for the target species and calibrate the relative efficiency of the software programs in identifying individuals.

LTmatch: A Method to Abstract Pattern from Unstructured Log

Logs record valuable data from different software and systems. Execution logs are widely available and are helpful in monitoring, examination, and system understanding of complex applications. However, log files usually contain too many lines of data for a human to deal with, therefore it is important to develop methods to process logs by computers. Logs are usually unstructured, which is not conducive to automatic analysis. How to categorize logs and turn into structured data automatically is of great practical significance. In this paper, LTmatch algorithm is proposed, which implements a log pattern extracting algorithm based on a weighted word matching rate. Compared with our preview work, this algorithm not only classifies the logs according to the longest common subsequence(LCS) but also gets and updates the log template in real-time. Besides, the pattern warehouse of the algorithm uses a fixed deep tree to store the log patterns, which optimizes the matching efficiency of log pattern extraction. To verify the advantages of the algorithm, we applied the proposed algorithm to the open-source data set with different kinds of labeled log data. A variety of state-of-the-art log pattern extraction algorithms are used for comparison. The result shows our method is improved by 2.67% in average accuracy when compared with the best result in all the other methods.

Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features

Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m.

Semantic Matching Efficiency of Supply and Demand Text on Cross-Border E-Commerce Online Technology Trading Platforms

With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross-border e-commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e-commerce platforms, this paper improves the logical and technical problems of cross-border e-commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one-hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F 1 value is 88.77%, and the result is better than other methods.