Traditional Evaluation Algorithm Research Articles

Gesture Scoring Based on Gaussian Distance-Improved DTW

The power industry has been dedicated to applying virtual reality (VR) technology to build training systems in virtual environments, enabling personnel to complete skill training in real simulated environments while ensuring their safety. Conventional action scoring systems struggle to provide accurate scores for fine movements. Accurate scoring of fine movements can help workers identify their shortcomings during power operations, thus improving learning efficiency. This is of great significance for training on virtual environment-based power operation. This paper proposes a power operation-orientated VR action evaluation method based on the Gaussian distance-improved dynamic time warping (DTW) algorithm and the temporal convolutional network (TCN) model. First, the adaptive adapter is used to extract one-dimensional features from the three-dimensional data of the data gloves. Then, based on the TCN model, action data with significant discrepancies are filtered out. Finally, the obtained data are input into the Gaussian distance-improved DTW algorithm, where the path size is calculated. Corresponding scoring criteria are established on the basis of the path size to evaluate the actions. The results demonstrate that the VR action evaluation method based on the Gaussian distance-improved DTW algorithm and the TCN model significantly improves the accuracy of evaluating fine movements compared to traditional evaluation algorithms.

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

This paper aims to enhance the effectiveness of intelligent evaluation in college English teaching and explore the role of artificial neural networks in evaluating college English teaching reform. A research study is conducted to develop an intelligent evaluation model by refining algorithm models and utilizing comparative research methods to address limitations of traditional algorithms. The neural network algorithms are improved through model construction techniques, with practical effectiveness assessed through simulation training methods. The algorithm model is applied to assess teaching quality in universities, with experimental research integrating questionnaire surveys and simulation data processing methods. The feasibility of the approach is verified through data evaluation. Simulation analysis demonstrates that the enhanced neural network algorithm reduces running time and enhances accuracy compared to traditional evaluation algorithms. Experimental studies indicate that employing artificial neural networks for evaluating the quality of college English teaching reform yields positive outcomes, aiding students and teachers in understanding their situations within mobile computing environments. This study confirms that combining a neural network model with data survey methods effectively meets the requirements of contemporary efficient teaching quality evaluation. The innovative integration of intelligent algorithms, factor analysis, surveys, and statistical methods enhances the progressive nature of teaching quality evaluation.

Evaluation and Analysis of the Informatization Degree of College English Education Based on Big Data Technology

Since the 21st century, thanks to the continuous achievements in the computer field, the Internet technology has developed rapidly in just a few years until it has swept the world. In the torrent of the information age, big data technology came into being. The application of big data technology is extremely high, and it is widely used in many industries. Based on the excellent performance of big data technology in the education industry, this paper will discuss the application of big data technology in the evaluation and analysis of college English education informatization. Under the condition that the evaluation standard is relatively abstract, the traditional method of evaluating through the subjective perspective of human beings has a large interference surface, and it is difficult to make the evaluation work substantive, data-based, and fair, which directly leads to evaluation and analysis work being difficult to carry out. Based on the abovementioned situation that the evaluation and analysis are relatively limited, this article will introduce the role of the big data technology algorithm model in the evaluation and analysis work. Through big data technology algorithms, qualitative problems that are difficult to evaluate are converted into quantitative problems that are easier to analyze and compare and then get rid of the limitations faced by traditional evaluation algorithms. The interference caused by human subjective factors to evaluation and analysis should be reasonably avoided as much as possible. This makes the evaluation and analysis results more objective and fair. This article is mainly based on the relevant algorithms under the big data technology to mine, classify, and purify the data with strong correlation with the important relevant indicators that affect the degree of college English informatization and carry out the experimental calculation of the evaluation of the relevant indicators. The results are as follows: we provide a reliable basis for the evaluation of the degree of informatization in college English and then conduct more scientific and rigorous evaluation and analysis. In the future development of big data technology, the database will be continuously supplemented with valuable teaching data, so that the results of big data technical analysis are more and more consistent with the real situation.

Evaluation of English Proficiency Based on Big Data Clustering Algorithm

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by K -means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt.

An Alternative Approach for Oil-immersed High Voltage Power Transformer Dissolved Gas Analysis Diagnostic Techniques

This article presents alternative analyzing method of extracted dissolved gases related to insulating oil of power transformers. Analysis of soluble and free gas is one of the most commonly used troubleshooting methods for detecting and evaluating equipment damage. Although the analysis of oil-soluble gases is often complex, it should be expertly processed during maintenance operation. The destruction of the transformer oil will produce some hydrocarbon type gases. The development of this index is based on two examples of traditional evaluation algorithms along with fuzzy logic inference engine. Through simulation process, the results of the initial fractures in the transformer are obtained in two ways by the "Duval Triangle method” and "Rogers’s ratios". In continue, three digit codes containing the fault information are created based on the fuzzy logic inference engine to achieve better results and eliminate ambiguous zones in commonly used methods, especially in the “Duval Triangle method”. The proposed method is applied to 80 real transformers to diagnose the fault by analyzing the dissolved oil based on fuzzy logic. The results illustrate the proficiency of this alternative proposed algorithm. Finally, with utilization of a neural network the alternative practical inference function is derived to make the algorithm more usable in the online condition monitoring of power transformers.

Inlining External Sources in Answer Set Programs

Abstracthex-programs are an extension of answer set programs (ASP) with external sources. To this end, external atoms provide a bidirectional interface between the program and an external source. The traditional evaluation algorithm for hex-programs is based on guessing truth values of external atoms and verifying them by explicit calls of the external source. The approach was optimized by techniques that reduce the number of necessary verification calls or speed them up, but the remaining external calls are still expensive. In this paper, we present an alternative evaluation approach based on inlining of external atoms, motivated by existing but less general approaches for specialized formalisms such as DL-programs. External atoms are then compiled away such that no verification calls are necessary. The approach is implemented in the dlvhex reasoner. Experiments show a significant performance gain. Besides performance improvements, we further exploit inlining for extending previous (semantic) characterizations of program equivalence from ASP to hex-programs, including those of strong equivalence, uniform equivalence, and $\langle\mathcal{H},\mathcal{B}\rangle$- equivalence. Finally, based on these equivalence criteria, we characterize also inconsistency of programs w.r.t. extensions. Since well-known ASP extensions (such as constraint ASP) are special cases of hex, the results are interesting beyond the particular formalism.

An improved multilevel fuzzy comprehensive evaluation algorithm for security performance

It is of great importance to take various factors into account when evaluating the network security performance. Multilevel fuzzy comprehensive evaluation is a relatively valid method. However, the traditional multilevel fuzzy comprehensive evaluation algorithm relies on the expert's knowledge and experiences excessively, and the result of the evaluation is usually less accurate. In this article, an improved multilevel fuzzy comprehensive evaluation algorithm, based on fuzzy sets core and entropy weight is presented. Furthermore, a multilevel fuzzy comprehensive evaluation model of P2P network security performance has also been designed, and the improved algorithm is used to make an instant computation based on the model. The advantages of the improved algorithm can be embodied in comparison with the traditional evaluation algorithm.