School of English, Xi'an Fanyi University, Xi'an City 710105, China.
University of Perpetual Help System DALTA, Alabang-Zapote Avenue, Pamplona 3, Las Piñas City, Mero Manila 1740, Philippines.
Comput Intell Neurosci. 2022 Jun 16;2022:9538917. doi: 10.1155/2022/9538917. eCollection 2022.
This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.
本研究旨在提高当代高校英语教学质量，培养更多的英语翻译人才。以语料库、英语翻译和教学实践为主要研究领域，本研究通过将物联网（IoT）下的任务迁移算法与自建语料库相结合，分析了硕士翻译与口译（MTI）课程设计、英语翻译和任务迁移的应用，以实现翻译人才的培养和教学实践的设计。构建了翻译语料库，设计了双向互动在线课程，并分析比较了英语教学实践课程中使用的完全局部迁移算法（CLM 算法）、随机迁移算法（RM 算法）和贪婪启发式迁移算法（GHM 算法）的实验结果。实验模拟结果表明，本研究提出的 GHM 算法系统稳定性良好，其持续时间比 CLM 算法提高了 60%，系统吞吐量比 CLM 算法提高了 50%。此外，最大延迟时间对系统吞吐量的影响较小。当系统时隙长度固定为 20ms 时，在不同的总用户数下，遗传算法的用户迁移率最高。此外，鉴于神经网络在英语翻译教学中的广泛应用，本研究建立了基于粒子群优化（PSO）算法和神经网络相结合的英语翻译评价模型，并在模拟实验中与传统神经网络模型进行了比较。结果表明，PSO 算法的加入可以有效提高人工神经网络（ANN）的收敛速度，减少模型的训练时间，提高 ANN 网络的准确性。使用 PSO 算法训练神经网络，可以得到不同粒子群的最优解，误差较小。PSO-ANNs 模型可以促进英语翻译教学质量的提高，提高学生的英语翻译能力。因此，将任务迁移算法和 PSO 算法应用于英语翻译教学实践，大大提高了英语课堂的教学效率。综上所述，本研究为当代高校英语教学的课程设计提供了新思路，对高校英语翻译人才的培养具有参考价值。
