Wang Zhengwu

Major courses：Digital circuit, analog circuit, high frequency circuit, C language, assembly language, signal and system, communication principle, digital signal processing, communication modeling, antenna and electromagnetic wave, microwave principle, neural network, 5G communication theory.

Major courses: Information retrieval, information mining, knowledge-based systems, distributed systems, point-to-point systems, recommendation systems, cloud network cognitive robot systems, natural language processing

Responsible for searching related learning materials, communicating with instructors, tracking project progress, and reporting tasks in stages

Summaring metadata of the dataset, manually annotating the training data and calculating the similarity, optimizing and correcting the dataset,establishing the sentiment analysis model, applying the induction analysis model to the bias detection of news data.

• Job description: engaged in the suitability testing of Qualcomm chip mobile phones and European operator networks

• Test objects: Samsung, XiaoMi, OPPO

• Test locations: Salzburg, Austria, Vienna, Austria

• Job content includes mobile phone base station adaptability testing, Qualcomm baseband testing, bug collection, test report return, and tracking solution.

Bachelor Thesis, 2019.01–2019.06

It was finished in the Mobile Communication Laboratory of the School of Information Engineering at Zhengzhou University under the direction of Professor Wang Ning. It mostly uses Matlab simulation to study the throughput and signal-to-noise ratio of edge users and central users using various 5G coding techniques.

Computer Lab Research Assistant, 2021.03-2021.08

Under Prof. Josef Pauli's direction, research on image recognition based on convolutional neural network (CNN), primarily research on handwriting identification based on convolutional neural network

Language Lab Research Assistant, 2022.02-2022.06

Under Prof. Torsten Zesch's direction, research on the use of intelligent systems in educational natural language processing (EduNLP) focuses on the differences and connections between Chinese and English automatic scoring (AES) technology and uses a traditional feature-based automatic scoring system.

Master Thesis, 2022.06.–2022.12

Under the guidance of Prof. Torsten Zesch, conduct research on the coherence of Chinese as a second language writing, mainly studying foreign studies in the Chinese Proficiency Test (HSK) database

The coherence of Chinese composition of students, the analysis of factors affecting coherence, the detection of correlation between coherence and scores, and the improvement of automatic scoring model performance based on Chinese connective words, automatic recognition of improper use of conjunctions