Wanhong Huang

Rui Geng, Yixuan Ma, Wanhong Huang

The YOLOv3 target detection algorithm is widely used in industry due to its high speed and high accuracy, but it has some limitations, such as the accuracy degradation of unbalanced datasets. The YOLOv3 target detection algorithm is based on a Gaussian fuzzy data augmentation approach to pre-process the data set and improve the YOLOv3 target detection algorithm. Through the efficient pre-processing, the confidence level of YOLOv3 is generally improved by 0.01-0.02 without changing the recognition speed of YOLOv3, and the processed images also perform better in image localization due to effective feature fusion, which is more in line with the requirement of recognition speed and accuracy in production.

Wanhong Huang

Automatic scoring system is extremely complex for any language. Because natural language itself is a complex model. When we evaluate articles generated by natural language, we need to view the articles from many dimensions such as word features, grammatical features, semantic features, text structure and so on. Even human beings sometimes can't accurately grade a composition because different people have different opinions about the same article. But a composition scoring system can greatly assist language learners. It can make language leaner improve themselves in the process of output something. Though it is still difficult for machines to directly evaluate a composition at the semantic and pragmatic levels, especially for Japanese, Chinese and other language in high context cultures, we can make machine evaluate a passage in word and grammar levels, which can as an assistance of composition rater or language learner. Especially for foreign language learners, lexical and syntactic content are usually what they are more concerned about. In our experiments, we did the follows works: 1) We use word segmentation tools and dictionaries to achieve word segmentation of an article, and extract word features, as well as generate a words' complexity feature of an article. And Bow technique are used to extract the theme features. 2) We designed a Turing-complete automata model and create 300+ automatons for the grammars that appear in the JLPT examination. And extract grammars features by using these automatons. 3) We propose a statistical approach for scoring a specify theme of composition, the final score will depend on all the writings that submitted to the system. 4) We design an grammar hint function for language leaner, so that they can know currently what grammars they can use.

WanHong Huang

Database system is an indispensable part of software projects. It plays an important role in data organization and storage. Its performance and efficiency are directly related to the performance of software. Nowadays, we have many general relational database systems that can be used in our projects, such as SQL Server, MySQL, Oracle, etc. It is undeniable that in most cases, we can easily use these database systems to complete our projects, but considering the generality, the general database systems often can't play the ultimate speed and fully adapt to our projects. In very few projects, we will need to design a database system that fully adapt to our projects and have a high efficiency and concurrency. Therefore, it is very important to consider a feasible solution of designing a database system (We only consider the relational database system here). Meanwhile, for a database system, SQL interpretation and execution module is necessary. According to the theory of formal language and automata, the realization of this module can be completed by automata. In our experiment, we made the following contributions: 1) We designed a small relational database, and used the database to complete a highly concurrent student course selection system. 2) We design a general automaton module, which can complete the operation from parsing to execution. The using of strategy model and event driven design scheme is used and some improvement on general automata, for example a memory like structure is added to automata to make it better to store context. All these make the automata model can be used in a variety of occasions, not only the parsing and execution of SQL statements.

Wanhong Huang, Rui Geng

Though current CV models have been able to achieve high levels of accuracy on small-scale images classification dataset with hundreds or thousands of categories, many models become infeasible in computational or space consumption when it comes to large-scale dataset with more than 50,000 categories. In this paper, we provide a viable solution for classifying large-scale species datasets using traditional CV techniques such as.features extraction and processing, BOVW(Bag of Visual Words) and some statistical learning technics like Mini-Batch K-Means,SVM which are used in our works. And then mixed with a neural network model. When applying these techniques, we have done some optimization in time and memory consumption, so that it can be feasible for large-scale dataset. And we also use some technics to reduce the impact of mislabeling data. We use a dataset with more than 50, 000 categories, and all operations are done on common computer with l 6GB RAM and a CPU of 3. OGHz. Our contributions are: 1) analysis what problems may meet in the training processes, and presents several feasible ways to solve these problems. 2) Make traditional CV models combined with neural network models provide some feasible scenarios for training large-scale classified datasets within the constraints of time and spatial resources.

Wanhong Huang, Chunxi Yang, TianHong Hou

Landmark Localization plays a very important role in processing medical images as well as in disease identification. However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, which is very simple and faster training , but larger dataset and deeper model are needed to achieve higher accuracy. Though with data augmentation and deeper network it can reach a reasonable accuracy, but the accuracy still not reach the requirement of medical field. In addition, a deeper networks also means larger space consumption. To achieve a higher accuracy, we contrived a new landmark regression method which combing heatmap regression and direct coordinate regression base on probability methods and system control theory.