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Unmanned Surface Vessels (USVs) are devices that use advanced, automated technologies for control and monitoring, with very little human intervention in this system. Today's un drone vehicles are developed for security, defense, trade, and civilian life purposes. Unmanned Surface Vessels is a device that uses advanced and automated technologies to control and monitor. This paper identifies the proportional - integral - differential controller. Results using MATLAB / SIMULINK simulation software.

Machine Reading Comprehension has attracted significant interest in research on natural language understanding, and large-scale datasets and neural network-based methods have been developed for this task. However, most developments of resources and methods in machine reading comprehension have been investigated using two resource-rich languages, English and Chinese. This article proposes a system called ViReader for open-domain machine reading comprehension in Vietnamese by using Wikipedia as the textual knowledge source, where the answer to any particular question is a textual span derived directly from texts on Vietnamese Wikipedia. Our system combines a sentence retriever component, based on techniques of information retrieval to extract the relevant sentences, with a transfer learning-based answer extractor trained to predict answers based on Wikipedia texts. Experiments on multiple datasets for machine reading comprehension in Vietnamese and other languages demonstrate that (1) our ViReader system is highly competitive with prevalent machine learning-based systems, and (2) multi-task learning by using a combination consisting of the sentence retriever and answer extractor is an end-to-end reading comprehension system. The sentence retriever component of our proposed system retrieves the sentences that are most likely to provide the answer response to the given question. The transfer learning-based answer extractor then reads the document from which the sentences have been retrieved, predicts the answer, and returns it to the user. The ViReader system achieves new state-of-the-art performances, with values of 70.83% EM (exact match) and 89.54% F1, outperforming the BERT-based system by 11.55% and 9.54% , respectively. It also obtains state-of-the-art performance on UIT-ViNewsQA (another Vietnamese dataset consisting of online health-domain news) and BiPaR (a bilingual dataset on English and Chinese novel texts). Compared with the BERT-based system, our system achieves significant improvements (in terms of F1) with 7.65% for English and 6.13% for Chinese on the BiPaR dataset. Furthermore, we build a ViReader application programming interface that programmers can employ in Artificial Intelligence applications.

Detecting the appearance of logos in images is applied to many applications, such as brand recognition for marketing analysis and intellectual property protection. It is a challenging problem in the computer vision field, especially in unconstrained images with a complex background, difference in sizes, a number of logos per image, contextual diversity with uncontrolled illumination, and low-resolution. In this work, we tackle this problem by first introducing a newly collected datasets containing 15 well-known brands, namely, U15-Logos (unconstrained logos) with 15000 images. In order to ease experimental evaluation and consider model adaptation, we use our standard dataset called F15-Logos (fundamental logos) with 15042 images for a crossing evaluation between U15-Logos and F15-Logos. Finally, we provide an evaluation of the state of the art models based on Deep learning including YOLO, Faster RCNN, Mask RCNN, RetinaNet in order to illustrate how well these models overcome unconstrained conditions. Comparative evaluations demonstrate the best performance on U15-Logos is about 89,4%.

How to find a person doing an action in a video database is a challenging problem because the result must be correct at an instance level with the specific person doing the appropriate action. Even though there have been many works about face recognition and action recognition, they often focus on only one separate task. In this paper, the problem could be formulated into an instance retrieval where input is a query consisting of examples of the target person and examples of the desired action, and the result is a list of ranked positive shots. In this work, we proposed a simple but efficient person-action retrieval system by combining multimodal features including visual feature and audio feature to deal with various types of instances by making use of available visual or audio cues. The evaluation results on a large-scale BBC Eastenders dataset with 3rd rank in a total of 6 teams in TRECVID INS 2019 has proved the effectiveness of the proposed method.