다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

비만아 부모의 돌봄 경험: q 방법론

http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

This work presents a real-time system for multiple objects tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320 /spl times/ 240, as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background.

/pdf/real-time-multiple-objects-tracking-with-occlusion-handling-49d0qzmju1.pdf

Real-time multiple objects tracking with occlusion handling in dynamic scenes

Data-driven proton exchange membrane fuel cell degradation predication through deep learning method

Depthmap embodies a theory of the city, as well as being a method for analysing the city. By solving outstanding problems of the normalisation of measures, most notably syntactic choice (mathematical betweenness), to permit comparison of cities of different sizes, we can gain new theoretical insights into their spatial structuring.

Normalising least angle choice in Depthmap - and how it opens up new perspectives on the global and local analysis of city space

Word embedding, where semantic and syntactic features are captured from unlabeled text data, is a basic procedure in Natural Language Processing (NLP) The extracted features thus could be organized in low dimensional space Some representative word embedding approaches include Probability Language Model, Neural Networks Language Model, Sparse Coding, etc The state-of-the-art methods like skip-gram negative samplings, noise-contrastive estimation, matrix factorization and hierarchical structure regularizer are applied correspondingly to resolve those models Most of these literatures are working on the observed count and co-occurrence statistic to learn the word embedding The increasing scale of data, the sparsity of data representation, word position, and training speed are the main challenges for designing word embedding algorithms In this survey, we first introduce the motivation and background of word embedding Next we will introduce the methods of text representation as preliminaries, as well as some existing word embedding approaches such as Neural Network Language Model and Sparse Coding Approach, along with their evaluation metrics In the end, we summarize the applications of word embedding and discuss its future directions

Word Embedding for Understanding Natural Language: A Survey

Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.

Tao Yang

Papers

Real-time multiple objects tracking with occlusion handling in dynamic scenes

Data-driven proton exchange membrane fuel cell degradation predication through deep learning method

Normalising least angle choice in Depthmap - and how it opens up new perspectives on the global and local analysis of city space

Word Embedding for Understanding Natural Language: A Survey

Behavioral recognition system