A hybrid approach for Thai word segmentation with crowdsourcing feedback system

2016 ◽  
Author(s):  
Kriangkrai Chaonithi ◽  
Santitham Prom-on
Keyword(s):  
Hybrid Approach ◽  
Feedback System ◽  
Word Segmentation

scholarly journals A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Phuoc Tran ◽  
Dien Dinh ◽  
Hien T. Nguyen
Keyword(s):  
Machine Translation ◽  
Hybrid Approach ◽  
Sparse Data ◽  
Word Segmentation ◽  
Experimental Results ◽  
Translation System ◽  
Word Level ◽  
Data Problem ◽  
Sparse Data Problem ◽  
Language Pair

Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English) and vice versa. However, it is a matter for consideration that words may or may not be segmented when translating between two languages in which spaces are not used between words, such as Chinese and Vietnamese. Since Chinese-Vietnamese is a low-resource language pair, the sparse data problem is evident in the translation system of this language pair. Therefore, while translating, whether it should be segmented or not becomes more important. In this paper, we propose a new method for translating Chinese to Vietnamese based on a combination of the advantages of character level and word level translation. In addition, a hybrid approach that combines statistics and rules is used to translate on the word level. And at the character level, a statistical translation is used. The experimental results showed that our method improved the performance of machine translation over that of character or word level translation.

scholarly journals A Hybrid Approach to Word Segmentation of Vietnamese Texts

2008 ◽  
pp. 240-249 ◽  
Author(s):  
L ê Hông Phuong ◽  
Nguyên Thi Minh Huyên ◽  
Azim Roussanaly ◽  
Hô Tuòng Vinh
Keyword(s):  
Hybrid Approach ◽  
Word Segmentation

scholarly journals A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2610
Author(s):  
Burhan A. Mudassar ◽  
Priyabrata Saha ◽  
Marilyn Wolf ◽  
Saibal Mukhopadhyay
Keyword(s):  
Object Detection ◽  
Closed Loop ◽  
Hybrid Control ◽  
Hybrid Approach ◽  
Feedback System ◽  
Tracking Accuracy ◽  
Loop Control ◽  
Action Detection ◽  
Active Sensor ◽  
Loop Feedback

Deep Neural Network (DNN) systems tend to produce overconfident or uncalibrated outputs. This poses problems for active sensor systems that have a DNN module as the main feedback controller. In this paper, we study a closed-loop feedback smart camera from the lens of uncertainty estimation. The uncertainty of the task output is used to characterize and facilitate the feedback operation. The DNN uncertainty in the feedback system is estimated and characterized using both sampling and non-sampling based methods. In addition, we propose a closed-loop control that incorporates uncertainty information when providing feedback. We show two modes of control, one that prioritizes false positives and one that prioritizes false negatives, and a hybrid approach combining the two. We apply the uncertainty-driven control to the tasks of object detection, object tracking, and action detection. The hybrid system improves object detection and tracking accuracy on the CAMEL dataset by 1.1% each respectively. For the action detection task, the hybrid approach improves accuracy by 1.4%.

Experiments with a scanning tunneling microscope (STM) mounted in a scanning electron microscope (SEM)

1986 ◽  
Vol 44 ◽  
pp. 636-639
Author(s):  
Oliver C. Wells ◽  
Mark E. Welland
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Mechanical Stability ◽  
Tunneling Current ◽  
Feedback System ◽  
Scanning Tunneling ◽  
Surface Profiling ◽  
Close Proximity ◽  
Metal Tip ◽  
Scanning Electron

Scanning tunneling microscopes (STM) exist in two versions. In both of these, a pointed metal tip is scanned in close proximity to the specimen surface by means of three piezos. The distance of the tip from the sample is controlled by a feedback system to give a constant tunneling current between the tip and the sample. In the low-end STM, the system has a mechanical stability and a noise level to give a vertical resolution of between 0.1 nm and 1.0 nm. The atomic resolution STM can show individual atoms on the surface of the specimen.A low-end STM has been put into the specimen chamber of a scanning electron microscope (SEM). The first objective was to investigate technological problems such as surface profiling. The second objective was for exploratory studies. This second objective has already been achieved by showing that the STM can be used to study trapping sites in SiO2.

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