A lightweight data-voting strategy for triple-modular redundant control computers

2022 ◽  
Author(s):  
Bo Liu ◽  
MengFei Yang ◽  
Yong Wang ◽  
Li Yuan ◽  
ChaoWei Liu ◽  
...  
Keyword(s):  
Redundant Control ◽  
Voting Strategy

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

scholarly journals Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 66
Author(s):  
Rahee Walambe ◽  
Aboli Marathe ◽  
Ketan Kotecha
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Test Time ◽  
Complex Task ◽  
Open Domain ◽  
End User ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

Automated Categorisation of Nailfold Capillaroscopy Images

2016 ◽  
pp. 134-145
Author(s):  
Niraj Doshi ◽  
Gerald Schaefer
Keyword(s):  
Early Detection ◽  
Texture Analysis ◽  
Ground Truth ◽  
Experimental Results ◽  
Nailfold Capillaroscopy ◽  
Image Region ◽  
Blood Capillaries ◽  
Non Invasive ◽  
Spectrum Disorders ◽  
Voting Strategy

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While automated approaches to analysing NC images are relatively rare, they are typically based on extraction and analysis of individual capillaries from the images in order to assign a patient to one of the commonly employed scleroderma patterns. In this chapter, we present a different approach that does not rely on individual capillaries but performs interpretation in a holistic way based on information gathered from an image or a selected image region. In particular, our algorithm employs texture analysis to characterise the underlying patterns, coupled with a classification stage to first identify patterns in fingers, and then, through a voting strategy, reach a decision for a patient. Experimental results on a set of NC images with known ground truth demonstrate the efficacy of the proposed approach.

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