Analyzing the impact of vibration on YOLO algorithm performance

2021 ◽  
Author(s):  
Jonathan G. Hixson ◽  
Brian P. Teaney ◽  
Bryan Vogel ◽  
Mark Jeiran ◽  
Georges Nehmetallah
Keyword(s):  
Algorithm Performance ◽  
The Impact

scholarly journals The impact of improved signal-to-noise ratios on algorithm performance: Case studies for Landsat class instruments

2016 ◽  
Vol 185 ◽  
pp. 37-45 ◽  
Author(s):  
John R. Schott ◽  
Aaron Gerace ◽  
Curtis E. Woodcock ◽  
Shixiong Wang ◽  
Zhe Zhu ◽  
...  
Keyword(s):  
Case Studies ◽  
Signal To Noise ◽  
Algorithm Performance ◽  
The Impact

Benchmarking Human Performance in Semi-Automated Image Segmentation

2020 ◽  
Vol 32 (3) ◽  
pp. 233-245
Author(s):  
Mark Eramian ◽  
Christopher Power ◽  
Stephen Rau ◽  
Pulkit Khandelwal
Keyword(s):  
Image Segmentation ◽  
Human Performance ◽  
State Of The Art ◽  
Automated Segmentation ◽  
Experimental Conditions ◽  
Algorithm Performance ◽  
Sharp Focus ◽  
Segmentation Algorithms ◽  
The Impact ◽  
Very High

Abstract Semi-automated segmentation algorithms hold promise for improving extraction and identification of objects in images such as tumors in medical images of human tissue, counting plants or flowers for crop yield prediction or other tasks where object numbers and appearance vary from image to image. By blending markup from human annotators to algorithmic classifiers, the accuracy and reproducability of image segmentation can be raised to very high levels. At least, that is the promise of this approach, but the reality is less than clear. In this paper, we review the state-of-the-art in semi-automated image segmentation performance assessment and demonstrate it to be lacking the level of experimental rigour needed to ensure that claims about algorithm accuracy and reproducability can be considered valid. We follow this review with two experiments that vary the type of markup that annotators make on images, either points or strokes, in tightly controlled experimental conditions in order to investigate the effect that this one particular source of variation has on the accuracy of these types of systems. In both experiments, we found that accuracy substantially increases when participants use a stroke-based interaction. In light of these results, the validity of claims about algorithm performance are brought into sharp focus, and we reflect on the need for a far more control on variables for benchmarking the impact of annotators and their context on these types of systems.

scholarly journals Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance

animal ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 409-417 ◽  
Author(s):  
D. Piette ◽  
T. Norton ◽  
V. Exadaktylos ◽  
D. Berckmans
Keyword(s):  
Dairy Cows ◽  
Window Length ◽  
Algorithm Performance ◽  
The Impact

Data processing with an Improved Hybrid Optimization Algorithm Base on PSO-GA

2014 ◽  
Vol 1014 ◽  
pp. 404-412 ◽  
Author(s):  
Fu Kun Zhang ◽  
Shu Wen Zhang ◽  
Gui Zhi Ba
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Hybrid Optimization Algorithm ◽  
Exchange Information ◽  
Algorithm Base ◽  
The Impact ◽  
Crossover And Mutation

This paper develops an improved hybrid optimization algorithm based on particle swarm optimization (PSO) and a genetic algorithm (GA). First, the population is evolved over a certain number of generations by PSO and the best M particles are retained, with the remaining particles excluded. Second, new individuals are generated by implementing selection, crossover and mutation GA operators for the best M particles. Finally, the new individuals are combined with the best M particles to form new a population for the next generation. The algorithm can exchange information several times during evolution so that the complement of two algorithms can be more fully exploited. The proposed method is applied to fifteen benchmark optimization problems and the results obtained show an improvement over published methods. The impact of M on algorithm performance is also discussed.

scholarly journals Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

2021 ◽  
Author(s):  
Nur Siyam ◽  
Sherief Abdallah
Keyword(s):  
Learning Algorithm ◽  
Autism Spectrum ◽  
Mobile App ◽  
Influential Factors ◽  
Algorithm Performance ◽  
Q Learning ◽  
Academic Activities ◽  
Markov Decision ◽  
The Right ◽  
The Impact

Abstract Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to solve the problem of selecting the right motivator for children with ASD using Reinforcement Learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov Decision Process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on Applied Behavior Analysis as well as learners’ individual preferences. We use a Q-Learning algorithm to solve the modelled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

Comparison of Noniterative Factor Analysis Spectral Resolution Algorithms

1993 ◽  
Vol 47 (8) ◽  
pp. 1161-1168 ◽  
Author(s):  
Sharon L. Neal
Keyword(s):  
Factor Analysis ◽  
Spectral Resolution ◽  
Pink Noise ◽  
Fluorescence Excitation ◽  
Algorithm Performance ◽  
Spectral Overlap ◽  
Noise Type ◽  
Resolution Algorithm ◽  
The Impact ◽  
Fluorescence Excitation Emission Matrices

The effects of fundamental and ancillary algorithm differences on the performance of three noniterative factor analysis spectral resolution algorithms on noisy and overlapped bilinear matrix-formatted spectral data are evaluated and compared. The evaluation consists of the analysis of simulated fluorescence excitation-emission matrices in which the spectral overlap, noise type, and level were systematically varied. The results indicate that the conventions used to exclude low-intensity, high-noise rows and columns from consideration as component spectra estimates and to choose the first estimates of the component spectra have significant impact on resolution algorithm performance. The results of the application of the algorithms to ideal data are nearly identical; however, there are several distinctions in the performance of the algorithms on noisy data. Verifiable estimates of the component spectra were resolved from data matrices degraded by white and Poisson noise that have signal-to-noise (S/N) ratios above 10 by all three algorithms regardless of the noise level and the degree of spectral overlap. The impact of pink noise was uniformly deleterious at S/N below 15.

An Efficient CGM-Based Parallel Algorithm Solving the Matrix Chain Ordering Problem

2014 ◽  
Vol 6 (2) ◽  
pp. 74-100 ◽  
Author(s):  
Jean Frédéric Myoupo ◽  
Vianney Kengne Tchendji
Keyword(s):  
Cost Model ◽  
Coarse Grain ◽  
Systolic Arrays ◽  
Algorithm Performance ◽  
Sequential Solution ◽  
The Matrix ◽  
Intensive Work ◽  
The Impact ◽  
Chain Ordering ◽  
Better Than

This study focuses on the parallel resolution of the matrix chain ordering problem and the optimal convex polygon triangulation problem on the Coarse grain multicomputer model (CGM for short). There has been intensive work on the parallelization of these dynamic programming problems in PRAM, including the use of systolic arrays, but a BSP/CGM solution is necessary for ease of implementation and portability. Our CGM algorithm is based on Yao's sequential solution running in O(n2) time and O(n2) space. This CGM algorithm uses p processors, each with O(n/p) local memory. It requires at most O(S/p×n2) running time with S communication rounds and with S/p<1. Our algorithm performs better than the algorithm proposed in 2012 by Dilson and Marco when S is less than n/p. We offer several ways of partitioning the problem to solve and study the impact of each partitioning algorithm performance. A CGM solution exists based on Yao's algorithm, but the subdivision of tasks is defined according to the BSP cost model. In this paper, we propose a solution based only on the CGM model specifications. Note that S is the number of super-steps of the CGM algorithm.

Global versus local search: the impact of population sizes on evolutionary algorithm performance

2016 ◽  
Vol 66 (3) ◽  
pp. 511-534 ◽  
Author(s):  
Thomas Weise ◽  
Yuezhong Wu ◽  
Raymond Chiong ◽  
Ke Tang ◽  
Jörg Lässig
Keyword(s):  
Local Search ◽  
Evolutionary Algorithm ◽  
Algorithm Performance ◽  
Population Sizes ◽  
The Impact
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