Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure

2016 ◽  
pp. 1-9 ◽  
Author(s):  
Koray Ozcan ◽  
Senem Velipasalar ◽  
Pramod K. Varshney
Keyword(s):  
Relative Entropy ◽  
Distance Measure ◽  
Fall Detection ◽  
Wearable Cameras ◽  
Entropy Distance

scholarly journals Quantum coherence, discord and correlation measures based on Tsallis relative entropy

2020 ◽  
Vol 20 (7&8) ◽  
pp. 553-569
Author(s):  
Anna Vershynina
Keyword(s):  
Relative Entropy ◽  
Quantum Coherence ◽  
Distance Measure ◽  
Upper And Lower Bounds ◽  
General Definition ◽  
Strong Monotonicity ◽  
Correlation Measure ◽  
Coherence Measure ◽  
Pure States ◽  
Correlation Measures

Several ways have been proposed in the literature to define a coherence measure based on Tsallis relative entropy. One of them is defined as a distance between a state and a set of incoherent states with Tsallis relative entropy taken as a distance measure. Unfortunately, this measure does not satisfy the required strong monotonicity, but a modification of this coherence has been proposed that does. We introduce three new Tsallis coherence measures coming from a more general definition that also satisfy the strong monotonicity, and compare all five definitions between each other. Using three coherence measures that we discuss, one can also define a discord. Two of these have been used in the literature, and another one is new. We also discuss two correlation measures based on Tsallis relative entropy. We provide explicit expressions for all three discord and two correlation measure on pure states. Lastly, we provide tight upper and lower bounds on two discord and correlations measures on any quantum state, with the condition for equality.

scholarly journals (QUANTUMNESS IN THE CONTEXT OF) RESOURCE THEORIES

2012 ◽  
Vol 27 (01n03) ◽  
pp. 1345019 ◽  
Author(s):  
MICHAL HORODECKI ◽  
JONATHAN OPPENHEIM
Keyword(s):  
State Space ◽  
Relative Entropy ◽  
General Framework ◽  
The State ◽  
Resource Theory ◽  
State Transitions ◽  
Restricted Class ◽  
Unique Measure ◽  
Entropy Distance

We review the basic idea behind resource theories, where we quantify quantum resources by specifying a restricted class of operations. This divides the state space into various sets, including states which are free (because they can be created under the class of operations), and those which are a resource (because they cannot be). One can quantify the worth of the resource by the relative entropy distance to the set of free states, and under certain conditions, this is a unique measure which quantifies the rate of state to state transitions. The framework includes entanglement, asymmetry and purity theory. It also includes thermodynamics, which is a hybrid resource theory combining purity theory and asymmetry. Another hybrid resource theory which merges purity theory and entanglement can be used to study quantumness of correlations and discord, and we present quantumness in this more general framework of resource theories.

Aggregation Similarity Measure Based on Hesitant Fuzzy Closeness Degree and Its Application to Clustering Analysis

2019 ◽  
Vol 7 (1) ◽  
pp. 70-89 ◽  
Author(s):  
Feng Wang
Keyword(s):  
Correlation Coefficient ◽  
Relative Entropy ◽  
Clustering Analysis ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Attribute Weights ◽  
Correlation Degree ◽  
Relative Closeness ◽  
New Formula

Abstract In order to distinguish with effect different hesitant fuzzy elements (HFEs), we introduce the asymmetrical relative entropy between HFEs as a distance measure for higher discernment. Next, the formula of attribute weights is derived via an optimal model according to TOPSIS from the relative closeness degree constructed by the discerning relative entropy. Then, we propose the concept of co-correlation degree from the viewpoint of probability theory and develop another new formula of hesitant fuzzy correlation coefficient, and prove their similar properties to the traditional correlation coefficient. To make full use of the existing similarity measures including the ones presented by us, we consider aggregation of similarity measures for hesitant fuzzy sets and derive the synthetical similarity formula. Finally, the derived formula is used for netting clustering analysis under hesitant fuzzy information and the effectiveness and superiority are verified through a comparison analysis of clustering results obtained by other clustering algorithms.

Image Fusion Base on K-Means Clustering and Contourlet Transform

2012 ◽  
Vol 500 ◽  
pp. 540-544
Author(s):  
Liang Ku Wang ◽  
Cheng Jin Li ◽  
Qing Wang ◽  
Zhao Hui Yang ◽  
Zhi Jie Wang
Keyword(s):  
Image Fusion ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Cross Entropy ◽  
Distribution Data ◽  
Spherical Distribution ◽  
Euclidean Distance Measure ◽  
Entropy Distance

The robustness of K-means clustering is poor in non-spherical distribution data, in order to improve the universal ability of clustering algorithms, the cross-entropy distance measure was used to replace the Euclidean distance measure . Contour let transform, not only has characteristics of multi-resolution, locality and critical sampling which wavelet has, but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lack. So we combine the modified K-means clustering and Contour let transform to apply for image fusion. Experimental results show that this method is feasible.

scholarly journals Application of Relative Entropy and Gradient Boosting Decision Tree to Fault Prognosis in Electronic Circuits

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 495 ◽  
Author(s):  
Ling Wang ◽  
Dongfang Zhou ◽  
Hao Zhang ◽  
Wei Zhang ◽  
Jing Chen
Keyword(s):  
Regression Analysis ◽  
Decision Tree ◽  
Relative Entropy ◽  
Gradient Boosting ◽  
Engineering Practice ◽  
Electronic Circuits ◽  
Measuring Methods ◽  
Fault Prognosis ◽  
Electronic Elements ◽  
Entropy Distance

Fault prognosis of electronic circuits is the premise of guaranteeing normal operation of a system and carrying out on-condition maintenance. In this work, the remaining useful life (RUL) of electronic elements was estimated by selecting fault features based on variance, measuring fault severity based on relative entropy distance, and conducting fault prognosis based on the gradient boosting decision tree (GBDT) model. At first, the corresponding voltages of amplitude-frequency response, under conditions of changing full-band element parameters, were extracted, and then the frequency bands with large change amplitude were further selected based on variance. Afterwards, using relative entropy distance, the degradation of element parameters was measured, and then the RUL of electronic elements was diagnosed through regression analysis by GBDT. By comparing the data with those arising from the use of other distance-measuring methods, the relative entropy distance shows a larger change range and less apt to suffer interference from noise, which is favorable to subsequent regression prediction. The regression analysis through GBDT is easy to understand and conveniently applied in engineering practice. The application of the method proposed in the study in two examples of electronic circuits indicates that the prediction accuracy of the method for RUL of electronic elements is higher than that of the other distance-measuring methods, and its application in engineering practice is convenient.

Context Quantization Based on the Modified K-Means Clustering

2013 ◽  
Vol 756-759 ◽  
pp. 4068-4072 ◽  
Author(s):  
Min Chen ◽  
Fu Yan Wang
Keyword(s):  
Conditional Probability ◽  
Relative Entropy ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Probability Distributions ◽  
Code Length ◽  
Adaptive Code

The context quantization forsource based on the modified K-means clustering algorithm is present in this paper. In this algorithm, the adaptive complementary relative entropy between two conditional probability distributions, which is used as the distance measure for K-means instead, is formulated to describe the similarity of these two probability distributions. The rules of the initialized centers chosen for K-means are also discussed. The proposed algorithm will traverse all possible number of the classes to search the optimal one which is corresponding to the shortest adaptive code length. Then the optimal context quantizer is achieved rapidly and the adaptive code length is minimized at the same time. Simulations indicate that the proposed algorithm produces better coding result than the result of other algorithm.

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