scholarly journals Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning

2017 ◽  
Author(s):  
Mojtaba Montazery ◽  
Nic Wilson
Keyword(s):  
Preference Relation ◽  
Computation Time ◽  
Preference Learning ◽  
Learning Approaches ◽  
Optimal Solutions ◽  
Scale Invariant ◽  
Maximum Margin ◽  
Invariance Properties ◽  
Test Vectors ◽  
Optimal Set

In the task of preference learning, there can be natural invariance properties that one might often expect a method to satisfy. These include (i) invariance to scaling of a pair of alternatives, e.g., replacing a pair (a,b) by (2a,2b); and (ii) invariance to rescaling of features across all alternatives. Maximum margin learning approaches satisfy such invariance properties for pairs of test vectors, but not for the preference input pairs, i.e., scaling the inputs in a different way could result in a different preference relation. In this paper we define and analyse more cautious preference relations that are invariant to the scaling of features, or inputs, or both simultaneously; this leads to computational methods for testing dominance with respect to the induced relations, and for generating optimal solutions among a set of alternatives. In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set. We also discuss connections with imprecise probability.

scholarly journals Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Prabal Poudel ◽  
Alfredo Illanes ◽  
Debdoot Sheet ◽  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
Active Contours ◽  
Computation Time ◽  
Machine Intelligence ◽  
Ease Of Use ◽  
Human Interaction ◽  
Ultrasound Images ◽  
Learning Approaches ◽  
Thyroid State ◽  
Advantages And Disadvantages

The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime importance to track the shape and size of thyroid over time in order to evaluate its state. Thyroid segmentation and volume computation are important tools that can be used for thyroid state tracking assessment. Most of the proposed approaches are not automatic and require long time to correctly segment the thyroid. In this work, we compare three different nonautomatic segmentation algorithms (i.e., active contours without edges, graph cut, and pixel-based classifier) in freehand three-dimensional ultrasound imaging in terms of accuracy, robustness, ease of use, level of human interaction required, and computation time. We figured out that these methods lack automation and machine intelligence and are not highly accurate. Hence, we implemented two machine learning approaches (i.e., random forest and convolutional neural network) to improve the accuracy of segmentation as well as provide automation. This comparative study intends to discuss and analyse the advantages and disadvantages of different algorithms. In the last step, the volume of the thyroid is computed using the segmentation results, and the performance analysis of all the algorithms is carried out by comparing the segmentation results with the ground truth.

scholarly journals A Filter of Minhash for Image Similarity Measures

2018 ◽  
Vol 22 (5) ◽  
pp. 689-698
Author(s):  
Jun Long ◽  
Qunfeng Liu ◽  
Xinpan Yuan ◽  
Chengyuan Zhang ◽  
Junfeng Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Computation Time ◽  
Image Features ◽  
Nearest Neighbor Search ◽  
Image Similarity ◽  
Scale Invariant ◽  
Minwise Hashing ◽  
Image Datasets

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific thresholdT(e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets.

scholarly journals A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

2015 ◽  
Vol 10 (4) ◽  
pp. 508 ◽  
Author(s):  
Ernestas Filatovas ◽  
Dmitry Podkopaev ◽  
Olga Kurasova
Keyword(s):  
Multiobjective Optimization ◽  
Dimensionality Reduction ◽  
Decision Maker ◽  
Optimization Problem ◽  
Interactive Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Visualization Technique ◽  
Preference Information ◽  
Optimal Set

<pre>Interactive methods of <span>multiobjective</span> optimization repetitively derive <span>Pareto</span> optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the <span>Pareto</span> optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. We propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. We describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method NIMBUS.</pre>

scholarly journals A Practical Approach to Optimising Distribution Transformer Tap Settings

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4889
Author(s):  
Joshua Paoli ◽  
Bernd Brinkmann ◽  
Michael Negnevitsky
Keyword(s):  
Low Voltage ◽  
Computation Time ◽  
Distribution Networks ◽  
Distribution Transformer ◽  
Average Case ◽  
Embedded Generation ◽  
Distribution Transformers ◽  
Real Distribution ◽  
Loading Case ◽  
Optimal Set

This paper proposes a method of determining the optimal tap settings for no-load distribution transformers with tap-changing capabilities that is practical to apply in real distribution networks. The risk of low voltage distribution networks violating voltage constraints is impacted by the increasing uptake of distributed energy resources and embedded generation. Some of this risk can be alleviated by suitably setting no-load transformer tap settings, however, modifying these taps requires customer outages and must be infrequent. Hence, loading over the entire year must be considered to account for seasonal variations when setting these taps optimally. These settings are determined using evolution strategy optimisation based on an average loading case. Monte Carlo simulations are used to calculate the probability that the terminal voltages on the distribution transformer secondary terminals violate the network voltage limits when the optimal set of taps for the average case is applied over a whole year. This algorithm was tested on several cases of a real distribution feeder of varying complexity, and produces a sufficiently-optimal set of taps without significant computation time.

scholarly journals Survey and Analysis on Machine Learning Approaches for Image Annotation

2021 ◽  
Vol 22 (12) ◽  
Author(s):  
Palekar V.R ◽  
◽  
Palekar V.R ◽  
Dr. Satish Kumar L ◽  
◽  
...  
Keyword(s):  
Social Networking Sites ◽  
Image Annotation ◽  
Main Idea ◽  
Computation Time ◽  
Image Data ◽  
Image Understanding ◽  
Model Complexity ◽  
Learning Approaches ◽  
Rural And Urban ◽  
Modal Semantic

In current years, a large amount of image data is being collected worldwide, which is majorly generated by corporate organizations, health industry and social networking sites. With the strength of substantial level depiction of images, Annotating image has numerous applications not only in image understanding and analysis but also in some of the concern domain like medical research, rural and urban management. Automatic Image Annotation (AIA) has been raised since the late 1990s due to inherent weaknesses of manual image annotation. In this paper, a deep review of the most recent stage in the development of AIA methods is presented by synthesizing 32 literatures published during the past decades. We classify AIA methods into five categories: 1) Kernel Logistic Regression (KLR), 2) Tri-relational Graph (TG), 3) Semantically Regularised CNN- RNN (S-CNN-RNN), 4) Label Correlation guided Deep Multi-view (LCDM), and 5) Multi-Modal Semantic Hash Learning (MMSHL). Considering inspiration on the basis of main idea, framework of model, complexity of computation, time complexity and accuracy in annotation Comparative analysis for various AIA methods are done.

scholarly journals Determining a Robust, Pareto Optimal Geometry for a Welded Joint

2014 ◽  
Vol 1016 ◽  
pp. 39-43
Author(s):  
Simon Barrans ◽  
H.E. Radhi
Keyword(s):  
Welded Joint ◽  
Optimization Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Geometry ◽  
Pareto Optimal Set ◽  
Particular Solution ◽  
Optimal Set ◽  
Selection Of

Multi-criteria optimization problems are known to give rise to a set of Pareto optimal solutions where one solution cannot be regarded as being superior to another. It is often stated that the selection of a particular solution from this set should be based on additional criteria. In this paper a methodology has been proposed that allows a robust design to be selected from the Pareto optimal set. This methodology has been used to determine a robust geometry for a welded joint. It has been shown that the robust geometry is dependent on the variability of the geometric parameters.

scholarly journals Deep Learning-Based Approach to Fast Power Allocation in SISO SWIPT Systems with a Power-Splitting Scheme

2020 ◽  
Vol 10 (10) ◽  
pp. 3634
Author(s):  
Huynh Thanh Thien ◽  
Pham-Viet Tuan ◽  
Insoo Koo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Performance ◽  
Short Term Memory ◽  
Computation Time ◽  
Power Splitting ◽  
Learning Approaches ◽  
Feed Forward Neural Network ◽  
Transmit Power ◽  
Single Input Single Output

Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.

scholarly journals Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Giuseppe Mascaro ◽  
Ara Ko ◽  
Enrique R. Vivoni
Keyword(s):  
Soil Moisture ◽  
Scale Invariance ◽  
Surface Soil ◽  
Agricultural Practices ◽  
Extreme Weather Events ◽  
Surface Soil Moisture ◽  
Scale Invariant ◽  
Invariance Properties ◽  
Terrestrial Water ◽  
Properties Of Soil

Abstract Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100 m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100 m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.

scholarly journals Measuring the Wave Height Based on Binocular Cameras

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1338 ◽  
Author(s):  
Yan Cang ◽  
Hengxiang He ◽  
Yulong Qiao
Keyword(s):  
Wave Height ◽  
Computation Time ◽  
Height Measurement ◽  
Scale Invariant ◽  
Epipolar Constraint ◽  
Marine Research ◽  
Key Points ◽  
System 2 ◽  
The Difference ◽  
Scale Invariant Feature

The wave is an important hydrological element in marine research. Accurately describing the characteristics of waves is therefore significant to the study of marine power. The contents of this article are as follows: (1) a wave height measurement system using binocular cameras is proposed, and the small tank experiments are conducted to prove the efficacy of the proposed system; (2) based on the scale invariant feature transition (SIFT) algorithm, sub-pixel Harris corners are calculated in the difference-of-Gaussian (DOG) space to locate key points more accurately; and (3) a bi-directional epipolar constraint is employed to decrease the mismatch rate and computation time.

scholarly journals A FAST APPROACH FOR STITCHING OF AERIAL IMAGES

2016 ◽  
Vol XLI-B3 ◽  
pp. 769-774 ◽  
Author(s):  
A. Moussa ◽  
N. El-Sheimy
Keyword(s):  
Computation Time ◽  
Image Data ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Stitching ◽  
Scale Invariant ◽  
Main Challenge ◽  
Transformation Parameters ◽  
Automatic Image Stitching ◽  
Scale Invariant Feature

The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image’s coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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