Point Selection Problem Research Articles

A Deep Neural Network-Based Approach to Media Hotspot Discovery

In recent years, with the rapid development of social network media, it has become a valuable research direction to quickly analyze these texts and find out the current hotspots from them in real time. To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. Then, the K-means algorithm is optimized for the initial point selection problem, and a clustering algorithm based on the maximum density selection method in the moving range is proposed. Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. The accuracy of the proposed media hotspot discovery method and the comprehensive evaluation of the computing time have been verified experimentally.

Fractal analysis of pore structures in transitional shale gas reservoirs in the Linxing area, Ordos Basin

Studying complex pore structures and fractal characteristics of gas shale provides significant guidance for clarifying the mechanism of shale gas accumulation and realizing its efficient development. In this paper, 12 samples of Taiyuan Formation shale are used as the research object, and the fractal theory is combined with mercury intrusion porosimetry and N2 adsorption technology to innovatively solve the problem of splicing point selection, which can reveal the full-scale pore size distribution of shale. The results demonstrate that the most common types of pores in the chosen samples are pores between or within clay minerals, micropores and mesopores inside organic matter, and microfractures, based on scanning electron microscopy imagery analyses. The pores of shale samples have fractal geometries. The fractal dimension DM1 values in the mercury intrusion porosimetry experiments range from 2.3060 to 2.6528. Two fractal dimensions, DN1 and DN2, may be obtained using the Frenkel-Halsey-Hill fractal method. DN1 values vary from 2.4780 to 2.6387, whereas DN2 values range from 2.5239 to 2.7388. Most macropores in shale samples have a size range of about 0.2 mm, with a wide pore size distribution, and the largest peak of the micro-mesopore volume is generally about 50 nm. The fractal dimension correlates positively with the corresponding pore volume, although the correlation between volume and composition is weak. The relatively strong correlation between fractals and the basic compositions of shale proves the fractal theory’s relevance in defining pore inhomogeneity. This study would contribute to the development of a fractal perspective-based method for pore splicing while also expanding our understanding of pore morphology and structure in transitional shale.

An improved adaptive surrogate model and application in thermal management system design

It is time-consuming to obtain the responses of real or high-precision simulation models in complex engineering problems. The surrogate model based on sample points can approximate the real model and thus greatly reduce the computational effort. The selection of sample points has a great influence on the accuracy of the surrogate model. Aiming at the problem of sample point selection in the process of establishing surrogate model, an adaptive sampling method based on distance density and local complexity is proposed. In this method, distance density is used to quantify the sparsity of new sample points, and local complexity is applied to quantify the change complexity of response values near new sample points. The high-quality of new sample point is added to improve the accuracy of the surrogate model. This method is compared with two other classical adaptive sampling methods through nine test functions. The results show that this method can make the new sample point more distributed in the key area of sample space, so that fewer sample points are used to establish a high-precision surrogate model. Finally, the effectiveness of this method is verified through an optimization of the thermal management system design for liquid-cooled cylindrical batteries.

Joint Transmission Reception Point Selection and Resource Allocation for Energy-Efficient Millimeter-Wave Communications

In this paper, we study the joint transmission reception point (TRP) selection and resource allocation problem to maximize the weighted sum energy efficiency under imperfect channel state information (CSI) for an uplink mmWave network considering beam training overheads and delay constraints with and without inter-beam interference, respectively. First, to guarantee the delay constraints of all UEs, we use the worst-case approach to cast the considered problem to a deterministic optimization problem. Then, for the scenario without inter-beam interference, the product of transmitting and receiving beams and the power allocation scheme for each pair of UEs and TRPs are derived iteratively, where the former is obtained by some relaxation operations under given power allocation policy, and the latter is searched by bisection under given beamwidth selection policy. After that, Kuhn-Munkres algorithm is used to find the joint TRP selection and resource allocation policy. For the scenario with inter-beam interference, coalition game is proposed to solve the considered problem, where UEs form coalitions to select their respective serving TRPs. At each iteration of the coalition game, the beamwidth control and power allocation are performed successively, where the transmitting and receiving beamwidths are solved by particle swarm algorithm under given power allocation policy, and the power allocation strategy is designed by a novel cooperative cost mechanism under given beamwidth selection policy. Finally, extensive simulation results are provided to verify that the proposed schemes achieve better weighted sum energy efficiency than existing schemes.

Resource Management for Maximizing the Secure Sum Rate in Dense Millimeter-Wave Networks

Due to the densifying of users and the reflections caused by small-scale objects, secure communication is an important research problem in dense millimeter-wave (mmWave) networks. In this paper, we investigate the resource management problem of joint transmission reception point (TRP) selection, power control and beamwidth selection for maximizing the secure sum rate in the dense mmWave network, while considering beamforming training overheads, blockage effect in mmWave communications, and imperfect channel state information (CSI) from a legitimate user equipment (LUE) to the corresponding malicious user equipment (MUE). To handle the problem with low complexity, we first break it down into two subproblems (i.e., TRP selection and joint power control and beamwidth allocation), and propose a two-stage game based decentralized resource management approach to solve them iteratively, where in the first stage we propose a matching game based distributed TRP selection algorithm to solve the first subproblem in which novel utilities for both LUEs and TRPs are designed to handle the high directivity transmission problem, and in the second stage we propose a weekly acyclic game based two-dimensional-strategy concurrent better response algorithm for the second subproblem to deal with the huge strategy space, which has been proved to converge to an invariant two-dimensional-strategy Nash equilibrium (NE). Moreover, to adapt to the three-dimensional optimization variables and the huge strategy space of the considered problem, we also propose a new three-dimensional-strategy iterative weekly acyclic game to solve it, where the three-dimensional optimization variables are optimized alternately during the decision-making process. Finally, extensive simulations are conducted to verify the effectiveness of the proposed schemes.

Optimising darts strategy using Markov decision processes and reinforcement learning

This article determines an aim point selection strategy for players in order to improve their chances of winning at the classic darts game of 501. Although previous studies have considered the problem of aim point selection in order to maximise the expected score a player can achieve, few have considered the more general strategical question of minimising the expected number of turns required for a player to finish. By casting the problem as a Markov decision process and utilising the reinforcement learning method of value iteration, a framework is derived for the identification of the optimal aim point for a player in an arbitrary game scenario. This study represents the first analytical investigation of the full game under the normal game rules, and is, to our knowledge, the first application of reinforcement learning methods to the optimisation of darts strategy. The article concludes with an empirical study investigating the optimal aim points for a number of player skill levels under a range of game scenarios.

Isometric 3D Shape Partial Matching Using GD-DNA

Isometric 3D shape partial matching has attracted a great amount of interest, with a plethora of applications ranging from shape recognition to texture mapping. In this paper, we propose a novel isometric 3D shape partial matching algorithm using the geodesic disk Laplace spectrum (GD-DNA). It transforms the partial matching problem into the geodesic disk matching problem. Firstly, the largest enclosed geodesic disk extracted from the partial shape is matched with geodesic disks from the full shape by the Laplace spectrum of the geodesic disk. Secondly, Generalized Multi-Dimensional Scaling algorithm (GMDS) and Euclidean embedding are conducted to establish final point correspondences between the partial and the full shape using the matched geodesic disk pair. The proposed GD-DNA is discriminative for matching geodesic disks, and it can well solve the anchor point selection problem in challenging partial shape matching tasks. Experimental results on the Shape Retrieval Contest 2016 (SHREC’16) benchmark validate the proposed method, and comparisons with isometric partial matching algorithms in the literature show that our method has a higher precision.

Caching AP Selection and Channel Allocation in Wireless Caching Networks: A Binary Concurrent Interference Minimizing Game Solution

Caching in wireless networks can alleviate redundant data transmissions and can assist cell base stations in providing service. In this paper, we focus on the problem of joint caching access point (AP) selection and channel allocation for mobile users (MUs) in wireless caching networks. Considering MUs’ dynamic probabilistic content demands, an asymmetric weighted interference graph is proposed to analyze the asymmetric interference among MU and caching AP pairs. We formulate the joint caching AP selection and channel allocation as a nonlinear optimization problem and then present a binary concurrent interference minimizing game theoretical scheme, where MUs and caching APs are two kinds of players with different kinds of actions and utilities. Unlike existing hierarchical game approaches, MUs and caching APs act concurrently without hierarchical constraints. Then, a novel pure strategy binary Nash equilibrium (BNE) and a concurrent binary better reply algorithm are proposed for the above game. It is proved that at least one BNE exists and the reply algorithm converges to a BNE. Moreover, simulation results show that the proposed approach can mitigate interference effectively and achieve a lower global interference level than the traditional better reply algorithm.

Quality of Service Oriented Access Point Selection Framework for Large Wi-Fi Networks

This paper addresses the problem of access point (AP) selection in large Wi-Fi networks. Unlike current solutions that rely on received signal strength to determine the best AP that could serve a wireless user's request, we propose a novel framework that considers the quality of service (QoS) requirements of the user's data flow. The proposed framework relies on a function reflecting the suitability of a Wi-Fi AP to satisfy the QoS requirements of the data flow. The framework takes advantage of the flexibility and centralised nature of software defined networking (SDN). A performance comparison of this algorithm developed through an SDN-based simulator shows significant achievements against other state-of-the-art solutions in terms of provided QoS and improved wireless network capacity.

The Improved Ant Colony Optimization for the Multi-Dominated QoE Evaluation Point Selection Problem

The Improved Ant Colony Optimization for the Multi-Dominated QoE Evaluation Point Selection Problem

Optimal back-off point determination and controller weight selection for multivariate systems under finite-horizon control

Plant economic performance is most often related to the operating point, specifically the mean values of the process variables; meanwhile, most existing performance assessment techniques involve examining the variances or covariances of the controlled variables. A combined approach is to determine the appropriate trade-off between variances of different process variables in order to operate the plant at the point that provides maximum economic benefit while satisfying the operating constraints. This problem is referred to as the minimum backed-off operating point selection, and previous works have formulated it as a non-convex constrained optimization problem. In the current work, a new technique is introduced that can provide the optimal plant operating point. Additionally, this method provides the weights for a finite horizon controller that results in the optimal trade-off in process variable variances that will allow satisfaction of the operating constraints at the optimal operating point. In this method, the plant and disturbance models for the given process are used to generate data representing possible trade-offs between process variable standard deviations. Employing a piecewise linear regression to describe the sample points of this standard deviations data allows for the operating point selection problem to be solved as a small number of linear programs. The advantages of this approach are demonstrated through the use of mathematical and simulation case studies.

A general method for analog test point selection using multi-frequency analysis

An important problem that arises in analog testing for fault dictionary approach is the test point selection. It consists of selecting a minimum set of test points to achieve the highest percentage of isolation. By using the concepts of ambiguity set and integer-coded dictionary, it can be formulated as a combinatorial optimization problem. On the basis of the formulation, this paper develops a general and accurate method for test point selection. This is achieved by presenting a new ambiguity set partition method based on hierarchical clustering and an improved entropy index method. Besides, multi-frequency analysis is also incorporated in the described method. The proposed method is tested and compared with exhaustive search through its application to two benchmark circuits. The results indicate that, compared with the extant methods, our method search for a test point set that not only includes a minimum number of test points but also has a good performance in terms of diagnostic accuracy and fault isolation rate. Meanwhile, the proposed method can be extended to deal with the problem of test point selection for some specific fault model.

Chaotic binary bat algorithm for analog test point selection

A set of novel methods are proposed to optimize analog test point selection in this paper. We have introduced chaos and elitist strategy into the binary bat algorithm so as to increase its global search mobility and effectiveness for robust global optimization. In the present study, nine well known chaotic maps are introduced and used to construct the chaotic binary bat algorithm (CBBA) respectively. As a result, we have developed nine different CBBAs and for the first time applied them to deal with the analog test point selection problem. The attractiveness of our proposed CBBA mainly lies in two aspects: the utilization of chaotic maps to tune the BBA parameter and the application of elitist strategy to store all the possible global best solutions. These improvements obviously enhance the performance of BBA and make our new algorithms (CBBAs) get all the best solutions (usually more than one) easily and effectively, which will give us more possible choices in practice. Analog circuits' examples and a group of statistical experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithms. The other reported algorithms are also used to do the comparison. The results indicate that the proposed algorithms have excellent performance in finding the optimum test point sets. Therefore, they are good solutions and applicable to actual circuits and engineering practice.

Automatic Feature Point Selection through Hybrid Metaheauristics based on Tabu Search and Memetic Algorithm for Augmented Reality

Abstract In recent years, augmented reality (AR) have received considerable attention along with the popularization of mobile devices (for example, iPhone, Android smartphone). However, under active use of AR applications on mobile devices, we need to take account of processing speed and precision due to mobile processors. Therefore, many researchers have studied about the algorithms which are computationally cheap and high accuracy. Under these circumstances, in this research, we formulate the new feature point selection problem as combinational optimization one and propose a hybrid metaheuristics through tabu search and memetic algorithm for the formulated feature point selection problem. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the proposed method.

Optimizing LBP Structure For Visual Recognition Using Binary Quadratic Programming

Local binary pattern (LBP) and its variants have shown promising results in visual recognition applications. However, most existing approaches rely on a pre-defined structure to extract LBP features. We argue that the optimal LBP structure should be task-dependent and propose a new method to learn discriminative LBP structures. We formulate it as a point selection problem: Given a set of point candidates, the goal is to select an optimal subset to compose the LBP structure. In view of the problems of current feature selection algorithms, we propose a novel Maximal Joint Mutual Information criterion. Then, the point selection is converted into a binary quadratic programming problem and solved efficiently via the branch and bound algorithm. The proposed LBP structures demonstrate superior performance to the state-of-the-art approaches on classifying both spatial patterns in scene recognition and spatial-temporal patterns in dynamic texture recognition.

Access Point Selection under QoS Requirements in Variable Channel-Width WLANs

This work investigates the access point (AP) selection problem in Variable channel-width WLANs (V-WLANs) using an evolutionary game-theoretic approach. A practical utility function is designed for each station in which the QoS requirement and energy consumption are considered. An Evolutionary Stable Strategy (ESS) of the game corresponds to the desired AP selection outcome, which remains stable even if the stations occasionally select inappropriate APs. A distributed learning algorithm based on replicator dynamics is developed to obtain the ESS with only local information. Simulation results verify the convergence and effectiveness of our algorithm.

Quantum-inspired evolutionary algorithm for analog test point selection

An important problem that arises in fault diagnosis of analog circuit for fault dictionary technique is the test point selection, which is known to be NP-hard. This paper develops a mathematical optimization model for analog test point selection (ATPS) problem and proposes a novel method to solve it based on quantum-inspired evolutionary algorithm (QEA). The proposed method uses the solution produced by the inclusive algorithm to initialize Q-bit individuals and presents a new fitness function to search the global minimum test point set. In addition, an approach for dynamically determining the magnitude of rotation angle is introduced to accelerate the convergent speed. The efficiency of the proposed algorithm is proven by one practical analog circuit example and a group of statistical experiments. Results show that the proposed algorithm, compared with other methods, finds the global minimum set of test points more efficiently and more accurately.

A Feature Selection process Optimization in multi-class Miner for Stream Data Classification

Multi-class miner resolves the problem of feature evaluation, data drift and concept evaluation of stream data classification. The process of stream data classification in multi-class miner based on ensemble technique of clustering and classification on feature evaluation technique. The process of feature evaluation technique faced a problem of correct point selection of cluster centre for the process of data grouping. For the proper selection of features point we used optimization technique for feature selection process. The feature selection process based on advance genetic algorithm (AGA). The advance genetic algorithm poses a process of feature point for neighbour class detection for finding a correct point in classification. Our proposed algorithm tested on some well know data set provided by UCI machine learning repository. Our empirical evaluation result shows that better result in comparison of multi-class miner for stream data classification.

QoS-aware dynamic MAP selection schemes in HMIPv6 networks

We consider a dynamic Mobile Anchor Point (MAP) selection problem when there are both real-time and non-real time sessions in a Hierarchical Mobile IPv6 (HMIPv6) network. We propose schemes in which Mobile Nodes (MNs) holding real-time sessions register with the root MAP in a hierarchy of MAPs to reduce the inter-domain handovers while those with non-real time sessions select one either to balance the load or to reduce handover frequencies.Both the simulation and analytical results show the effectiveness of the proposed schemes with respect to the number of inter-domain handovers, to the average signaling cost, and to the load distribution. In addition, we could also confirm that our MAP selection schemes provide better QoS to the MNs holding real-time sessions, in that they reduce the inter-domain handovers for those MNs and the average handover delay, resulting in a shorter service disruption.

Economic back-off selection based on optimal multivariable controller

This paper discusses the minimum backed off operating point selection problem based on process economics. In this work, we consider the case where the nominal operating point is not completely constrained, i.e., there are some unconstrained degrees of freedom or manipulations available. In this regard, we propose a stochastic formulation that ensures feasible dynamic operating region within the prescribed confidence limit. Furthermore, the formulation also finds a suitable multivariable controller to achieve economic benefits. The problem is nonlinear and non-convex and hence an iterative solution procedure is proposed such that at each step in the iteration, a convex problem is solved. Finally, the approach is illustrated using an evaporation process.