An Algorithmic Weakening of the Erd?s-Hajnal Conjecture

We study the approximability of the Maximum Independent Set (MIS) problem in H-free graphs (that is, graphs which do not admit H as an induced subgraph). As one motivation we investigate the following conjecture: for every fixed graph H, there exists a constant ? > 0 such that MIS can be n^{1-?}-approximated in H-free graphs, where n denotes the number of vertices of the input graph. We first prove that a constructive version of the celebrated Erd?s-Hajnal conjecture implies ours. We then prove that the set of graphs H satisfying our conjecture is closed under the so-called graph substitution. This, together with the known polynomial-time algorithms for MIS in H-free graphs (e.g. P?-free and fork-free graphs), implies that our conjecture holds for many graphs H for which the Erd?s-Hajnal conjecture is still open. We then focus on improving the constant ? for some graph classes: we prove that the classical Local Search algorithm provides an OPT^{1-1/t}-approximation in K_{t, t}-free graphs (hence a ?{OPT}-approximation in C?-free graphs), and, while there is a simple ?n-approximation in triangle-free graphs, it cannot be improved to n^{1/4-?} for any ? > 0 unless NP ? BPP. More generally, we show that there is a constant c such that MIS in graphs of girth ? cannot be n^{c/(?)}-approximated. Up to a constant factor in the exponent, this matches the ratio of a known approximation algorithm by Monien and Speckenmeyer, and by Murphy. To the best of our knowledge, this is the first strong (i.e., ?(n^?) for some ? > 0) inapproximability result for Maximum Independent Set in a proper hereditary class

Negative positional externality of conspicuous and positional goods on society: An empirical analysis on income and clothing consumption for 9 EU countries.

Objectives The main objectives of this study were first, through secondary sources, to analyze the way positional externality exist and its negative impact of on society, focusing on conspicuous and positional goods. Second, it tries to find the empirical evidence for the effect of positional externality in income and clothing consumption. Third, the thesis discuss various ways to reduce positional externality. Summary The thesis first analyzes the literatures which shows the negative impact of conspicuous and positional goods on society. Then, using data from Life in Transition survey III, the thesis tests four hypotheses on the effect of positional externality on life satisfaction. Two hypotheses are related to income comparisons, while the others are related to clothing consumption. Finally, the author discusses some of the measures to reduce the effect of positional externality. Conclusions The main findings shows controlled for income of each individual, GDP per capita and average clothing consumption has negative correlation with life satisfaction of each individual. This result shows the existence of positional externality and support the argument that positional externality has a negative impact on society

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system

Energy-efficient Trajectory Design for UAV-enabled Wireless Communications with Latency Constraints

This paper studies a new energy-efficient unmanned aerial vehicle (UAV)-enabled wireless communications, where the UAV acts as a flying base station (BS) to serve the ground users (GUs) within some predetermined latency limitations, e.g., requested timeout (RT). Our goal is to design the UAV trajectory to minimize the total energy consumption while satisfying the RT requirement from every GU, which is accomplished via two consecutive subproblems: traveling time minimization and energy minimization problems. Firstly, we propose two exhaustive search and heuristic algorithms based on the traveling salesman problem with time window (TSPTW) in order to minimize the UAV’s traveling time without violating the GUs’ RT requirements. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm achieves a trade-off between the performance and complexity. Secondly, we minimize the total energy consumption, for a given trajectory, via a joint optimization of the UAV’s velocity along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. In particular, it is shown that the proposed solutions outperform the reference in terms of both energy consumption and outage performance

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Damage detection for a large-scale truss bridge using Tranmissibility and ANNAOA

In this paper, we propose an efficient approach to enhance the capacity of Artificial Neural Network (ANN) to deal with Structural Health Monitoring (SHM) problems.  Over the last decades, ANN has been extensively utilized for damage detection in structures. In order to identify damages, ANN frequently utilizes input information that is based on dynamic features such as mode shapes or natural frequencies. However, this type of data may not be able to detect minor damages if the structural defects are insignificant. To transcend these limitations, in this work, we propose utilizing transmissibility to create input data for the input layer of ANN. Moreover, to deal with local minimum problems of ANN, a combination between the Arithmetic Optimization Algorithm (AOA) and ANN is proposed. The global search capacity of AOA is employed to remedy the local minima of ANN. To evaluate the effectiveness of the proposed approach, a numerical model with different damage scenarios is considered. The suggested approach detects damage location precisely and with higher severity detection precision than the conventional ANN method