Songfeng Lu

Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization

This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. To show feasibility and effectiveness of the proposed method, different case studies, such as economic load scheduling (ELS), economic emission scheduling (EES) and combined economic emission scheduling (CEES) in hydrothermal scheduling, are carried out and the test results are compared with those of other methods reported in the literature. It is also very important to note that the proposed method is capable of yielding higher-quality solutions while strictly satisfying all constraints of the test system.

Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling

Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions.

Quantum decision tree classifier

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object.

Coevolutionary Quantum-Behaved Particle Swarm Optimization with Hybrid Cooperative Search

Based on the previous introduced quantum-behaved particle swarm optimization (QPSO), in this paper, a revised novel QPSO with hybrid cooperative search is proposed. Taking full advantages of the characteristics of mutualism among swarms, the cooperative search is carried out to improve the diversity of the swarms, so as to help the system escape from local optima and converge to global optima. With the help of the cooperative search among different swarms, hybrid cooperative quantum-behaved particle swarm optimization (HCQPSO) makes the swarms more efficient in global search. The experimental results on test functions show that HCQPSO with hybrid cooperative search outperforms the QPSO. In addition, simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

A density-based approach for text extraction in images

In this paper we describe a new approach to distinguish and extract text from images with various objects and complex backgrounds. The goal of our approach is to present characters in images with clear background and without other objects. The proposed approach mainly includes two steps. Firstly, a density-based clustering method is employed to segment candidate characters by integrating spatial connectivity and color feature of characterspsila pixels. In most images, colors of pixels in one character are commonly non-uniform due to the noise. So a new histogram segmentation method is proposed in this step to obtain the color thresholds of characters. Secondly, priori knowledge and texture-based method are performed on the candidate characters to filter the non-characters. Experimental results show that the proposed approach has a good performance in character extraction rate.

Image Analysis of the Relationship between Changes of Cornea and Postmortem Interval

Opacity of the cornea is one of the important indices for estimating time of death. Now the work is done by forensic medical experts. An unbiased estimation method is needed. This paper proposed a method for finding the relationship between changes of cornea and postmortem intervals by processing and analyzing images. Firstly, a histogram based image segmentation method is proposed to extract corneal regions from pictures of rabbits eye. Secondly, texture and color features are used to describe the extracted corneal regions. Those features are carefully chosen to represent the changes of cornea in different postmortem intervals. A KNN classifier is used to reveal the association of image features and postmortem intervals. The experimental results show that cornea image features can be used to automatically estimate postmortem interval.

Quantum-Behaved Particle Swarm Optimization with Cooperative-Competitive Coevolutionary

Based on the previous introduced quantum-behaved particle swarm optimization (QPSO), in this paper, a revised QPSO with hybrid cooperative and competitive mechanism is proposed. The cooperative and competitive mechanism improves the diversity of the swarm, so as to help the system escape from local optima and converge to global optima. Take full advantages of the cooperative and competitive search among different swarms, cooperative competitive quantum-behaved particle swarm optimization (COQPSO) makes the swarms more efficient in global search. The experimental results on test functions show that COQPSO with cooperative and competitive mechanism outperforms the QPSO and even can search out the minimum value for some test functions.

Adiabatically implementing quantum gates

We show that, through the approach of quantum adiabatic evolution, all of the usual quantum gates can be implemented efficiently, yielding running time of order O(1). This may be considered as a useful alternative to the standard quantum computing approach, which involves quantum gates transforming quantum states during the computing process.

Quantum differential cryptanalysis

In this paper, we propose a quantum version of the differential cryptanalysis which offers a quadratic speedup over the existing classical one and show the quantum circuit implementing it. The quantum differential cryptanalysis is based on the quantum minimum/maximum-finding algorithm, where the values to be compared and filtered are obtained by calling the quantum counting algorithm. Any cipher which is vulnerable to the classical differential cryptanalysis based on counting procedures can be cracked more quickly under this quantum differential attack.

Partial adiabatic quantum search algorithm and its extensions

In this paper, we again discuss quantum search by partial adiabatic evolution, which was first proposed by Zhang et al. In contrast to previous conclusions, we show that partial adiabatic search does not improve the time complexity of a local adiabatic algorithm. Firstly, we show a variant of this algorithm and find that it is equivalent to the original partial adiabatic algorithm, in the sense of the same time complexity. But we give two alternate viewpoints on this “new” adiabatic algorithm—“global” adiabatic evolution and local adiabatic evolution approaches, respectively. Then, we discuss how global and local adiabatic quantum search can be recast in the framework of partial adiabatic search algorithm. It is found here that the former two algorithms could be considered as special cases of the later one when appropriately tuning the evolution interval of it. Also this implies the flexibility of quantum search based on partial adiabatic evolution.