Hui Lu

Beihang University (BUAA) | BUAA · Department of Electronic and Information Engineering

The cooperative exploration in unknown environment is a tough task for the multi-robot system. The imbalance of individual workload caused by the weak autonomous cooperation ability will affect the working efficiency of the multi-robot system. In this paper, a two-objective cooperative exploration algorithm (TOCEA) is proposed, where the working ef...

Multipath effect is one crucial impact on the performance of wireless communication systems. Multipath measurement has attracted more attention. While in the multipath measurement system, the band-limited issue causes chip distortion and brings errors in results. This paper proposed a chip distortion error post-processing method for multipath estim...

To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the cha...

Various niching methods have been widely adopted for solving multimodal optimization. However, keeping a balance between exploitation and exploration is still a tough task for designers of multimodal optimization algorithms. An essential niching method is encouraged to deal with optimization problems. In this paper, we proposed an adaptive niching...

For an imbalanced dataset, traditional machine learning methods usually misclassify minority samples due to the indicator evaluating classification accuracy biased toward majority class. To address the issue, manifold cluster-based evolutionary ensemble imbalance learning is proposed, with the purpose of providing a more effective framework for bui...

For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a...

Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to ins...

Fast path planning in unknown environment is important to reduce the loss of human and material resources. To reduce planning time while obtaining a short path, this paper proposes a Bidirectional Associative Learning Algorithm (BALA). In the proposed algorithm, an episode is defined as a bidirectional movement between the start point and the targe...

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particula...

Parameter control is critical for the performance of any swarm intelligence algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a swarm intelligence algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy...

Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method...

In order to solve the problem of slow convergence rate and long planned path when the robot plans a path in unknown environment by using Q-learning algorithm, we propose an Experience-Memory Q-Learning (EMQL) algorithm based on the continuous update of the shortest distance from the current state node to the start point. The autonomous learning abi...

Maintenance support resource scheduling (MSRS) problem has attracted increasing attention in modern battle. Its objective is minimizing execution time of allocating multiple resources from multiple supply centers to tasks. As a NP-hard problem, there exist various constraints in the MSRS problem, including resource demands of tasks, resource reserv...

Combinatorial optimization problems (COPs) are discrete problems arising from aerospace, bioinformatics, manufacturing, and other fields. One of the classic COPs is the scheduling problem. Moreover, these problems are usually multimodal optimization problems with a quantity of global and local optima. As a result, many search algorithms can easily...

The feature extraction problem for flight data has aroused increasing attention in the practical and the academic aspects. It can reveal the inherent correlation relation among different parameters for the conditional maintenance of the aircraft. However, the high-dimensional and continuous features in the real number field bring challenges to the...

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications. It is one kind of combinatorial optimization problem with the property of multimodal. There are a lot of global optima and local optima in its huge solution space. Brain storm optimization algorithm (BSO) is a new...

Brain storm optimization (BSO) algorithms is a framework that indicates algorithms using converging operation and diverging operation to locate the optima of optimization problems. Hundreds of articles on the BSO algorithms have been published in different journals and conference proceedings, even though there are more questions than answers. In th...

Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series datasets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are...

Fitness landscape analysis has been effectively used to analyze the characteristics of combinatorial optimization problems (COPs) while investigating the behavior of applied algorithms. However, most COPs are high dimensional and an intuitive understanding of traditional fitness landscape analysis is challenging. To address this issue, the present...

Job-based scheduling problems have inherent similarities and relations. However, the current researches on these scheduling problems are isolated and lack references. We propose a unified solution framework containing two innovative strategies: the packet scheduling strategy and the greedy dispatching rule. It can increase the diversity of solution...

Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, the particle swarm optimization algorithm has attracted many researchers’ attention. Through the convergent operation and divergent operation, individuals in PSO group and diverge in...

The test task scheduling problem (TTSP) is a combinatorial optimization problem still under investigation. A multi-objective evolutionary algorithm based on Pareto prediction (PP-MOEA) is proposed fully considering the characteristics of TTSP. In a multi-objective TTSP, multiple solutions in the decision space correspond to a point in the objective...

Job-based scheduling problems as one type of combinational optimization problem have relevance in several aspects. However, the current studies on these scheduling problems are isolated and lack mutual references and general analysis. To address this issue, we propose several evaluation measures to explore the similarities and differences among dif...

Particle swarm optimization has been applied to solve many optimization problems because of its simplicity and fast convergence performance. In order to avoid precocious convergence and further improve the ability of exploration and exploitation, many researchers modify the parameters and the topological structure of the algorithm. However, the bou...

Dynamic optimization problems (DOPs) have attracted increasing attention in recent years. Analyzing the fitness landscape is essential to understand the characteristics of DOPs and may provide guidance for the algorithm design. Existing measures for analyzing the dynamic fitness landscape, such as the dynamic fitness distance correlation and the se...

Demand response is an essential issue in smart grid. The central problem is balancing the user cost and the social utility. We focus on the multi-objective energy consumption scheduling problem based on the third-party management. The aim is to provide diverse, uniformly-distributed, and accurate solutions to the third-party decision-maker. The nov...

The multi-objective energy consumption scheduling problem based on the third-party management is one essential issue of smart grid. The minimal energy cost and the maximal utility are two optimization objectives. One characteristic of the multi-objective energy consumption scheduling problem is that the magnitude difference between the two objectiv...

The optimization of test task scheduling problem (TTSP) is an important issue in automatic test system (ATS). TTSP is a complex combination optimization problem and includes two sub-problems. They are test task sequencing and test scheme combination. According to the characteristic of TTSP, a non-integrated algorithm based on estimation of distribu...

The greedy strategy of geographical routing may cause the local minimum problem when there is a hole in the routing area. It depends on other strategies such as perimeter routing to find a detour path, which can be long and result in inefficiency of the routing protocol. In this paper, we propose a new approach called Intermediate Target based Geog...

Test task scheduling problem (TTSP) is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme...

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close...

The Global Environment for Network Innovations (GENI) is a virtual laboratory which provides the infrastructure and resources for setting up network experiments. At present, GENI experimenters need to draw the topology in detail with a tool such as Flack, describing every node and every link in the experiment. This is not a problem for small-scale...

Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects on EAs. This paper focuses on exploring the effects of chaotic maps and giving comprehensive guidance for improving multiobjective ev...

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorit...

Solving a task scheduling problem is a key challenge for automatic test technology to improve throughput, reduce test time, and operate the necessary instruments at their maximum capacity. Therefore, this paper attempts to solve the automatic test task scheduling problem (TTSP) with the objectives of minimizing the maximal test completion time (mak...

A new code concept is used for the L1 civil (L1C) signal of the global positioning system (GPS). The generation of L1C codes is quite different from the generation of traditional ranging codes. Thus, it is necessary to find a method for the correct generation to pave the way for future research. L1C codes are based on only one Legendre sequence whi...

Task scheduling problem is one of the key technologies for automatic test systems. This study proposes a novel and integrated multicriteria decision mechanism called the chaotic non-dominated sorting genetic algorithm plus analytic hierarchy process (CNSGA-AHP) for the automatic test task scheduling problem (ATSP). This mechanism contains two parts...

Parallel test task scheduling is one of the key technologies used in parallel test. A hybrid Particle swarm optimization and Taboo search algorithm (PSO-TS) is proposed to solve parallel test task scheduling with constraints. The scheduling process is divided into two subproblems: task scheduling sequence with constraints and resource optimization....

To investigate the value and clinical application of the Three-Step Sperm Retrieval method in improving the sperm retrieval rate for non-obstructive azoospermia (NOA) patients. Seventy-three NOA patients underwent Three-Step Sperm Retrieval in the following order of procedures: testicular fine needle aspiration (TFNA), testicular sperm extraction (...

To analyze the distribution characteristics of the main semen parameters of healthy semen donors and normal fertile men in Shanghai, compare the semen quality between the two groups, and investigate the normal reference values of the semen parameters of the fertile population in Shanghai. We obtained semen samples from 100 healthy donors and 41 fer...

To study the effect of direct fumigation on the post-thaw recovery rate of cryopreserved spermatozoa, and to search for a best method for human sperm cryopreservation. We collected semen samples from 100 donors conforming to the normal reference values in WHO Laboratory Manual for the Examination and Processing of Human Semen (5th Ed), divided them...

The second Zagreb index of a graph G is an adjacency-based topological index, which is defined as ∑ uv∈E(G) (d(u)d(v)), where uv is an edge of G,d(u) is the degree of vertex u in G. In this paper, we consider the second Zagreb index for bipartite graphs. Firstly, we present a new definition of ordered bipartite graphs, and then give a necessary con...

Flight Parameters stage classification is the premise of the fault diagnosis and trend forecast based on flight parameters. Stage classification belongs to the classification optimization problem of multi-attribute data through analysis the flight data. This paper carried out the research for the two-class classification based on the semi-supervise...

It is an important direction for the development of aircraft health management system to utilize flight data to conduct fault diagnosis and trend prediction. The status of fault is usually correlated with specific flight stage, but flight data recorded by the recorder usually have no corresponding stage feature, which presents great challenges to t...

Steganography it is an art of secret transmission by embedding data into multimedia and steganalysis is an attacking technology on steganography, which can be used to judge the existence of secret data, decode and even destroy it. An effective steganalysis method should measure the characteristics of embedded messages and digital image and give dec...

A systematic steganographic detecting framework is analyzed and some factors that make detection effective are given. As embedded message is important object in steganography, different types of embedded massage are analyzed. Statistical characteristics of embedded message give clue to steganalysis. An analytical method, generalized character seque...

Flight data is a kind of time correlated observations to each other as a time series in nature, which is not comply with some certain models, detecting outliers from the time series is a big challenge. In this article an effective two-sided median filtering method to detect outliers in flight data is proposed, which makes use of the median computed...