先进控制技术在热风炉燃烧优化控制系统中的应用

结合热风炉的实际运行情况,采用先进的燃烧优化控制技术,实现了热风炉燃烧系统的自动控制,通过动态优化算法,发现了热风炉的最佳空燃比,实现了炉内燃烧过程的优化控制。运行结果表明,该技术提高了穹顶和排气温度的控制精度,降低了燃气消耗。该控制技术可以明显提高热风炉的经济效益,具有巨大的推广价值。

一种水声通信Turbo均衡中的软迭代信道估计算法

Turbo均衡技术是水声相干通信克服信道多径、消除码间干扰(ISI)的有效工具。Turbo均衡实际使用时需要对时变、多径信道进行良好的估计。为了提高信道估计的效果,该文基于时变横向滤波和相位旋转信道模型,提出一种水声通信Turbo均衡中的软迭代信道估计算法。该算法采用快速自优化最小均方算法得到各数据符号处的横向滤波器系数矢量并与二阶锁相环联合优化计算。通过仿真比较,该算法明显优于硬迭代信道估计算法,且相位估计性能优于其他文献中的软迭代信道估计算法。在海上试验中,水声通信距离5 km,方向近似垂直,接收阵起伏周期10 s,起伏幅度5 m左右,在此情况下进行数据采集。将该算法用于对海试数据的单通道Turbo均衡处理,实现无误码输出,验证了所提算法在软迭代信道相位估计方面的优势。

一种用于水声相干通信的快速自适应自最佳均衡器(英文)

多通道判决反馈均衡器(MC-DFE)是水声相干通信克服信道多径效应、消除码间干扰(ISI)的主要手段。本文提出了一种用于水声相干通信系统的分集合并自最佳自适应多通道判决反馈均衡器。该算法将快速自优化LMS分集合并(FOLMSDC)算法、快速自优化LMS(FOLMS)算法和快速自优化LMS相位补偿(FOLMSPC)算法有机地结合在一起。该算法一方面可以进一步减少运算复杂度,另一方面,由于算法中的步长因子和相位跟踪因子可以自适应地更新,因此算法可以更好地跟踪水声信道的变化,进一步提高通信系统的接收性能。仿真结果表明,本文提出的算法的性能优于现有算法。

BLDC电机温度退化多段Wiener过程建模

无刷直流(BLDC)电机应用广泛,其温度退化过程呈现多段性,需建立多段退化模型,而模型参数较多时,参数估计过程对初始值敏感且易陷入局部最优。首先,针对电机的加速退化数据进行研究,采用正态加权平均(Gauss滤波)的方式滤波,改进实际数据在模型参数的估计中的应用。然后,引入转换函数对Wiener模型改进,建立多段Wiener模型。其次,以极大化似然函数进行参数估计,计算时采用改进粒子群优化(PSO)算法得到估计值,对比非线性模型的残差的正态性,同时分析各模型理论寿命分布及实际该批次失效分布,确定多段模型合理性;得到的模型结果说明电机在退化过程中发生了退化机理的改变,且变换速度快。最后,以非线性模型不同时刻的寿命分布给出该应力下电机的寿命预测,这对电机的预测与健康管理(PHM)有重要意义。

Adaptive Optimal Capacity Perception and Control for Wireless Multi-Hop Networks

In wireless multimedia communications, it is extremely difficult to derive general end-to-end capacity results because of decentralized packet scheduling and the interference between communicating nodes. In this paper, we present a state-based channel capacity perception scheme to provide statistical Quality-of-Service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 wireless multi-hop networks. The proposed scheme first perceives the state of the wireless link from the MAC retransmission information and extends this information to calculate the wireless channel capacity, particularly under a saturated traffic load, on the basis of the interference among flows and the link state in the wireless multi-hop networks. Finally, the adaptive optimal control algorithm allocates a network resource and forwards the data packet by taking into consideration the channel capacity deployments in multi-terminal or multi-hop mesh networks. Extensive computer simulations demonstrate that the proposed scheme can achieve better performance in terms of packet delivery ratio and network throughput compared to the existing capacity prediction schemes.

A Grey Wolf Optimization-Based Tilt Tri-rotor UAV Altitude Control in Transition Mode

To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.

Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm

A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.

一种基于机器自学习的LTE容量规划方法

随着业务的高速发展,对网络资源的需求也不断升高,有限的投资下,资源的投放应更加关注建网效益,更加精准的进行网络建设。因此,如何做好网络质量与网络效益之间的平衡,做好基于网络效益的无线网精确规划成为首要任务。文章提出一种基于机器自学习的LTE容量规划方法,实现从传统话务预测到机器化自动识别容量需求、再到后评估的自优化闭环管理。

A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation

By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

An improved self-adaptive membrane computing optimization algorithm and its applications in residue hydrogenating model parameter estimation

In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.

Real-valued multi-area self set optimization in immunity-based network intrusion detection system

The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.

Vibration mechanism analysis and algorithm optimization of contactor contact system

In order to solve the problem of vibration bounce caused by the contact between moving and stationary contacts in the process of switching on,two-degree-of-freedom motion differential equation of the contact system is established.Genetic algorithm is used to optimize the pull in process of AC contactor.The whole process of contact bounce was observed and analyzed by high-speed photography experiment.The theory and experimental results were very similar.The iron core has collided before the contact is separated,which further aggravates the contact bounce.When the iron core bounces collided again,the bounce of the contact was not affected.During the operation of the contactor,the movement of the moving iron core will cause slight vibration of the system.The contact bounce time and the maximum amplitude are reduced.The research results provide a theoretical basis for further control and reduction of contact bounce.

Research on Network-on-chip Dynamic and Adaptive Algorithm and Choice Strategy

With further increase of the number of on-chip device, the bus structure has not met the requirements. In order to make better communication between each part, the chip designers need to explore a new structure to solve the interconnection of on-chip device. The paper proposes a network-on-chip dynamic and adaptive algorithm which selects NoC platform with 2-dimension mesh as the carrier, incorporates communication energy consumption and delay into unified cost function and uses ant colony optimization to realize NOC map facing energy consumption and delay. The experiment indicates that compared with random map, single objective optimization can separately saves （30% - 47 %） and （ 20% - 39%） in communication energy consumption and execution time compared with random map, and joint objective optimization can further excavate the potential of time dimension in mapping scheme dominated by the energy.

Intelligent Metal Detection and Disposal Automation Equipment Based on Geometric Optimization Driving Algorithm

In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.

Optimized Ewald sum for electrostatics in molecular self-assembly systems at interfaces

We extend the recent formulation of the Ewald sum for electrostatics in a two-dimensionally periodic three-dimensional multi- atom layer or two-dimensional single-atom layer system with a rectangular periodic boundary condition （J Chem Theory, Comput, 2014, 10： 534-542） to that with a parallelogrammic periodic boundary condition in general. Following the discussion of an efficient implementation of the formula, we suggest a simple setup of parameters using a relatively smaller screening factor and the associated larger real space cutoff distance to reach an optimized algorithm of an order N computational cost. The connection between the previous application of the Ewald sum to ionic crystal systems and the future application to mo- lecular self-assembly or disassembly systems on solid surfaces or at liquid-liquid interfaces ate illustrated to demonstrate the applicability of the present work to simulate the self-assembly process and to produce dynamical, structural and thermody- namic properties of experimental self-assembly systems of interest.

Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum(minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization(SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.

An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive en- gineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization （E-HAM） is first proposed in this work. Important region update method （IRU） and different sampling size strategies are proposed in the opti- mization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be up- dated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson＇s ratio （NPR） crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive prob- lems and can be widely used in engineering industry.

Graph and Hint Based Algorithm for Machining Feature Automation Recognition and Mapping

Feature recognition is a process of extracting machining features which has engineering meaning from solid model, and it is a key technology of CAD/CAPP/CAM integration. This paper presents an effective and efficient methodology of recognizing machining feature. In this approach, features are classified into two categories： pocket feature and predefined feature. Different feature type adopts its special hint and heuristic rule, and is helpful to recognize intersection feature. Feature classification optimizes search algorithm and shortens search scope dramatically. Meanwhile, extension and split algorithm is used to handle intersecting feature. Moreover, feature mapping based on machining knowledge is introduced to support downstream application better. Finally, case studies with complex intersecting features prove that the developed approach has stronger recognizing ability.

Identification of a combination of SNPs associated with Graves' disease using swarm intelligence

Graves' disease,the production of thyroid-stimulating hormone receptor-stimulating antibodies leading to hyperthyroidism,is one of the most common forms of human autoimmune disease.It is widely agreed that complex diseases are not controlled simply by an individual gene or DNA variation but by their combination.Single nucleotide polymorphisms(SNPs),which are the most common form of DNA variation,have great potential as a medical diagnostic tool.In this paper,the P-value is used as a SNP pre-selection criterion,and a wrapper algorithm with binary particle swarm optimization is used to find the rule for discriminating between affected and control subjects.We analyzed the association between combinations of SNPs and Graves' disease by investigating 108 SNPs in 384 cases and 652 controls.We evaluated our method by differentiating between cases and controls in a five-fold cross validation test,and it achieved a 72.9% prediction accuracy with a combination of 17 SNPs.The experimental results showed that SNPs,even those with a high P-value,have a greater effect on Graves' disease when acting in a combination.