A deep learning-based binocular perception system

An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.     

Multi-Objective Optimization of Urban Bus Network Using Cumulative Prospect Theory

Multi-objective optimization of urban bus network can help improve operation efficiency of the transit system and develop strategies for reducing urban traffic congestion in China. The work used cumulative prospect theory, currently the most influential model for decision under uncertainty,to optimize urban bus network. To achieve the research objective, the work developed the theoretical framework of urban bus network optimization, including optimization principle, optimization objectives and constraints. Furthermore, optimization objectives could comprehensively reflect expectations of passengers and bus companies from the dimension of time, space and value. It is more scientific and reasonable compared with only one stakeholder or dimension alone in the previous studies. In addition,the technique for order preference by similarity to ideal solution(TOPSIS) was used to determine the positive and negative ideal alternative. The correlations between the optimization alternatives and the ideal alternatives were estimated by grey relational analysis simultaneously. The cumulative prospect theory(CPT) was used to determine the best alternative by comparing comprehensive prospect value of every alternative, accurately describing decision-making behavior compared with expected utility theory in actual life. Finally, Case of Xi’an showed that the method can better adjust the bus network,and the optimization solution is more reasonable to meet the actual needs.     

The game theoretical approach for multi-phase complex systems in chemical engineering

This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale(EMMS) model proposed by Li and Kwauk(1994) and Li, et al.(2013) which identifies the multi-scale structure as a result of ‘compromise-in-competition between dominant mechanisms' and tries to solve a multi-objective optimization problem. However,the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ‘compromise-in-competition' mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidization(GSF) system and turbulent flow in pipe. Two different cases for generalized Nash equilibrium in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.     

STEADY-STATE HIERARCHICAL INTELLIGENT CONTROL OF LARGE-SCALE INDUSTRIAL PROCESSES

This paper considers the fourth stage of development of hierarchical control ofindustrial processes to the intelligent control and optimization stage, and reviews what theauthor and his Group have been investigating for the past decade in the on-line steady-state hierarchical intelligent control of large-scale industrial processes (LSIP). This papergives a definition of intelligent control of large-scale systems first, and then reviews the useof neural networks for identification and optimization, the use of expert systems to solvesome kinds of hierarchical multi-objective optimization problems by an intelligent decisionunit (ID), the use of fuzzy logic control, and the use of iterative learning control. Severalimplementation examples are introduced. This paper reviews other main achievements ofthe Group also. Finally this paper gives a perspective of future development.     

On Intelligent Systems of the Data Acquisition in the Schedule Risk Analysis

It is an important task to analyze the schedule risk in a project management.As a semi-constructed or non-constructed complex system, there are many difficulties in the quantitative analysis of the schedule risk (SRA). The paper integrates intelligent techniques to obtain massive basic data required in the risk analysis process. It greatly improves the precision and efficiency of the SRA. In addition, the paper presents a mechanism and architecture of the integrated intelligent systems. Finally, the concluding remarks are provided for basic data acquisition in the SRA.     

Intelligent coordinated control of power-plant main steam pressure and power output

An intelligent coordinated control strategy has been proposed and successfully applied to a 300MW boiler-turbine unit i. e. Unit 1 of Yuanbaoshan power plant in China. Load following operation of coal-fired boiler-turbine unit in the power plant leads to changes in operating points which result in nonlinear variations of the plant variables and parameters. For the variation of operating condition and slowly varying dynamics, an intelligent control scheme has been developed by combining fuzzy self-tuning with adaptive control and auto-tuning techniques. As there exist strong couplings between control loops of main steam pressure and power output in the unit, a new design for static decoupler aimed at decoupling for setpoints and unmeasured pulverized coal disturbance of the system at the same time is presented. Satisfactory industrial application results show that such a control system has enhanced adaptability and robustness to the complex process, and better control performance and high economic benefi     

Progress in reentry trajectory planning for hypersonic vehicle

The reentry trajectory planning for hypersonic vehicles is critical and challenging in the presence of numerous nonlinear equations of motion and path constraints, as well as guaranteed satisfaction of accuracy in meeting all the specified boundary conditions. In the last ten years, many researchers have investigated various strategies to generate a feasible or optimal constrained reentry trajectory for hypersonic vehicles. This paper briefly reviews the new research efforts to promote the capability of reentry trajectory planning. The progress of the onboard reentry trajectory planning, reentry trajectory optimization, and landing footprint is summarized. The main challenges of reentry trajectory planning for hypersonic vehicles are analyzed, focusing on the rapid reentry trajectory optimization, complex geographic constraints, and coop- erative strategies.     

A transfer forecasting model for container throughput guided by discrete PSO

Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.     

Evidence for self-organized criticality in unscheduled downtime data of equipment

How to reduce downtime and improve availability of the complex equipment is very important. Although the unscheduled downtime（USDT） issues of the equipment are very complex, the self-organized criticality（SOC） is the right theory to study complex systems evolution and opens up a new window to the investigation of disasters, such as the sudden failure of the equipment. Firstly,SOC theory and its validation method are introduced. Then an SOC validation method for USDT of the equipment is proposed based on the above theory. Case study is done on bottleneck equipment in a factory and corresponding data pre-process work is done. The rescaled-range（R/S） analysis method is used to calculate the Hurst exponent of USDT time-series data in order to determine the long-range correlation of USDT data on time scale;at the same time the spatial power-law characteristic of USDT time series data is studied. The result shows that the characteristics of SOC are revealed in USDT data of the equipment according to the criterion of SOC. In addition, based on the characteristics of SOC,the overall framework of the prediction method for major sudden failure of the equipment is proposed based on SOC.     

New theoretical analysis method for the phase center in the Fresnel region of complex structure antennas

A finite domain time difference （FDTD） and second-derivative combined method is proposed for the evaluation of phase center in the Fresnel region of complex structure millimeter antennas. This method adopts FDTD＇s near to far field transformation to obtain the fields in Fresnel region and then applies the second-derivative method to calculate the phase center. The adoption of FDTD efficiently overcomes the difficulties arising from the existing calculation methods＇ requirements for the radiation analytical formula of some complex antennas, which makes the existing second-derivative method more applicable in engineering. Also, FDTD increases the precision owing to the superposition field calculation from its extrapolation. The correctness of this proposed method is certified with typical examples and the phase center in the Fresnel region of a microwave radiometry calibration corrugate horn antenna is evaluated with the key features.     

大规模MIMO系统的智能天线选择与功率分配方法研究

为了满足大规模多输入多输出(multiple input multiple output, MIMO)系统的数据传输需求并降低系统能耗,提出一种基于量子化学反应优化的智能天线选择与功率分配方法。根据大规模MIMO系统不同时段的用户传输需求建立智能天线选择与功率分配模型,推导出其最大能效方程。为有效求解该非线性、多约束的混合优化难题,结合量子计算和化学反应优化机制的优势设计了量子化学反应优化算法,可得到最佳的天线选择与功率分配方案。仿真结果表明,所提的智能天线选择与功率分配方法能实时满足用户的信息传输需求,显著提高系统能效。针对不同的仿真场景,所提方法与现有的智能算法与分配策略相比均可得到最高的系统能效。     

基于遗传算法的相位调制波形智能优化

随着智能化程度的提高,雷达发射信号更加复杂多变.为有效应对复杂的、未知的威胁信号,需要提升对抗系统智能对抗的能力,提出一种基于智能优化算法的对抗波形智能优化方法,并通过仿真实验对基于遗传算法的相位调制波形智能优化进行研究.不同优化参数、不同实施条件、不同雷达信号及信号变化条件下的实验结果表明,遗传算法能够以较少的迭代次...     

大规模MIMO系统的智能天线选择与功率分配方法研究

为了满足大规模多输入多输出(multiple input multiple output, MIMO)系统的数据传输需求并降低系统能耗,提出一种基于量子化学反应优化的智能天线选择与功率分配方法。根据大规模MIMO系统不同时段的用户传输需求建立智能天线选择与功率分配模型,推导出其最大能效方程。为有效求解该非线性、多约束的混合优化难题,结合量子计算和化学反应优化机制的优势设计了量子化学反应优化算法,可得到最佳的天线选择与功率分配方案。仿真结果表明,所提的智能天线选择与功率分配方法能实时满足用户的信息传输需求,显著提高系统能效。针对不同的仿真场景,所提方法与现有的智能算法与分配策略相比均可得到最高的系统能效。     

求解模糊机会约束规划模型的微粒群算法

针对现在不确定规划领域中存在的模糊机会约束规划模型和群智能优化方法-微粒群算法,提出了一种求解模糊机会约束规划模型的新混合智能算法.该算法利用微粒群算法并运用模糊模拟技术对模糊机会约束规划模型的规划问题进行分析和数值求解,因而无需像传统的基于遗传算法的混合智能算法那样需要很长时间并经过复杂的计算才能得到结果.通过对实际模型的分析和数值求解,给出了应用该方法的具体步骤,说明了该混合智能算法的合理性和有效性.     

液压集成系统智能化虚拟设计中的核心算法研究

通过对液压集成系统设计中存在的问题进行分析,提出以集成块布局布孔集成方案的智能优化设计算法为核心,以具有沉浸感的虚拟现实环境为人机交互界面的智能化虚拟设计新方法。针对集成块这种复杂的立体空间带约束布局问题,介绍了智能型虚拟设计系统的构造方案,重点对其核心算法—运用智能计算方法及其混合优化策略实现集成块布局布孔自动优化设计的方法和实现技术进行阐述,为建造高度智能的液压集成块虚拟设计系统提供了可靠的理论依据。     

基于改进智能水滴算法的动态车辆配送路径优化

针对当前车辆配送过程中存在的配送路径不合理、配送效率低和需求不确定性等问题,提出一种基于改进智能水滴算法的动态车辆配送路径优化方法。构建软时间窗惩罚函数,考虑顾客对配送时间的要求,建立顾客满意度函数。综合车辆配送过程的车速、货损成本、惩罚成本、顾客满意度等特征,建立车辆路径优化模型。采用智能水滴算法对车辆路径优化模型进行求解,使用灰狼优化算法改善智能水滴算法的搜索能力,获取最优路径。实验结果表明该方法能够提供实时优化的路径,减少调配成本。     

基于绿灯时间等饱和度的离线Q学习配时优化模型

首先对传统的绿灯时间等饱和度概念进行了扩展,提出了分级绿灯时间等饱和度。在此基础上,针对分级绿灯时间等饱和度目标,构造了奖赏函数,建立了定周期和变周期两种模式下的四种离线Q学习配时优化模型。相对于在线Q学习模型,离线Q学习模型更适合交叉口信号配时优化,变周期模式的离线Q学习模型可以获得解的结构、最优解的分布,这是传统配时理论不具备的。算例结果表明,定周期模式下最优解是唯一的。变周期模式下最优解是不唯一的,呈带状,奖赏分级模型比奖赏不分级的最优解更加集中。     

基于粒子群优化与高斯过程的协同优化算法

对于适应度函数计算耗时较大的工程优化问题,采用仿生智能优化算法求解时常遇到由于适应度函数评价次数过大而导致计算量过高的瓶颈问题。针对上述问题,提出一种基于粒子群优化（particle swarm optimization, PSO）算法与高斯过程（Gaussian process, GP）机器学习方法的协同优化算法（PSO-GP）。该算法在寻优过程中采用GP近似模型来构建决策变量与适应度函数值之间的映射关系,在PSO全局寻优过程中不断地总结寻优历史经验的基础上,预测可能包含全局最优解的搜索区域,以优化粒子群飞行的方向。多个测试函数的优化结果表明,该算法是可行的,与基本PSO算法相比,在获得全局最优解的前提下,可显著减小寻优过程中的适应度函数评价次数,寻优效率较高,在高计算代价复杂工程优化问题的求解上具有良好的应用前景。     

天基预警调度的启发式优化方法

天基预警过程可以看作一种多维离散时间序列监控与预测问题,其调度的决策要素、优化目标和约束条件较多,故往往采用智能优化算法求解该非线性优化问题.而它们在指定时间内却是概率性收敛到Pareto解集.对此,提出基于贝叶斯方法提供多类别决策树挖掘调度中的启发信息,以及引入局部搜索算子等方法提高智能优化算法的快速性和鲁棒性.预警仿真实验表明融入上述方法的免疫克隆选择算法收敛性能提高了10.1%,遗传算法提高了9.8%.     

在轨空间智能制造:分布式调度建模与优化

在轨空间制造系统是在行星大气层外的需要地面工厂、在轨空间工厂、天地运载工具协同的以进行空间设施建造为目标的一类分布式制造系统.分布式调度建模和高效优化求解技术是实现在轨空间智能制造的关键技术之一.本文针对一类具有组件地面分布式制造及运输、地空分批次运输、组件在轨装配等典型特点的在轨空间智能制造系统,将其分解为分布式同质流水线调度,考虑运输时间的同速并行机调度,考虑工件释放时间、机器可用时间、机器处理能力的单机批调度以及考虑组件释放时间、优先约束的单机调度等问题,并基于模型协调思想建立以最小化组件生产到产品装配总时长为目标的分布式多阶段调度模型.进而,将用于求解连续优化问题的易理优化算法扩展到离散调度问题,提出求解该分布式调度问题的基于易理优化的模因算法.基于中规模、大规模算例的仿真结果和算法分析比较表明:相较于粒子群算法、教学算法、水波算法等智能优化算法,所提算法是一种求解分布式多阶段调度问题的可行、有效算法.值得一提的是,这是第一篇关于在轨空间智能制造系统调度优化的研究.