受强弱关系理论启发的改进人工蜂群算法

针对原人工蜂群算法在寻优过程中存在收敛精度不高、容易陷入局部最优的问题,提出一种改进人工蜂群算法(SWT-ABC)。将社会学中强弱关系模型化并引入多子群矩阵式蜂群结构,定义了强关系个体从三个方向随机引导搜索,加快算法收敛速度和提高收敛精度;为增强算法跳出局部最优的能力,定义了弱关系个体交互以实现子群间信息交流来提升种群多样性;增加侦查蜂反向学习机制并确定合适的蜜源上限,能有效提升目标函数评价次数的利用效率。通过基准测试函数的数值实验并与12种改进算法进行对比,改进后的人工蜂群算法收敛精度更高、全局寻优能力更强,并且在高维优化问题求解中仍具备良好的收敛性能。     

融合了K近邻与密度峰值算法的K-means算法

初始聚类中心的随机选择,根据主观经验确定类簇数等问题时常伴随着原始K-means算法。为了攻克以上问题,改进算法采用峰值法以及融合了K近邻算法的密度峰值算法逐一调整。通过在UCI数据集上测试及与原始K-means算法、最大最小距离距离算法在准确率、稳定性和处理数据速率方面的比较,其中最为突出的是,改进算法的准确率达到了96%以上。     

Guaranteeing performance for uncertain nonlinear systems with bounded state constraint and mismatching condition

The control design problem for the uncertain nonlinear system with bounded state constraint and mismatching condition is considered in this paper. The uncertainty in the system, which may be due to unknown system parameters and external disturbance, is nonlinear and time‐varying. The state of the system is constrained to be bounded. The system does not satisfy the (global) matching condition. A creative one‐to‐one state transformation is proposed by converting the bounded states into the unbounded ones. A step‐by‐step state transformation is proposed to convert the mismatched system into a matched system. The robust control is then proposed based on the transformed system. The control is demonstrated to be able to guarantee the uniform boundedness and uniform ultimate boundedness of the system in the presence of uncertainty, while the state constraint can be always guaranteed.     

边界约束对弯曲-伸展元三轴波速试验的影响

利用弯曲-伸展元开展干砂小应变剪切波试验和除气水压缩波试验,研究了三轴试样的不同约束边界对弯曲-伸展元量测剪切波速和压缩波速的影响,并对波速试验结果进行分析。研究结果表明:以干砂为试验对象的剪切波速试验结果介于砂土中剪切波速与边界材料的剪切波速之间,受到约束边界的材质、长度和壁厚的显著影响;而以除气水为试验对象的压缩波速试验结果受样品约束边界的影响较小。在不同约束材质下,剪切波在干砂中的传播速度与约束材料的刚度有关,材料的刚度越小对干砂剪切波速测试结果影响越小。弯曲-伸展元激发频率的相关性与边界约束无关,只与测试材料有关。研究成果有助于获取准确的弯曲-伸展元波速试验结果。     

Probabilistic and interval reliability design of the composite pressure vessel

Considering the randomness or interval character of physical parameters and applied loads of composite pressure vessels (COPV), the COPV reliability-analyzing model is built. And then the computational expressions for the mean value, standard deviation and deviation of fiber stress are deduced by the random factor method. The probabilistic and interval reliability designs on COPV are implemented by utilizing the probabilistic method and interval method combined with the improved particle swarm optimization (PSO) algorithm, respectively. The influence of fluctuation of structure uncertain parameters on COPV design thickness is inspected. Finally, examples are given to demonstrate that both probabilistic and interval reliability methods can satisfy the safety requirement and both are of higher rationality than the traditional safety factor method. And probabilistic method has a lower relative error but a higher computational complexity contrasted with the interval method.     

Cooperative Multi-Agent Reinforcement Learning with Constraint-Reduced DCOP

Cooperative multi-agent reinforcement learning (MARL) is an important topic in the field of artificial intelligence,in which distributed constraint optimization (DCOP) algorithms have been widely used to coordinate the actions of multiple agents.However,dense communication among agents affects the practicability of DCOP algorithms.In this paper,we propose a novel DCOP algorithm dealing with the previous DCOP algorithms' communication problem by reducing constraints.The contributions of this paper are primarily threefold:① It is proved that removing constraints can effectively reduce the communication burden of DCOP algorithms.② An criterion is provided to identify insignificant constraints whose elimination doesn't have a great impact on the performance of the whole system.③ A constraint-reduced DCOP algorithm is proposed by adopting a variant of spectral clustering algorithm to detect and eliminate the insignificant constraints.Our algorithm reduces the communication burdern of the benchmark DCOP algorithm while keeping its overall performance unaffected.The performance of constraint-reduced DCOP algorithm is evaluated on four configurations of cooperative sensor networks.The effectiveness of communication reduction is also verified by comparisons between the constraint-reduced DCOP and the benchmark DCOP.     

Development of a six drillhead roof bolting machine

In underground mining, machine design is predominantly dictated by mine conditions and individual customer desires. In partnership with Foresight Energy, J. H. Fletcher Company was tasked to design and manufacture a new roof bolting machine with six independent drilling apparatus on board capable of drilling and bolting the roof and ribs with material handling. The objective was to produce a machine capable of drilling and installing six bolts simultaneously with a limited number of operators. The goal of the mine is to decrease the time to bolt a cut to improve the safety level of the current roof bolting method, improve efficiency and to improve the bottom line cost of entry development. The customer wanted four drills at the front of the machine dedicated to installing roof bolts and then another two drills behind them dedicated to rib bolts. This dictated the requirement of latched controls, which would allow the operator to start drilling one hole and then latch the controls to be able to move on to the next.The result of the design is a machine with a single platform and six independent masts with drillheads:four masts strictly for drilling and installing roof bolts on the front of the platform and two masts on the back of the platform for rib bolts. The controls at each operator's station include a latch control for drilling. The six-head roof bolter allows fewer operators to drill and install roof and rib bolts, which in turn lowers the miners' roof exposure per cut. This design reduces the operator's exposure from the inherent pinch points and rotary hazards once he has engaged the latch drilling. Therefore, the machine will help to decrease the time to bolt a cut, improving productivity while enhancing the ability to operate the machine safely.     

超声左心耳图像的轮廓自动提取

针对经食道超声左心耳图像的分辨率低、对比度低、含有斑点噪声等问题,提出了一种结合左心耳解剖位置和超声图像灰度及相位信息的方法,全自动定位常规切片中的左心耳。首先,根据医生采集习惯,以左心耳在标准切面中的解剖位置为先验知识,结合其灰度特性,自动确定分割模型中的初始轮廓;然后,通过线型加权相位和梯度信息构造新的外力项,改进向量场卷积模型,完成左心耳轮廓的自动提取。300张左心耳超声图片测试结果表明,以医生手动勾勒的轮廓作为“金标准”,该方法自动提取左心耳的准确性为0.8969 0.0494、敏感性为0.9058 0.0762、特异性为0.9645 0.1687。分割效果优于传统的向量场卷积模型,能够解决自动定位超声图像中左心耳的初始轮廓和弱边界分割的问题。     

模糊系统结合蚁群算法的金具温升建模

为了得到准确可靠的阀厅连接金具温升模型,运用模糊系统结合蚁群算法的方法进行建模。在分析基本蚁群算法与梯度下降法优缺点的基础上,将两种方法结合形成改进蚁群算法,即在基本蚁群算法基础上应用梯度下降算法。通过试验得到的训练数据分别用基本蚁群算法、梯度下降算法、改进蚁群算法训练模糊系统,改进蚁群算法的收敛效果优于其他两种方法;通过试验得到的测试数据对4种方法所得的模型进行测试,由改进蚁群算法训练模糊系统所得模型的测试效果是最好的。结果表明,若能通过试验得到足量训练数据,用改进蚁群算法训练模糊系统的方法对阀厅连接金具的温升进行建模是可行的。     

大型复杂跨流域调水工程电站-水库-泵站群多目标优化调度

考虑调水量、发电最大和耗能最小3个目标,建立电站-水库-泵站群多目标优化调度模型。基于参数调整策略、邻域变异和加速策略,提出了求解多目标优化调度模型的综合改进布谷鸟新算法并求解模型,获得了发电、调水、泵站耗能多目标Pareto解集。以引汉济渭大型复杂跨流域调水工程为实例,将该模型与模拟调度模型和NSGA-Ⅱ算法多目标优化调度模型的结果进行比较,结果表明,该优化调度模型的发电、调水、耗能、弃水等各项指标合理,具有相对优势。