The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Analysis of Transit Network Design Using GIS and Honeybee Algorithm in the City of Sanandaj, Iran

Public transit planning is a user-oriented problem, respectful of financial issues and involves different stakeholders such as the general public, the transportation provider and the local government. One of the main components of public transit planning is the transit network design (TND) problem. This research is an attempt to perform transit network design and analysis in the city of Sanandaj, Iran using the capabilities of GIS and Honeybee algorithm. Objectives of this study are formulating a multi-objective model of the TND problem, developing a GIS-based procedure for solving the TND problem and examination of the solutions using artificial metaheuristic methods such as honeybee algorithm. The transit network design approach in this research, aims to reduce the walking distance, the total travel distance and the total number of stops needed for a suitable transit service in Sanandaj, Iran. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modelling functionalities and using the abilities of the artificial intelligence in modelling and assessment of the transit network.

Development of an electrode intelligent design system based on adaptive fuzzy neural network and genetic algorithm

The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ＋＋ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Helicopter Sizing Based on Genetic Algorithm Optimized Neural Network

It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are some difficulties in using NN, e.g., how to select a proper network structure and the number of hidden layers. In this paper, structure and connection weight of a three-layer NN are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. The proposed method can not only give an optimal NN structure and connection weight, but also reduce the prediction error and has the capability of self-learning when the latest data are available. Furthermore, this method can be easily applied to helicopter design systems.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

无线网络覆盖方案仿真实验设计与实现

以无线信号传播模型为基础,以实现对某区域内无线网络覆盖为目标,设计了覆盖半径随无线参数变化的基础实验、基于三种经典启发式算法的无线网络覆盖方案提高实验和基于改进算法的拓展创新实验,形成了一个完整的实验系统。通过实验教学,学生可以了解到无线通信信号传播和网络覆盖理论知识,能够掌握无线网络覆盖方案设计方法,该实验是一个理论与实践相结合、有层次、有创新的综合设计性实验。

双目标优化与生成对抗网络结合的框架结构阻尼器布置方案智能设计方法

为实现框架结构的阻尼器智能化布置,结合减震设计原理和智能算法,采用双目标优化算法和生成对抗网络算法分别进行阻尼器竖向和水平智能布置研究,并将该方法应用到两个框架结构减震设计工程案例中。在框架结构减震设计中,采用双目标优化算法进行阻尼器竖向布置,并与逐层逼近法、工程师设计和非减震设计进行对比,结果表明,采用该优化算法得到的阻尼器竖向布置方案能有效降低层间位移角和楼层加速度,提高结构的抗震性能。在确定各楼层的阻尼器数量后,利用训练好的生成对抗网络生成模型,可快速、自动地选择和确定各楼层阻尼器的平面安装位置,生成的平面布置与工程师设计的平面布置在相似性差异度综合评价指标上小于临界值0.1,说明两者相似度较高,且有利于提高原结构的抗扭能力。将双目标优化算法与生成对抗网络相结合,不仅能满足框架结构的减震性能目标,而且可实现阻尼器布置方案的智能设计,提升减震工程设计效率。

军事舰艇计算机生成兵力仿真系统的结构设计

虚拟环境训练和测试可以实现对真实海洋战场环境的模拟,并有效减少对真实舰艇、人员和装备的需求。本文对军事舰艇计算机生成兵力仿真系统进行研究,分析了系统需求,设计的系统结构包括实体行为模型和人工智能模型,对系统中的关键技术进行研究和设计,包括实体模型设计、舰艇兵力生成及模拟、人工智能决策实现等。实体行为模型和人工智能模型不断融合和交互,结合神经网络算法和模糊逻辑控制器,实现对海洋复杂战场环境以及敌对行为的模拟。

三相磁保持继电器的性能分析与优化设计

建立了三相磁保持继电器的虚拟样机模型,基于正交试验结果,使用BP神经网络与NSGA2遗传算法对继电器进行了优化设计。使用ANSYS和Adams/View分别建立三相磁保持继电器的静态和动态模型,进行了试验验证。以电磁系统部分结构的尺寸为变量,以分断电流值和吸合后的磁保持力矩为目标,进行五因素五水平正交试验,建立数据集;使用BP神经网络对数据集进行训练,分别得到两个优化目标的网络函数;使用NSGA2遗传算法对目标函数进行寻优并更新数据集,如此循环,直至最优解满足要求。动态特性仿真表明,优化后样机的分断时间减少了23.89%,吸合时间和弹跳时间分别减少了29.2%和47.05%,为样机的制造提供了参考。

不确定需求条件下考虑风险公平的危险品运输网络设计问题

危险品运输事故具有低概率、高后果的特征,降低事故风险是危险品运输研究的重点。危险品运输产生风险的同时伴随着风险在空间上的分布不均衡,此前研究关注总风险最小和风险公平的权衡,但较少考虑不确定需求下的运输网络。运输需求波动可能引起风险增加,为了解决不确定需求下考虑风险公平的危险品网络设计问题,构建了危险品多起点-终点(OD)对运输网络设计的双层混合整数规划模型,上层和下层问题分别为监管者选择关闭路段以及承运人选择最优路径,并通过自适应遗传算法和局部搜索算法进行求解。通过实例分析发现,不同需求情景特别是部分OD对中断时产生不同的最优网络设计方案,采用的遗传算法相比局部搜索算法运行时间稍长情况下获得的解更优。

基于区块链隐私保护的工业网络输出指令安全控制算法设计

当前工业网络输出指令加密机制多为目标式和独立式,导致指令安全控制效果不理想,丢包率增加,为此提出基于区块链隐私保护的工业网络输出指令安全控制算法。设计工业网络指令安全控制辨识目标,将此目标导入初始的安全指令控制加密程序中,设置一个基础的控制指令。以其作为引导,进行安全控制目标的扩展及生成,对指令的内容进行多层级加密。在指令加密处理的基础上,根据输出指令安全控制指标及参数的限制,设计不同的安全控制标准和双向控制条件,将安全控制辨识目标导入当前的区块链双向控制结构中,形成一个具体细化的控制结构。在控制结构中,利用区块链技术将区块控制程序与重叠指令的控制程序关联起来,实现动态化的指令输出安全控制。实验结果表明:这种安全控制算法能够对全部工业网络输出指令数据进行加密处理,能够最大程度上保证工业网络输出指令隐私安全,丢包率在5.2%以下,控制正确率较高,具有较高的实际应用价值。

低碳视角下多式联运网络设计优化问题研究

网络设计与低碳补贴激励措施,是推进多式联运可持续发展的重要途径。基于此,从低碳视角研究水陆联运网络设计优化与补贴模式问题,考虑政府管理部门与物流用户的互动博弈行为,构建基于双层规划的水陆联运物流网络优化模型。该模型中上层规划(政府层)确定网络扩容投资决策及其补贴方案,从而最小化扩容投资与低碳补贴总成本,以及系统中碳排放量;下层模型(物流用户)则是基于广义费用的用户均衡分配模型。针对上述双层优化模型的特点,设计了基于相继平均配流算法(MSA)的改进快速非支配排序遗传算法(NSGA-Ⅱ)。以长江经济带中游地区的水陆联运物流网络为例,进行相应的实证研究,验证上述优化模型及算法的有效性;并且,在对网络进行扩容投资的情况下,对比4种补贴方案,即1)对铁路及水运弧段按固定值进行补贴;2)不进行补贴;3)不同地区的铁路及水运弧段的补贴不同;4)补贴值随机连续。研究结果表明:扩容投资可以减少网络中的超载弧段数量,同时提升网络性能;对铁路及水运弧段进行运输补贴能有效降低碳排放量;若政府关注预算限制,则倾向于按固定值补贴的方案;若更重视碳减排效果,则倾向于采取不同地区不同补贴值的方案。

基于神经网络与遗传算法的离心泵汽蚀性能优化设计

为了研究离心泵发生汽蚀过程时其内部流动规律和对其汽蚀性能的优化设计,在结合传统优化方法的基础上,提出神经网络与遗传算法结合的智能优化方法。通过Plackett-Burman试验设计从离心泵的叶轮进出口直径、进出口安放角、叶片数、叶片包角等7个设计参数中筛选出显著影响的3个优化设计变量。叶片出口宽度、叶片包角和叶轮出口直径3个优化设计变量,其影响汽蚀性能的显著性由大到小。运用拉丁超立方抽样方法抽取30组设计方案,分别进行数值模拟计算,得出相应的汽蚀余量值。建立神经网络模型,结合遗传算法在规定范围内进行寻优,得到最优设计变量组合及最优汽蚀余量值。取优化后的参数进行数值模拟计算,优化后的离心泵在相同工况下汽蚀余量降低了43.1%,说明优化后的离心泵抗汽蚀性能显著提高。

神经网络模型在大学生在线学习预测中的应用研究

为了较为准确地预测大学生在线学习效果,提出一套基于神经网络的学生在线学习行为分析模型。首先介绍该模型的输入项、输出项、数据处理方法以及整体拓扑结构。利用学生日常学习行为数据对神经网络模型进行训练,并通过该模型对学生的期末考试成绩进行预测。实验研究发现,所提出的神经网络模型在学生学习效果预测方面体现出了较为理想的准确度水平,具有一定的应用价值。

基于改进Dijkstra算法的民机信号敷设路径规划方法

针对规划电气原理信号在全机布线网络中敷设路径时存在不兼容线束通道、计算效率低、敷设路径长等问题,设计了一种基于改进Dijkstra算法的信号敷设方法。由全机布线网络建立敷设通道模型,在信号端接点处采用映射扩展连接,综合布线网络形成整体计算结构,并根据待敷设信号的隔离代码与余度动态定义线束通道的权值大小。结合Blender建模软件与某机型数据进行实例分析,结果表明:改进的Dijkstra算法能够准确地将信号敷设至兼容的线束通道中,路径搜索耗时更短,同时在信号数量较大的情况下能够得到总长度与质量更小的敷设结果。

基于配重式铂热电阻的表面温度计设计与研究

针对现有表面温度计在测量过程中受环境和人为因素影响较大的问题,该文通过硬件设计和软件算法的创新性改进,研制一款基于配重式铂热电阻表面温度计。在硬件方面,选择铂热电阻作为温度传感器以提高测量准确性。热传导材料方面,采用铜作为最佳热传导基底,结合独特的配重探头装置和防风罩设计,有效降低测量误差和环境干扰。软件方面,通过优化的温度标定算法和引入BP神经网络,进一步提高温度测量的准确度和稳定性。与传统热电偶型表面温度计相比,该文研制的配重式表面温度计在最大示值误差和稳定性方面均有显著提升,最大示值误差仅为-0.2°C,达到稳定状态的时间仅需140 s。该研究在减少人为误差和外界环境干扰方面取得显著成果,为工业生产、校准实验室、环境监测和食品加工等领域提供一种高准确度、高稳定性的表面温度测量解决方案。未来研究将聚焦于软件算法的智能化和新型传感器材料的探索,以进一步提高测量精度和仪器适用范围。

基于ControlNet的儿童床设计效果图生成技术

基于深度学习的计算机生成技术,可实现产品设计效果图快速生成,对提升产品设计效率、降低人工劳动强度具有积极作用,也为高质量的图像生成提供了新思路。本研究提出了一种基于ControlNet的儿童床设计效果图生成方法。首先,采用Canny边缘检测算法对原始图像进行处理,得到对应的边缘图像;然后,利用控制分支网络从边缘图像中提取特征;最后,将提取的边缘特征融合到原有的扩散模型特征层中,并作为条件来控制ControlNet的生成过程,通过设置不同的随机数种子,可生成具有相同结构但不同配色的目标图像。以FID值、Inception Score值为指标,对试验结果进行定量评估,结果表明:相较于CycleGAN、ChipGAN、Style2Paints 3种图像生成模型,基于ControlNet生成的图像不仅拥有较好的多样性,其图像细节质量也有所提高,证明了所应用模型的有效性和合理性。同时,对基于ControlNet的儿童床设计效果图方法进行了专家评估,评估满意度达92.22%,进一步证明该方法的可靠性和适用性。将ControlNet应用于儿童床设计效果图工作是可行的,不仅可为设计师提供设计灵感,也可节省人力与物力成本,提高设计效率。