Conceptual Warehouse Design Algorithm Using a Network Flow Model

Currently, third-party logistics (3PL) services are growing rapidly. In the current business climate, the goal of each 3PL firm is to reduce the time required for warehouse design. In this paper, a warehouse design problem is discussed. The problem is to determine the number of machines used in a warehouse and the product flow among the devices. In the conventional design process, such a problem would be solved with experimental and intuitional approaches. However, conventional approaches require large amounts of time. In addition, the design outcome is dependent on the skill of the designer. In order to provide theoretical guidelines for the design itself and shorten the design process, we propose a new design method using an extended network flow model. The network flows are expressed as the combination of the flow in the preparation time and that in the lead time. The mathematical model is constructed and solved as a threshold optimization algorithm including the mixed integer linear programming problem. We then evaluated the feasibility of the proposed model and method using real data, and confirmed the effect of the proposed method with regard to time reduction and the obtained design results.     

Probabilistic sensitivity analysis for novel second-order reliability method (SORM) using generalized chi-squared distribution

Reliability-based design optimization (RBDO) requires evaluation of sensitivities of probabilistic constraints. To develop RBDO utilizing the recently proposed novel second-order reliability method (SORM) that improves conventional SORM approaches in terms of accuracy, the sensitivities of the probabilistic constraints at the most probable point (MPP) are required. Thus, this study presents sensitivity analysis of the novel SORM at MPP for more accurate RBDO. During analytic derivation in this study, it is assumed that the Hessian matrix does not change due to the small change of design variables. The calculation of the sensitivity based on the analytic derivation requires evaluation of probability density function (PDF) of a linear combination of non-central chi-square variables, which is obtained by utilizing general chi-squared distribution. In terms of accuracy, the proposed probabilistic sensitivity analysis is compared with the finite difference method (FDM) using the Monte Carlo simulation (MCS) through numerical examples. The numerical examples demonstrate that the analytic sensitivity of the novel SORM agrees very well with the sensitivity obtained by FDM using MCS when a performance function is quadratic in U-space and input variables are normally distributed. It is further shown that the proposed sensitivity is accurate enough compared with FDM results even for a higher order performance function.     

An Analytic Center Machine

Support vector machines have recently attracted much attention in the machine learning and optimization communities for their remarkable generalization ability. The support vector machine solution corresponds to the center of the largest hypersphere inscribed in the version space. Recently, however, alternative approaches (Herbrich, Graepel, & Campbell, In Proceedings of ESANN 2000) have suggested that the generalization performance can be further enhanced by considering other possible centers of the version space like the center of gravity. However, efficient methods for calculating the center of gravity of a polyhedron are lacking. A center that can be computed efficiently using Newton's method is the analytic center of a convex polytope. We propose an algorithm, that finds the hypothesis that corresponds to the analytic center of the version space. We refer to this type of classifier as the analytic center machine (ACM). Preliminary experimental results are presented for which ACMs outperform support vector machines.     

Towards designing a green data center farm for Internet services: Iran’s case study

Energy consumption has become a critical design factor in today’s data centers. In recent years, extensive research has been done to address power–performance trade-off in data centers considering both IT equipments and cooling infrastructures (e.g., thermal-aware task scheduling, server consolidation, load balancing and geographical load balancing to name a few). This paper introduces a design-time technique that targets energy-efficient design of a green data center farm in Iran. Workload predictions, geographical maps of wind speed and solar radiation, data center and renewable resources configurations are used as a priori to design an energy-efficient data center farm for Internet services. The proposed problem is mathematically formulated as a nonlinear optimization problem and is effectively solved using a coordinate descent-based method. We also show that with some minor modification, our proposed technique can be applied at run-time for the purpose of change management. The experimental results show that the proposed method can lead to 11.6 % cost saving on average over conventional approaches.     

An improved algorithm for vessel centerline tracking in coronary angiograms

For automated visualization and quantification of artery diseases, the accurate determination of the arterial centerline is a prerequisite. Existing tracking-based approaches usually suffer from the inaccuracy, inflexion and discontinuity in the extracted centerlines, and they may even fail in complicated situations. In this paper, an improved algorithm for coronary arterial centerline extraction is proposed, which incorporates a new tracking direction updating scheme, a self-adaptive magnitude of linear extrapolation and a dynamic-size search window for matched filtering. A simulation study is conducted for the determination of the optimal weighting factor which is used to combine the geometrical topology information and intensity distribution information to obtain the proposed tracking direction. Synthetic and clinical examples, representing some difficult situations that may occur in coronary angiograms, are presented. Results show that the proposed algorithm outperforms the conventional methods. By adopting the proposed algorithm, centerlines are successfully extracted under these complicated situations, and with satisfactory accuracy.     

用于统计测试概率分布生成的自动搜索方法

提出一种基于自动搜索的概率分布生成方法,设计对概率分布的表示形式与评估函数,同时结合模拟退火算法设计基于马尔可夫模型的自动搜索过程。实验结果表明,该方法能够有效地提高自动搜索的准确性,在一定时间内成功找到接近最优的概率分布,生成高效的测试数据,同时达到降低统计测试成本的目的。     

Automated process planning method to machine A B-Spline free-form feature on a mill–turn center

In this paper, we present a methodology for automating the process planning and NC code generation for a widely encountered class of free-form features that can be machined on a 3-axis mill–turn center. The free-form feature family that is considered is that of extruded protrusions whose cross-section is a closed, periodic B-Spline curve. In this methodology, for machining a part with B-Spline protrusion located at the free end, the part is first rough turned to the maximum profile diameter of the B-Spline, followed by rough profile cutting and finish profiling with axially mounted end mill tools. The identification and sequencing of machining volumes is completely automated, as is the generation of actual NC code. The approach supports both convex and non-convex profiles. In the case of non-convex profiles, the process planning algorithm ensures that there is no gouging of the work piece by the tool. The algorithm also identifies when sections of the tool path lie outside the work piece and utilizes rapid traverses in these regions to reduce cutting time. This methodology presents an integrated turn–mill process planning where by making the process fully automated from design with no user intervention making the overall process planning efficient. The algorithm was tested on several examples and test parts using the unmodified NC code obtained from the implementation were run on a Moriseiki mill–turn center. The parts that were produced met the dimensional specifications of the desired part.     

An automated approach for the design of Mechanically Stabilized Earth Walls incorporating metaheuristic optimization algorithms

Considered as cost-efficient, reliable and aesthetic alternatives to the conventional retaining structures, Mechanically Stabilized Earth Walls (MSEWs) have been increasingly used in civil engineering practice over the previous decades. The design of these structures is conventionally based on engineering guidelines, requiring the use of trial and error approaches to determine the design variables. Therefore, the quality and cost effectiveness of the design is limited with the effort, intuition, and experience of the engineer while the process transpires to be time-consuming, both of which can be solved by developing automated approaches. In order to address these issues, the present study introduces a novel framework to optimize the (i) reinforcement type, (ii) length, and (iii) layout of MSEWs for minimum cost, integrating metaheuristic optimization algorithms in compliance with the Federal Highway Administration guidelines. The framework is conjoined with optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE) and tested with a set of benchmark design problems that incorporate various types of MSEWs with different heights. The results are comparatively evaluated to assess the most effective optimization algorithm and validated using a well-known MSEW analysis and design software. The outcomes indicate that the proposed framework, implemented with a powerful optimization algorithm, can effectively produce the optimum design in a matter of seconds. In this sense, DE algorithm is proposed based on the improved results over GA, PSO, and ABC.     

Bandwidth-aware energy efficient flow scheduling with SDN in data center networks

Nowadays the energy consumption has become one of the most urgent issues for Data center networks. For general network devices, the power is constant and independent from the actual transfer rate. Therefore the network devices are energy efficient when they are in full workload. The flow scheduling methods based on the exclusive routing can reduce the network energy consumption, as the exclusive routing paths can fully utilize all their links. However, these methods will no longer guarantee the energy efficiency of switches, as they handle flows in priority order by greedily choosing the path of available links instantaneously. In a previous work we proposed an extreme case of flow scheduling based on both link and switch utilization. Herein we consider general scenarios in data center networks and propose a novel energy efficient flow scheduling and routing algorithm in SDN. This method minimizes the overall energy for data center traffic in time dimension, and increases the utilization of switches and meet the flow requirements such as deadline. We did a series of simulation studies in the INET framework of OMNet++. The experiment results show that our algorithm can reduce the overall energy with respect to the traffic volume and reduce the flow completion time on average.     

基于二值检测器的交通异常事件传感器设计

针对高速公路交通异常事件检测难度大、成本高等问题,提出了一种基于二值检测器的高速公路异常事件传感器的设计方法;利用二值检测器的特点,通过对SCM设计并结合状态识别算法获得车流量、时间占有率等交通状态参数;为了降低管控中心异常事件检测算法的设计难度,提高整体系统的运行效率,传感器选择性地提供必要的交通状态参数;最后管控中心通过对交通状态参数分析处理,实现异常事件的检测;仿真实验结果表明,该设计具备可行性。     

对象存储系统中一种高效的分层对象布局算法

在对象存储系统中,如何有效地在对象存储设备上分布对象是其面临的重大挑战.需要一个能够常数时间内定位对象,同时能公平地分布对象以及自适应存储规模变化的对象布局算法.目前大部分布局算法只能适应单层模式,少数的多层模式对设备配置有严格的要求,而且无法在常数时间内定位对象,自适应性较差.提出了一种新的分层对象布局算法,首先使用最大最小聚类算法将设备集合进行分类,支持灵活的设备配置.然后使用提出的EFAH Hashing算法在集群间和集群内分布对象.理论和实验证明,新的分层对象布局算法可以在常数时间内定位对象,从而减轻元数据服务器的计算量.同时可以在设备之间较公平地分布对象,达到I/O负载均衡的目的.而且在设备集合变化时,迁移较少的对象数以满足对象再次分布的公平性.     

基于多目标自适应遗传算法的逻辑电路门级进化方法

提出一种改进的遗传算法，通过网表级编码、多目标评估和遗传参数自适应等措施，可依据多个设计目标，以较少的运算量自动生成和优化逻辑电路．在数字乘法器、偶校验器等进化设计实验中，通过比手工设计和同类方法更优的新奇设计结果展示了该方法的有效性和先进性．     

Structured synthesis of MEMS using evolutionary approaches

In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical synthesis process for MEMS. The paper first introduces the flow of a structured MEMS design process and emphasizes that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. At the system level, an approach combining bond graphs and genetic programming can lead to satisfactory design candidates as system-level models that meet the predefined behavioral specifications for designers to trade off. Then at the physical layout synthesis level, the selection of geometric parameters for component devices and other design variables is formulated as a constrained optimization problem and addressed using a constrained genetic algorithm approach. A multiple-resonator microsystem design is used to illustrate the integrated design automation idea using these evolutionary approaches.     

基于几何着色器的流场动态可视化研究与实现

为有效解决复杂流场可视化效率低下问题，加快可视化速度，提出了一种基于几何着色器的快速流场可视化算法。在流场可视化的过程当中引入几何着色器，利用GPU的并行处理能力和强大的图像处理能力对流场实时地进行箭头和流线的绘制，然后采用积分颜色映射方法，与常规的线性颜色映射法相比较，改善了颜色的均匀分布，增强流场强度层次感。实验表明，该算法可以有效地反映流场特征分布，减少可视化过程中的数据传输量，降低资源浪费，提高可视化渲染效率。     

面向惯性约束聚变实验靶图像的快速椭圆检测

目的：针对惯性约束核聚变实验中靶图像轮廓模糊、亮度不均匀等问题,并从提高图像处理实时性角度出发,提出了一种高可靠性和高精度的快速椭圆检测方法。方法：首先利用椭圆边缘点在它与圆心相连方向上具有较大灰度变化率这一特点,以预估中心点为极点建立极坐标系,通过从极点出发的射线上灰度变化率极值点搜索实现椭圆边缘点检测,极值点搜索在图像局部范围进行保证边缘点检测的有效性和实时性;其次利用基于RANSAC的自适应椭圆参数提取算法得到最终椭圆参数,该方法利用椭圆参数空间聚类分析选取最优椭圆参数,从而实现了一致样本集的自适应选择,保证了椭圆参数拟合精度的同时提高了算法的适应性和鲁棒性。结果：采用本文算法检测一幅图像的平均时间约为110ms,与常用椭圆检测方法相比检测速度有显著提高。结论：对比实验表明,本文提出的椭圆检测方法与其他方法相比具有更高的精度、更快的实时性和更强的鲁棒性。     

基于数据驱动多输出ARMAX建模的高炉十字测温中心温度在线估计

高炉（Blast furnace,BF）炼铁中,十字测温作为炉顶温度和煤气流分布监测的最主要手段,对高炉的安全、稳定和高效运行起着重要作用.然而,由于高炉炉顶中心部位温度较高,造成十字测温装置中心位置传感器极易损坏,并且更换周期长,因而无法及时判断炉顶煤气流分布.针对这一实际工程问题,本文基于时间序列建模思想,集成采用多输出自回归移动平均（Multi-output autoregressive moving average,M-ARMAX）建模、因子分析、Pearson相关分析、基于赤池信息准则（Akaike information criterion,AIC）与模型拟合优度联合定阶等混合技术,提出一种模型结构简单、精度较高且易于工程实现的十字测温中心温度在线估计方法.首先,提出利用因子分析与Pearson相关分析相结合的稳健特征选择方法选取多输出建模输入变量.然后,采用样本均值消去法预处理采集的高炉样本数据,使其成为离散随机数.基于离散随机数,建立算法简单、易于工程实现的M-ARMAX温度模型:为了克服传统基于AIC阶数确定造成模型阶次高、结构复杂的问题,提出在AIC准则基础上进一步引入模型拟合优度来选取模型最小阶,可保证模型估计精度的同时降低模型阶次;同时,采用可快速收敛的递推最小二乘算法辨识M-ARMAX模型参数,并用残差分析方法检验模型.工业试验和比较分析表明:建立的M-ARMAX模型能够根据实时数据同时对十字测温装置多个中心温度点进行准确和稳定估计,且模型估计误差符合高斯白噪声特性.     

Robust multiple-model LPV approach to nonlinear process identification using mixture t distributions

In this paper, we propose a robust multiple-model linear parameter varying (LPV) approach to identification of the nonlinear process contaminated with outliers. The identification problem is formulated and solved under the EM framework. Instead of assuming that the measurement noise comes from the Gaussian distribution like conventional LPV approaches, the proposed robust algorithm formulates the LPV solution using mixture t distributions and thus naturally addresses the robust identification problem. By modulating the distribution tails through degrees of freedom, the proposed algorithm can handle various outliers. Two simulated examples and an experiment are studied to verify the effectiveness of the proposed approach.     

A virtual prototyping approach to product disassembly reasoning

An important aspect of Design for the Life Cycle is assessing the disassemblability of products. After an artifact has completed its useful life, it must be disassembled then recycled, remanufactured or scrapped. Disassemblability of a product can be evaluated by performing disassembly activities on prototypes. Virtual prototyping (VP) is an alternative to hardware prototyping in which analysis of designs can be done without manufacturing physical samples. In recent years, disassembly processes have been generated either by using interactive or automated approaches, but these approaches have limitations. Interactive approaches require extensive user input usually in the form of answering questions, whereas automated approaches can only be used to generate disassembly processes for products with simple component configuration and geometry. In this paper automated and interactive techniques are combined, using VP, to generate complete disassembly processes of a product design. To support generation of disassembly processes of a product, a virtual environment and VP method were developed that will support disassembly activities performed by a designer. The product model of the virtual prototype is generated from the CAD model. The disassembly process model for the prototype is generated using automated reasoning techniques and is completed by interactively disassembling the product in the virtual environment. Extensions to automatic reasoning techniques to compute ranges of feasible directions of component removal were developed to facilitate the generation of the disassembly process. A scheme to represent the disassembly process for disassemblability evaluation was developed and implemented. In this paper a Chrysler LHS center console has been used to illustrate our approach of generating disassembly processes via VP.     

Harris角点结合金字塔光流法的目标跟踪算法设计研究

针对现存很多跟踪算法在速度和准确度方面很难满足嵌入式跟踪开发的需要,提出一种基于Harris角点和金字塔光流法的快速跟踪算法,并详细给出了DSP-FPGA的硬件设计。首先,使用Harris角点提取目标角点特征;然后,使用金字塔光流法为后续视频帧匹配角点;最后,基于角点的质心跟踪算法用于匹配目标的重心,确定目标的位置,重心跟踪算法可以较好地抵消由于旋转或扭曲带来的形变问题。在硬件实现过程中,FPGA方便电路设计,使用硬件描述程序语言实现硬件算法、逻辑控制和外部接口,DSP则运行目标跟踪算法。实验结果验证了本文硬件实现算法的有效性,相比于AVT21开发板的质心跟踪算法、相位相关跟踪算法和金字塔相关性跟踪算法相比,本文算法在平均重叠和平均中心误差方面具有一定优势,在720p的视频流上可以满足25fps。     

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.