Large-scale parallel topology optimization using a dual-primal substructuring solver

Parallel computing is an integral part of many scientific disciplines. In this paper, we discuss issues and difficulties arising when a state-of-the-art parallel linear solver is applied to topology optimization problems. Within the topology optimization framework, we cannot readjust domain decomposition to align with material decomposition, which leads to the deterioration of performance of the substructuring solver. We illustrate the difficulties with detailed condition number estimates and numerical studies. We also report the practical performances of finite element tearing and interconnection/dual–primal solver for topology optimization problems and our attempts to improve it by applying additional scaling and/or preconditioning strategies. The performance of the method is finally illustrated with large-scale topology optimization problems coming from different optimal design fields: compliance minimization, design of compliant mechanisms, and design of elastic surface wave-guides. The authors acknowledge the support of the Air Force Office of Scientific Research (AFOSR) under grant FA9550-05-1-0046. The computational facility was obtained under the grant AFOSR-DURIP FA9550-05-1-0291.     

Churn management optimization with controllable marketing variables and associated management costs

In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model after relative management costs of controllable marketing variables are estimated through a triangulation method. Finally, a PLS optimization model with marketing objectives and constraints are specified and solved via a sequential quadratic programming method. In our experiments, we observe that while the training and test data sets are dramatically different in terms of churner distributions (50% vs. 1.8%), four controllable variables in three marketing strategies significantly changed through optimization process while other variables only marginally changed. We also observe that the most significant variable in a PLS prediction model does not necessarily change most significantly in our PLS optimization model due to the highest management cost associated, implying differences between a prediction and an optimization model. Finally, two marketing models designed for targeting the subsets of customers based on churn probability or management costs are presented and discussed.     

Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices

Load balancing in electricity grids becomes a more sophisticated problem by the increased availability of time-varying stochastic supply of electricity from conversion of renewable resources like wind or sunlight. Due to the fact that large quantities of electrical energy cannot be stored easily, demand side management by shifting electrical loads is one attempt to cope with this problem. In this paper we discuss and compare two types of control signals to use the thermal storage of electrical household appliances as balancing power. As the system of our research consists of a high number of controllable refrigerators with independent parameters and behaviour, we investigate the synergetic behaviour by a simulation model. For this objective we analyze a simulation model of controllable refrigerators with respect to their ability to shift their energy demand depending on parameterized external signals. We show that both types of control signals can be used for short term reserves with delivery within 15 min of time, but they differ in possible shapes of the resulting load curves and in the reaction time of the controlled system.In addition to the simulation model we develop a model of the synergetic behaviour of an ensemble of refrigerators' reaction on control signals. This mathematical model predicts the electricity demand of ensembles of controlled appliances. As it reduces the simulation model's complexity it could be used in a sophisticated control strategy, e.g. in a model predictive control approach. The general attempt to integrate the load shift potential of cooling devices into the control of an electricity grid can probably be transferred to other electrical appliances with thermal storage capacities.     

Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.     

Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models

Stochastic Flow Models (SFMs) are stochastic hybrid systems that abstract the dynamics of many complex discrete event systems and provide the basis for their control and optimization. SFMs have been used to date to study systems with a single user class or some multiclass settings in which performance metrics are not class-dependent. In this paper, we develop a SFM framework for multiple classes and class-dependent performance objectives, where competing classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. In this framework, we analyze new phenomena that result from the interaction of the different classes and give rise to a new class of “induced” events that capture delays in the SFM dynamics. We derive Infinitesimal Perturbation Analysis (IPA) estimators for derivatives of various class-dependent objectives, and use them as the basis for on-line optimization algorithms that apply to the underlying discrete event system (not the SFM). This allows us to contrast system-centric and user-centric objectives, thus putting the resource contention problem in a game framework. The unbiasedness of IPA estimators is established and numerical results are provided to illustrate the effectiveness of our method for the case where there are no constraints on the controllable thresholds and to demonstrate the gap between the results of system-centric optimization and user-centric optimization.     

动态双足机器人的控制与优化研究进展

对动态双足机器人的可控周期步态的稳定性、鲁棒性和优化控制策略的国内外研究现状与发展趋势进行了探讨.首先,介绍动态双足机器人的动力学数学模型,进一步,提出动态双足机器人运动步态和控制系统原理;其次,讨论动态双足机器人可控周期步态稳定性现有的研究方法,分析这些方法中存在的缺点与不足;再次,研究动态双足机器人的可控周期步态优化控制策略,阐明各种策略的优缺点;最后,给出动态双足机器人研究领域的难点问题和未来工作,展望动态双足机器人可控周期步态与鲁棒稳定性及其应用的研究思路.     

弹性需求下易变质物品定价、营销与生产计划的联合优化

针对易变质的商品,分析基于弹性需求的定价、营销及生产计划的联合优化问题,并建立用于描述该问题的非线性规划模型。考虑到模型是高度非线性的,提出基于几何规划的求解方法。首先将高度非线性的问题简化为只含有一个变量的问题;然后利用黄金分割法获得原问题高质量的近优解;最后通过算例验证了所提出求解方法的可操作性和正确性,并分析了主要参数的灵敏度。     

Topology optimization of a cantilevered piezoelectric energy harvester using stress norm constraints

Vibrational piezoelectric energy harvesters are devices which convert ambient vibrational energy into electric energy. Here we focus on the common cantilever type in which an elastic beam is sandwiched between two piezoelectric plates. In order to maximize the electric power for a given sinusoidal vibrational excitation, we perform topology optimization of the elastic beam and tip mass by means of the SIMP approach, leaving the piezoelectric plates solid. We are interested in the first and especially second resonance mode. Homogenizing the piezoelectric strain distribution is a common indirect approach increasing the electric performance. The large design space of the topology optimization approach and the linear physical model also allows the maximization of electric performance by maximizing peak bending, resulting in practically infeasible designs. To avoid such problems, we formulate dynamic piezoelectric stress constraints. The obtained result is based on a mechanism which differs significantly from the common designs reported in literature.     

Two-Color Capillary Electrophoresis with On-Column Excitation and Wave-Guide Based Fluorescent Detection

An optical wave-guide based two-color capillary electrophoresis laser induced fluorescence (CE-LIF) instrument is described. The wave-guide based approach allows for on column excitation and detection with two-color discrimination. The instrument is designed to allow either electrokinetic or hydrodynamic injections. In its present configuration, the attainable limit of detection (LOD, S/N = 3) was 50 X 10⁻²¹ moles of fluorescein with a 488-nm excitation source. This study was designed to test the instrument design for applications in protein analyses. Fluorescent dyes with two different wavelengths were simultaneously separated and detected as were complexes formed by labeled antibodies to NFκB p65 and cdc2p34. Quantification of both proteins in THP-1 cell lysates performed using this approach illustrates a rapid screening application of this instrument.     

Coordinating orders in a two echelon supply chain with controllable lead time and ordering cost using the credit period

In this research, a coordination mechanism based on a credit period in a two echelon supply chain with one buyer and one supplier, is designed. The buyer is faced with uncertain demand by coping with normal distribution. Both lead time and ordering cost for receiving his order can be reduced at an added cost; in other words, they are controllable. The optimization models with and without integration are proposed. Then a way to coordinate orders in supply chain based on the credit period so that the total cost of supply chain would be minimized is designed. By using this mechanism we also discuss how the credit period is to be determined in order to achieve channel coordination and a win-win outcome. Finally, numerical examples are solved to illustrate the theoretical results and obtain the managerial insights.     

A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.     

Elastic threshold-based admission control for QoS satisfaction with reward optimization for servicing multiple priority classes in wireless networks

We analyze performance characteristics of a class of call admission control (CAC) algorithms designed for servicing multiple priority classes in wireless networks with the goal of quality of service (QoS) satisfaction and reward optimization. By reward, we mean some sort of “value” obtained by the system as a result of servicing multiple priority classes. In this paper we design and evaluate the performance of a new algorithm, elastic threshold-based CAC, in terms of the maximum reward obtainable with QoS satisfaction from servicing multiple priority classes with distinct QoS requirements, and compare it with existing partitioning, threshold, and spillover CAC algorithms. We also develop a heuristic-based search method to determine the best threshold-value sets used for multiple service classes by sequentially adjusting these thresholds based on the reward and rejection rate obtainable vs. QoS constraints of each service class. We demonstrate through test cases and simulation that elastic threshold-based CAC outperforms existing CAC algorithms for QoS satisfaction and reward optimization in solution optimality for serving multiple QoS service classes in wireless networks.     

Stochastic H 2 /H ∞-control for a dynamical system with internal noises multiplicative with respect to state, control, and external disturbance

We consider an optimal control problem for a dynamical system under the influence of disturbances of both deterministic and stochastic nature. The system is defined on a finite time interval, and its diffusion coefficient depends on the control signal. The controller in the feedback circuit is assumed to be static, nonstationary, linear in the state vector, and satisfying the condition ‖L‖_∞ < γ that bounds the norm of operator L: v ?z for the transition of external disturbance to the controllable output signal. Solving the optimization H ₂/H _∞-control problem, we get three matrix functions satisfying a system of two differential equations of Riccati type and one matrix algebraic equation. In the special case of a stochastic system whose diffusion coefficient does not depend on the control signal, the system is reduced to two related Riccati equations.     

Service Time Optimization of Mixed-Line Flow Shop Systems

We consider deterministic mixed-line flow shop systems that are composed of controllable and uncontrollable machines. Arrival times and completion deadlines of jobs are assumed to be known, and they are processed in the order they arrive at the machines. We model these flow shops as serial networks of queues operating under a non-preemptive first-come-first-served policy, and employ max-plus algebra to characterize the system dynamics. Defining completion-time costs for jobs and service costs at controllable machines, a non-convex optimization problem is formulated where the control variables are the constrained service times at the controllable machines. In order to simplify this optimization problem, under some cost assumptions, we show that no waiting is observed on the optimal sample path at the downstream of the first controllable machine. We also present a method to decompose the optimization problem into convex subproblems. A solution algorithm utilizing these findings is proposed, and a numerical study is presented to evaluate the performance improvement due to this algorithm.      

Smart monopole antenna with pattern and frequency reconfiguration characteristics based on programmable metasurface

Metasurfaces having ultrathin and planar structure with sub‐wavelength unit cell, have recently gained significant potential for use thanks to their control capabilities over the electromagnetic waves from microwave to the visible range. The structure and the dimensions of the sub‐wavelength elements determine the electromagnetic properties, capabilities, and functionalities of the metasurfaces providing a full control of the reflected and transmitted fields and these metasurfaces are referred to as analog metasurfaces. When adjustability is added to the unit cells, programmable or digital metasurfaces are obtained, allowing us to take multiple unique functionality advantages controlled by external stimuli. In this study, we propose a metasurface structure, also known as 1‐bit coding metasurface, which is controlled depending on the “On/Off” state. The “On/Off” state is controlled by a computer program using genetical algorithm. Depending on the operating state, electromagnetic waves can be manipulated and different functionalities of the metasurfaces can be realized. The contribution and innovation of the study is the demonstration of the beam rotation, resonance frequency shift and radiation pattern reconfiguration properties of a simple monopole antenna by using controllable metasurface composed of T shaped resonator and circular patch with an operating frequency between 4.3 and 5.6 GHz.     

QoC elastic scheduling for real-time control systems

The elastic task model, a significant development in scheduling of real-time control tasks, provides a mechanism for flexible workload management in uncertain environments. It tells how to adjust the control periods to fulfill the workload constraints. However, it is not directly linked to the quality-of-control (QoC) management, the ultimate goal of a control system. As a result, it does not tell how to make the best use of the system resources to maximize the QoC improvement. To fill in this gap, a new feedback scheduling framework, which we refer to as QoC elastic scheduling, is developed in this paper for real-time process control systems. It addresses the QoC directly through embedding both the QoC management and workload adaptation into a constrained optimization problem. The resulting solution for period adjustment is in a closed-form expressed in QoC measurements, enabling closed-loop feedback of the QoC to the task scheduler. Whenever the QoC elastic scheduler is activated, it improves the QoC the most while still meeting the system constraints. Examples are given to demonstrate the effectiveness of the QoC elastic scheduling.     

Modular formal verification of specifications of concurrent systems

In this paper, we propose a bottom‐up approach for the verification of systems with modular structure: we prove that when the modules are composed in specific ways, the complete software system verifies a composition of the properties each component does. We focus on the process of upgrading systems with new functionalities, where the validity of old requirements needs to be ensured, but also an understanding of the new properties the upgraded system would enjoy is useful. In this work, we assume each component to be specified by a CCS process, and the properties to be expressed by selective mu‐calculus formulae. Copyright © 2007 John Wiley & Sons, Ltd.     

Policy gradient learning for quadruped soccer robots

In real-world robotic applications, many factors, both at low level (e.g., vision, motion control and behaviors) and at high level (e.g., plans and strategies) determine the quality of the robot performance. Consequently, fine tuning of the parameters, in the implementation of the basic functionalities, as well as in the strategic decisions, is a key issue in robot software development. In recent years, machine learning techniques have been successfully used to find optimal parameters for typical robotic functionalities. However, one major drawback of learning techniques is time consumption: in practical applications, methods designed for physical robots must be effective with small amounts of data. In this paper, we present a method for concurrent learning of best strategy and optimal parameters using policy gradient reinforcement learning algorithm. The results of our experimental work in a simulated environment and on a real robot show a very high convergence rate.     

Model predictive control for perturbed max-plus-linear systems

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the (max,+) algebra. In our previous work, we have only considered MPC for the deterministic noise-free case without modeling errors. In this paper, we extend our previous results on MPC for max-plus-linear systems to cases with noise and/or modeling errors. We show that under quite general conditions the resulting optimization problems can be solved very efficiently.     

An improved visualization-based approach for project portfolio selection

We propose a 2-step interactive approach for solving a project portfolio selection problem as a single-criterium optimization problem. Our approach innovates by using two coordinated charts: an interactive project timeline with drag-and-drop functionalities for project reallocation in time; and an interactive cost and risk chart that combines line charts and bar charts in order to present multidimensional time-based datasets. We also use bar charts related to the allocation of man-hour resources. These functionalities enable users to refine the model that is fed into optimization software in order to achieve results that better correspond to their expectations. We discuss results of a heuristic-based usability evaluation of our prototype, its use in real scenarios, and present preliminary positive feedback from users.