Simultaneous Optimization of Skew and Control Step Assignments in RT-Datapath Synthesis

The numerical method to solve simultaneous design and control optimization problems is considered. Firstly, the simultaneous optimization problem dealt with in this paper is defined, and this problem is transformed into a new formulated nonlinear programming problem. Considering that the nonlinear programming problem is solved by using a sequential quadratic programming method, the diagonal form of the Hessian matrix for the Lagrange function is introduced in a new formulation named Block Diagonal Hessian (BDH) method. Secondly, the BDH method is applied to the simultaneous spaceplane design and trajectory optimization problem, where the best size and its optimal trajectory are estimated to realize it. Through the several calculations, it is confirmed that the proposed BDH method is effective for simultaneous design and control optimization problems.

Simultaneous optimization for several characteristics concerning percutaneous absorption and skin damage of ketoprofen hydrogels containing d-limonene

With the advance of process technologies, delay variation becomes relatively larger. As a result, it becomes difficult to improve a performance such as a clock frequency in conventional worst-case design. A post-silicon approach is one of the promising approaches to overcome this serious problem. In this paper, two techniques are introduced and combined: The first technique, named the stall insertion, is to insert pauses, and shift all the following operation schedules by one or more control steps. The second technique, named the timing skew adjustment, is a temporal difference of the arrival of clock signals at registers. This paper proposes a novel datapath which is tuned by the stall insertion with the timing skew adjustment after the fabrication. The tuned datapath in proposed design can be expected to operate correctly with minimum degradation while keeping clock period unchanged. Since the stall insertion degrades latency, the stall minimization problem is formulated incorporating with timing skew optimization. Two solution algorithms are presented: one is a mixed integer linear programming (MILP) formulation, and the other is the repetition of skew adjustability test. Experimental results show the effectiveness of our MILP.

Simultaneous plant-controller design optimization of a two-link planar manipulator

• A new control logic for the SCMSHPD system under low meteorological conditions was proposed. • Performance of the SCMSHPD system operating under the new control logic were analyzed. • The effects of the waste heat recovery on the combined drying system under different meteorological parameters were obtained. • Drying process of the combined drying system was optimized. To solve the problem that the high drying temperature demand could not be met when combined drying system operated under low ambient temperature and weak solar irradiance, a new control logic of the solar combined multi-heat source heat pump drying (SCMSHPD) system was proposed and its simulation model was established. By modifying its operating strategy, the SCMSHPD system achieved a higher drying temperature under low environment temperature and weak irradiance, which ensured the implementation of the drying process. The simulation results was verified through experimental study. Additionally, to address the issue of significant heat loss of the exhaust air, waste heat recovery was implemented and its impact on the system’s operating characteristics was analyzed. At last, the drying process was optimized due to the significant improvement of the specific moisture extraction rate ( SMER ) and the system performance was further improved. The results showed that the errors in coefficient of performance ( COP ), energy consumption, and SMER between the experimental and simulation results were 4.04%, 1.84% and 1.53% respectively, which verified the reliability of the simulation model. In October, the COP of the SCMSHPD system without waste heat recovery was 3.66% higher than that of the ASHPD system, but with the decrease of ambient temperature and solar irradiance, the COP of the combined drying system increased by 5.86% in December, demonstrating the advantage of the SCMSHPD system with the new control logic under low meteorological parameters. The waste heat recovery could not improve the system performance of the SCMSHPD system operating with the new control logic proposed, which caused a decrease of 1.06% and 1.19% in October and December respectively. However, the SMER was significantly increased by 70.67% and 12.78% respectively, and the energy consumption was significantly decreased by 41.33% and 10.94%. The results indicated that although waste heat recovery resulted in a slight decrease in system COP , the significant reduction in energy consumption and notable increase in SMER demonstrated a marked improvement in the overall system performance. Due to the significant increase of the SMER , the drying time was shortened, which caused an increase of 1.77% in COP in October. With meteorological parameters became lower, the COP get a bigger increase of 3.34%. Therefore, it could be concluded that the combination of waste heat recovery and shortened drying time could effectively improve the system performance, and the lower the meteorological parameters, the greater the improvement in the system performance. This study can provide important guidance for the selection of control logic and the optimization of the drying process for the SCMSHPD system under low meteorological parameters.

This paper proposes a new static simultaneous optimization of dispatching and conflict-free routing for unidirectional Overhead Hoist Transport (OHT) systems. Our objective is to minimize the entire transportation time where the given transportation tasks are finished. A hybrid system modeling is applied for OHT system that is constituted by the OHT vehicles at the lower level and governed by the upper level scheduler. In the proposed modeling, it is clarified that the simultaneous optimization problems are recast as a binary integer linear problem by using model predictive control. This paper presents a numerical example and discusses limitation of proposed modeling.

Population-based (gradient-free) methods have become attractive for solving optimization problems since they have been known to locate "optimal" (best) positions within a search space. In this study, we investigate the use of three different gradient-free optimization methods namely: Iterative Latin Hypercube Sampling (ILHS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for well placement and well controls optimization problem of the CO2 underground storage in a 2D saline aquifer with bound constraints on the design variables. We also consider sequential optimization of well locations and well controls using the gradient-free methods. To evaluate the performance of these algorithms, we test the methods on well-known test (or toy) problems such as the multi-dimensional (40 design variables) Rosenbrock function, as well as the Traveling Salesman Problem (TSP) with 20 cities (20 design variables). We further extend the study to a more complex problem which involves finding the optimal well types, well locations, or well controls for a CO2 injection problem in a synthetic saline aquifer compositional model built in a commercial simulator. The objective function formulated for the CO2 injection problem is the net present environmental value (NPEV) involving discrete design variables (well types and well locations) and real design variables (well controls). Results obtained from the performance comparison of these gradient-free methods on the toy problems demonstrate the superior performance of the ILHS over PSO and GA for the Rosenbrock test function, while GA outperforms ILHS and PSO in solving the TSP. We also observe that for the simple well locations optimization problems involving one or two wells, all three algorithms obtained comparable results, given the same number of samples and number of iterations. However, for more complex cases such as the sequential or simultaneous optimization problems, when multiple wells which may include injectors and producers are present, we observe significant differences in the selection of the optimal well types and well locations among the three optimizers. In the simultaneous optimization of well types, well locations, and well controls, when both injectors and producers are BHP controlled, the results obtained showed that it is more optimal for at least one producer to be present, and for the injectors to be operated at the upper bound of the injector BHP, while the producers operated at the lower bound of the producer BHP to maximize the NPEV.

This paper presents a high-level synthesizer to map a complete program efficiently on a dynamically reconfigurable processor (DRP). Initially, we introduce our DRP architecture, which is suitable for control-intensive programs since it has a stand-alone finite state machine that switches “contexts” consisting of many processing elements (PEs). Then, we propose three new techniques optimized for our DRP. Firstly, we explain how synthesized control steps are mapped onto the contexts. Several control steps are combined as a context to utilize PEs efficiently since each control step does not require the same amount of operational units. Secondly, we describe a modulo scheduling algorithm for loop pipelining, considering both spatial and time dimensions of our DRP. Lastly, we explain a scheduling technique to optimize clock frequency, which can take advantage of multiplexer, wire and routing switch delays. We have demonstrated a JPEG-based image decoder example to evaluate our methods. Experimental results show that high area efficiency is achieved by balancing the number of PEs between contexts. Despite an overall increase in performance on pipelining of 3.6 times that without pipelining, the number of operational units increased by a factor of 2.2. The clock frequency is maximized with accurate delay estimation.

Traditionally, the application of a neural network (Haykin, 1999) to solve a problem has required to follow some steps before to obtain the desired network. Some of these steps are the data preprocessing, model selection, topology optimization and then the training. It is usual to spend a large amount of computational time and human interaction to perform each task of before and, particularly, in the topology optimization and network training. There have been many proposals to reduce the effort necessary to do these tasks and to provide the experts with a robust methodology. For example, Giles et al. (1995) provides a constructive method to optimize iteratively the topology of a recurrent network. Other methods attempt to reduce the complexity of the network structure by mean of removing unnecessary network nodes and connections like in (Morse, 1994). In the last years, evolutionary algorithms have been shown as promising tools to solve this problem, existing many competitive approaches in the literature. For example, Blanco et al. (2001) proposed a master-slave genetic algorithm to train (master algorithm) and to optimize the size of the network (slave algorithm). For a general view of the problem and the use of evolutionary algorithms for neural network training and optimization, we refer the reader to (Yao, 1999). Although the literature about genetic algorithms and neural networks is very extensive, we would like to remark the recent popularity of multi-objective optimization (Coello et al., 2002, Jin, 2006), specially to solve the problem of simultaneous training and topology optimization of neural networks. These methods have shown to perform suitably for this task in previous works, although most of them are proposed for feedforward models. They attempt to optimize the structure of the network (number of connections, hidden units or layers), while training the network at the same time. Multi-objective algorithms may provide important advantages in the simultaneous training and optimization of neural networks: They may force the search to return a set of optimal networks instead of a single one; they are capable to speed-up the optimization process; they may be preferred to a weight-aggregation procedure to cover the regularization problem in neural networks; and they are more suitable when the designer would like to combine different error measures for the training. A recent review of these techniques may be found in (Jin, 2006).

Simultaneous outline shape and size optimization for stiffeners in practical engineering structures

A method of simultaneous optimization of structure and control using mixed H2 and H∞ norms of the transfer function as the objective function is proposed and the modeling and formulation of simultaneous optimization problems associated with this approach are discussed in this paper. Simultaneous optimization is realized by iteratively executing structural optimization and controller optimization. Both serial and parallel approaches to combine structural optimization and controller optimization are investigated. They are applied to the simultaneous optimization of the cross-sectional parameters of a spring-supported beam and the parameters of the controller used to actively suppress the vibration of the beam. The performance of both displacement output and control input is improved significantly after simultaneous optimization. The simulation results show the great potential advantages of simultaneous optimization over traditional design methods and the effectiveness of the proposed approach.

This paper is concerned with the sampled-data based adaptive linear quadratic (LQ) control of hybrid systems with both unmeasurable Markov jump processes and stochastic noises. By the least matching error estimation algorithm, parameter estimates are presented. By a double-step (DS) sampling approach and the certainty equivalence principle, a sampled-data based adaptive LQ control is designed. The DS-approach is characterized by a comparatively large estimation step for parameter estimation and a sufficient small control step for control updating. Under mild conditions, the closed-loop system is shown to be stable. It is found that the key factor determining the perfor- mance index is the estimation step rather than the control step. When the estimation step becomes too small, the system performance will become worse. When the estimation step is fixed, the system performance can indeed be improved by reducing the control step, but cannot reach the optimal value. The index difference between the sampled-data based adaptive LQ control and the conventional LQ optimal control is asymptotically bounded by a constant depending on the estimation step and the priori information of the parameter set.

A composite framework for co-optimization of spacecraft trajectory and propulsion system

As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.

Database systems allow quick creation of performance problems. The goal of database systems is to allow the computer-illiterate to write complex and complete applications. It is the job of the syst...