Iterative improvement of parameter estimation for model migration by means of sequential experiments

Determining an optimal design for estimation of parameters of a class of complex models expected to be built at a minimum cost is a growing trend in science and engineering. We adopt a scale-bias adjustment migration strategy for integrating base and new models based on similar nature underlying processes. Further, we propose a Bayesian sequential algorithm for obtaining the statistically most informative data about the migrated model for use in parameter estimation. The benefits of the proposed strategy over traditional approaches presented in recent reported work are demonstrated using Monte Carlo simulations.     

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 72B线材在绕簧过程中发生了断裂现象。利用金相显微镜对断口附近的金相组织、非金属夹杂物和表面形貌等进行了分析。结果表明：钢丝表面有损伤，且损伤部位出现横向裂纹和形变马氏体，这是导致绕簧过程中发生断裂的主要原因。针对断裂产生的原因，提出具体的改进措施。     

A bi-objective integrated procurement,production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA

Efficient management of supply chain (SC) requires systematic considerations of miscellaneous issues in its comprehensive version. In this paper, a multi-periodic structure is developed for a supply chain network design (SCND) involving suppliers, factories, distribution centers (DCs), and retailers. The nature of the logistic decisions is tactical that encompasses procurement of raw materials from suppliers, production of finished product at factories, distribution of finished product to retailers via DCs, and the storage of raw materials and end product at factories and DCs. Besides, to make the structure more comprehensive, a flow-shop scheduling model in manufacturing part of the SC is integrated in order to obtain optimal delivery time of the product that consists of the makespan and the ship time of the product to DCs via factories. Moreover, to make the model more realistic, shortage in the form of backorder can occur in each period. The two objectives are minimizing the total SC costs as well as minimizing the average tardiness of product to DCs. The obtained model is a bi-objective mixed-integer non-linear programming (MINLP) model that is shown to belong to NP-Hard class of the optimization problems. Thus, a novel algorithm, called multi-objective biogeography based optimization (MOBBO) with tuned parameters is presented to find a near-optimum solution. As there is no benchmark available in the literature, the parameter-tuned multi-objective simulated annealing algorithm (MOSA) and the popular non-dominated sorting genetic algorithm (NSGA-II) are developed to validate the results obtained and to evaluate the performance of MOBBO using randomly generated test instances.     

New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

This paper addresses the field of stereophonic acoustic echo cancelation (SAEC) by adaptive filtering algorithms. Recently, simplified versions of the fast transversal filter (SFTF)‐type algorithm has been proposed. In this paper, we propose two major contributions. In the first contribution, we propose two new FTF‐type algorithms with low complexity and good convergence speed characteristics. These two proposed algorithms are mainly on the basis of a forward prediction scheme to estimate the so called dual Kalman gain, which is inherent in the filtering part update. This computation complexity is achieved by the introduction of new relations for the computation of the likelihood variables that are simple and lead to further simplifications on the prediction part of the two proposed algorithms. In the second contribution, we propose to adapt then apply these four new SFTF‐type algorithms, (the two proposed algorithms in this paper and their original versions) in the SAEC applications. A fair comparison of the proposed algorithms with the original SFTF and the normalized least mean square algorithms, in mono and SAEC applications, is presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic facility layout problem in footwear industry

Dynamic facility layout problem (DFLP) deals with the arrangement of machines in a site as to minimize the sum of materials handling and re-layout costs by considering multi periods. The DFLP studies in the literature provide several different algorithms and utilize the well known test problems to assess their performance. However, real life applications are overlooked. The industries such as footwear and clothing are prone to seasonal demand changes. Therefore, time horizons and layout/re-layout of the machines within the facility should be studied carefully. This study considers a footwear facility and several scenarios are generated by using the real life data. A clonal selection based algorithm is proposed to solve the real life DFLP. The performance of the algorithm, further the effect of time periods on solution quality and applicability of the results are tested and promising results are obtained.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.     

低碳时变城配送车辆路径-发车调度集成优化

针对交通拥挤环境下日益增长的城市配送需求，通过分析时序依赖对成本和碳排放的影响，引入车辆在节点等待和离散调度策略，研究基于时序依赖的低碳城市配送车辆路径与离散调度问题。为求解该问题，设计基于遗传算法与局部搜索相结合的混合进化搜索算法对模型求解，用积极的局部搜索机制替代随机的变异操作，并通过可行解构造算法、变概率交叉和多种局部搜索策略来提高算法求解质量和求解效率。通过对比仿真实验对算法和模型的有效性进行了验证。     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations

The increasing demand for low power consumption and high computational performance is outpacing available technological improvements in embedded systems. Approximate computing is a novel design paradigm trying to bridge this gap by leveraging the inherent error resilience of certain applications and trading in quality to achieve reductions in resource usage. Numerous approximation methods have emerged in this research field. While these methods are commonly demonstrated in isolation, their combination can increase the achieved benefits in complex systems. However, the propagation of errors throughout the system necessitates a global optimization of parameters, leading to an exponentially growing design space. Additionally, the parameterization of approximated components must consider potential cross-dependencies between them. This work proposes a systematic approach to integrate and optimally configure parameterizable approximate components in FPGA-based applications, focusing on low-level but high-bandwidth image processing pipelines. The design space is explored by a multi-objective genetic algorithm which takes parameter dependencies between different components into account. During the exploration, appropriate models are used to estimate the quality-resource trade-off for probed solutions without the need for time-consuming synthesis. We demonstrate and evaluate the effectiveness of our approach on two image processing applications that employ multiple approximations. The experimental results show that the proposed methods are able to produce a wide range of Pareto-optimal solutions, offering various choices regarding the desired quality-resource trade-off.