Try and error-based scheduling algorithm for cluster tools of wafer fabrications with residency time constraints

To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.

Laser-Based Guided Wave Propagation and Mode Decomposition in Detecting the Integrity of Structural I-Beams

This paper addresses the studies carried out on an I-beam to reveal the wave propagation characteristics and tackle the multi-mode propagation of Lamb waves. The experimental setup consisted of a new 3D Scanning Laser Doppler Vibrometer manufactured by Polytec (3D-SLDV) and was used to acquire high resolution time-space Lamb waves that were propagating in the I-beam. A high power and pulsed Nd:YAG laser was used to emit the required Lamb waves. The emission and sensing of the waves were carried out simultaneously. The wave propagation data was recorded by scanning the surface of the I-beam in a sequential manner. The measured data was used to construct the wave patterns that were propagating in the I-beams at different time instants. Furthermore, as the waves in an I-Beam propagate with multiple modes even at low frequency range, filtering was carried out in the frequency-wavenum- ber domain in order to decompose the modes. The results presented thereby confirm that the new 3D-SLDV possesses tremendous capability in revealing the wave propagation characteristics and its interaction with defect. The results could be the first time that the waves propagating in a real I-beam can be visually observed, whilst in the past, it can only be visualized through simulation. The capability of using such totally laser-based 3D inspection system to reveal the characteristics of Lamb wave and its interaction with defects are substantial.

Mechanical analysis of double-layered circular graphene sheets as building material embedded in an elastic medium

Possessing the unique and highly valuable properties, graphene sheets(GSs) have attracted increasing attention including that from the building engineer due to the fact that Graphene can be utilized to reinforce concrete and other building materials. In this work, the nonlocal elastic theory and classical plate theory(CLPT) are used to derive the governing equations. The element-free framework for analyzing the buckling behaviors of double layer circular graphene sheets(DLCGSs) relying on an elastic medium is proposed. Pasternak-type model is adopted to describe the elastic medium. Accordingly, the influences of boundary conditions, size of GSs and nonlocal parameters on the buckling behavior of DLCGSs are investigated. The results show that the OP buckling modes are only sensible to the van der Waals forces.

Heuristic tabu search scheduling algorithm for wet-etching systems in semiconductor wafer fabrications

To improve overall equipment efficiency(OEE) of a semiconductor wafer wet-etching system,a heuristic tabu search scheduling algorithm is proposed for the wet-etching process in the paper,with material handling robot capacity and wafer processing time constraints of the process modules considered.Firstly,scheduling problem domains of the wet-etching system(WES) are assumed and defined,and a non-linear programming model is built to maximize the throughput with no defective wafers.On the basis of the model,a scheduling algorithm based on tabu search is presented in this paper.An improved Nawaz,Enscore,and Ham(NEH) heuristic algorithm is used as the initial feasible solution of the proposed heuristic algorithm.Finally,performances of the proposed algorithm are analyzed and evaluated by simulation experiments.The results indicate that the proposed algorithm is valid and practical to generate satisfied scheduling solutions.

Importance measure-based maintenance strategy optimization:Fundamentals,applications and future directions in AI and IoT

Numerous maintenance strategies have been proposed in the literature related to reliability.This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies.We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research.The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies.Additionally,it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies.Finally,it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

由微孔棒状羟基磷灰石为模板合成的新型层次孔炭材料的电化学电容性能(英文)

电化学电容器已经成为极具潜力的可满足高功率需求的储能系统器件.多孔炭具有大比表面积、高导电性、化学惰性、廉价及可调孔结构等优势,因此成为电化学电容器最为常用的电极材料.本文报道由微孔棒状羟基磷灰石为模板及蔗糖为碳源合成的新型具有层次孔道结构的孔炭材料的电化学电容器的性能.采用X射线衍射分析仪、扫描电子显微镜、透射电子显微镜、X射线光电子能谱及BET表面分析仪表征了合成的多孔炭的形貌及表面特性.采用循环伏安法、交流阻抗图谱分析及恒流充放电评价多孔炭材料在1 mol·L^(-1)硫酸中的电化学电容性能.多孔炭具有高的比表面积(719.7 m^2·g^(-1))和大的孔容(1.32 cm^3·g^(-1)),其无序的孔道由任意分布的微孔、坍塌的中孔及类模板形状的相互交织的棒状中孔组成.随着炭化温度的增加,微孔及棒状中孔的密度随之降低,在炭化温度高达900℃时,孔径分布图上出现了三个峰.正是由于这些特殊的结构特征,山900℃炭化得到的多孔炭制成的电极展示出很好的电化学电容性能.

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

COMPETITIVE STRATEGY IN REMANUFACTURING AND THE EFFECTS OF GOVERNMENT SUBSIDY

In this paper, we consider a single-period model comprised of an original manufacturer （OM） who produces only new products and a remanufacturer who collects used products from consumers and produces remanufactured products. The OM and the remanufacturer compete in the product market. We examine the effects of government subsidy as a means to promote remanufacturing activity. In particularly, we consider three subsidy options： subsidy to remanufacturer, subsidy to consumers, and subsidy shared by remanufacturer and consumers. We find that the introduction of government subsidy on remanufacturer or consumers always increases remanufacturing activity. We also find that subsidy to remanufacturer is the best subsidy option, because subsidy to remanufacturer results in lower price of remanufactttred products, thus leading to higher consumer surplus.

Control for Intelligent Manufacturing： A Multiscale Challenge

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include： multi-time scale, spacetime scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

SOME EXTENDED KNAPSACK PROBLEMS INVOLVING JOB PARTITION BETWEEN TWO PARTIES

Some novel applications and pragmatic variations of knapsack problem （KP） are presented and constructed, which are formulated and developed from a model initiated in this paper on profit allocation from partition of jobs in terms of two-person discrete cooperation game.

CAPACITY ALLOCATION AND COORDINATION ISSUES FOR THE TIMELY PROCESSING OF OUTSOURCED OPERATIONS

We consider dynamic capacity booking problems faced by multiple manufacturers each outsourcing certain operations to a common third-party firm. Each manufacturer, upon observing the current state of the third-party schedule, books capacity with the objective to jointly minimize holding costs that result from early deliveries, tardiness penalties due to late deliveries, and third-party capacity booking costs. When making a reservation, each manufacturer evaluates two alternative courses of action： （i） reserving capacity not yet utilized by other manufactures who booked earlier; or （ii） forming a coalition with a subset or all of other manufacturers to achieve a schedule minimizing coalition costs, i.e., a centralized schedule for that coalition. The latter practice surely benefits the coalition as a whole; however, some manufacturers may incur higher costs if their operations are either pushed back too much, or delivered too early. For this reason, a cost allocation scheme making each manufacturer no worse than they would be when acting differently （i.e., participating in a smaller coalition or acting on their own behalf,） must accompany centralized scheduling for the coalition. We model this relationship among the manufacturers as a cooperative game with transferable utility, and present optimal and/or heuristic algorithms to attain individually and eoalitionally optimal schedules as well as a linear program formulation to find a core allocation of the manufacturers＇ costs.

Applications of gauge duality in robust principal component analysis and semidefinite programming

Gauge duality theory was originated by Preund （1987）, and was recently further investigated by Friedlander et al. （2014）. When solving some matrix optimization problems via gauge dual, one is usually able to avoid full matrix decompositions such as singular value and/or eigenvalue decompositions. In such an approach, a gauge dual problem is solved in the first stage, and then an optimal solution to the primal problem can be recovered from the dual optimal solution obtained in the first stage. Recently, this theory has been applied to a class of semidefinite programming （SDP） problems with promising numerical results by Friedlander and Mac^to （2016）. We establish some theoretical results on applying the gauge duality theory to robust principal component analysis （PCA） and general SDP. For each problem, we present its gauge dual problem, characterize the optimality conditions for the primal-dual gauge pair, and validate a way to recover a primal optimal solution from a dual one. These results are extensions of Friedlander and Macedo （2016） from nuclear norm regularization to robust PCA and from a special class of SDP which requires the coefficient matrix in the linear objective to be positive definite to SDP problems without this restriction. Our results provide further understanding in the potential advantages and disadvantages of the gauge duality theory.

Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion

To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.

Semantic Similarity between Ontologies at Different Scales

In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims (plant, yeast, and candida, among which the latter two belong to the same kingdom-fungi) using four popular measures commonly applied to biomedical ontologies (Resnik, Lin, Jiang-Conrath, and SimRel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of Jiang-Conrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar (as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales. This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering. © 2014 Chinese Association of Automation.

MARKOWITZ STRATEGIES REVISED

Continuous-time Markowitz＇s by parameterizing a critical quantity. It mean-variance efficient strategies are modified is shown that these parameterized Markowitz strategies could reach the original mean target with arbitrarily high probabilities. This, in turn, motivates the introduction of certain stopped strategies where stock holdings are liquidated whenever the parameterized Markowitz strategies reach the present value of the mean target. The risk aspect of the revised Markowitz strategies are examined via expected discounted loss from the initial budget. A new portfolio selection model is suggested based on the results of the paper.

ELECTRONIC DISCLOSURE AND FINANCIAL KNOWLEDGE MANAGEMENT

In this paper, we reported the benefits of using eXtended Markup Language （XML） to support financial knowledge management and discussed number of issues associated with developing an XML-based financial knowledge management system. Current searching engines do not provide sufficient performance in terms of recall, precision, and extensibility for financial knowledge management, because the data represented in HTML format cannot support fmancial knowledge management effectively. On the other hand, XML provides a vendor-neutral approach to structure and organize contents as XML authors are allowed to create arbitrary tags to describe the format or structure of data. A prototype of XML-based ELectronic Financial Filing System （ELFFS-XML） is developed, and value-added ated informationservices such as automatic tag generation and cross-linking rel from different data sources are provided to enable knowledge representation and knowledge generation. We compared the XML-based ELFFS with the original HTML-based ELFFS and SEDAR - an electronic filing system used in Canada, and we found that ELFFS-XML is able to provide much more functionalities to support knowledge management. We also compared our automatic tag generation result with the experts＇ and investors＇ choices, and recommended some directions for future development of similar electronic filing systems.

Grading the Severity of Mispronunciations in CAPT Based on Statistical Analysis and Computational Speech Perception

Computer-aided pronunciation training（CAPT） technologies enable the use of automatic speech recognition to detect mispronunciations in second language（L2） learners＇ speech. In order to further facilitate learning, we aim to develop a principle-based method for generating a gradation of the severity of mispronunciations. This paper presents an approach towards gradation that is motivated by auditory perception. We have developed a computational method for generating a perceptual distance（PD） between two spoken phonemes. This is used to compute the auditory confusion of native language（L1）. PD is found to correlate well with the mispronunciations detected in CAPT system for Chinese learners of English,i.e., L1 being Chinese（Mandarin and Cantonese） and L2 being US English. The results show that auditory confusion is indicative of pronunciation confusions in L2 learning. PD can also be used to help us grade the severity of errors（i.e.,mispronunciations that confuse more distant phonemes are more severe） and accordingly prioritize the order of corrective feedback generated for the learners.

Measuring the resilience of an airport network

Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.

Alternating Direction Method of Multipliers for Sparse Principal Component Analysis

We consider a convex relaxation of sparse principal component analysisproposed by d' Aspremont et al. (SIAM Rev. 49:434 448, 2007). This convex relax-ation is a nonsmooth semidefinite programming problem in which the ξ1 norm of thedesired matrix is imposed in either the objective function or the constraint to improvethe sparsity of the resulting matrix. The sparse principal component is obtained by arank- one decomposition of the resulting sparse matrix. We propose an alternating di-rection method based on a variable-splitting technique and an augmented I agrangianframework for solving this nonsmooth semidefinite programming problem. In con-trast to the first-order method proposed in d' Aspremont et al. (SIAM Rev. 49:434448, 2007), which solves approximately the dual problem of the original semidefiniteprogramming problem, our method deals with the primal problem directly and solvesit exactly, which guarantees that the resulting matrix is a sparse matrix. A globalconvergence result is established for the proposed method. Numerical results on bothsynthetic problems and the real applications from classification of text data and senatevoting data are reported to demonstrate the efficacy of our method.

A Dispatching Method for Integrated Energy System Based on Dynamic Time-interval of Model Predictive Control

In integrated energy systems(IESs),traditional fixed time-interval dispatching scheme is unable to adapt to the need of dynamic properties of the transient network,demand response characteristics,dispatching time scales in energy subsystems and renewable power uncertainties.This scheme may easily result in uneconomic source-grid-load-storage operations in IES.In this paper,we propose a dispatching method for IES based on dynamic time-interval of model predictive control(MPC).We firstly build models for energy sub-systems and multi-energy loads in the power-gas-heat IES.Then,we develop an innovative optimization method leveraging trajectory deviation control,energy control,and cost control frameworks in MPC to handle the requirements and constraints over the timeinterval of dispatching.Finally,a dynamic programming algorithm is introduced to efficiently solve the proposed method.Experiments and simulation results prove the effectiveness of the method.