A Robust Adaptive Dynamic Programming Principle for Sensorimotor Control with Signal-Dependent Noise

As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.     

Strategic Bidding Model for Power Generation Company Based on Repast Platform

： The bidding strategies of power generations in the market are a dynamic and complex problem. It is difficult to analyze and compute with the traditional mathematical methods, which is conspicuous in the middle or long-term transactions. This paper pro- poses a modelity which is the middle or long-term bidding strategy in two-tiers electricity market that is based on the optimal power flow （OPF）. Uncertainties in the outside world are regarded as the agent （Agent） of ＂external environment.＂ Under this conditions, Agent through environment evaluation judges to select viable strategic. Through learning from experiences and opponent＇s behaviors, Agent guides the purpose of the best production. The adaptability and superiority of this model is tested based on Repast with a standard IEEE-5 bus 6 notes test system.     

News event prediction by trigger evolution graph and event segment

Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information. Current datasets for event prediction, naturally, can be used for supervised learning. Event chains are either from document-level procedural action flow, or from news sequences under the same column. This paper leverages graph structure knowledge of event triggers an...     

Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem

Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.     

Dynamic portfolio choice under the time-varying, jumps, and knight uncertainty of asset return process

By introducing a stochastic element to the double-jump diffusion framework to measure the Knight uncertainty of asset return process,this paper builds the model of dynamic portfolio choice, which maximizes the expected utility of terminal portfolio wealth.Through specifying the state function of uncertainty-aversion,it utilizes the max-min method to derive the analytical solution of the model to study the effect of the time-varying,jumps,and Knight uncertainty of asset return process on dynamic portfolio choice and their interactions.Results of comparative analysis show:The time-varying results in positive or negative intertemporal hedging demand of portfolio,which depends on the coefficient of investor’s risk aversion and the correlation coefficient between return shift and volatility shift;the jumps in asset return overall reduce investor’s demand for the risky asset,which can be enhanced or weakened by the jumps in volatility;due to the existing of the Knight uncertainty,the investor avoids taking large position on risky asset,and the resulting is the improving of portfolio’s steady and immunity.At last,an empirical study is done based on the samples of Shanghai Exchange Composite Index monthly return data from January 1997 to December 2009,which not only tests the theoretical analysis but also demonstrates that the proposed method in the paper is useful from the aspect of portfolio’s equivalent utility.     

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts’ evaluation information in group decision-making,the existing methods of determining experts’ weights based on cluster analysis take into account the expert’s preferences and the consistency of expert’s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts’ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.     

Manufacturing and services: From mass production to mass customization

Manufacturing and services constitute two of the five sectors of every country’s economy;depending on the maturity of the economy,they are-in terms of employment-typically the two largest sectors.The outputs or products of an economy can also be divided into goods products(due to manufacturing,construction,agriculture and mining) and services products.To date,the goods and services products have,for the most part,been mass produced;it is the premise of this paper that recent technological advances-including flexible manufacturing,cloud computing,nanotechnology and smart sensing-can better enable the transformation from mass production to mass customization.We regard mass customization as the simultaneous and real time management of supply and demand chains,based on a taxonomy that can be defined in terms of its underpinning component and management foci.From a components perspective,we first consider the value chain of supplier,manufacturer,assembler,retailer,and customer,and then develop a consistent set of definitions for supply and demand chains based on the location of the customer order penetration point.From a management perspective,we classify the methods that are employed in the management of these chains,based on whether supply and/or demand are flexible or fixed.Interestingly,our management taxonomy highlights a very critical research area at which both supply and demand are flexible,thus manageable.Simultaneous management of supply and demand chains sets the stage for mass customization which is concerned with meeting the needs of an individualized customer market.Simultaneous and real time management of supply and demand chains set the stage for real time mass customization(e.g.,wherein a tailor first laser scans an individual’s upper torso and then delivers a uniquely fitted jacket within a reasonable period,while the individual is waiting).The benefits of real time mass customization cannot be over-stated as goods and services become indistinguishable and are co-produced-as "servgoods"-in real time,resulting in an overwhelming economic advantage.     

Fast and accurate analysis of electromagnetic scattering from targets situated in dielectric half space

Electromagnetic scattering from targets situated in half space is solved by applying fast inhomogeneous plane wave algorithm combined with a tabulation and interpolation method. The integral equation is set up based on derivation of dyadic Green’s functions in this environment. The coupling is divided into nearby region and well-separated region by grouping. The Green’s function can be divided into two parts: primary term and reflected term. In the well-separated region, the two terms are both expressed as ...     

Extended research on software hybrid testing combining reliability and directed testing

The software reliability testing has many disadvantages in practice,such as high complexity of constructing operational profiles and poor fault detection efficiency.Oppositely,the directed testing with a high fault detection rate is incapable of estimating reliability quantificationally.To solve this problem,a hybrid testing combining reliability and directed testing as well as a reliability model based on the order statistic(OS) model were presented by Mitchell.An extended research on Mitchell’s work is proposed.Firstly,the most proper distribution of the fault’s failure rate which tends to be lognormal is suggested,and a detailed form of the OS model based on lognormal and the corresponding parameter estimation method are proposed,respectively.Secondly,an implementing framework for the hybrid testing is proposed.Finally,the hybrid testing and the OS model are applied on a real website system.The experimental results indicate:the hybrid testing has more efficient fault detection power and lower testing cost than the reliability testing;compared with three traditional software reliability growth models,the OS model has a best or pretty estimation or prediction power for each data set;and for the failure data set collected from hybrid testing,the OS model also achieves an acceptable estimation result.     

Coordinating a two-echelon supply chain through different contracts under price and promotional effort-dependent demand

Retail price and promotional effort are two important parameters on which demand of a commodity largely depends.This paper develops and analyzes a two-echelon supply chain where market demand depends on both retail price and sales effort.The centralized model is studied as the benchmark case,and the wholesale price-only contract is studied as the base case in which each entity tries to maximize its individual profit.Different contract mechanisms are implemented to outperform the base case in terms of both total chain’s as well as individual profits.Comparisons among the coordinating contracts are provided so that any entity may choose the better one from available contracts after the contract parameters are negotiated.The model is extensively examined through a numerical example.