Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

ON CHANNEL ESTIMATION USING OPTIMAL TRAINING SEQUENCES IN CYCLIC-PREFIX-BASED SINGLE-CARRIER SYSTEMS WITH SPACE-TIME BLOCK-CODING

In this paper, a new scheme that combines Space-Time Block-Coding (STBC) based on an Alamouti-like scheme and the Least Squares (LS) channel estimation using optimal training sequences in Cyclic-Prefix-based (CP)\Single-Carrier (SC) systems is proposed. With two transmit antennas, based on Cramer-Rao lower bound for channel estimation, it is shown that the Periodic Comple- mentary Set (PCS) is optimal over frequency-selective fading channels. Compared with the normal scheme without STBC, 3dB Mean Square Error (MSE) performance gains and fewer restrictions on the length of channel impulse response are demonstrated.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Recognition mechanism and sequence optimization of organophosphorus pesticides aptamers for better monitoring contaminations in food

Aptamers as a kind of biological recognition element have shown great potential in monitoring and the rapid quantification of organophosphorus pesticides(OPPs). However, molecules of OPPs are structurally similar and original aptamers selected by systematic evolution of ligands by exponential enrichment are usually long-chain bases, which hamper the further application under OPPs-aptamer recognition. The aim of the research was to develop a new strategy to design oligonucleotide sequences for binding OPPs by combination of experimental and molecular modeling methods. 3D models of aptamers binding OPPs were constructed, and binding energy and the most probable binding site for the OPPs were then determined by molecular docking, and the binding sites were further confirmed by the results of 2-AP replaced experiments. Based on the docking results, a new aptamer for detection 4 representative OPPs with only 29 bases was designed by reasonable truncation and mutation of the reported aptamer(named S4-29). The interaction between this new aptamer and OPPs were analyzed by molecular docking, microscale thermophoresis, circular dichroism and fluorometric analysis. The results revealed that the new aptamer exhibit more superior recognition performance to OPPs, which can be promote the monitoring ability of OPPs contaminations in food.

Optimal Maintenance Policy for a Storage System with Finite Number of Inspections

The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with finite number of inspections. This paper gives the algorithm for an optimal inspection schedule and specifies the computing procedure for a Weibull distribution. Using this algorithm, optimal inspection times are computed as a numerical result. Compared with the periodic point inspection, the policies in this paper reduce the cost successfully.

Optimization method for diagnostic sequence based on improved particle swarm optimization algorithm

To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.

Sequence optimization of hull structure assembly based on prediction of welding deformation

Optimization of assembly process is significant for ship construction, thus reducing the time and related costs of construction. Welding Structure Deformation Analysis （Weld-sta） was used to predict welding deformation of a hull block. After the reliability of simulation was proved by comparing with measured results, four kinds of welding sequences was determined. By considering welding deformation and assembly process with the e]ficieney of automation, decreasing the overturn times （times of turning the erections upside down during welding） and working hours etc, one of the welding sequences is assumed to be the most reasonable one. The study shows this method is very useful in optimal assembly program determination of practical engineering structures.

Distillation Sequence Optimization Considering Extractive Distillation under Multiple Conditions: A Methanol to Propylene Case Study

This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA) with relative cost function was used to solve a meaningful optimization problem. It was observed that different conditions had differed on the flowsheet. Case study shows the effectiveness of the proposed method.

Stability analysis of backflling in subsiding area and optimization of the stoping sequence

In underground mining by sublevel caving method, the deformation and damage of the surface induced by subsidence are the major challenging issues. The dynamic and soft backflling body increases the safety risks in the subsiding area. In this paper, taking Zhangfushan iron mine as an example, the ore body and the general layout are focused on the safety of backflling of mined-out area. Then, we use the ANSYS software to construct a three-dimensional(3D) model for the mining area in the Zhangfushan iron mine. According to the simulation results of the initial mining stages, the ore body is stoped step by step as suggested in the design. The stability of the backflling is back analyzed based on the monitored displacements, considering the stress distribution to optimize the stoping sequence. The simulations show that a reasonable stoping sequence can minimize the concentration of high compressive stress and ensure the safety of stoping of the ore body.

Low-complexity PTS scheme based on phase factor sequences optimization

In this paper, a new partial transmit sequence（PTS）scheme with low computational complexity is proposed for the problems of high computational complexity in the conventional PTS method. By analyzing the relationship of candidate sequences in the PTS method under the interleaved partition method, it has been discovered that some candidate sequences generated by phase factor sequences have the same peak average power ratio（PAPR）. Hence, phase factor sequences can be optimized to reduce their searching times. Then, the computational process of generating candidate sequences can be simplified by improving the utilization of data and minimizing the calculations of complex multiplication. The performance analysis shows that, compared with the conventional PTS scheme, the proposed approach significantly decreases the computational complexity and has no loss of PAPR performance.

STACKING SEQUENCE OPTIMIZA-TION OF LAMINATED COMPOSITE CYLINDER SHELL FOR MAXIMAL BUCKLING LOAD

A new optimization method for the optimization of stacking of composite glass fiber laminates is developed. The fiber orientation and angle of the layers of the cylindrical shells are sought considering the buckling load. The proposed optimization algorithm applies both finite element analysis and the mode-pursuing sampling （MPS）method. The algorithms suggest the optimal stacking sequence for achieving the maximal buckling load. The procedure is implemented by integrating ANSYS and MATLAB. The stacking sequence designing for the symmetric angle-ply three-layered and five-layered composite cylinder shells is presented to illustrate the optimization process, respectively. Compared with the genetic algorithms, the proposed optimization method is much faster and efficient for composite staking sequence plan.

Welding sequences optimization of box structure based on genetic algorithm method

In this article, The genetic algorithm method was proposed, that is, to establish the box structure＇s nonlinear three-dimension optimization numerical model based on thermo-mechanical coupling algorithm, and the objective function of welding distortion has been utilized to determine an optimum welding sequence by optimization simulation. The validity of genetic algorithm method combining with the thermo-mechanical nonlinear finite element model is verified by comparison with the experimental data where available. By choosing the appropriate objective function for the considered case, an optimum weldiing.sequence is determined by a genetic algorithm. All done in this study indicates that the new method presented in this article will have important practical application for designing the welding technical parameters in the future.

Strategy of Concurrent Optimization for an Assembly Sequence

An effective constraint release based approach to realize concurrent optimization for an assembly sequence is proposed. To quantify the measurement of assembly efficiency, a mathematical model of concurrency evaluation index was put forward at first, and then a technology to quantify assembly constraints was developed by application of some fuzzy logic algorithms. In the process of concurrent optimization of the assembly sequence, two kinds of constraints were involved. One was self-constraints of components, which was used to evaluate the assemble capability of components under the condition of full-freedom. Another was an assembly constraint between components represented by geometric constraints between points, lines and planes under physical restriction conditions. The concept of connection strength degree （CSD） was introduced as one efficient indicator and the value of it was evaluated by the intersection of the two constraints mentioned above. The equivalent constraints describing the connection weights between components were realized by a well designed constraints reduction, and then the connection weights based complete assembly liaison graph was applied to release virtual connections between components. Under a given threshold value, a decomposition and reconstituting strategy for the graph with the focus on high assembly concurrency was used to realize an optimized assembly concurrency evaluation index. Finally, the availability of the approach was illustrated in an example to optimize the assembly of a shift pump.

An Uncertainty Analysis and Reliability-Based Multidisciplinary Design Optimization Method Using Fourth-Moment Saddlepoint Approximation

In uncertainty analysis and reliability-based multidisciplinary design and optimization(RBMDO)of engineering structures,the saddlepoint approximation(SA)method can be utilized to enhance the accuracy and efficiency of reliability evaluation.However,the random variables involved in SA should be easy to handle.Additionally,the corresponding saddlepoint equation should not be complicated.Both of them limit the application of SA for engineering problems.The moment method can construct an approximate cumulative distribution function of the performance function based on the first few statistical moments.However,the traditional moment matching method is not very accurate generally.In order to take advantage of the SA method and the moment matching method to enhance the efficiency of design and optimization,a fourth-moment saddlepoint approximation(FMSA)method is introduced into RBMDO.In FMSA,the approximate cumulative generating functions are constructed based on the first four moments of the limit state function.The probability density function and cumulative distribution function are estimated based on this approximate cumulative generating function.Furthermore,the FMSA method is introduced and combined into RBMDO within the framework of sequence optimization and reliability assessment,which is based on the performance measure approach strategy.Two engineering examples are introduced to verify the effectiveness of proposed method.

Tool Sequence Optimization Based on Three Logical Modes of Robot Control via CPLEX Optimization

Cluster tools have advantages of shorter cycle times,faster process development,and better yield for less contamination.The sequence of dual-arm cluster tools is a complex logistics process during the semiconductor production.Efficient use of cluster tools is naturally very significant to competitive fab operations.Generating an optimized sequence in a computationally efficient manner and assessing the quality of the requirements to improve the fab production are the key factors for semiconductor manufacturing productivity.The Petri net modeling is introduced to minimize the makespan of the process for the three different logical modes and select a better mode after comparing the makespan among the three logical modes.The tool sequence optimization problem is formulated as optimization firing transition sequences based on the Petri net and then the formulation is converted to be linearly solved by the branch-and-cut method in the standard commercial solver CPLEX.Special methods for the linear conversion are highlighted.Due to the limited calculation time requirement for the real production and the large scale of the problem,special methods for the efficiency tuning are applied according to the characteristics of the problem.Numerical testing is supported by one of the most advanced semiconductor enterprises and the computational results show significant improvement compared with the traditional manual sequence results.

A modified predictive control strategy of three-phase grid-connected converters with optimized action time sequence

Due to the excellent dynamic performance,the Finite Control Set Model Predictive Control has been widely used in various types of converters.However,when Finite Control Set Model Predictive Control is adopted,the switching frequency of converters varies significantly with system operating conditions.Consequently,constant-frequency predictive control strategy has been proposed.Two active voltage vectors and a zero voltage vector are selected within each sampling period.The action time sequence is then calculated.Due to the unsymmetrical distribution of current variation rates around zero,the calculated value of the voltage-vector action time will turn up negative.According to common sense,the voltage-vector action time is greater than or equal to zero.The action time is normally forced to zero whenever a negative value is predicted,resulting in the control failure and performance deterioration.To solve this problem,this paper proposes modified strategy.The modified strategy examines the action time calculated out.When negative action time comes out,the modified strategy reselects the active voltage vector accordingly,instead of forcing the action time to be zero.Optimized action time sequence is further determined by minimizing the cost function.The effectiveness of the modified strategy is clearly verified by experimental tests,and analytical remarks are all founded in practical results.

Assembly Sequence Planning for Mechanical Products

A method for assembly sequence planning is proposed in this paper. First, two methods for assembly sequence planning are compared, which are indirect method and direct method. Then, the limits of the previous assembly planning system are pointed out. On the basis of indirect method, an improved method for assembly sequence planning is put forward. This method is composed of four parts, which are assembly modeling for products, assembly sequence representing, assembly sequence planning, and evaluation and optimization. The assembly model is established by human machine interaction, and the assembly model contains components' information and the assembly relation among the components. The assembly sequence planning is based on the breaking up of the assembly model. And/or graph is used to represent assembly sequence set. Every component which satisfies the disassembly condition is recorded as a node of an and/or graph. After the disassembly sequence and/or graph is generated, heuristic algorithm AO * algorithm is used to search the disassembly sequence and/or graph, and the optimum assembly sequence planning is realized. This method is proved to be effective in a prototype system which is a sub project of a state 863/CIMS research project of China ‘Concurrent Engineering’.

Optimal Training Design and Placement for MIMO Orthogonal Frequency Division Multiplexing Channel Estimation

This paper deals with optimal training design and placement over multiple orthogonal frequency division multiplexing(OFDM) symbols for the least squares(LS) channel estimation in multiple-input multipleoutput(MIMO) OFDM systems.First,the optimal pilot sequences over multiple OFDM symbols are derived by co-cyclic Jacket matrices based on the minimum mean square error(MSE) of the LS channel estimation.Then,an enhanced channel estimation method using sliding window is proposed to improve further the performance for the optimal pilot sequences in fast-varying channels.Simulation results show that the enhancedmethod can efficiently improve the performances for the optimal pilot sequences over two and four OFDM symbols,especially in fast-varying channels.

MULTI-SCALE METHODS FOR INVERSE MODELING IN 1-D MOS CAPACITOR

In this paper, we investigate multi-scale methods for the inverse modeling in 1-D Metal-Oxide-Silicon (MOS) capacitor. First, the mathematical model of the device is given and the numerical simulation for the forward problem of the model is implemented using finite element method with adaptive moving mesh. Then numerical analysis of these parameters in the model for the inverse problem is presented. Some matrix analysis tools are applied to explore the parameters' sensitivities. And third, the parameters are extracted using Levenberg-Marquardt optimization method. The essential difficulty arises from the effect of multi-scale physical difference of the parameters. We explore the relationship between the parameters' sensitivities and the sequence for optimization, which can seriously affect the final inverse modeling results. An optimal sequence can efficiently overcome the multi-scale problem of these parameters. Numerical experiments show the efficiency of the proposed methods.

Solution of a zero-sum linear quadratic differential game with singular control cost of minimiser

We consider a finite horizon,zero-sum linear quadratic differential game.The feature of this game is that a weight matrix of the minimiser’s control cost in the cost functional is singular.Due to this singularity,the game can be solved neither by applying the Isaacs MinMax principle nor using the Bellman–Isaacs equation approach,i.e.this game is singular.Aprevious paper of one of the authors analysed such a game in the case where the cost functional does not contain the minimiser’s control cost at all,i.e.the weight matrix of this cost equals zero.In this case,all coordinates of the minimiser’s control are singular.In the present paper,we study the general case where the weight matrix of the minimiser’s control cost,being singular,is not,in general,zero.This means that only a part of the coordinates of the minimiser’s control is singular,while others are regular.The considered game is treated by a regularisation,i.e.by its approximate conversion to an auxiliary regular game.The latter has the same equation of dynamics and a similar cost functional augmented by an integral of the squares of the singular control coordinates with a small positive weight.Thus,the auxiliary game is a partial cheap control differential game.Based on a singular perturbation’s asymptotic analysis of this auxiliary game,the existence of the value of the original(singular)game is established,and its expression is obtained.The maximiser’s optimal state feedback strategy and the minimising control sequence in the original game are designed.It is shown that the coordinates of the minimising control sequence,corresponding to the regular coordinates of the minimiser’s control,are point-wise convergent in the class of regular functions.The optimal trajectory sequence and the optimal trajectory in the considered singular game also are obtained.An illustrative example is presented.