A membrane evolutionary algorithm for DNA sequence design in DNA computing

DNA sequence design has a crucial role in successful DNA computation,which has been proved to be an NP-hard(non-deterministic polynomial-time hard) problem.In this paper,a membrane evolutionary algorithm is proposed for the DNA sequence design problem.The results of computer experiments are reported,in which the new algorithm is validated and out-performs certain known evolutionary algorithms for the DNA sequence design problem.     

DNA computing model based on lab-on-a-chip and its application on solving the timetabling problem

The essential characteristic of DNA computation is its massive parallelism in obtaining and managing information. With the develop- ment of molecular biology technique, the field of DNA computation has made a great progress. By using an advanced biochip technique, laboratory-on-a-chip, a new DNA computing model is presented in the paper to solve a simple timetabling problem, which is a special version of the optimization problems. It also plays an important role in education and other industries. With a simulated biological experiment, the result snggested that DNA comnutation with lab-on-a-chin has the notential to solve a real comtplex timetabling problem.     

Unique optimal solution instance and computational complexity of backbone in the graph bi-partitioning problem

As an important tool for heuristic design of NP-hard problems, backbone analysis has become a hot spot in theoretical computer science in recent years. Due to the difficulty in the research on computa- tional complexity of the backbone, many researchers analyzed the backbone by statistic ways. Aiming to increase the backbone size which is usually very small by the existing methods, the unique optimal solution instance construction (UOSIC) is proposed for the graph bi-partitioning problem (GBP). Also, we prove by using the UOSIC that it is NP-hard to obtain the backbone, i.e. no algorithm exists to obtain the backbone of a GBP in polynomial time under the assumption that P ≠ NP. Our work expands the research area of computational complexity of the backbone. And the UOSIC provides a new way for heuristic design of NP-hard problems.     

Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment

In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.     

ANALYSIS OF THE HEAT TRANSFER IN FIN ASSEMBLIES BY THE BOUNDARY ELEMENT METHOD

The special formulation that allows an accurate and efficient solution to the heat transfer problems within fin assemblies with very large aspect ratios has been developed in this paper Numerically, it consists of the boundary element method in the wall region and the analytical solution in the fin region This modified BEM makes tractable a large class of heat transfer problems in the long and thin domains which are frequently encountered in practice.     

Counting Problems in Parameterized Complexity

Parameterized complexity is a multivariate theory for the analysis of computational problems. It leads to practically efficient algorithms for many NP-hard problems and also provides a much finer complexity classification for other intractable problems. Although the theory is mostly on decision problems, parameterized complexity naturally extends to counting problems as well. The purpose of this article is to survey a few aspects of parameterized counting complexity, with a particular emphasis on some general frameworks in which parameterized complexity proves to be indispensable.     

Combined Timetabling Procedure and Complete Local Search for No-Wait Job Shop Scheduling with Total Tardiness

The strong non-deterministic polynomial-hard （NP-hard） character of job shop scheduling problem （JSSP） has been acknowledged widely and it becomes stronger when attaches the no-wait constraint, which widely exists in many production processes, such as chemistry process, metallurgical process. However, compared with the massive research on traditional job shop problem, little attention has been paid on the no-wait constraint. Therefore, in this paper, we have dealt with this problem by decomposing it into two sub-problems, the timetabling and sequencing problems, in traditional frame work. A new efficient combined non-order timetabling method, coordinated with objective of total tardiness, is proposed for the timetabling problems. As for the sequencing one, we have presented a modified complete local search with memory combined by crossover operator and distance counting. The entire algorithm was tested on well-known benchmark problems and compared with several existing algorithms. Computational experiments showed that our proposed algorithm performed both effectively and efficiently.     

Method of So.Sensor Modeling for Fermentation Process Based on Geometric Support Vector Regression

The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.     

Similar component analysis

A new unsupervised feature extraction method called similar component analysis (SCA) is proposed in this paper. SCA method has a self-aggregation property that the data objects will move towards each other to form clusters through SCA theoretically, which can reveal the inherent pattern of similarity hidden in the dataset. The inputs of SCA are just the pairwise similarities of the dataset, which makes it easier for time series analysis due to the variable length of the time series. Our experimental results on many problems have verified the effectiveness of SCA on some engineering application.     

VISUALIZATION AND MEASUREMENT OF VORTEX FLOW IN SPINNING SHELL-CASE

The characteristics of a flow in spinning devices are one of the fundamental problems innew-type spinning.In this paper the vortex flow in interior of the shell-case was studied by usingexperimental methods,which include flow visualization and laser doppler techniques.The flow sur-vey by measurement has been compared with that photographed by flow visualization method,andshows a fairly good conformity.The results of experiments are similar to those by numericalevaluation.     

AGVs Dispatching Using Multiple Cooperative Learing Agents

AGVs dispatching,one of the hot problems in FMS,has attracted widespread interest in recent years.It is hard to dynamically schedule AGVs with pre-designed rule because of the uncertaintty and dynamic nature of AGVs dispatching progress,so the AGVs system in this paper is treated as a cooperative learning multiagent system,in which each agent adopts multilevel decision method,which includes two level decisions:the option level and the action level,On the option level,an agent learns a policy to execute a subtask with the best response to the other AGVs‘ current options.On the action level,an agent learns an optimal policy of actions for achieving his planned option.The method is applied to a AGVs‘ dispatching simulation,and the performance of the AGVs system based on this method is verified.     

A Fault Detection and Isolation Scheme Based on Parity Space Method for Discrete Time-delay System

A Fault detection and isolation （FDI） scheme for discrete time-delay system is proposed in this paper, which can not only detect but also isolate the faults. A time delay operator ↓△ is introduced to resolve the problem brought by the time-delay system. The design and computation for the FDI system is carried by computer math tool Maple, which can easily deal with the symbolic computation. Residuals in the form of parity space can be deduced from the recursion of the system equations. Further more, a generalized residual set is created using the freedom of the parity space redundancy. Thus, both fault detection and fault isolation have been accomplished. The proposed method has been verified by a numerical example.     

GENERATION OF STRANGE ATTRACTOR IMAGES WITH GENETIC ALGORITHM

This paper presents a method for the generation of satisfied strange attractor images, which is based on the idea of Genetic Algorithm and is realized by adding a controller to a chaotic system.The principle of the method is introduced. Some problems which exist in genetic algorithm's parameter optimization are discussed in detail. Finally, the effectiveness of the method for finding pretty strange attractors is verified. It is helpful to pattern design and works of arts and crafts.     

A Nonsmooth Iterative Algorithm for Solving Obstacle Problems

This paper discussed a numerical method for solving obstacle problems .Discrete problem were obtained by the finite difference method ,and an iterative algorithm ,which comes from the nonsmooth Newton method with a special choice of generalized Jacobian ,for solving the problems was presented .The algorithm is monotonic and will stop in finite steps .The numerical results were listed at the end of the paper .     

Multi-Seed Key Distribution Scheme Test

The key problem of securing multieast is to generate, distribute and update Session Encryption Key（SEK）. Polynomial expansion with multi-seed （MPE） scheme is an approach which is based on Polynomial expansion （PE） scheme and overcomes PE＇s shortage. Its operation is demonstrated by using multi-seed, the group member is partitioned to many subgroups. While updating the SEK, computation is needed only in one of subgroups, the other of them will use the computation history to update their SEK. The key problems to design a MPE scheme application includes to find a feasible one way function as well as to generate a Strict Prime Number （SPN）. Those technologies with multi-seed and computation history concepts make MPE as a good choice in practical applications. A prototype test system is designed and solutions of all above mentioned problems are included in this proposed paper.     

Remote intelligent identification system of structural damage

The focus of this paper is to build the damage identify system, which performs “system identification“ to detcct the positions and extents of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However. neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (P, BFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The resuits show that the RBFNN identification technology can he used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies.     

SIGNGD with Error Feedback Meets Lazily Aggregated Technique:Communication-Efficient Algorithms for Distributed Learning

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems. However, the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning systems. In this paper, we design two communication-efficient algorithms for distributed learning tasks. The first one is named EF-SIGNGD, in which we use the 1-bit(sign-based) gradient quantization method to save the communication b...     

An effective PN blind estimation reconstruction strategy to solve data reversions interference

In a direct spectrum （DS） system, the PN code can be estimated by analyzing the singular vectors of the received data matrix in order to blind despread in a non-cooperative context. But as there are informa-tion data reversions in the analyzed data matrix, some parts of the estimated PN code may be invertible to the original PN code, which may bring about problems in the following despreading process. In order to solve this problem, a method to well reconstruct the PN code is proposed. This method is based on power detection. The combination scheme which has the maximum power is the best combination scheme that is most suitable to the original PN code. Simulation results show that the method can reconstruct the PN code very well,even if the signal-to-noise ratio is low.     

DNA media storage

In 1994, University of Southern California computer scientist, Dr. Leonard Adleman solved the Hamiltonian path problem using DNA as a computational mechanism. He proved the principle that DNA computing could be used to solve computationally complex problems. Because of the limitations in discovery time, resource requirements, and sequence mismatches, DNA computing has not yet become a commonly accepted practice. However, advancements are continually being discovered that are evolving the field of DNA computing. Practical applications of DNA are not restricted to computation alone. This research presents a novel approach in which DNA could be used as a means of storing files. Through the use of multiple sequence alignment combined with intelligent heu- ristics, the most probabilistic file contents can be determined with minimal errors.     

An Optimization Algorithm for Service Composition Based on an Improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing(SOC). Quality of Service(Qo S) has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA(Web Service composition based on Fruit Fly Optimization Algorithm) for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.