Two-Phase Algorithm for Multi-warehouse and Multi-task Based Logistics Delivery

To a scaled logistic company, assigning is an important part of logistic, and further development will make the optimized assigning of multi-warehouse and multi-task possible. This paper provided a two-phase multiwarehouse and multi-task based algorithm which has two phases. In the first phase, it combines sweep algorithm,saving algorithm and virtual task point to present a method. And in the second phase it provides an algorithm for the arrangement of goods loading which is based on the constraints of time-window and attributes of goods and vehicle. It uses the computing results of the first phase to form more detailed delivery scheme based on the constraints of time-window and attributes of vehicle and goods.     

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to provide quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in management information base, the algorithm is compatible with the existing simple network management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identifiers are acquired from the network simulation. A k-nearest neighbor classifier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, Which prove the accuracy of the algorithm. The identification errors are discussed to illustrate that the novel fault identification algorithm is adaptive in the fault scenarios with network traffic change.     

Delay Restoration of Single-Track Railway Rescheduling

On the basis of analysis of the principle of delay restoration in a disturbed schedule, a heuristic algorithm for rescheduling trains is developed by restoring the total delay of the disturbed schedule. A discrete event topologic model is derived from the original undisturbed train diagram and a back propagation analysis method is used to label the maximum buffer time of each point in the model. In order to analyze the principle of delay restoration, the concept of critical delay is developed from the labeled maximum buffer time. The critical delay is the critical point of successful delay restoration. All the disturbed trains are classified into the strong-delayed trains and the weak-delayed trains by the criterion of the critical delay. Only the latter, in which actual delay is less than its critical delay, can be adjusted to a normal running state during time horizon considered. The heuristic algorithm is used to restore all the disturbed trains according to their critical details. The cores of the algorithm are the iterative repair technique and two repair methods for the two kinds of trains. The algorithm searches iteratively the space of possible conflicts caused by disturbed trains using an earfiest-delay-first heuristics and always attempts to repair the earliest constraint violation. The algorithm adjusts the weak-delayed trains directly back to the normal running state using the buffer time of the original train diagram. For the strong-delayed trains,the algorithm uses an utility function with some weighted attributes to determine the dynamic priority of the trains, and resolves the conflict according to the calculated dynamic priority. In the end, the experimental results show that the algorithm produces ＂good enough＂ schedules effectively and efficiently in disturbed situations.     

Direct algorithms for steady-state solution of long slender marine structures

The steady state solution of long slender marine structures simply indicates the steady motion response to the excitation at top of the structure.It is very crucial especially for deep towing systems to find out how the towed body and towing cable work under certain towing speed.This paper has presented a direct algorithm using Runge-Kutta method for steady-state solution of long slender cylindrical structures and compared to the time iteration calculation;the direct algorithm spends much less time than the time-iteration scheme.Therefore, the direct algorithm proposed in this paper is quite efficient in providing credible reference for marine engineering applications.     

A NOVEL METHOD FOR NETWORK WORM DETECTION BASED ON WAVELET PACKET ANALYSIS

Objective To detect unknown network worm at its early propagation stage. Methods On the basis of characteristics of network worm attack, the concept of failed connection flow （FCT） was defined. Based on wavelet packet analysis of FCT time series, this method computed the energy associated with each wavelet packet of FCT time series, transformed the FCT time series into a series of energy distribution vector on frequency domain, then a trained K-nearest neighbor （KNN） classifier was applied to identify the worm. Results The experiment showed that the method could identify network worm when the worm started to scan. Compared to theoretic value, the identification error ratio was 5.69%. Conclusion The method can detect unknown network worm at its early propagation stage effectively.     

Feature Selection for SVM Classifiers Based on Discretization

The rough sets and Boolean reasoning based discretization approach （RSBRA） is not suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A modified RSBRA for feature selection was proposed and evaluated with SVM classifiers. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of the RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accuracy and less training time than the original RSBRA.     

Distributed cooperative coverage of mobile robots with consensus-based connectivity estimation

This paper deals with the discrete-time connected coverage problem with the constraint that only local information can be utilized for each robot. In such distributed framework, globM connectivity characterized by the second smallest eigenvalue of topology Laplacian is estimated through introducing distributed minimal- time consensus algorithm and power iteration algorithm. A self-deployment algorithm is developed to disperse the robots with the precondition that the estimated second smallest eigenvalue is positive at each time-step. Since thus connectivity constraint does not impose to preserve some certain edges, the self-deployment strategy developed in this paper reserves a sufficient degree of freedom for the motion of robots. Theoretical analysis demonstrates that each pair of neighbor robots can finally reach the largest objective distance from each other while the group keeps connected all the time, which is also shown by simulations.     

Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization

Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.     

TDOA Passive Location Based on Cuckoo Search Algorithm

This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.     

Synthesis of Stephenson Ⅲ Six-Link Mechanism for Function Generation Using an Improved Homotopy Method

This paper presents solutions to the function generation problems of Stephenson Ⅲ six-link mechanism with the maximum precision positions for the first time using homotopy method with an improved path-tracking scheme. The new path-tracking scheme is based on the characteristics of the zero sets of homotopy function for polynomial mapping and thus the tracking speed is dramatically increased.     

An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem

This paper considers the parallel machines scheduling problem where jobs are subject to different releas etimes. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem‘s scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.     

Experimental Study of Natural Gas Storage in Hydrates

Hydrate formation rate plays an important role in the making of hydrates for natural gas storage. The effect of sodium dodecyl sulfate (SDS), alkyl polysaccharide glycoside (APG) and cyclopentane (CP) on natural gas hydrate formation rate, induction time and storage capacity was studied. Micellar surfactant solutions were found to increase hydrate formation rate in a quiescent system and improve hydrate formation rate and natural gas storage capacity. The process of hydrate formation includes two stages with surfactant presence. Hydrate forms quickly in the first stage, and then the formation rate is slowed down. Surfactants (SDS or APG) reduce the induction time of hydrate formation. The effect of an anionic surfactant (SDS) on gas storage in hydrates is more pronounced compared to a nonionic surfactant (APG). CP also reduces the induction time of hydrate formation, but can not improve the natural gas storage capacity in hydrates.     

Influences of clock resolution of bandwidth measurement on packet pair algorithm

Influences of the clock resolution of bandwidth estimator on the accuracy and stability of the packet pair algorithm was analyzed. A mathematic model has been established to reveal the relationship between the result deviation coefficient and the packet size, clock resolution and real bandwidth（value）of the measured route. A bandwidth self-adapting packet pair algorithm was presented based on the mathematic model to reduce the estimation error resulting from the clock resolution and to improve the accuracy and stability of measurement by adjusting the deviation coefficient. Experimental results have verified the validity and stability of the algorithm.     

Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.     

A New Wireless Packet Scheduling Algorithm Based on EDF and Its Application in Multi-bottleneck Networks

Most of current wireless packet scheduling algorithms aim at resource allocation as fairly as possible or maximizing throughput. This paper proposed a new packet scheduling algorithm that aims at satisfying delay requirement and is the improvement of earliest due first (EDF) algorithm in wired networks. The main idea is to classify the packets based on their delay bound, scheduling the most "urgent" class of user and the users that have the best channel condition with higher priority. This algorithm can easily integrate with common buffer management algorithms, when buffer management algorithm cannot accept new arrival packets, try to modify scheduling policy. Packet scheduling algorithms in multiple bottleneck wireless networks were also discussed. A new variable multi-hop factor was defined to estimate the congestion situation (including channel condition) of future hops. Multi-hop factor can be integrated into packet scheduling algorithms as assistant and supplement to improve its performance in multi-bottleneck wireless networks.     

Multimodal Evolution Approach to Multidimensional Intrusion Detection

An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.     

Research on Network Delays of Networked Control Systems

In networked control systems (NCS), the main problem is time delays induced by communication network, which can deteriorate the performance of the systems, even cause the systems instability. If we know the exact network delays, we can compensate for their effect by modifying the parameters of the controller. Hence how to get the knowledge of these delays in the network is critical. This paper analyzed the different characteristics of network delays from sensor to controller and from controller to actuator and presented the methods of online evaluaticm of these delavs. The experiment shows these methods are valid.     

Efficient scheduling with random network coding in multi-relay wireless network

In this paper, we propose two novel efficient scheduling schemes with network coding in multi-relay wireless network to maximize the transmission efficiency. The first one uses adaptive forwarding with network coding （AF-NC）, in which each relay adaptively calculates the number of packets having innovative information according to the feedback from the sink. With AF-NC, duplicate packets are not sent, and the total number of time slots needed to complete transmission can be significantly reduced. The second scheme, named adaptive forwarding with network coding and retransmission （AFR-NC）, combines AF-NC with automatic repeat request （ARQ） to guarantee reliable end-to-end communication with limited resource occupation. Numerical results show that compared with simple forwarding with network coding （F-NC）, AF-NC has close successful delivery rate with dramatically less time slots, while AFR-NC achieves strict reliability with limited resource cost.     

A Method of Clustering Components into Modules Based on Products＇ Functional and Structural Analysis

Modularity is the key to improving the cost-variety trade-off in product development. To achieve the functional independency and structural independency of modules, a method of clustering components to identify modules based on functional and structural analysis was presented. Two stages were included in the method. In the first stage the products＇ function was analyzed to determine the primary level of modules. Then the objective function for modules identifying was formulated to achieve functional independency of modules. Finally the genetic algorithm was used to solve the combinatorial optimization problem in modules identifying to form the primary modules of products. In the second stage the cohesion degree of modules and the coupling degree between modules were analyzed. Based on this structural analysis the modular scheme was refined according to the thinking of struc- tural independency. A case study on the gear reducer was conducted to illustrate the validity of the presented method.     

Surge Response of a Compliant Offshore Structure by Time Domain Simulation

A single-degree-of-freedom equation of motion was used for modeling a compliant offshore structure exposed to viscous hydrodynamic loads. The equation of motion contains nonlinearities in the forms of both Duffing stiffness and Morison drag force with current. The water particle velocity and acceleration for calculating the Morison inertia and drag forces are modeled as Gaussian processes based on a Pierson-Moskowitz (P-M) elevation spectrum. The single-degree-of-freedom equation of motion containing different ocean current values are then numerically integrated via a fourth-order Runge-Kutta scheme. Time trajectories of the surge response displacements of the offshore structure and the response probability density curves are obtained. Furthermore, the ocean current influences on the response central moments up to the fourth order are studied. A literature review reveals that this is the first treatment of such a pair of nonlinearities in time domain. The simulation results are analyzed, and some conclusions valuable for engineering design are pointed out.