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The materials supply for sea islands is the basis for ensuring survival needs of persons on sea islands. Considering factors such as transportation cost，storage cost，recycling demand and transportation capacity, it is a vehicle path planning problem with time window and capacity limit to use many transportation tools to supply materials for sea islands within a time cycle. The intelligent algorithm provides a preferable solution to solve this complex irregular optimization problem. The paper establishes a mathematical model of mission planning of materials supply for sea islands, explores the solving process of genetic algorithm， and uses an example to calculate it. The optimization result shows that the scheme can save transportation costs and storage costs， and also meet the need of materials supply cycle，which can provide a reference for scientific mission planning of materials supply for sea islands. [ABSTRACT FROM AUTHOR]

This paper proposed the mathematical model which based on the open location routing problem with fuzzy demands considering with the sum of location cost,routing cost,lost opportunity and additional distances.For solving the mathematical model,this paper proposed a new swarm intelligence algorithm named hybrid discrete mushroom algorithm.The algorithm used partly mapping crossover and path relinking algorithm to replaced artificial wind of original mushroom reproduction algorithm(MRA),and used the method of selecting different neighborhoods with a certain probability in the local search.It used stochastic simulation program to simulate the designed routes to compute additional distances and lost opportunity resulting from serving failure.The algorithm was successfully applied to the combinatorial optimization problems.Finally,this paper verifies the validity of the model and the performance of hybrid discrete mushroom reproduction algorithm through a series of tested instances and comparisons. [ABSTRACT FROM AUTHOR]

In the view of the problem of project makespan extended randomly which background with specific product moving assembly line under uncertain environment. Firstly, the paper broke an original project into multiple projects virtually by using the idea of project splitting. And it established an optimization mathematical model to make the objective function of minimizing project makespan on the basis of considering the maximum robustness constraints. This paper developed a two-level iterative algorithm. At the project splitting level, it adjusted jobs between projects by a job allocation heuristic algorithm. At the project scheduling level, it applied a self-adaption cuckoo algorithm scheme to generate feasible schedules and returned the result to the last level for iteration. To analyze project outcome, it changed selecting examples of different scales in PSPLIB. The results show that the proposed algorithm can improve the utilization rate of project resources and shorten the activity durations under uncertain environment. [ABSTRACT FROM AUTHOR]

In order to solve the problem of optimal aHocation of non-paper resource in chemical laboratories, a mathematical model is set up. We abstract the problem to find an optimal solution in a six-dimensional space. A constraint rule and an optimization rule are presnted, cost distance and equilibration value are given, wait time is defined based on open queuing network too. A fitness function is defined, optimal aHocation algorithm based on particle swarm algorithm is established. Simulation experiment on this algorithm shows that it is fast and efficient. [ABSTRACT FROM AUTHOR]

In order to give a comprehensive understanding of SLAM, this paper first gave an overview of the progress of visual SLAM community has made over the last 30 years in this survey. Then it presented the non-linearity of the mathematical model and computational complexity in visual SLAM algorithms. This paper mostly focused on the latest achievements and approached improving the accuracy of pose estimation, building a globally consistency representation of the environment and promoting computation efficiency. It also surveyed the failure modes of current visual SLAM algorithms for large scale, full life cycle implementation and the different ways to address that. Finally, it discussed the potential connections between deep learning architectures and visual SLAM state estimations. [ABSTRACT FROM AUTHOR]

The constrained competitive location problem is a classic NP-hard problem in combinatorial optimization. When the existing algorithms study this problem, the optimal solution cannot be obtained or the solution speed is slow. Aiming at the shortcomings of the existing algorithms, this paper modified this classic problem and constructed a new mathematical model. Then it studied the mathematical properties of the model, and proposed upper and lower bound algorithms and lower bound algorithms on the basis of mathematical properties. The order sub-algorithm reduced the order of the problem and achieved the purpose of reducing the search solution space of the problem. There were both single order reduction and batch reduction in the order reduction process. Then it designed a backtracking sub-algorithm on the basis of the previous one to solve the optimal solution of the problem. Finally, the principle of the algorithm was explained more clearly through the analysis of two examples, and the result proves that the algorithm can find the optimal solution faster. [ABSTRACT FROM AUTHOR]

Aiming at the global optimization problem, this paper proposed a new intelligent algorithm called RGSO, based on SVDD and the root algorithm existed. The root system differentiated into the taproot and the lateral root group. Inspired by this growth behavior, this paper described the growth behavior of the taproot tips based on SVDD. The algorithm constructed the root growth model, and used the point of the highest concentration of nutrients in the soil as the target of global optimization. It analyzed the mathematical model of RGSO, and proved its convergence theoretically. In the experiment, this paper compared RGSO with the other three advanced algorithms, and tested the optimization effect with different parameters. The result of the experiment verified the convergence and effectiveness of RGSO, and it indicates that RGSO is an effective algorithm to solve global optimization problem. [ABSTRACT FROM AUTHOR]

This paper proposed a new anti-aliasing algorithm based on delay coloring and dynamic target light effect to solve the problem of aliasing in aerial simulation. Firstly, for the macro static scene, it extracted the edge region of the scene by using the normal information and position information, and smoothed the edge by jitter sampling, and optimized the anti-aliasing result by using certain multiple of super-sampling. Secondly, on the basis of the previous step, for the dynamic target in the scene, it established the mathematical model of geometric transformation and light effect. The final display effect was determined by calculating the pseudo-distance and the position coordinates of dynamic targets in the virtual scene, so as to solve the problem of aliasing and pixel loss in the real-time motion of dynamic targets. Finally, it verified the algorithm in a flight simulation platform. Experimental results show that the algorithm can solve the problem of aliasing in static scene and dynamic target in aviation simulation, and it is suitable for aviation simulation training. [ABSTRACT FROM AUTHOR]

gence of smoothing results due to the influence of ionospheric delay variation. In this paper, a moving window method is proposed to reduce the influence of unmodeled ionospheric delay, and a mathematical model is established. Formula deduction shows that moving window method can eliminate the influence of ionospheric delay trend term. In the static positioning experiment, the accuracy of this algorithm in three directions of north, east and up is 9.5 cm, 18.0 cm and 13.7 cm, which is 2.4, 1.5 and 3.6 times higher than the original pseudorange positioning results. The results show that the proposed algorithm can significantly reduce the ionospheric influence and greatly improve the positioning accuracy. It is also suitable for both static and dynamic positioning. Its optimal result accuracy is comparable to that of weighted Hatch filtering, but the window selectivity is wider and the result is smoother. On various application scenarios, the uniform smoothing window is recommended for 7 minutes.The traditional single-frequency carrier phase smoothing pseudorange algorithm is prone to diver- [ABSTRACT FROM AUTHOR]

The proposed a new method for model checking the consistency of the semantics of sequence diagram and state machine in the process of design. The method formalized the description of the consistency of sequence and state machine to establish the theoretical basis of model checking. This paper presented some state reduction rules and a state reduction algorithm which proved not affect the consistency to decrease the redundancy of states and transitions. Moreover, it translated the UML model to PROMELA which was the specification language of the model checker SPIN which was used to verify the consistency. Experiments indicate that, consistency of sequence diagram and state machine diagram is effectively verified, states and transitions are decreased during the model checking process, and the codes generated are simpler and more effective. [ABSTRACT FROM AUTHOR]

To find the optimal scheduling for the production job in the production process, using the production quality, efficiency and cost threshold value as constraints, this paper built a multi-objective relative degree of nearness optimization model for quality-efficiency-cost control based on set pair analysis (SPA). It solved the optimization model by using non-dominated sorting generic algorithm (NSGA-II) and acquired the Pareto-optimal solutions under the relative certainty conditions. Each job selected reasonable solution according to the optimal alternative. Finally, the experiment verifies the multi-objective optimization problems that illustrates the accuracy, effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Because the boundary layer method of traditional sliding mode control algorithm restrains buffeting had steady-state error, a tracking error of the integral term was added to the traditional sliding mode surface and this was the traditional integration sliding mode controller. Although the introduction of the integral term could reduce the steady-state error and offset the uncertainty of model, in a large initial error condition, large overshoot and long regulation time would cause deterioration of system transient performance, Integral windup effect might even occured and it would lead to instability of the whole system, meanwhile in order to overcome the shortcomings that the traditional sliding mode control didn't have the robustness in the reaching phase, the paper designed the nonlinear replacement integral sliding mode controller at the limited initial state. The simulation results show that this sliding mode controller not only can weakened steady-state error and restrain the buffeting well but also has a good robustness. [ABSTRACT FROM AUTHOR]

This paper proposed two kinds of measurements based on close degree between the cloud models had the same nature concepts. Algorithms were simple, especially still maintained the accuracy in few cloud drops. The experimental results show that the proposed algorithm has obvious optimization accuracy and consumption, meanwhile appeares good feasibility and validity in classification problems based on cloud model. [ABSTRACT FROM AUTHOR]

Research for fast and energy-efficient task scheduling algorithm on heterogeneous cluster, this paper proposed a duplication-based task scheduling algorithm, which based on the initial allocation of tasks, duplicated tasks from two aspects of energy aware and performance-energy balance. It established mathematical models for the energy consumption caused by computing and communication, and did extensive experiments. Results show that compared with an existing algorithm-balanced energy-aware task allocation (BEATA), this method can significantly reduce the schedule length and energy consumption of processing parallel applications on heterogeneous cluster, and task duplication method can reduce the schedule length but meanwhile will increase energy consumption. Thus, the task scheduling method which can simultaneously reduce schedule length and energy consumption is the direction of future research. [ABSTRACT FROM AUTHOR]

The estimation method of tire-road friction coefficient is studied in this paper. Firstly simple Brush tire model is built and verified by single wheel bench test. Then a dynamics model for steering system and a 3 DOF vehicle dynamics model are established. Finally, a nonlinear observer for tire side-slip angle and wheel-road friction coefficient is designed based on tire cornering characteristics. On this basis, a combined estimation algorithm is proposed, in which a threshold dynamic control is added for being also adapted to the case of sudden change in road friction. The results of simulation and test show that the estimation method proposed is effective and feasible. [ABSTRACT FROM AUTHOR]

This paper considers a problem of uniform-machine scheduling with machine release times to minimize makespan. Firstly, four properties of swapping jobs are proposed. Then, based on the properties, a heuristic algorithm is presented, which takes the LPT schedule as its initial solution and improves the solution by repeatedly swapping the jobs between two machines with the maximal and minimal makespan. Finally, computational results show that the presented heuristic is efficient and effective. [ABSTRACT FROM AUTHOR]

Timed influence net (TIN) is a powerful formalism to model uncertain causal relationship, and has been widely used in effectiveness evaluation of military operation alternatives. Stochastic timed influence net (STIN) is proposed to enhance TIN formalism by relaxing the constant delay assumption. Two key parameters, stochastic delay and stochastic belief sequence, are introduced in STIN to evaluate the effectiveness of alternatives under uncertain delays in the generation and propagation of operational effect. The mathematical description of and belief propagation algorithm in STIN is discussed in detail. This paper presents an example to illustrate the modeling process of STIN and the effectiveness of the belief propagation algorithm. [ABSTRACT FROM AUTHOR]