Research on steering control performance of electric forklift with steer by wire

Forklift plays an important role in cargo handling in the warehouse; therefore, it is necessary to ensure the stability of the forklift when turning to guarantee the safety of transportation. In this study, the particle swarm optimization (PSO) algorithm was improved by a genetic algorithm (GA), and the parameters of the proportion, integration, and differentiation (PID) controller were calculated using the improved algorithm for forklift steering control. Then simulation experiments were carried out using MATLAB. The results showed that the convergence speed of the improved PSO algorithm was faster than that of GA, and its adaptive value after convergence stability was significantly lower than that of the PSO algorithm; whether it was low-speed or high-speed steering, the three algorithms responded to the steering signal quickly; the yaw velocity and sideslip angle of the forklift steering under the improved PSO algorithm were more suitable for stable steering, and the increase of the steering speed would increase the yaw velocity. The novelty of this paper is that the traditional PSO algorithm is improved by GA and the particle swarm jumps out of the locally optimal solution through the crossover and mutation operations.

Pursuit Strategies for a Target Tracking Game Around a Circular Obstacle

In this paper, we address a visibility-based target tracking game for the scenario when the environment contains a circular obstacle. The game is originally formulated in four dimensions, but due to the symmetry of the environment, the dimension of the state space can be reduced to three. In the reduced state space, we formulate a game of degree. We use Isaacs techniques to obtain the locally optimal solution of the players. These solutions are extended back in the state space keeping in mind the geometry of the environment to obtain complete solution in the solution. Finally, we construct vector fields whose integral curves are the optimal solutions in the state space.

The complex method for definition of effective decisions on data set composition and shedule for their processing in conveyor systems

Построение комплексных расписаний обработки партий данных предполагает формирование взаимосвязанных решений по составам партий и расписаниям их обработки. В результате выполненной декомпозиции обобщенной задачи построения расписаний обработки партий ее решение представляется в виде решений двух подзадач — определения составов партий и непосредственно расписаний. В рассмотрение введена модель двухуровневого программирования для определения эффективных решений по составам партий и расписаниям их обработки. Для нахождения эффективных решений выполнено обоснование методов определения составов партий и расписаний. Проведены исследования особенностей обработки партий в конвейерных системах с точки зрения входных параметров, характеризующих обрабатываемые данные и конвейерную систему. The task of controlling the pipelined processing for various types of remote sensing data of the Earth is important. The task of managing the data processing process is complex and represents a set of two interrelated subtasks — the definition of the composition of batches and the schedules of their processing. The complex task of managing the processing of data lots is represented in the form of hierarchy of the required subtasks. The realization of effective decisions on the composition of parties is realized at the top level, schedules and their processing — on the lower level. A hierarchical game model (two-level programming) for determining solutions at the appropriate levels is accordingly designed. The final time for processing all data entering the system is considered as a criterion at the upper level (to determine the composition of data lots). The total downtiming of the pipeline segments when processing batches of data is the criterion at the lower level. Local optimization methods are proposed at each level of the hierarchy to determine effective solutions. The method for determining solutions by party composition provides for the search for a better solution in the vicinity of different types of the current locally optimal solution. The approaches of making decisions on the composition of parties in the vicinity of the respective species are formulated. The proof of both theorems determining the conditions for the end of the change in the composition of a given number of batches and that eliminates duplication of solutions in a certain neighborhood is accomplished. A generalized algorithm for determining locally optimal solutions by party composition, acting on the upper level of the hierarchy, is formulated based on these methods.The algorithm for determining effective schedules is based on “greedy” strategies and involves adding each new batch to the processing sequences optimized in the previous steps and determining an effective position for them in this order. Software implementation of the developed methods of local optimization of solutions at each level of the hierarchy is completed. Studies for the dependence of the effectiveness of the proposed methods (in particular, the method of forming batches of batches) on the parameters of the system and the data processed are carried out. The obtained results have shown that use of the offered method of the definition of batches allows considerable reducing for the time of processing of batches of the data in the conveyor system. The efficiency of the treatment is then increased by 10% to 80 %.

Feature Selection Clustering for Gene Data

Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper , an algorithm is proposed which makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solution in both clustering and feature selection. The proposed method uses genetic algorithm to preserve the population diversity and prevent premature convergence. The algorithm is implemented in Matlab 7.4 under windows operating system. The results show that the proposed algorithm outperforms existing algorithms in terms of accuracy.

A Fast-Iterative Data Association Technique for Multiple Object Tracking

A massive amount of video data is recorded daily for forensic post analysis and computer vision applications. The analyses of this data often require multiple object tracking (MOT). Advancements in image analysis algorithms and global optimization techniques have improved the accuracy of MOT, often at the cost of slow processing speed which limits its applications only to small video datasets. With the focus on speed, a fast-iterative data association technique (FIDA) for MOT that uses a tracking-by-detection paradigm and finds a locally optimal solution with a low computational overhead is introduced. The performance analyses conducted on a set of benchmark video datasets show that the proposed technique is significantly faster (50–600 times) than the existing state-of-the-art techniques that produce a comparable tracking accuracy.

Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

In this letter, the interference mitigation in a canonical communication network is discussed from the perspective of intelligent location selection. A potential game model is constructed and a location-selection algorithm is designed combining no-regret procedure. With the proposed algorithm, all nodes can update their strategies with limited information exchange. Specifically, our proposed algorithm can converge to a set of correlated equilibria which are the globally or locally optimal solution to the problem of interference minimization. Moreover, our proposed algorithm can achieve distributed implementation without a central node. Simulation results demonstrate that the total interference can be mitigated efficiently with our proposed algorithm. And the proposed algorithm can converge fast.

GeneticTKM

The clustering problem under the criterion of minimum sum square of errors is a non-convex and non-linear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presented based on tabu search algorithm for the proposed hybrid genetic method. The key idea of the new operator is to produce tabu space for escaping from trap of local optimal and finding better solution. The results of the proposed algorithm are compared with other clustering algorithms such as genetic algorithm; tabu search and particle swarm optimization by implementing them and using standard and simulated data sets. The authors also compare the results of the proposed algorithm with other researchers' results in clustering the standard data sets. The results show that the proposed algorithm can be considered as an effective and efficient algorithm to find better solution for the clustering problem.

Center-based l1–clustering method

In this paper, we consider the l1-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the corresponding algorithm is given. The method is illustrated by and compared with some other clustering methods, especially with the l2-clustering method, which is also known in the literature as a smooth k-means method, on a few typical situations, such as the presence of outliers among the data and the clustering of incomplete data. Numerical experiments show in this case that the proposed l1-clustering algorithm is faster and gives significantly better results than the l2-clustering algorithm.

Nonsingularity Conditions for FB System of Reformulating Nonlinear Second-Order Cone Programming

This paper is a counterpart of Bi et al., 2011. For a locally optimal solution to the nonlinear second-order cone programming (SOCP), specifically, under Robinson’s constraint qualification, we establish the equivalence among the following three conditions: the nonsingularity of Clarke’s Jacobian of Fischer-Burmeister (FB) nonsmooth system for the Karush-Kuhn-Tucker conditions, the strong second-order sufficient condition and constraint nondegeneracy, and the strong regularity of the Karush-Kuhn-Tucker point.

GeneticTKM

The clustering problem under the criterion of minimum sum square of errors is a non-convex and non-linear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presented based on tabu search algorithm for the proposed hybrid genetic method. The key idea of the new operator is to produce tabu space for escaping from trap of local optimal and finding better solution. The results of the proposed algorithm are compared with other clustering algorithms such as genetic algorithm; tabu search and particle swarm optimization by implementing them and using standard and simulated data sets. The authors also compare the results of the proposed algorithm with other researchers’ results in clustering the standard data sets. The results show that the proposed algorithm can be considered as an effective and efficient algorithm to find better solution for the clustering problem.