Incremental Search Method Research Articles

Driving line-based two-stage path planning in the AGV sorting system

The path planning problem is the core issue in the automatic guided vehicle (AGV) sorting system. The current AGV sorting system has the following characteristics: (1) a great number of AGVs; (2) dynamic sorting of tasks; and (3) two-stage tasks involving transporting express packages and then leaving the sorting area. Therefore, existing path planning methods face challenges in terms of achieving timeliness and optimality. In this paper, the path planning problem of the AGV sorting system in a mesh topology area is modeled. A Driving Line-based Two-Stage (DLTS) path planning algorithm is proposed. First, to avoid conflicts among AGVs with different driving directions, time–space resources are divided according to the driving line mechanism, which specifies the driving direction, possible planned locations and specific driving rules. Second, we propose an incremental search method to plan continuous paths for two-stage tasks while simultaneously avoiding conflicts among AGVs moving in the same direction. Finally, we verify the effectiveness of our method in terms of real-time and optimal performance through simulation experiments.

Optimal number and locations of automatic vehicle identification sensors considering link travel time estimation

AbstractThe problem of optimally locating Automatic Vehicle Identification (AVI) sensors on a traffic network for travel time estimation has been a topic of growing interests in recent years. Even though great progresses have been made on AVI sensor deployment for path‐level travel time estimation, very few contributions exist in the literatures that address the AVI sensor deployment for link‐level travel time estimation on an urban network. In this paper, considering the link travel time estimation, two deployment sub‐problems are addressed: (1) where to deploy a certain number of AVI sensors? (2) What is a cost‐effective number of AVI sensors to deploy? To address the first problem, a potential game of sensors is developed to find their optimal locations which maximize the objective function that consists of estimation coverage and estimation accuracy. Then, based on the optimal locations, an incremental search method is proposed to find the optimal number of sensors considering the cost. The case in Shanghai shows the proposed game‐theoretic method is superior to other two heuristic algorithms. Moreover, compared to the real‐world sensor locations, the optimally redeployed locations improve both the estimation coverage and estimation accuracy. The case in Xuancheng City validates the proposed incremental search uses less computations to find an optimal number that close to the global optimal number solved from the brute‐force search.

Discrete Phase Shifts of Intelligent Reflecting Surface Systems Considering Network Overhead.

In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20% of coverage extension can be accomplished by deploying IRS with the proposed method.

A new improved maximal relevance and minimal redundancy method based on feature subset

Feature selection plays a very significant role for the success of pattern recognition and data mining. Based on the maximal relevance and minimal redundancy (mRMR) method, combined with feature subset, this paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset. In ImRMR, the Pearson correlation coefficient and mutual information are first used to measure the relevance of a single feature to the sample category, and a factor is introduced to adjust the weights of the two measurement criteria. And an equal grouping method is exploited to generate candidate feature subsets according to the ranking features. Then, the relevance and redundancy of candidate feature subsets are calculated and the ordered sequence of these feature subsets is gained by incremental search method. Finally, the final optimal feature subset is obtained from these feature subsets by combining the sequence forward search method and the classification learning algorithm. Experiments are conducted on seven datasets. The results show that ImRMR can effectively remove irrelevant and redundant features, which can not only reduce the dimension of sample features and time of model training and prediction, but also improve the classification performance.

Deep Reinforcement Learning-Based Radar Network Target Assignment

This study focuses on the problem of target assignment when a phased-array radar network detects hypersonic-glide vehicles in near-space and proposes a method for target assignment based on deep reinforcement learning. The state, action, and reward functions of the agent and the structure of the deep Q network are designed. To solve the problem of the large scale of the agent's action space, an incremental search method is proposed. The proposed incremental search method reduces the scale of the search space by at least 16 orders of magnitude. To improve the agent's solution quality during a search, an agent soft search constraint is designed. Setting the soft search constraint filters out the obviously inferior solutions, thereby improving the solution quality. The performance of the proposed method is verified in two scenarios: a multitarget attack and a saturation attack. The simulation results showed that the performance of the method proposed in this paper is significantly better than those achieved by the heuristic algorithm and the random assignment method in terms of the number of target assignments, the target threat degree, the number of radar switches and the cumulative detection duration.

Cascade Forward Neural Network for Time Series Prediction

Cascade-forward neural network is a class of neural network which is similar to feed-forward networks, but include a connection from the input and every previous layer to following layers. In a network which has three layers, the output layer is also connected directly with the input layer beside with hidden layer. As with feed-forward networks, a two-or more layer cascade-network can learn any finite input-output relationship arbitrarily well given enough hidden neurons. Cascade-forward neural network can be used for any kind of input to output mapping. The advantage of this method is that it accommodates the nonlinear relationship between input and output by not eliminating the linear relationship between the two. In this study, we apply the network in time series field. The optimal architecture was determined computationally by using incremental search method in both input and hidden units. The simple one was built first, and then the more complex is constructed by adding the units one by one. The optimal one is chosen then by using the mean square error criteria.

Vibrations of Shells of Revolution with Branched Meridian

A method for determining the natural frequencies of compound shells of revolution with a branched meridian is proposed. This method combines the Fourier method, the incremental search method (∆(λ)-method), and the orthogonal-sweep method. The method is tested against specific examples. The dependence of the lower frequencies of a cylinder–ring-plate system on the relative stiffness of its components is studied.

A combinatorial search method for the quasi-static payload capacity of serial modular reconfigurable robots

This paper presents a new combinatorial search method for the quasi-static stiffness performance evaluation and payload capacity analysis of serial modular reconfigurable robots (MRRs). The goal in this paper is to optimize the external payload capacity to achieve a global set of satisfactory kineto-elastic performance requirements for all module configurations. This problem exhibits considerable numerical complexities since the maximum payload capacity is dependent on a large number of possible module configurations, the worst-case stiffness poses for each configuration, and multiple kineto-elastic performance requirements. Therefore, to alleviate these difficulties, the problem is decomposed into an elimination search algorithm to reduce the configuration search space, a genetic algorithm to directly search the configuration workspaces and find the worst-case stiffness poses, and a bisection method to determine the maximum payload capacity at the worst-case configurations and poses. It is demonstrated that the worst-case stiffness search method in this paper is superior in computational time and numerical accuracy compared to previous hierarchical or incremental search methods, which require searching through a pre-determined number of poses. Through case studies, the new combinatorial search method is proven to be computationally efficient and can obtain accurate results for the worst-case stiffness poses and maximum payload capacity.

Nonaxisymmetric Waves in Layered Hollow Cylinders with Radially Polarized Piezoceramic Layers

The problem of the propagation of nonaxisymmetric waves in layered hollow cylinders with axially polarized piezoceramic layers is solved using an effective numerical-analytic method. The three-dimensional problem of electroelasticity for partial differential equations is reduced to a boundary-value eigenvalue problem for ordinary differential equations by representing the components of the stiffness tensor, displacement vectors, electric-flux density, and electrostatic potential as a combination of standing circumferential waves and traveling axial waves. The problem is solved with the stable discrete-orthogonalization and incremental search methods. The dispersion equations are analyzed numerically over a wide range of geometrical characteristics of the cylinders.

Numerical Study of Plastic Strains on Slope Instability

This article simulates an open pit slope stability using the ANSYS software, which is based on the finite element strength reduction theory, three kinds of slope instability criterion of the strength reduction method are applied to judge whether the slope is on the limit equilibrium state, the incremental search method is used to search the safety factor of the slope stability, and the results show that, the slope body damages when the plastic zone developed from the top to the bottom, in the numerical simulation the finite element iteration calculation didn’t just converge, the corresponding former level of reduction factor is the safety factor, This article can have a guiding significance on the safety production of the open-pit mine.

Free nonaxisymmetric vibrations of radially polarized hollow piezoceramic cylinders of finite length

The three-dimensional problem of free nonaxisymmetric vibrations of hollow piezoceramic cylinders with axial polarization is considered. An efficient numerical analytic method to solve boundary-value problems is proposed. The original three-dimensional problem of electroelasticity is reduced to a two-dimensional problem by representing the displacement components as standing circumferential waves. Spline collocation with respect to the axial coordinate is used to reduce this two-dimensional problem to an eigenvalue boundary-value problem with respect to the radial coordinate. This problem is solved by the stable discrete-orthogonalization and incremental-search methods. Numerical results are presented and the natural frequencies of the cylinders are analyzed in a wide range of their geometric characteristics

Free vibrations of axially polarized piezoceramic hollow cylinders of finite length

The problem of the free axisymmetric vibrations of longitudinally polarized piezoceramic hollow cylinders is solved by a numerical analytic method. The spline-collocation method with respect to the longitudinal coordinate is used to reduce the original problem of electroelasticity to an eigenvalue boundary-value problem for ordinary differential equations with respect to the radial coordinate. This problem is solved by the stable discrete-orthogonalization and incremental search methods. Numerical results are presented and the natural frequencies of the cylinders are analyzed for a wide range of their geometric characteristics

Using spline-approximation to solve problems of axisymmetric free vibration of thick-walled orthotropic cylinders

The three-dimensional theory of elasticity is used to study the free vibrations of an anisotropic hollow cylinder with different boundary conditions at the ends. The relevant problem is solved by a numerical-and-analytic method. Spline approximation and collocation is used to reduce the partial differential equations of elasticity to a boundary-value problem for a system of ordinary differential equations of high order for the radial coordinate, which is solved using the stable discrete-orthogonalization and incremental-search methods. The calculated results for an orthotropic inhomogeneous cylinder with boundary conditions of several types are presented

Lifelong Planning A∗

Heuristic search methods promise to find shortest paths for path-planning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar path-planning problems faster than is possible by solving each path-planning problem from scratch. In this article, we develop Lifelong Planning A∗ (LPA∗), an incremental version of A∗ that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A∗ that breaks ties in favor of vertices with smaller g-values but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A∗ and experimental results that demonstrate its potential advantage in two different domains if the path-planning problems change only slightly and the changes are close to the goal.

ELECT++: Faster conformational search method for docking flexible molecules using molecular similarity

We have developed a program, ELECT++ (Effective LEssening of Conformations by Template molecules in C++), to speed up the conformational search for small flexible molecules using the similar property principle. We apply this principle to molecular shape and, importantly, to molecular flexibility. After molecules in a database are clustered according to flexibility and shape (FCLUST++), additional reagents are generated to screen the conformational space of molecules in each cluster (TEMPLATE++). We call these representative reagents of each cluster template reagents. Template reagents and clustered reagents produce, after reaction, template molecules and clustered molecules, respectively (tREACT++). The conformations of a template molecule are searched in the context of a macromolecular target. Acceptable conformational choices are then applied to all molecules in its cluster, thus effectively biasing conformational space to speed up conformational searches (tSEARCH++). In our incremental search method, it is necessary to calculate the root-mean-square deviations (RMSD) matrix of distances between different conformations of the same molecule to reduce the number of conformations. Instead of calculating the RMSD matrix for all molecules in a cluster, the RMSD matrix of a template molecule is chosen as a reference and applied to all the molecules in its cluster. We demonstrate that FCLUST++ clusters the primary amine reagents from the Available Chemicals Directory (ACD) successfully. The program tSEARCH++ was applied to dihydrofolate reductase with virtual molecules generated by tREACT++ using clustered primary amine reagents. The conformational search by the program tSEARCH++ was about 4.8 times faster than by SEARCH++, with an acceptable range of errors. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1834–1852, 1998

Simultaneous measurement of the acoustical properties of a thin-layered medium: The inverse problem

This paper presents a frequency-domain ultrasonic technique for a simultaneous determination of the thickness (h) and wave speed (c) of the individual layers comprising a multilayered medium. The layers may be ‘‘thin’’; by thin we mean that the successive reflections of an ultrasonic pulse from the two faces of a layer are nonseparable in the time domain. Plane longitudinal waves which are normally incident upon the medium are considered. A systematic analysis of the sensitivity of the complex-valued transfer function to the acoustical parameters of each layer has been carried out. An inverse algorithm, which utilizes either the Newton–Raphson or the Simplex method in conjunction with the incremental search method, has been developed to reconstruct simultaneously the thickness and phase velocity of each layer by minimizing the difference between the theoretical and the experimental results in the mean-sum-square sense; the entire complex spectrum, i.e., the amplitude as well as the phase spectrum, was used. The technique is fully automated and computer controlled and can be readily used for in situ NDE applications. Results are presented for several three-layer specimens; aluminum/water/aluminum, aluminum/water/titanium, and titanium/water/titanium. Successful inversion was obtained for the following cases (1) simultaneous determination of h and c of any one of the three layers, given h and c of the remaining two layers; (2) simultaneous measurement of the three thicknesses, given the three wave speeds; (3) simultaneous measurement of the three wave speeds, given the three thicknesses; (4) simultaneous determination of all three thicknesses and one wave speed, given the remaining two wave speeds. The precision of our measurements was found to be excellent; typically, ±3 μm in h (for h of the order of 1 mm) and ± one part per thousand in c. The accuracy was found to be about one order of magnitude lower than the precision; typically, ±10 μm in h and ±2% in c.

Optimization techniques applied to induction motor design—A comparative study

This paper deals with the optimized design of cage induction motors considering cost of active materials as the objective function and performance quantities as constraints. All the expressions involved are highly nonlinear and constraints are of inequality type. Three different optimization methods have been used, i.e. Incremental Search Method, Rosenbrock's method and Complex-Box method, to obtain the optimum design. The explicit independent variables considered are ampere conductors/ m, stator stack length to pole pitch and slot depth to slot width ratios, stator core depth, average air gap flux density and stator and rotor winding current densities. Finally, a comparative study of these methods is presented.