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An Improved A∗ algorithm based on time window is proposed to solve conflict-free path planning problem for the Automated Guided Vehicles (AGV) in flexible manufacturing systems. Based on the planned AGV paths, conflict-free routing was received by utilizing the improved A∗ algorithm in the next AGV routing. The algorithm of improving evaluation function to introduce the turning factors not only avoids...
In reality, traffic demands are quite random, which usually deviate from the application conditions of a certain kind of signal control method and compromise the efficiency of traffic control. To solve such a problem, taking a typical isolated intersection as the research object, the adaptive control of an isolated intersection based on a complete phase group is proposed, which can automatically optimize...
This paper focuses on presenting a human-in-the-loop reinforcement learning theory framework and foreseeing its application to driving decision making. Currently, the technologies in human-vehicle collaborative driving face great challenges, and do not consider the Human-in-the-loop learning framework and Driving Decision-Maker optimization under the complex road conditions. The main content of this...
Grey Wolf Optimizer (GWO) is a new meta-heuristic optimization. It is inspired by the unique predator strategy and organization system of grey wolves. Since the GWO algorithm is easy to fall into local optimum especially when it is used in the high-dimensional data, an improved GWO algorithm combined with Cuckoo Search (CS) is proposed in this paper. By introducing the global-search ability of CS...
At present, radio frequency identification technology has been widely used in many fields, such as data acquisition, transportation, logistics management and so on. However, the collision problem in RFID technology seriously affects the performance of RFID system. Aimed at the shortcomings of search times and traffic by the traditional binary search algorithm, this paper propose an improved binary...
In this paper we consider the data caching problem in next generation data services in the cloud, which is characterized by using monetary cost and access trajectory information to control cache replacements, instead of exploiting capacityoriented strategies as in traditional research. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first...
This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle (HFV) models by using neural network (NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbance-observer-based-control (DOBC) algorithm with...
Lack of relevant data is a major challenge for Bayesian network (BN) parameters learning. For the issue, this paper proposes a constrained parameter evolutionary learning algorithm (CPEL) which is based on the qualitative knowledge and evolutionary strategy. In detail, firstly qualitative knowledge is employed into BN parameters learning process to reduce the parameter search space where two types...
In this paper, we deal with a consensus control problem for a group of high dimensional agents which are networked by digraphs. Assuming that the control input of each agent is constructed based on the weighted difference between its states and those of its neighbor agents, we aim to propose an algorithm on computing the weighting coefficients in the control input. The problem is reduced to designing...
We have introduced a novel metro network architecture called Ultra-Dense Wavelength Switched Network (UD-WSN) [1], which enables an even fine spectrum granularity so as to efficiently accommodate low-speed metro services (e.g., 1GE/10GE services). We evaluate the performance of this type of networks with spectrum defragmentation and partial optical transport network (OTN) switching. An efficient spectrum...
In this paper, a novel guidance-control scheme is presented for the unmanned surface vessel (USV) trajectory tracking. First, the guidance system is designed to give the reference speed and heading for the control system. Based on the SFLOS algorithm, the guidance system can track the desired curve in high precision. Then, control system is proposed based on backstepping technique and bio-inspired...
For the irregular nesting problem widely existing in modern manufacturing industry, this paper makes a research on it and presents an optimization algorithm based on no-fit polygon (NFP) method and hybrid heuristic strategy to solve it. The proposed algorithm first uses the composition method of trace line segment to calculate no-fit polygons (NFPs) between every two pieces in piece set, and extracts...
Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance...
The Smith-Waterman algorithm, which produces the optimal local alignment between pairwise sequences, is universally used as a key component in bioinformatics fields. It is more sensitive than heuristic approaches, but also more time-consuming. To speed up the algorithm, Single-Instruction Multiple-Data (SIMD) instructions have been used to parallelize the algorithm by leveraging data parallel strategy...
Determination of source-destination connectivity in networks has long been a fundamental problem, where most existing works are based on deterministic graphs that overlook the inherent uncertainty in network links. To overcome such limitation, this paper models the network as an uncertain graph where each edge e exists independently with some probability p(e). The problem examined is that of determining...
Learning distributed representations of symbolic data were introduced by Hinton[1], and first developed in modeling networks for learning the node vectors by Perozzi et al (2014). In this work, we proposed Dnps, a novel nodes embedding approach for acquiring distributed representations of large-scale dynamic social networks. Dnps is suitable for many types of social networks: dynamic/static, directed/undirected,...
In the automated storage and retrieval system (AS/RS), a reasonable path-planning is fundamental for the effective work of AGV. For the rectangular environment map of the AS/RS system, the traditional Dijkstra algorithm can only find one shortest path, and skip over other paths with the same distance. An improved Dijkstra algorithm is proposed to solve the path-planning problem in the rectangular...
In software-defined networking (SDN), as data plane scale expands, scalability and reliability of the control plane have become major concerns. To mitigate such concerns, two kinds of solutions have been proposed separately. One is multi-controller architecture, i.e., a logically centralized control plane with physically distributed controllers. The other is control devolution, i.e., delegating control...
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper,...
Backpressure-based adaptive routing algorithms have been studied extensively in the literature. Although backpressure-based adaptive routing algorithms have been shown to be network-wide throughput optimal, they typically have poor delay performance under light or moderate loads because packets may be sent over unnecessarily long routes. Further, backpressure-based algorithms have required every node...
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