Logistics is a driver of countries’ and firms’ competitiveness and plays a vital role in economic growth. However, the current logistics industry still faces high costs and low efficiency. The development of smart logistics brings opportunities to solve these problems. As one of the important technologies of the modern information and communication technology (ICT), the Internet of Things (IoT) can create oceans of data and explore the complex relationships between the transactions represented by these data with the help of various mathematical analysis technologies. These features are helpful to promote the development of smart logistics. In this article, we provide a comprehensive survey on the literature involving IoT technologies applied to smart logistics. First, the related work and background knowledge of smart logistics are introduced. Then, we highlight the enabling technologies for IoT in smart logistics. Furthermore, we review how IoT technologies are applied in the realm of smart logistics from the perspectives of logistics transportation, warehousing, loading/unloading, carrying, distribution processing, distribution, and information processing. Finally, some challenges and future directions are discussed.

Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.

Training radial basis function networks using biogeography-based optimizer

Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field

A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation

Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky–Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.

A machine learning approach to predict drilling rate using petrophysical and mud logging data

The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.

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Using Radial Basis Function Networks for Function Approximation and Classification

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Evolutionary computation and its applications in neural and fuzzy systems

The Hop field model is a well-known dynamic associative-memory model. In this paper, we investigate various aspects of the Hop field model for associative memory. We conduct a systematic simulation investigation of several storage algorithms for Hop field networks, and conclude that the perceptron learning based storage algorithms can achieve much better storage capacity than the Hebbian learning based algorithms.

Storage Capacity of the Hopfield Network Associative Memory

The invention discloses a license plate character segmenting method which comprises the following steps of: S10. correcting an inclined license plate; S11. removing an upper frame and a lower frame of the license plate; S12. segmenting license plate characters; and S13. normalizing the character size. The license plate character segmenting method overcomes the shortcomings that the traditional license plate character segmenting method is greatly affected by the background of the license plate, is high in the partitioning error rate, huge in calculation amount and long in execution time; the license plate character segmenting method is based on vertical projections and center-to-center spacing of the characters; and according to the license plate character segmenting method, firstly, a vertical projection method is used for realizing the coarse segmentation of the license plate characters, and then a first or second character segmentation region is found according to the center-to-center spacing of the license plate characters, the widths of the license plate characters and the prior knowledge of the license plate, and finally the merging of Chinese characters and the fine segmentation of numbers and English characters are realized. The license plate character segmenting method has the advantages that the computation complexity is low, the capacity of resisting disturbance is strong, the characters can be segmented from the background and the reliability is very high.

License plate character segmenting method

A lane video detection method based on half-machine study includes the following steps: (1) a road surface background picture is analyzed from a video and dynamically updated; (2) the road surface background picture is preprocessed by means of median filtering according to characteristics of the road surface picture; (3) edge information of a lane is extracted by means of an edge detection technology, and interference information of a distant view portion is eliminated with combination of characteristics of the lane; (4) linear multi-dimensional digital characteristics are limited by means of an improved Hough transformed detection straight line, and after points in an image space are mapped in a parameter space each time, whether counting of each accumulator exceeds an accumulated threshold is examined; (5) a multi-dimensional characteristic vector cluster of the straight line is extracted, and clustering is achieved by means of a K-means algorithm of the half-machine study according to the slope, the position and the length of the straight line; (6) a straight line in the center of the cluster is taken as a boundary, and a final lane is distinguished. The method is good in applicability, instantaneity and stability.

Yue Wu

Papers

Using Radial Basis Function Networks for Function Approximation and Classification

Evolutionary computation and its applications in neural and fuzzy systems

Storage Capacity of the Hopfield Network Associative Memory

License plate character segmenting method

Lane video detection method based on half-machine study