Analysis and Design of Intelligent Logistics System Based on Internet of Things

Based on Internet of things, .NET software development technology and GIS technology, this paper analyzes and designs a system of intelligent distribution information with software engineering life cycle theory as the guide to solve the problem of high complexity and low efficiency of manual operation in logistics and distribution, improve the level of intelligent operation and then improve the operating efficiency. It analyzes the business requirements of the system, then designs its physical architecture, software architecture and system structure, and constructs the terminal node distribution dynamic model of transmission route, realizing the main function modules of the system and verifying the correctness and effectiveness of the system results through systematic and comprehensive tests. DOI: 10.17762/ijritcc2321-8169.15065

An Adaptive Optimization Spiking Neural P System for Binary Problems

© 2020 World Scientific Publishing Company. Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios

Digital Implementation of Bio-Inspired Spiking Neuronal Networks

Spiking Neural Network as the third generation of artificial neural networks offers a promising solution for future computing, prosthesis, robotic and image processing applications. This thesis introduces digital designs and implementations of building blocks of a Spiking Neural Networks (SNNs) including neurons, learning rule, and small networks of neurons in the form of a Central Pattern Generator (CPG) which can be used as a module in control part of a bio-inspired robot. The circuits have been developed using Verilog Hardware Description Language (VHDL) and simulated through Modelsim and compiled and synthesised by Altera Qurtus Prime software for FPGA devices. Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapseastrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX II FPGA which saves up to 4.4% of FPGA resources in comparison to other designs. Learning rule is an essential part of every neural network including SNN. In an SNN, a special type of learning called Spike Timing Dependent Plasticity (STDP) is used to modify the connection strength between the spiking neurons. A pair-based STDP (PSTDP) works on pairs of spikes while a Triplet-based STDP (TSTDP) works on triplets of spikes to modify the synaptic weights. A low cost, accurate, and configurable digital architectures are proposed for PSTDP and TSTDP learning models. The proposed circuits have been compared with the state of the art methods like Lookup Table (LUT), and Piecewise Linear approximation (PWL). The circuits can be employed in a large-scale SNN implementation due to their compactness and configurability. Most of the neuron models represented in the literature are introduced to model the behavior of a single neuron. Since there is a large number of neurons in the brain, a population-based model can be helpful in better understanding of the brain functionality, implementing cognitive tasks and studying the brain diseases. Gaussian Wilson-Cowan model as one of the population-based models represents neuronal activity in the neocortex region of the brain. A digital model is proposed for the GaussianWilson-Cowan and examined in terms of dynamical and timing behavior. The evaluation indicates that the proposed model is able to generate the dynamical behavior as the original model is capable of. Digital architectures are implemented on an Altera FPGA board. Experimental results show that the proposed circuits take maximum 2% of the resources of a Stratix Altera board. In addition, static timing analysis indicates that the circuits can work in a maximum frequency of 244 MHz

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs