Consumer decision-making and smart logistics planning based on FPGA and convolutional neural network

Liang Tianbao; Wang Hu

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Consumer decision-making and smart logistics planning based on FPGA and convolutional neural network

机译：基于FPGA和卷积神经网络的消费者决策和智能物流规划

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In the fourth Industrial Revolution, cost-effective planning and rational management were the key to the success of the revolution. This paper mainly studies the development and application of models in machine learning technology. The abnormal activities monitored in real time are rectified so that the customer's electronic orders can be displayed through the support of big data, thus laying the foundation for the development of intelligent logistics. Under the data system, an exception model is created and classified and regressed. In this model, the security and stability of customer orders in the network can be automatically detected, and the abnormal data can be analyzed and evaluated. Unusual circumstances of this kind need to be in an intelligent logistics environment, and delivery tasks must be called intuitive for special care. Early detection of abnormal order events is expected to improve the accuracy of delivery planning. To enable new technical solutions, the logistics industry and economic decision-makers often lack the IT background and expertise needed to start developing new systems and technical solutions. Evaluate the benefits of using. Implementation and integration complexity is seen as one of the three major obstacles to the success of the IoT above. This is by hindering long-term investment in new technologies from slowing down digitization.

机译：在第四次工业革命中，经济高效的规划和理性管理是革命成功的关键。本文主要研究机器学习技术模型的开发和应用。实时监测的异常活动得到纠正，以便通过对大数据的支持来展示客户的电子订单，从而为智能物流的发展奠定基础。在数据系统下，创建和分类和回归异常模型。在该模型中，可以自动检测网络中客户订单的安全性和稳定性，并且可以分析和评估异常数据。这种不寻常的情况需要处于智能物流环境中，并且必须对特殊护理称为直观的交付任务。预计早期检测异常订单事件将提高交付规划的准确性。为了实现新的技术解决方案，物流业和经济决策者往往缺乏开始开发新系统和技术解决方案所需的IT背景和专业知识。评估使用的好处。实施和整合复杂性被视为上面的IOT成功的三大障碍之一。这是通过妨碍新技术的长期投资减缓数字化。

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来源
《Microprocessors and microsystems》 |2021年第2期|103628.1-103628.5|共5页
作者
Liang Tianbao; Wang Hu;
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作者单位

Zhongkai Univ Agr & Engn Sch Management Guangzhou 510225 Guangdong Peoples R China;

Wuhan Univ Technol Sch Management Wuhan 430000 Hubei Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Anomaly detection; Fpga; Smart logistics; Cnn classification; Machine learning;

机译：异常检测;FPGA;智能物流;CNN分类;机器学习;

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1. FFConv: An FPGA-based Accelerator for Fast Convolution Layers in Convolutional Neural Networks [J] . AFZAL AHMAD, MUHAMMAD ADEEL PASHA ACM Transactions on Embedded Computing Systems . 2020,第2期

机译：FFCONV：卷积神经网络中的快速卷积层的基于FPGA的加速器
2. Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network [J] . Liu Hui, Mi Xiwei, Li Yanfei Energy Conversion & Management . 2018,第JUNa期

机译：基于小波包分解，卷积神经网络和卷积长期短期记忆网络的基于智能深度学习的风速预测模型
3. OPU: An FPGA-Based Overlay Processor for Convolutional Neural Networks [J] . IEEE transactions on very large scale integration (VLSI) systems . 2020,第1期

机译：OPU：用于卷积神经网络的基于FPGA的覆盖处理器
4. Smart logistics using convolutional neural networks and sensor data fusion [C] . D. Pamela, Mohana Krishna Chitoor 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies . 2017

机译：使用卷积神经网络和传感器数据融合的智能物流
5. FPGA-based Accelerators for Convolutional Neural Networks on Embedded Devices [D] . Perera Miro, Jordi. 2020

机译：基于FPGA的嵌入式设备卷积神经网络的加速器
6. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network [O] . Baoding Zhou, Jun Yang, Qingquan Li 2019

机译：基于智能手机的基于卷积神经网络的室内定位活动识别
7. An Efficient Task Assignment Framework to Accelerate DPU-Based Convolutional Neural Network Inference on FPGAs [O] . Jiang Zhu, Lizan Wang, Haolin Liu, 2020

机译：一个有效的任务分配框架，可以在FPGA上加速基于DPU的卷积神经网络推断

Consumer decision-making and smart logistics planning based on FPGA and convolutional neural network

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