Radio Frequency IDentification (RFID) is one of the prominent wireless technologies for implementing a complete smart environment and is preferred over magnetic tapes, bar codes and smart cards due...

A Novel and Efficient Reader-to-Reader and Tag-to-Tag Anti-Collision Protocol

With the widespread use of mobile devices having powerful processing capabilities, there is an ever-increasing demand to localize essential mobile services on these mobile devices to increase convenience. Most of the current computation concentrates on collecting service information from mobile devices and processing it at the server side. This is because semantic analysis of the service description in the mobile device is a resource intensive process. One of the main processes involved in the semantic analysis is the Parts of Speech (POS) Tagging. Currently, POS tools are not available for mobile devices. POS tagging however is a resource intensive application which is a challenge in the context of mobile devices due to limited availability of resources such as power, memory and processing capability. This chapter discusses a new, lightweight, context based web service composition model for mobile devices. The main idea is to build a lightweight POS tagger in the mobile device itself. The POS tagger finds its application in the context of identifying services requested by users in the form of natural language queries. Once the service names are identified in the mobile device, the request is sent to the web-service providers for their response and these responses are composed in the mobile device.

Lightweight Context-Based Web-Service Composition Model for Mobile Devices

Application of GNSS displacement monitoring system in measurement of open sea structure

https://iopscience.iop.org/article/10.1088/1742-6596/1617/1/012065/pdf

A Novel Approach for Indoor Positioning Based on RFID and UWB

Radio frequency identification (RFID) technology has become one of the most prosperous positioning technologies due to its advantages in non-contact and non-line-of-sight sensing. Most of the current positioning applications using RFID were implemented by setting a few RFID readers in some known locations, which were only suitable for the indoor or small area of outdoor positioning. This paper proposed a positioning algorithm by combining vehicle Global Navigation Satellite System (GNSS) and mobile RFID readers. The positioning accuracy is better than ±5 m with an identification distance of 160 m. This method can be used in the outdoor positioning with a rapid and accurate locating of the target objects, which is very helpful for the specific objects positioning and change detection in daily urban management and regulations.

https://link.springer.com/content/pdf/10.1186%2Fs41445-016-0002-8.pdf

Tag location method integrating GNSS and RFID technology

In recent years, the economy of Guangdong province has ranked first in China for many consecutive years, with a strong growth momentum. In the future, whether Guangdong’s GDP can maintain high-quality growth is an important issue. Neural network is an important tool for prediction. The neural network is mainly divided into BP neural network and RBF neural network. Combining the economic forecasting theory and the characteristics of BP neural network algorithm and RBF neural network, this paper studies the economic growth prediction of Guangdong province based on the artificial neural network algorithm and RBF neural network algorithm. The results of empirical research show that the artificial neural network has good prediction accuracy, but the data of economic growth prediction by various neural networks are different, and some errors are very large. Compared with BP neural network, RBF neural network model is one of good economic prediction models with high accuracy and less time.

Economic Forecast Model and Development Path Analysis Based on BP and RBF Neural Network

The goal of circular economy is to save energy and reduce emissions. It is a new economic development mode of comprehensive development, full utilization of resources, waste reuse and energy-saving production. The development of circular economy needs to consider regional differences in economy, society, environment, resources, etc. This paper uses AHP-FCE method to build the evaluation index system of regional circular economy development, so as to measure the actual situation of regional circular economy development. On this basis, in order to predict the future development trend of circular economy based on historical evaluation index, we utilize the grey theory to build the prediction model. After inputting the original data into the model, we can get the forecast value of the circular economy development level of province A from 2022 to 2024. The prediction result shows that, although the growth rate of circular economy in A Province has basically stagnated from 2018 to 2021, it will enter the development stage in the next three years.

Construction of Regional Circular Economy Development Prediction Model Based on AHP-FCE Algorithm

In this paper, an intelligent hybrid algorithm for regional economic development difference analysis is proposed: firstly, the FCM algorithm is optimized by organically integrating the principal component distance weighting, genetic algorithm and simulated annealing algorithm, and the optimized FCM algorithm is used to classify the economic development level of each sub region. This paper studies and analyzes the differences in regional economic development. Taking the economic development difference of the Yangtze River Delta urban agglomeration as an example, this paper shows that the algorithm can effectively carry out a comprehensive analysis of the economic development difference of the Yangtze River Delta, well reflects the characteristics of the economic development of each city, and is an improved and feasible analysis method of regional economic development level difference.

Meng Du

Papers

Tag location method integrating GNSS and RFID technology

Economic Forecast Model and Development Path Analysis Based on BP and RBF Neural Network

Construction of Regional Circular Economy Development Prediction Model Based on AHP-FCE Algorithm

Differences Analysis and Development Strategies of Regional Economies Based on Intelligent Hybrid Algorithm