Hybrid journal (It can contain Open Access articles) ISSN (Print) 1741-847X - ISSN (Online) 1741-8488 Published by Inderscience Publishers[451 journals]
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Yajuan Zhang, Tongtong Zhang Pages: 3 - 10 Abstract: The purposes are to improve the air quality of the urban ecological environment and increase the green rate of the urban garden ecological landscape. Machine Learning (ML) algorithms are used to analyse and calculate the dust retention outcomes of different plants. Dust retention capabilities and spectral characteristics of several different plants are researched. Results demonstrate a significant correlation between plants and dust retention rate. Red sandalwood has 150 inversion bands, and the optimal inversion algorithm is Random Forest (RF). Zhu Jiao has 74 inversion bands, and the optimal inversion algorithm is the Support Vector Machine (SVM). Ficus microcarpa has 80 inversion bands, and the optimal inversion algorithms are SVM and RF. ML algorithms provide better accuracy than correlation analysis, more suitable for calculating plants' dust retention capabilities. To sum up, ML algorithms can calculate the dust retention amounts of plants to better plan and design regional ecological landscapes. Keywords: dust retention effect; spectral characteristics; correlation analysis method; machine learning algorithm Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 3 - 10 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121415 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Peifu Han, Junjun Guo, Hua Lai, Qianli Song Pages: 11 - 20 Abstract: With the increasing trade among China and Southeast Asian countries, cultural exchanges have become more and more intensified. Convenient language communication constitutes an important part of the cooperation channels among different countries. To explore the named entity recognition (NER) in the field of knowledge graph construction, the Vietnamese grammar and word formation are analysed deeply in this study, aiming to solve the low recognition precision and low network calculation efficiency in Vietnamese named entity recognition. Firstly, the Vietnamese person names, location names, and institution names in Vietnamese corpus are collected statistically to build a corresponding entity database to assist the Vietnamese named entity recognition. Then, a Vietnamese named entity recognition model is proposed based on residual dense block (RDB) convolutional neural network (CNN). Keywords: Vietnamese; named entity recognition; residual network; knowledge graph Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 11 - 20 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121423 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Peifu Han, Junjun Guo, Hua Lai, Qianli Song Pages: 21 - 29 Abstract: With the advent of the information age, there appear many problems in cargo transportation, such as traffic jams, delayed information transmission, and low freight efficiency. The purpose of the study is to make freight transportation better adapt to the intelligent logistics and study the application of de-noising automatic coding networks based on deep learning in freight volume prediction. The de-noising auto-coding network and the stack de-noising auto-coding network are deeply discussed, and a freight volume prediction model based on the stack de-noising auto-coding network is constructed. The de-noising auto-coding prediction method is compared with the traditional prediction method and the deep-learning prediction method of the same kind. According to the comparative analysis, the average error of the stack de-noising auto-coding prediction method is 5.96% in 2019 and 2020, which is smaller than that of traditional prediction methods. Keywords: intelligent logistics; deep learning; de-noising auto-coding; cargo volume prediction Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 21 - 29 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121425 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Jingcheng Tian, Lingbo Yang, Yutao Zhang, Wei Qian Pages: 30 - 39 Abstract: The study aims to ensure the security and authentication efficiency of the bill image, and the encryption and decryption methods and security protection of the electronic bill are studied in the experiment. First, aiming at the not high traditional electronic bill security performance, a method is proposed, namely, embedding a watermark into a binary image with edge information. Second, aiming at the weak compression robust character of electronic bill image, the method of chaotic encryption of digital watermark through wavelet coefficient matrix algorithm is proposed to be combined with the binary sequence. Finally, the deep learning algorithm combined with the convolution algorithm can detect the quality of electronic bill watermark images. The results show that the method of embedding watermark with edge information effectively has improved the confidentiality of electronic bills. The method of chaotic encryption of digital watermarks by wavelet coefficient matrix algorithm combined with binary sequence has improved the anti-compression ability of digital watermarks. The multi-watermark encryption method has enhanced the tamper-proof ability of electronic bills and has improved the security performance of bills, and the deep convolution algorithm has improved the security and efficiency of electronic bill processing. Keywords: electronic bills; machine learning algorithms; deep convolution algorithm; multiple watermark encryption; binary image; chaotic encryption Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 30 - 39 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121413 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Jingcheng Tian, Lingbo Yang, Yutao Zhang, Wei Qian Pages: 40 - 48 Abstract: The purposes are to study how the multi-sensor Internet of Things (IoT) data fusion algorithm calculates in data fusion systems and improves the systems' fusion efficiency. An improved Weighted Least-Squares (WLS) algorithm is proposed for IoT data fusion, and how it processes massive data in a multi-sensor system is studied. Accordingly, a multi-sensor system for sports fitness is designed based on a data fusion algorithm. First, the purposes and demand for sports Application (APP) are analysed, to understand the problems and necessary improvements of such APP. Then, the implementation of the IoT and the classification of the data fusion algorithm of the IoT are explored. The WLS method is selected through comparison, and its implementation process and the data processing process are analysed and explained. Finally, the sports fitness system based on the IoT data fusion algorithm is designed and analysed. The results show that the wireless communication of a multi-sensor data fusion system is feasible and reliable. The variances calculated through the WLS method and the Arithmetic Mean (AM) method in data fusion are compared. The former value is about one-thousandth of the latter value, indicating that the data fusion based on WLS is advantageous over the traditional data fusion. Keywords: IoT; internet of things; multi-sensors; data fusion; weighted least-squares method; intelligent solutions Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 40 - 48 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121419 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xiaoyan Xu, Zhenhuan Yang Pages: 49 - 56 Abstract: The purpose is to translate loan words accurately, standardise their use, and better integrate with Chinese culture. The role of intelligent translation in loan words translation is studied based on deep learning and intelligent Internet of things. Moreover, a new neural translation mechanism of divide and conquer strategy is proposed based on the original Artificial Intelligence (AI) translation technology. Its translation quality is tested and compared with manual translation. The new neural translation mechanism can achieve the accuracy of human daily language. The accuracy rate of system Mobile Number Portability (MNP) identification is 74.67%, the recall rate is 71.42% and the F-value is 74.01%. Moreover, the average time is 0.01s/character. Therefore, the research results suggest that the constructed AI translation can more efficiently complete the translation task, save a lot of time cost and labour cost and produce a crucial reference for the intelligent development of the translation industry. Keywords: intelligent translation; loan word translation; deep learning; intelligent internet of things Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 49 - 56 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121427 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Li Cao, Chongjiang Zhan Pages: 57 - 65 Abstract: The purpose is to better mine the fitness motion data for intelligent wearable devices and promote the development of the new community fitness mode. First, the defects of the traditional fitness motion recognition system are analysed. Then, software engineering technology and Deep Learning (DL) technology are used to build a multi-layer fitness motion monitoring system. Finally, the data of running, riding, race walking, and rope skipping in the PAMAP2 data set are used for system evaluation. The results show that the proposed motion data monitoring system has an average accuracy of 97.622%, an average precision of 96.322% and a recall rate of 96.021% for fitness data recognition. The experimental results suggest that intelligent wearable devices with the proposed monitoring system can effectively mine wears' motion data and promote the development of the new community fitness mode. Keywords: AIoT; motion recognition; intelligent life technology; intelligent wearable device Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 57 - 65 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121416 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Li Cao, Chongjiang Zhan Pages: 66 - 75 Abstract: The purpose is to implement fitness and health management services more scientifically, enhance people's awareness of health management, prevent diseases caused by long-term sub-health, and comprehensively improve people's fitness and health status physically and mentally. Specifically, the data of people's health indicators are analysed, and a fitness and health management service system is established using deep learning and Internet of Things (IoT) technologies. First, people's fitness and health indicators are detected using IoT technology and integrated and pre-classified into text, number, and image. Afterward, the pre-classified data are input into the Convolutional Neural Network (CNN), their features are extracted for modelling and analysis, and the results are input into the constructed BP BackPropagation Neural Network (BPNN) model. Consequently, a preliminary prediction result about the user's fitness and health is obtained for the user's fitness and health status. The results show that the constructed fitness and health management system based on the proposed ensemble prediction model is more optimised than those constructed by a traditional simple model. With the proposed intelligent fitness and health management system composed of IoT devices, users can gain a better health status by self-monitoring, self-control, self-discovery, self-analysis and self-search. Keywords: health management; backpropagation neural network; DS evidence theory; composite prediction model Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 66 - 75 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121424 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xin Ju, Ruixin Gou, Yanli Xiao, Zheng Wang, Shangke Liu Pages: 76 - 86 Abstract: The purpose of this research is to apply the Internet of Things (IoT) and big data technology to the power management and control platform, and improve the intelligent level of power management and control. Based on edge computing and cloud platform technology, this research designed a power intelligent management and control platform and a lightning positioning system based on the Internet of Things technology. Finally, taking the Shaanxi regional power grid as an example, the power consumption data of 8000 users was selected as the data source for cluster analysis. The results found that the power intelligent management and control platform can monitor power equipment online, realise intelligent analysis of multi-source data, and is better than traditional centralised algorithms in classification accuracy and platform running time, in the hope that its good management and control performance can be applied in practice. Keywords: edge computing; internet of things; big data; power intelligent management and control platform; cloud platform Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 76 - 86 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121426 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xiangxi Du, Yanhua Sun Pages: 87 - 95 Abstract: To improve the performance and service life of the bearing and improve the overall performance of the mechanical system, the characteristics of the electromagnetic bearing and the elastic foil gas bearing are analysed based on the machine learning algorithm. First, the bearing capacity of electromagnetic bearing is analysed, including the non-linear stiffness of electromagnetic bearing, the influence of air gap on the electromagnetic force, the determination of optimal linear range and the improvement of PID control based on support vector machine. At the same time, the characteristics of the elastic foil gas bearing are analysed, including the static characteristics of the bearing aeroelastic coupling calculation process and the calculation of dynamic stiffness and damping coefficient. The results show that with the gradual increase of the current, the radial electromagnetic force of the electromagnetic bearing also increases, and the increase range is larger and larger; when the current is constant, the electromagnetic force decreases with the increase of the air gap. When the frequency is small, the response curve of electromagnetic force of electromagnetic bearing fluctuates greatly with the change of control square wave. The research has practical reference value for the optimal design of electromagnetic bearing and elastic foil. Keywords: aeroelastic coupling; machine learning; support vector machine; electromagnetic bearing; elastic foil gas bearing Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 87 - 95 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121414 Issue No:Vol. 13, No. 1 (2022)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Jianhua Deng, Songyan Mai, Ji Zeng, He Zhang, Bowen Jin, Longyu Bu, Chaochun Huang, Hui Jiang Pages: 96 - 105 Abstract: The purpose is to carry out remote monitoring of ships and realise intelligent recording of ship energy efficiency. Big data technology, 6G communication technology and embedded technology are used to build a remote monitoring system of ship operation energy efficiency from the level of hardware and software. The specific research work can be divided into three aspects. First, the existing ship operation energy efficiency indicators are analysed, and the ship Energy Efficiency Operation Indicator (EEOI) with the most balanced performance is found as the evaluation indicator of ship operation energy efficiency. Then, the requirement analysis is carried out, and the overall framework design is completed according to the analysed requirements. Moreover, the hardware module selection and peripheral circuit design are completed based on the framework parameters. Finally, the software part of the system is built. The test results prove that the functionality and stability of the platform meet the actual needs. Then, the system is tested on board for 20 days. The system proposed has a certain reference value to help ships in China achieve energy saving and emission reduction. Keywords: big data; ship energy efficiency management; ship remote monitoring; internet of things Citation: International Journal of Grid and Utility Computing, Vol. 13, No. 1 (2022) pp. 96 - 105 PubDate: 2022-03-11T23:20:50-05:00 DOI: 10.1504/IJGUC.2022.121412 Issue No:Vol. 13, No. 1 (2022)
Hybrid journal (It can contain Open Access articles)
[451 journals]
