Open Access Published since March 20, 2012

Nonlinear Engineering

Modeling and Application

ISSN: 2192-8029

Editor-in-chief: Luigi Fortuna

Managing Editor: Katarzyna Gajewska

About this journal

Please see the latest table of contents to have access to all the papers published in Nonlinear Engineering – Modeling and Application in Issues 2021 and 2020!

Table of contents: Issues 2021 and 2020

Objective
The purpose of the Journal of Nonlinear Engineering is to provide a medium for dissemination of original research results in theoretical, experimental, practical, and applied nonlinear phenomena in engineering. The journal serves as a forum to exchange new ideas and applications of nonlinear problems occurring in automation and control, aeronautical, biological, civil, chemical, communication, electrical, industrial, mechanical, mathematical, physical, and structural systems engineering

The articles will be considered for publication if they examine nonlinearities in any engineering systems to provide a more realistic mathematical modeling, new design using nonlinearities of the system, stabilizing the system by taking advantage of nonlinearity, discovering the real behavior of the system by including nonlinearity, optimizing the system based on nonlinear interactions, development algorithms to tame nonlinearities and taking advantage of them.

Topics

Nonlinear engineering
Theoretical, experimental, practical, and applied nonlinear phenomena in engineering
Nonlinear problems occurring in aeronautical, biological, civil, chemical, communication, electrical, industrial, mechanical, mathematical, physical, and structural systems
Engineering systems
Designing and stabilizing behavior of the system
Optimizing and developing the algorithms

The journal considers submissions of different types of articles. “Research Articles” are focused on fundamental theoretical aspects, as well as on significant applications in science, engineering etc. “Rapid Communications” are intended to present information of exceptional novelty and exciting results of significant interest to the readers. “Review articles” and “Commentaries”, which present the existing literature on the specific topic from new perspectives, are welcome as well.

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You are welcome to submit your paper to our journal! The publication fee (APC – Article Processing Charges) amounts to €500.

Your benefits

Prominent members of the editorial board from all over the world
International outreach of the journal
Rich publishing traditions
Original research articles
New ideas and applications of nonlinear problems

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Volume 12 Issue 1

January 2023

Special Issue: Recent trends and emergence of technology in nonlinear engineering and its applications

Open Access January 9, 2023

Research on optimization of crane fault predictive control system based on data mining

Xu Yanbin, Zhang Jianhua, Xiongwei Wang, Mohammad Shabaz, Mohd Wazih Ahmad, Samrat Ray

Article number: 20220202

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Abstract

To ensure the safe functioning of lifting equipment, a data mining-based optimization study of a crane failure predictive control system is provided. To diagnose lifting machinery faults, the system employs decision tree categorization. Using association rules, a correlation study of hoisting machinery defect and failure was performed. When the minimal confidence and support degree are entered, a total of 244 instances of 18 frequent itemset A9 (safety protection device) may be obtained, indicating that lifting machinery does not perform well in this category. A6 (main parts) and A9 appeared a total of 98 times, with support and confidence of 29.4 and 35.6, respectively, indicating that the main parts can detect that the safety protection device is also having problems. A7 (electrical control system) and A9 appeared a total of 67 times, with support and confidence of 20.1 and 27.3, respectively, indicating that the electrical control system can detect that the safety protection device is also having problems; the correlation between them was also quite large. The system’s feasibility and efficacy shows that it has some application value.

Open Access January 14, 2023

Nonlinear computer image scene and target information extraction based on big data technology

Jiaqi Wang

Article number: 20220245

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Abstract

To explore the extraction of computer image scene and target information, a nonlinear method based on big data technology is proposed. The method can decompose the computer image into a plurality of components when the SAR computer image is processed such as target extraction and computer image compression, which represent different captured image features, respectively. Selecting the most suitable processing method according to the characteristics of different components can greatly improve the performance. Using nonlinear diffusion method, the computer image is decomposed into structural components representing large-scale structural information and texture components representing small-scale detailed information, and the automatic threshold estimation in the diffusion process is studied. The LAIDA criterion is introduced into the automatic threshold solution of nonlinear diffusion-based computer image decomposition to test and evaluate the diffusion process of various diffusion parameter forms. The results show that the experimental outcome of the diffusion decomposition based on automatic threshold estimation is very close on each index, which shows that using automatic threshold estimation, no matter what diffusion index is used, very close results can be obtained. Specifically, for each algorithm, the parameter estimation threshold l for outliers plays an obvious role. The third is the degree of initiative of the estimation process. The larger the L , the larger the outlier, which will lead to a greater extent of the diffusion process, resulting in a continuous decrease in the structural similarity index and compositional correlation. It is proved that the algorithm has strong global search ability, can effectively avoid premature convergence, has fast convergence speed, and good long stability. It can be widely used for optimization of various multimodal functions.

Open Access January 17, 2023

Nonlinear analysis and processing of software development data under Internet of things monitoring system

Peipeng Zhang, Junya Li, Anjali Sharma

Article number: 20220248

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Abstract

The most important and core technology of the Internet of things (IoT) is still the internet, and it also includes many other technical fields and is applied to many fields. The various processes of IoT data are the guarantee that the IoT can meet the management and management requirements in a certain application field, so that each application field can better meet the requirements of people’s lives. In order to study the nonlinear analysis and processing of software development data under the IoT monitoring system, this work uses QT software and socket programming to build the detection, control, and transmission module of the system, and uses multiple processes to classify and process the data. The user interface technology is used to display the data in real time. The system can control the type of transmitted data through interface operation. The classified transmission of data is realized, and the transmission speed is guaranteed to be about 50 MB/s. The experimental results show that after the functional design of each module is completed, the whole system is finally tested to determine whether the system can meet the requirements of data transmission, control, and processing. Since these data are simulated data, video playback is used to simulate the occurrence of a real accident. When an abnormal situation occurs, the whole system starts to work. If an accident occurs, a signal is sent after the accident. The system not only ensures the intellectualization of control, but also ensures the rapidity of data transmission.

Open Access January 18, 2023

Nonlinear remote monitoring system of manipulator based on network communication technology

Qing Liu, Liye Peng, Kang Shang

Article number: 20220246

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Abstract

In order to study the nonlinear remote monitoring system of the manipulator based on the network communication technology, a research method of the nonlinear remote monitoring system of the manipulator based on the network communication technology is proposed. In this article, in the Visual C++ environment, the transmission control protocol/internet protocol technology is used to build a remote monitoring system for the robotic arm, and it mainly realizes the function of the robotic arm running according to the trajectory in the remote. When storing video, you need to specify the ID of the video source, i.e. , the synchronization source in the real-time transport protocol data packet header identifies the robot, which can monitor more clearly. The remote monitoring technology is widely used in the modern automation industry, which not only further extends the human space activity ability, but also removes the human from the dangerous and complex working environment. Therefore, the remote monitoring technology has broad application prospects.

Open Access January 23, 2023

Nonlinear bridge deflection monitoring and prediction system based on network communication

Yanli Lei, Zhiqiang Li

Article number: 20220251

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Abstract

In order to study the bridge deflection monitoring and prediction system based on network communication, first, the development status of the bridge deflection monitoring system, overall demand of the system, hardware composition of the system, realization of the system software, and the timely processing and analysis of monitoring data are discussed. Then, the dynamic prediction of the change trend of the external load and the decay information of the structural resistance contained in the deflection monitoring data is carried out, and the prediction function of the external effect of the structure is established at the same time. Finally, a rapid monitoring system specially designed to deal with accidental bridge disasters was developed, and it was installed and applied in the engineering experiment of Chongqing Gaojia Garden Bridge. The deflection data analysis of Gaojia Huayuan Bridge was carried out, and suggestions for the operation and maintenance of the bridge were put forward, and the safety status assessment of the bridge was realized. The results show that the system has strong practicability, real-time monitoring and accuracy. It provides a convenient and accurate way for bridge managers to supervise bridges and formulate specific bridge maintenance plans in a timely manner. During the radio frequency modulation of communication data, harmonic oscillations occur due to the nonlinear characteristics of oscillating data, so it is difficult to improve the wireless ability to modulate and demodulate transmitted data in communications. The traditional method uses neural network fuzzy control distribution estimation harmonic balance algorithm and nonlinear rolling. The performance of dynamic predictive control is poor in quality, harmonic balance, and stability control. An improved harmonic-based communication network is proposed to balance the stability control model of nonlinear communication system, construct nonlinear communication system model, and extract signal and channel characteristics of the communication system. The channel model is designed and the communication network control method is adopted to improve the control algorithm. The simulation results show that the proposed algorithm can be used to improve the stability of nonlinear communication system, reduce the bit error rate, overcome the interference of coherent component in sidelobe, and autocorrelated the impulse response of receiver. The stability of the cumulative output is good, which can overcome the communication error caused by the harmonic oscillation due to the nonlinear characteristics of the oscillating data, and improve the communication quality.

Open Access January 24, 2023

Cross-modal multi-label image classification modeling and recognition based on nonlinear

Shuping Yuan, Yang Chen, Chengqiong Ye, Mohammed Wasim Bhatt, Mhalasakant Saradeshmukh, Md Shamim Hossain

Article number: 20220194

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Abstract

Recently, it has become a popular strategy in multi-label image recognition to predict those labels that co-occur in a picture. Previous work has concentrated on capturing label correlation but has neglected to correctly fuse picture features and label embeddings, which has a substantial influence on the model’s convergence efficiency and restricts future multi-label image recognition accuracy improvement. In order to better classify labeled training samples of corresponding categories in the field of image classification, a cross-modal multi-label image classification modeling and recognition method based on nonlinear is proposed. Multi-label classification models based on deep convolutional neural networks are constructed respectively. The visual classification model uses natural images and simple biomedical images with single labels to achieve heterogeneous transfer learning and homogeneous transfer learning, capturing the general features of the general field and the proprietary features of the biomedical field, while the text classification model uses the description text of simple biomedical images to achieve homogeneous transfer learning. The experimental results show that the multi-label classification model combining the two modes can obtain a hamming loss similar to the best performance of the evaluation task, and the macro average F 1 value increases from 0.20 to 0.488, which is about 52.5% higher. The cross-modal multi-label image classification algorithm can better alleviate the problem of overfitting in most classes and has better cross-modal retrieval performance. In addition, the effectiveness and rationality of the two cross-modal mapping techniques are verified.

Open Access February 2, 2023

Application of nonlinear clustering optimization algorithm in web data mining of cloud computing

Yan Zhang

Article number: 20220239

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Abstract

To improve data mining and data clustering performance to improve the efficiency of the cloud computing platform, the author proposes a bionic optimized clustering data extraction algorithm based on cloud computing platform. According to the Gaussian distribution function graph, the degree of aggregation of the categories and the distribution of data points of the same category can be judged more intuitively. The cloud computing platform has the characteristics of large amount of data and high dimension. In the process of solving the distance between all sample points and the center point, after each center point update, the optimization function needs to be re-executed, the author mainly uses clustering evaluation methods such as PBM-index and DB-index. The simulation data object is the Iris dataset in UCI, and N = 500 samples are selected for simulation. The experiment result shows that when P is not greater than 15, the PBM value changes very little, and when P = 20, the PBM performance of all the four clustering algorithms decreased significantly. When the sample size is increased from 50,000 to 100,000, the DB performance of this algorithm does not change much, and the DB value tends to be stable. In terms of clustering operation time, the K -means algorithm has obvious advantages, the DBSCAN algorithm is the most time-consuming, and the operation time of wolf pack clustering and Mean-shift is in the middle. In the actual application process, the number of samples for each training can be dynamically adjusted according to the actual needs, in order to improve the applicability of the wolf pack clustering algorithm in specific application scenarios. Flattening in cloud computing for data clusters, this algorithm is compared with the common clustering algorithm in PBM. DB also shows better performance.

Volume 7 (2018)

Volume 6 (2017)

Volume 5 (2016)

Volume 4 (2015)

Volume 3 (2014)

Volume 2 (2013)

Volume 1 (2012)

Cite Score	4.8
Journal Citation Indicator	0.98
SCImago Journal Rank	0.520
Source Normalized Impact per Paper	1.046

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Editorial

Editor-in-Chief
Luigi Fortuna, University of Catania, Catania (UNICT), Italy
E-mail: luigi.fortuna@unict.it

Associate Editors
Musa Bashir, Liverpool John Moores University, UK
Rosa M. Benito, Universidad Politécnica de Madrid, Spain
Arturo Buscarino, University of Catania, Italy
Ciro Del Negro, Etna Volcano Observatory (EVO), Catania, Italy
Daniel Espino, University of Birmingham, UK
Carlo Famoso, University of Catania, Italy
Nan Gao, University of Birmingham, UK
David Lambert, University of North Texas, Denton, USA
Mario Lavorgna, STMicroelectronics, Naples, Italy
Korosh Mahmoodi, US Army Research Laboratory, Aberdeen, USA
Cristina Masoller, Universitat Politecnica de Catalunya, Barcelona, Spain
Giovanni Francesco Massari, Politecnico di Bari, Italy
Viet-Thanh Pham, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Mofeed Turky Rashid, University of Basrah, Iraq
Ashutosh Sharma, University of Petroleum and Energy Studies, Dehradun, India
Shiyang Tang, University of Birmingham, UK　　　

Editorial Board
Hazizan Md Akil, Universiti Sains Malaysia, Penang, Malaysia
Zi Chen, Harvard Medical School, Birgham and Women’s Hospital, Boston, USA
Kuan Yew Cheong, University Sains Malaysia, Penang, Malaysia
Xiaowei Deng, University of Hong Kong, Hong Kong
Volkan Esat, Middle East Technical University – Northern Cyprus Campus (METU-NCC), Turkey
Kheng Lim Goh, Newcastle University, Singapore
Joseph N Grima, University of Malta, Malta
Huali Hao, City University of Hong Kong, Hong Kong
Enrique Herrera-Viedma, University of Granada, Spain
Nagma Irfan, Sharda University, India
Haichao Li, Harbin Engineering University, China
Mohd Adzir Mahdi, Universiti Putra Malaysia, Serdang, Malaysia
M.S.A. Majid, University of Malaysia Perlis, Malaysia
Saad Mekhilef, University of Malaya, Kuala Lumpur, Malaysia
Rui Rao, Guangzhou University, China
Hamid M. Sedighi, Shahid Chamran University of Ahvaz, Iran
Mohamed Thariq Hameed Sultan, University Putra Malaysia, Serdang, Malaysia
Chak Yin Tang, Hong Kong Polytechnic University, Hong Kong
Pak-Kin Wong, University of Macau, China
Seong Ling Yap, University of Malaya, Kuala Lumpur, Malaysia　
Jingjie Yeo, Cornell University, New York, USA　

Editorial Advisory Board
Mizi Fan, Brunel University, London, UK
Sabyasachi Ghosh, Indian Institute of Technology, Kharagpur, India
Denvid Lau, City University of Hong Kong, Hong Kong
Rama Layek, LUT University, Lappeenranta, Finland
Maxwell Omeje, Convenant University, Nigeria　
Duc Pham, University of Birmingham, UK
S.M. Sapuan, University Putra Malaysia, Serdang, Malaysia
Lik Ho Tam, Beihang University, Beijing, China　

Honorary Editor
David Hui, University of New Orleans, New Orleans, USA

(Deutsch)

Type: Journal
Language: English
Publisher: De Gruyter
First published: March 20, 2012
Publication Frequency: 1 Issue per Year
Audience: scholars, students, researchers and practitioners interested in nonlinearity and its applications