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Artificial neural network models for prediction of net radiation over a tropical region

neural COMPUTING & APPLICATIONS 2021年第12期33卷 6865-6877页

作者： Ojo, Olusola Samuel Adeyemi, Babatunde Oluleye, Daniel Oluwagbenga Fed Univ Technol Akure Dept Phys PMB 704 Akure Nigeria

In this study, net radiation and temperature data were collected from the Nigeria Meteorological Agency, Lagos, covering a period of 1983-2013. The study uses a multiple layer perceptron (MLP) model of artificial neural network (ANN) with three algorithms namely the Gradient Descent, Conjugate Gradient, and Broyden-Fletcher-Goldfarb-Shanno for the prediction of net radiation using the temperature series as input variables in place of commonly used empirical statistical methods. The investigation is conducted in a tropical region where 16 meteorological stations spatially distributed across the four climatic regions of Nigeria viz semi-arid (SAR), sub-humid dry (SHD), sub-humid humid (SHH), and humid (HUM) regions were used as a case study. Analyses showed that minimum temperature has a greater positive impact on net radiation than maximum temperature. This has been attributed among other factors to have contributed to higher magnitudes of net radiation in humid regions than those of arid regions. Meanwhile, the performance assessment of the ANN models using the refined index (d(r)) metric showed that among the three models proposed, the MLP-BFGS model performed best having maximum values of 0.91 in the SAR, 0.65 in the SHD, 0.80 in the SHH, and 0.88 in the HUM regions. It also has the lowest error analysis with magnitudes of root mean square error values of 7.92 W/m(2) in the SAR, 9.95 W/m(2) in the SHD, 12.91 W/m(2) in the SHH, and 8.73 W/m(2) in the HUM regions. Finally, it can be inferred from the results that MLP neural networks with the BFGS algorithm can predict net radiation accurately better than any empirical statistical methods over Nigeria.

关键词： Net radiation Prediction neural network algorithms Performance assessment Tropical region

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Software reliability prediction: A survey

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第1期39卷 412-453页

作者： Oveisi, Shahrzad Moeini, Ali Mirzaei, Sayeh Farsi, Mohammad Ali Univ Tehran Coll Engn Sch Engn Sci Dept Algorithms & Computat Tehran Iran Minist Sci Res & Technol Aerosp Res Inst Tehran Iran Univ Tehran Coll Engn Sch Engn Sci Dept Algorithms & Computat Tehran *** Iran

Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems may cause irreparable damages;therefore, it is of great importance to be able to predict software faults and estimate the reliability of softwares. In this survey, we present a classification of various methods proposed in the literature to predict software reliability. This study summarizes the results of more than 200 research papers in the field. We also discuss the challenges involved in prediction methods along with proposed partial solutions (i.e., Bayesian methods) to improve the accuracy of such predictions. Moreover, we review numerous evaluation measures introduced so far to assess the performance of prediction models, the datasets they are based on, and also the results they yield.

关键词： Bayesian methods fuzzy logic inference systems neural network algorithms software reliability SRGM supervised algorithms

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Monitoring and mitigation framework for contaminant spills in a river

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2025年第3期11卷 1-14页

作者： Bonet, Enrique Yubero, Maria Teresa Sanmiquel, Lluis Bascompta, Marc Univ Politecn Cataluna Barcelona Spain

The global use of freshwater has increased six-fold over the past 100 years and has been growing by about 1% per year since the 1980s. By 2021, 40% of the water bodies on which Spain depends were in poor condition due to overexploitation, pollution, or ecological deterioration. To address this challenging situation, a Monitoring and Mitigation Framework (MMF) was developed to minimize damage from contaminant spills, identify the location of contamination sources, and estimate the Chemical Oxygen Demand (COD) flow concentration rates. The framework is designed to detect environmental crimes, identify the companies responsible for contamination spills, and schedule mitigation actions in coordination with law enforcement authorities for future events. Although the framework includes real-time modules to identify contamination sources and estimate COD concentration rates, this manuscript focuses solely on the offline module, where Inverse Estimation (IE) algorithms form the core. The IE algorithms are based on neural networks: a classification algorithm to locate the contamination source and a regression algorithm to estimate the COD flow concentration at the source location over time. These algorithms provide evidence to help local police specifically the Malaga City Council (Spain) identify potential companies involved in environmental crimes. The MMF was developed and tested at a Llobregat river reach (Barcelona, Spain), and it was considered 2621 test cases with different spill location and COD concentrations ranging from 0.1 to 80,000 mg/l. In those tests, IE algorithms were able to estimate COD concentration at source with a mean absolute error (MAE) lower than 2.6 mg/l or a Mean Square Relative Error (MSRE) lower than 10 mg/l, and it correctly identified the contamination source location in 85% of cases.

关键词： Agricultural and industrial pollution Contamination source location COD concentration neural network algorithms Water contamination algorithms

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Optimization method of machining parameters based on intelligent algorithm

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 737-752页

作者： Cai, Jie Zhang, Wei Deng, Jinlian Zhao, Weisheng Zhejiang Inst Mech & Elect Engn Sch Intelligent Mfg Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Creat Arts & Design Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Automat Hangzhou 310053 Zhejiang Peoples R China

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

关键词： Intelligent algorithms Processing parameters Heavy-duty CNC machine tools neural network algorithms

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Application and Outlook of Intelligent Big Data Analysis in Internet of Things Security

Application and Outlook of Intelligent Big Data Analysis in ...

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作者： Tan, Fei Guo, Huiyong Wuhan Donghu University Hubei Wuhan China

As the Internet of Things (IoT) technology develops rapidly, the interconnection of various devices and sensors has brought great convenience to people's lives and work. However, at the same time, IoT systems also face security threats and intrusion risks. Therefore, designing an efficient and reliable IoT intrusion detection model has become an important issue that urgently needs to be addressed today. The main function of the perception layer of the IoT is to achieve intelligent perception of information acquisition, capture, and target recognition, and generate massive amounts of new data. In the IoT, the transmission and transmission of information are mainly completed through the network layer. At the application level, it can achieve fields such as smart grids, monitoring services, smart homes, environmental monitoring, public safety, green agriculture, and industrial monitoring. Intelligent processing and analysis of the large amount of data generated by the perception layer can effectively manage the risk of IoT intrusion. This article explores an IoT intrusion detection model based on convolutional neural network (CNN) algorithm and random forest (RF) algorithm, aiming to improve the security and stability of IoT systems by combining the advantages of deep learning and traditional machine learning algorithms. How to promote big data analysis methods to intelligent management and operational optimization is the core of the development of the IoT industry. This article ultimately proves through experiments that the AUC (area under the curve) of the CNN-RF model is closer to 1 than the CNN model, and has high classification ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Big data analysis Information safety Internet of things security neural network algorithms

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Exploring the Usability of Adaptive Weighting Factors in Transformer Health Diagnostic Practices 14

Exploring the Usability of Adaptive Weighting Factors in Tra...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Prasojo, Rahman Azis Maharani, Devira Anggi Safarina, Liska Suwarno Putra, Muhammad Akmal A. Ekojono Politeknik Negeri Malang Department of Electrical Engineering Malang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350374605

This paper examines the potential of using adaptive weighting factors in the Health Index (HI) method for transformer health diagnostics. Typically, HI methods rely on weighting factors that are set by experts. While effective, this approach often static and may not always incorporate the latest insights or data. This study aims to enhance the HI method's accuracy and reliability by integrating adaptive, data-driven weighting factors. To achieve this, the usability of deep neural network algorithms to derive these weighting factors from actual transformer oil data is investigated. The dataset includes 500 high-voltage transformer oil samples, each with five oil testing features and known oil quality conditions. This data is used to develop and evaluate the approach. The dataset is analyzed at different scales (5, 25, 50, 100, 250, and 500 randomized samples) to ensure a comprehensive and repeatable evaluation. The main procedure involves feeding the dataset into a neural network, which then extracts the optimal weighting factors. These factors are subsequently applied in the conventional HI formula to assess their accuracy in classifying transformer oil quality. The study focuses on three key areas: firstly, the influence of the dataset size on the precision of the adaptive weighting factors, secondly, the effect of varying the neural network's structure, and third, the choice of optimizers. The aim of this study is to shift from a predominantly expertbased approach to a more data-driven methodology for reliable transformer health diagnostics framework. © 2024 IEEE.

关键词： Adaptive Weighting Factors Health Index Methodology neural network algorithms Transformer Health Diagnostics Transformer Oil Data Analysis

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Hardware Accelerators for Real-Time Face Recognition: A Survey

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IEEE ACCESS 2022年 10卷 83723-83739页

作者： Baobaid, Asma Meribout, Mahmoud Tiwari, Varun Kumar Pena, Juan Pablo Khalifa Univ Coll Engn Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates

Real-time face recognition has been of great interest in the last decade due to its wide and varied critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable of detecting and recognizing a large number of faces at almost a video frame rate. Due to the increasing volume of reference faces, traditional general-purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Units (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuits (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them have reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

关键词： Face recognition Field programmable gate arrays Feature extraction Central Processing Unit Real-time systems Hardware acceleration Graphics processing units Face recognition face detection FPGA GPU multicore CPU neural network algorithms non-neural network algorithms

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Research on Intrusion Detection Method Based on Machine Learning Algorithm and Big Data Technology 3

Research on Intrusion Detection Method Based on Machine Lear...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Li, Junhua Geely University of China Sichuan Province Chengdu city641423 China

ISBN: (纸本)9798350319125

With the popularization and application of Internet technology, people are easy to steal valuable information by cyber attackers in the process of using computer networks and electronic products. In order to maintain a good network environment and protect people's interests, network security issues need to be solved. The role of intrusion detection technology in big data platform and feature selection is particularly important, but there is a problem of inaccurate result evaluation. With the rapid development of network technology, the problem of network security has become increasingly prominent. As an important part of network security, intrusion detection plays an important role in preventing network attacks and maintaining system stability. In recent years, the application of machine learning algorithm and big data technology in the field of network intrusion detection has gradually attracted wide attention. This paper mainly discusses the role of these technologies in the process of intrusion detection and its impact on detection results. © 2023 IEEE.

关键词： Big data platform cybersecurity Machine learning algorithms neural network algorithms

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Design of Industrial and Commercial Systems based on neural networks

Design of Industrial and Commercial Systems based on Neural ...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Liao, Haifeng Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798350313413

Model construction is the main content of industrial and commercial system design, but in the process of industrial and commercial system use, data collection and characteristics Extraction has an impact on the accuracy and completeness of the data and reduces the usability of commercial systems. To improve the effectiveness of the system, this paper proposes a neural network algorithm to carry out the industrial and commercial systems neural network analysis improves the development level of industrial and commercial systems and shortens the time for model construction and formulation. A comprehensive assessment of the business system is then carried out. Finally, continuous monitoring is used to build the business system model and output the final processing results. The neural network algorithm perform data processing in industrial and commercial systems, improve the level and rationality of industrial and commercial systems More than 95% is better than traditional data processing. Therefore, the neural network algorithm can meet the requirements of industrial and commercial system collation and is suitable for continuous analysis of the development of industrial and commercial system. © 2023 IEEE.

关键词： business systems data processing model building neural network algorithms traditional data processing

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Use of Digital Twins and Digital Threads for Subway Infrastructure Monitoring 22nd

Use of Digital Twins and Digital Threads for Subway Infrastr...

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22nd International Conference on Computational Science and its Applications (ICCSA)

作者： Vodyaho, Alexander Stankova, Elena Zhukova, Nataly Subbotin, Alexey Chervontsev, Michael St Petersburg State Electrotech Univ Prof Popov St 5 St Petersburg 197376 Russia St Petersburg State Univ 7-9 Univ Skaya Nab St Petersburg 199034 Russia Russian Acad Sci St Petersburg Inst Informat & Automat Fed Res Ctr 14th Line VO 39 St Petersburg 199178 Russia

ISBN: (纸本)9783031105425;9783031105418

The article deals with the problem of using digital twins and digital threads in complex distributed cyber-physical systems with a high level of structural and functional dynamics. A three-level model of the life cycle of a complex cyber-physical system is proposed. At the upper level, the observed system is described in terms of a continuous architecture. At the middle level, the observed system is described in terms of an agile architecture. At the lower level, the observed system is described in terms of a multigraph, which allows describe both the observed system structure and behavior. In the case study the solution of the problem of monitoring the state of the subway infrastructure is considered. The proposed approach has shown its effectiveness and can be applied in other domains such as smart cities.

关键词： Digital twin Digital thread Event monitoring and visualization Smart city Machine learning neural network algorithms

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