syedzayyan/paper_mill_benchmark huggingface dataset:
An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves.This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone.On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research.Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy rbf neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams.Injury assessment.The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
An Image Fusion Algorithm Based on Improved RGF and Visual Saliency Map

To solve the artifact problem in fused images and the lack of enough generalization under different scenarios of existing fusion algorithms, the paper proposes an image fusion algorithm based on improved rgf and visual saliency map to realize fusion for infrared and visible light images and a multimode medical image.Firstly, the paper uses rgf (rolling guidance filter) and gaussian filter to decompose the image into the base layer, interlayer, and detail layer by a different scale.Secondly, the paper obtains a visual weight map by the calculation of the source image and uses the guided filter to better guide the base layer fusion.Then, it realizes the interlayer fusion through maximum local variance and realizes the detail layer fusion through the maximum absolute value of the pixel.Finally, it obtains the fused image through weight fusion.The experiment demonstrates that the proposed method shows better comprehensive performance and obtains better results in fusion for infrared and visible light images and medical images compared to the contrast method.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;
Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement

The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3d visual image recognition of aerobics movements.A lot of results have been achieved by applying deep neural networks to the 3d visual image recognition of aerobics movements, but there are still many problems to be overcome.After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model.The model is improved on the basis of the traditional model and unifies the process of aerobics 3d visual image segmentation, target feature extraction, and target recognition.The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3d visual image test set for recognition and comparison.We improve the accuracy of network recognition by adjusting the configuration parameters in the network model.The experimental results show that compared with other simple models, the model based on the improved ada boost algorithm can improve the final result significantly when the accuracy of each model is average.Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model.

Journal: Complexity (2020)
Publication types: Research Article;
A Three-Dimensional Complex Measurement Model-Based Avionic Radio-Frequency Power Source Health Assessment Method

Health assessment is an important part of phm technology, which is crucial to product state monitoring and management.With the complexity of product structure and diversification of functions, the product health presents uncertainty under the complex influence of multiple factors.Nowadays, the general health assessment method is relatively simple and only considers the product function completeness, and the assessment effect accuracy still needs to be strengthened in the actual working environment.Most assessment parameters and assessment methods are selected ignoring the impact of environmental changes and operating time performance.Therefore, a new health measurement method for the avionic radio-frequency power source based on a three-dimensional complex measurement model is proposed.Firstly, the product health definition is proposed, and the health connotation is analyzed from three-dimensional aspects containing functional integrity, environmental adaptability, and temporal sustainability.According to the function and structure characteristics of radio-frequency power source, the health characterization parameters are then obtained and the health assessment parameter system is established.Finally, the three-dimensional complex measurement model is given, and the rf power source health assessment is carried out comprehensively from integrity, stability, and reliability aspects.The three-dimensional health assessment method provides a new way to solve the uncertainty of product health state under multiple factor effects, which is conducive to the targeted optimization management strategy.

Journal: Complexity (2020)
Publication types: Research Article;
A Systematic Pipelaying Control Method Based on the Sliding Matrix for Dynamically Positioned Surface Vessels

A pipelaying control method is presented in this paper which includes path planning, path guidance, and path tracking controller for dynamically positioned (dp) surface vessels based on the characteristics of the predefined path in marine pipelaying operation.The pipelaying control method depends on path coding, path selection logic system, and a sliding matrix.The sliding matrix contains a vessel local path and its specified control requirements, which can be updated by sliding down the waypoint table line by line as the vessel is traveling from one path to the next.A line of sight (los) algorithm is developed to calculate the desired vessel position and heading on a circular arc path.The motion controller, which can simultaneously control the vessel speed at the directions of surge and sway, is designed by decomposing the desired inertial resulting velocity into the desired body velocity components.A dp simulator for pipelaying operation is developed, and in order to verify the proposed method, a pipelaying simulation is carried out.The simulation results show that the proposed method enables the vessel to move along the desired path while maintaining a set crab angle, a specified speed, and a turning radius.The pipeline can be laid onto the specified waypoints even when the vessel is subjected to drift forces caused by ocean currents, wind, and waves.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses.In india, agriculture is a prime source of income in most rural areas.Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner.In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, cnn, and various image-processing techniques along with their associated merits and demerits have already been discussed.In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model.As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the plant village dataset, 25% utilized real-field images, and 20% utilized open image datasets.The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%.In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification.Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy.We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.

Journal: Journal of Sensors (2022)
Publication types: Review Article;
A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (io t) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for io t applications.Data is the foundation and core of cloud io t platform applications, and massive multisource heterogeneous io t data aggregation and storage have basic requirements such as real-time, security, and scalability.This paper focuses on the aggregation and storage methods of massive heterogeneous cloud io t data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types.A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency.A protocol adaptation layer is set up in the io t virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of io t nodes, and on this basis, data is transmitted to the cloud io t platform through a unified interface, shielding the variability of io t sensing devices.Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for io t devices based on “api key” and implant the device authentication api key into the protocol adaptation layer of the virtual gateway to realize the source authentication of io t nodes and ensure the authenticity of data.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
A Study of the Spatial Effects of Financial Support and Economic Growth under Optimal Control of Nonlinear Generalized Complex Systems

This paper provides an in‐depth analysis and study of the spatial effects of financial support and economic growth with the help of nonlinear generalized complex systems.Taking the industrial sector as the research object and combining the relevant contents of neoclassical investment theory, information economics, and institutional economics, this paper clearly defines and argues that the main feature of current financial policy is financial constraint rather than financial inhibition based on an in‐depth understanding of the theoretical connotation and policy rationality of financial constraint and, as a premise, further analyzes the financial constraint policy causing excessive investment and capital mismatch in the corporate sector.It further analyzes the mechanism of the role of financial constraint policies in causing overinvestment and capital mismatch in the corporate sector and conducts empirical tests from three research perspectives of measuring investment efficiency, output efficiency of investment, allocation efficiency of industry capital, and investment behavior of microenterprises, and finally puts forward relevant policy recommendations in conjunction with the evaluation of the efficiency of financial constraint policies.This paper selects three dimensions of the financial system, namely, financial structure, financial efficiency, and financial scale, and studies the adaptability between these three dimensions and the development of the real economy, respectively, and then uses different empirical methods to analyze the dynamic adaptability effects between the development of the real economy and these three dimensions of the financial system and finally explores the way of adaptability between the financial system and the development of the real economy.This paper provides a medium and micro theoretical basis and new empirical evidence for understanding the importance of financial system reform on economic growth and also opens up a space for exploring the exit path of financial constraints and using interest rate marketization as a general grip to reasonably guide financial resources to achieve economic transformation and upgrading and sustainable and healthy development through supporting high‐quality investment, using more interprovincial level data in the analysis, so it is more comprehensive and detailed than previous scholars’ studies.The examination is more comprehensive and detailed than previous scholars’ studies.

Journal: Discrete Dynamics in Nature and Society (2022)
Publication types: Research Article;
A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply.It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production.It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies.Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy.Initially, the segmentation of the image is carried out.To this end, the parcel data is used to generate the region of interest (roi) of each parcel.Second, the homogeneity of each roi is detected by its statistical indices (mean value and standard deviation).Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous rois.Finally, two classification results are combined as the final classification result.The new algorithm was applied to a complex planting area of 103.60 km2 in central china based on the ownership parcel data and gaofen-1 pms and wfv remote sensing images in this paper.The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.

Journal: Journal of Mathematics (2021)
Publication types: Research Article;
A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model

Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on.So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements.In this paper, a multiscale and high-precision lstm-gasvr short-term traffic flow prediction algorithm is proposed.This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (lstm) and inverse normalization; and proposing the ga-svr model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow.The prediction experiment is carried out according to the charge data of a toll station in xi’an, shaanxi province, from may 2018 to may 2019.The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the lstm, gru, cnn, sae, arima, and svr, and the r 2 can reach 0.982, the explanatory variance is 0.982, and the mape is 0.118.The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.

Journal: Complexity (2020)
Publication types: Research Article;
A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

The current robotics field, led by a new generation of information technology, is moving into a new stage of human-machine collaborative operation.Unlike traditional robots that need to use isolation rails to maintain a certain safety distance from people, the new generation of human-machine collaboration systems can work side by side with humans without spatial obstruction, giving full play to the expertise of people and machines through an intelligent assignment of operational tasks and improving work patterns to achieve increased efficiency.The robot’s efficient and accurate recognition of human movements has become a key factor in measuring robot performance.Usually, the data for action recognition is video data, and video data is time-series data.Time series describe the response results of a certain system at different times.Therefore, the study of time series can be used to recognize the structural characteristics of the system and reveal its operation law.As a result, this paper proposes a time series-based action recognition model with multimodal information fusion and applies it to a robot to realize friendly human-robot interaction.Multifeatures can characterize data information comprehensively, and in this study, the spatial flow and motion flow features of the dataset are extracted separately, and each feature is input into a bidirectional long and short-term memory network (bi lstm).A confidence fusion method was used to obtain the final action recognition results.Experiment results on the publicly available datasets ntu-rgb + d and msr action 3d show that the method proposed in this paper can improve action recognition accuracy.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
5-Fluorouracil-Loaded PLGA Nanoparticles: Formulation, Physicochemical Characterisation, and In Vitro Anti-Cancer Activity

The major goal of this investigation was to prepare a drug delivery of polymeric nanoparticles (nps) from 5-fluorouracil (fu) that could be delivered intravenously and improve the therapeutic index of the fu.In order to achieve this, interfacial deposition method was used to prepare fu entrapped poly-(lactic-co-glycolic acid) nanoparticles (fu-plga-nps).The influence of various experimental settings on the effectiveness of fu integration into the nps was assessed.Our findings show that the technique used to prepare the organic phase and the ratio of the organic phase to the aqueous phase had the greatest impact on the effectiveness of fu integration into nps.The results show that the preparation process produced spherical, homogenous, negatively charged particles with a nanometric size of 200 nm that are acceptable for intravenous delivery.A quick initial release over 24 h and then slow and steady release of fu from the formed nps, exhibiting a biphasic pattern.Through the human small cell lung cancer cell line (nci-h69), the in vitro anti-cancer potential of the fu-plga-nps was evaluated.It was then associated to the in vitro anti-cancer potential of the marketed formulation fluracil®.Investigations were also conducted into cremophor-el (cre-el) potential activity on live cells.The viability of nci-h69 cells was drastically reduced when they were exposed to 50 μg·m l−1 fluracil®.Our findings show that the integration of fu in nps significantly increases the drug cytotoxic effect in comparison to fluracil®, with this potential effect being particularly important for extended incubation durations.

Journal: Bioinorganic Chemistry and Applications (2023)
Publication types: Research Article;