Problem Of Uneven Distribution Research Articles

Detection Algorithm of Surface Defect Word on Printed Circuit Board

For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is preserved for PCB defect feature information extraction, and secondly, the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process. Then, the K-means++ clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection, which further improves the recognition and localization of small PCB defects. Finally, the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms. The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%, which has better detection capability, lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

Hagnifinder: Recovering magnification information of digital histological images using deep learning

Background and objectiveTraining a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports can be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images. MethodsHagnifinder is a regression model based on a modified convolutional neural network (CNN) that contains 3 modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features. Secondly, the ASM is proposed to address the learned feature values uneven distribution problem. Finally, the Regression Module estimates the mapping between the regularized extracted features and the magnifications. We construct a new dataset for training a robust model, named Hagni40, consisting of 94 643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancer based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measure the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, and 5) between the predicted magnification and the actual magnification. We compare Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40. ResultsThe Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 is used as the feature extractor. Compared with the state-of-the-art methods focusing on 4–5 levels of magnification classification, the Hagnifinder achieves the best and most comparable performance in the BreakHis and Hagni40 datasets. ConclusionsThe experimental results suggest that Hagnifinder can be a valuable tool for predicting the associated magnification of any given histology image.

E-BOOK SEBAGAI INOVASI DALAM PEMENUHAN SARANA DAN PRASARANA PENDIDIKAN BERBASIS TEKNOLOGI DI ERA DIGITAL PASCA PANDEMI COVID-19

This article discusses an innovation of facilities and infrastructure to support the quality of education in Indonesia. Difficulties in the distribution and outreach of facilities and infrastructure such as textbooks to schools that are far from the center of government are also an obstacle. This causes the problem of uneven distribution of education in Indonesia. For this reason, innovation is needed so that students access the information they should get easily and affordable throughout the archipelago. The purpose of writing this article is to explain the effect of using technology media, namely electronic books or ebooks for schools. This research is a literature study. This article discusses the use of electronic books or ebooks as supporting educational facilities and infrastructure after the Covid-19 pandemic.

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era.

"Re-visits and drug renewal" is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of "Re-visits and drug renewal" actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era.

Multiscale image dehazing network based on spatially weighted sparse representation

Due to the influence of hazy weather, the images captured often suffer from severe degradation, which will reduce the effectiveness of the devices for computer-related applications. To eliminate the degradation of hazy images, a multiscale image dehazing network based on spatially weighted sparse representation is proposed. The model is mainly divided into two modules: multiscale module (MSM) and detail enhancement module (DEM). The MSM can effectively obtain more feature information in more hazy images. Specifically, the degradation of hazy image is considered in terms of haze distribution and image details. A spatially weighted nonlocal sparse attention module was developed to focus on channel-level features adaptively and to pay more attention to the recovery of dense haze areas in response to the problem of uneven distribution of haze in images. In addition, an independent feature fusion module is proposed to capture better the contextual information, which can also effectively address the traditional fusion, leading to redundant feature extraction and hindering the reconstructive performance of the network. Finally, a DEM is employed by a combination of pyramid pooling and resblock group, which guides the adaptive fusion of latent features, where different details and plausible appearances are integrated. Experimental results on public datasets show that this method is effective and superior in the metrics of structure similarity (SSIM), peak-signal-to-noise ratio, multiscale-SSIM, and root-mean-square error.

Deep learning-based remote-photoplethysmography measurement from short-time facial video

Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model. Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets. Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively. Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.

Cattle Number Estimation on Smart Pasture Based on Multi-Scale Information Fusion

In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE.

Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application

Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.

Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data

AbstractWafer yield prediction, as the basis of quality control, is dedicated to predicting quality indices of the wafer manufacturing process. In recent years, data-driven machine learning methods have received a lot of attention due to their accuracy, robustness, and convenience for the prediction of quality indices. However, the existing studies mainly focus on the model level to improve the accuracy of yield prediction does not consider the impact of data characteristics on yield prediction. To tackle the above issues, a novel wafer yield prediction method is proposed, in which the improved genetic algorithm (IGA) is an under-sampling method, which is used to solve the problem of data overlap between finished products and defective products caused by the similarity of manufacturing processes between finished products and defective products in the wafer manufacturing process, and the problem of data imbalance caused by too few defective samples, that is, the problem of uneven distribution of data. In addition, the high-dimensional alternating feature selection method (HAFS) is used to select key influencing processes, that is, key parameters to avoid overfitting in the prediction model caused by many input parameters. Finally, SVM is used to predict the yield. Furthermore, experiments are conducted on a public wafer yield prediction dataset collected from an actual wafer manufacturing system. IGA-HAFS-SVM achieves state-of-art results on this dataset, which confirms the effectiveness of IGA-HAFS-SVM. Additionally, on this dataset, the proposed method improves the AUC score, G-Mean and F1-score by 21.6%, 34.6% and 0.6% respectively compared with the conventional method. Moreover, the experimental results prove the influence of data characteristics on wafer yield prediction.

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Improved YOLOv4 algorithm for safety management of on-site power system work

To achieve real-time safety management of on-site power system work, the object detection algorithm of the cloud platform is transplanted into the edge computing device. However, due to the limited capacity and arithmetic power of the AI chip carried in the edge computing device, the algorithm needs to be studied in a lightweight manner. Therefore, this paper proposes an improved edge-side lightweight YOLOv4 (You Only Look Once version 4) algorithm, which is compressed and accelerated by improving the network structure and the confidence loss function of the algorithm without losing too much detection accuracy. Firstly, a lightweight convolutional neural network based on depthwise separable convolution and mobile inverted bottleneck convolution is constructed as the backbone network of YOLOv4, and the depthwise separable convolution is used instead of the conventional convolution, which can greatly reduce the parameters number and computation of the algorithm. Secondly, an improved bidirectional feature fusion network is constructed as the neck network of YOLOv4 algorithm to improve the information availability of multi-semantic features and enhance the extraction capability of small-scale features. Then, the confidence loss function of the algorithm is improved to alleviate the problems of uneven distribution of positive samples and negative samples and uneven distribution of easy-to-classify samples and hard-to-classify samples. Finally, the improved YOLOv4 algorithm is tested on an edge computing device equipped with an NVIDIA Jetson Xavier NX chip, and the dataset based on the compliance of on-site dress code in the electric power system field is tested. The experimental results show that, compared with the original YOLOv4 algorithm, the improved YOLOv4 algorithm reduces the parameters number by 93.11%, and the real-time video stream detection speed on the edge computing device is increased by 22.00%, reaching 15.05 FPS. The lightweight improved YOLOv4 algorithm is suitable for deployment at the edge end and can achieve real-time accurate identification of risks in the on-site work.

Cotton canopy airflow simulation and velocity attenuation model based upon 3D phenotype and stratified sub-regional porous medium

Airflow assisted with sprayers can change not only the canopy’s porosity, but also allow the droplets to enter its interior. It is important to depict the airflow velocity distribution in a dense crop canopy quantitatively to optimize the goal of uniform deposition of droplets and full coverage within the canopy. However, there is yet no effective way to do so. The contribution of this paper is to propose a digital method to predict airflow distribution in the canopy using computational fluid dynamics (CFD) and machine-learning (ML) methods. Firstly, a virtual, simplified 3D cotton plant is generated based upon the phenotypic traits and the Logistic growth function to describe such canopy architecture parameters as leaf area density (LAD) and porosity quantitatively at any position in the canopy. Secondly, to solve the problem of uneven distribution of airflow in the canopy, in the CFD model, the target area surrounded by the main stems of 4 adjacent 3D cotton plants is selected as a stratified sub-regional porous medium (SSPM), and the accuracy of the CFD simulations is compared to the indoor measurements. Thereafter, a range of ML algorithms are trained with the velocity dataset obtained from the CFD results under different spray operating parameters (initial air velocity, canopy depth, LAD, and porosity). The comparison of CFD simulations and actual airflow measurements shows that the mean normalized mean absolute errors (NMAEs) of the lower, middle, and upper layers are 17.38 %, 21.35 %, and 9.75 % respectively. The Random Forest method has greater prediction accuracy, with a Coefficient of Determination (R2) of 0.9882 and Root Mean Squared Error (RMSE) of 0.5071, which indicates excellent agreement with the CFD simulation results. Therefore, such a data-driven model of airflow velocity distribution in a dense crop canopy can be used to optimize the operation parameters and eliminate complex CFD simulations or tedious physical experiments.

Research on E-Commerce Database Marketing Based on Machine Learning Algorithm.

From simple commercial relations to complex online transactions at this stage, it not only highlights the progress of science and technology, but also indirectly explains the diversified evolution of marketing methods and means. In marketing, database marketing has been favored by more marketers with its low cost and high efficiency and has become the “rookie” in marketing in recent years. However, as a kind of prediction and ferry, database marketing tends to be applied after simple data analysis in unpredictable market and in practice. In contrast, database marketing combined with machine learning algorithms has always been a depression in the marketing field. Therefore, this paper takes e-commerce as the research object and carries out database marketing research based on machine learning algorithm from four stages: theoretical preparation, status analysis, model construction, and results application. Firstly, the connotation, advantages, and specific operation procedures of database marketing are discussed. At the same time, four excellent machine learning algorithms including logistic regression, random forest, support vector machine, and gradient boosted decision tree (GBDT) are selected to explain the basic principles and algorithm introduction, respectively, laying a theoretical foundation for the model training chapter. Secondly, it analyzes the current situation of e-commerce from the distribution of marketing objects, the proportion of marketing channels, and the composition of marketing methods and finds new marketing ideas based on the main problems existing at the present stage of database marketing using machine learning algorithm. Thirdly, on the premise of marketing ideas, data acquisition, data processing, and positive and negative sample setting. At the same time, four machine learning algorithms are used to combine features from the perspectives of consumers, stores, and the relationship between consumers and stores. Finally, by substituting the predicted sample into the model for testing, the crowd whose predicted score is between 80 and 99 is selected to be put into the market as the model predicted crowd, and it is proposed that e-commerce should mainly adopt the database marketing method of model prediction. On the one hand, machine learning algorithm can solve the problem of uneven distribution of marketing objects, and on the other hand, it can effectively prevent the loss of potential consumers. In addition, the application strategy of optimizing other database marketing methods and assisting model prediction to improve marketing effect is also put forward.

Engineering hollow mesoporous silica supported cobalt molybdate catalyst by dissolution-regrowth strategy for efficiently aerobic oxidative desulfurization

Heterogeneous catalysts show superior separable and recyclable property in comparison with homogeneous catalysts. However, the conventional synthetic method faces the problem of uneven distribution of active sites. Here, a facile dissolution-regrowth strategy was developed to prepare hollow mesoporous silica nanospheres supported cobalt molybdate (CoMoO4/HMS) under mild conditions, simultaneously, CoMoO4 was obtained in situ. The homogenous dispersion of the active species made the catalyst manifest an outstanding desulfurization efficiency in aerobic oxidative desulfurization compared with the supported single-metal oxides catalysts or supported CoMoO4 catalyst prepared by wet impregnation. The removal efficiency of three sulfur-containing substances, such as dibenzothiophene, 4-methyldibenzothiophene and 4,6-dimethyldibenzothiophene, could reach 100% by 5 h with molecular oxygen as an oxidant. Besides, the sulfur removal of DBT almost did not reduce after at least 8 cycles under the optimal reaction conditions. Through electron spin resonance spectroscopy and radical quenching experiments, O2− was determined as the main active oxygen species for sulfur oxidation. This dissolution-regrowth strategy can be used to prepare a series of supported multi-metal oxides for various applications.

A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

A Secure Private Cloud Storage Platform for English Education Resources Based on IoT Technology.

The contemporary ubiquitous “cloud” network knowledge and information resources, as well as ecological pedagogy theory, have enlarged teaching research's perspective, widened teaching research's innovation area, and created practical options for English classroom reform. Cloud education relies on the Internet of Things, cloud computing, and big data to have a huge impact on the English learning process. The key to the integration of English education resources is the storage of huge amount of English teaching data. Applying the technology and methods of cloud storage to the construction of English education resource integration can effectively save the educational resources of schools, improve the utilization rate of English education resources, and thus enhance the teaching level of English subjects. In this work, we examine the existing state of English education resource building and teaching administration and offer a way for creating a “private cloud” of English education materials. We not only examined the architecture and three-layer modules of cloud computing in depth, but we also analyzed the “private cloud” technology and built the cloud structure of English teaching materials on this foundation. We hope that this paper can help and inspire us to solve the problems of uneven distribution, irregular management, and difficult sharing in the construction of English education resources.

Network Intrusion Detection Method Based on Improved CNN in Internet of Things Environment

In view of most existing intrusion detection technologies that cannot meet the actual needs of the Internet of Things and facing the problems of poor detection effect of complex network intrusion methods, a network intrusion detection method based on deep learning algorithm in the environment of the Internet of Things is proposed. Firstly, the Internet of Things intrusion detection model is constructed based on edge computing, in which the concept of gated convolution is introduced to improve the convolution neural network model. Data passes through convolution layer, pooling, dropout, full connection, and Softmax function to realize multiclassification. Finally, the Focal Loss function is used to modulate the training ratio of positive and negative samples to solve the problem of uneven distribution of sample data. The proposed algorithm is demonstrated experimentally based on KDD99 data set. The results show that the accuracy, precision, recall, and F1 values are 92.14%, 95.97%, 90.89%, and 90.03%, which are better than other comparison algorithms. The proposed method can better meet the needs of Internet of Things intrusion detection.

Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment.

Medical facilities are an important part of urban public facilities and a vital pillar for the survival of citizens at critical times. During the rapid spread of coronavirus disease (COVID-19), Wuhan was forced into lockdown with a severe shortage of medical resources and high public tension. Adequate allocation of medical facilities is significant to stabilize citizens’ emotions and ensure their living standards. This paper combines text sentiment analysis techniques with geographic information system (GIS) technology and uses a coordination degree model to evaluate the dynamic demand for medical facilities in Wuhan based on social media data and medical facility data. This study divided the epidemic into three phases: latent, outbreak and stable, from which the following findings arise: Public sentiment changed from negative to positive. Over half of the subdistricts in three phases were in a dysfunctional state, with a circular distribution of coordination levels decreasing from the city center to the outer. Thus, when facing major public health emergencies, Wuhan revealed problems of uneven distribution of medical facilities and unreasonable distribution of grades. This study aims to provide a basis and suggestions for the city to respond to major public health emergencies and optimize the allocation of urban medical facilities.

The Key Technology of Wireless Sensor Network and Its Application in the Internet of Things

At present, sensor networks and the Internet of Things have made rapid development and received more and more attention. Wireless sensor network technology is widely applied in many industries. As an important part of IoT sensor layer, it is an important infrastructure for IoT development. The purpose of this paper is to enhance the existing wireless sensor networks on the basis of existing technologies for better application in IoT. In this article, we introduce the structural features, key techniques, and some specific applications of wireless sensor networks in the IoT. Secondly, this paper studies and analyzes the principle of the cell membrane algorithm and its own characteristics. Aiming at the problem of energy constraint in wireless sensor network, an energy equilibrium clustering algorithm based on the cell membrane optimization algorithm is proposed in order to realize energy balance of nodes and distribution of network cluster heads. This algorithm divides the nodes by concentration and energy factors and divides them into globally balanced clusters by combining distance factors, which can well solve the problems of uneven distribution of cluster heads and unbalanced global power usage in sensor networks. On the basis of a lot of previous researches, this paper presents a QoS model, which is based on the routing protocol, clustering protocol, and data fusion scheme discussed in this paper. Through analysis and experiments, it is proved that this scheme improves energy consumption and flexibility by 78% compared with the previous one and achieves better network performance.

Improvement of the model by preprocessing big data of tapping temperature prediction industry

The quality of steel is highly related to the tapping temperature. At present, many models can predict the tapping temperature to a certain extent. However, after in-depth exploration of the model, it was found that most of the models have a better performance for the tapping temperature prediction of common steel, while the special steel was ignored. It is because that most models are rough in the data processing. The neglect of incomplete data, uneven distribution problems have caused models to over-emphasize some types of steel, while it does not agree with the production demand of steel mills. This paper proposes Bayes neural network model with the improved filling method based on K-Means algorithm and data homogenization method based on K-Means algorithm. The statistical results show that the model has a better performance at error within ±5°C ratio, mean absolute error, root means square error, etc. than the traditional ones.