Precision Indicators Research Articles

Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN

Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator's experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters.

Early prediction of Ibex 35 movements

AbstractIn this paper, we examine the early predictability of the market's directional movement using intraday high‐frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high‐frequency time series of both price and certain technical indicators.The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.

EBE-YOLOv4: A lightweight detecting model for pine cones in forest.

Pine cones are important forest products, and the picking process is complex. Aiming at the multi-objective and dispersed characteristics of pine cones in the forest, a machine vision detection model (EBE-YOLOV4) is designed to solve the problems of many parameters and poor computing ability of the general YOLOv4, so as to realize rapid and accurate recognition of pine cones in the forest. Taking YOLOv4 as the basic framework, this method can realize a lightweight and accurate recognition model for pine cones in forest through optimized design of the backbone and the neck networks. EfficientNet-b0 (E) is chosen as the backbone network for feature extraction to reduce parameters and improve the running speed of the model. Channel transformation BiFPN structure (B), which improves the detection rate and ensures the detection accuracy of the model, is introduced to the neck network for feature fusion. The neck network also adds a lightweight channel attention ECA-Net (E) to solve the problem of accuracy decline caused by lightweight improvement. Meanwhile, the H-Swish activation function is used to optimize the model performance to further improve the model accuracy at a small computational cost. 768 images of pine cones in forest were used as experimental data, and 1536 images were obtained after data expansion, which were divided into training set and test set at the ratio of 8:2. The CPU used in the experiment was Inter Core i9-10885@2.40Ghz, and the GPU was NVIDIA Quadro RTX 5000. The performance of YOLOv4 lightweight design was observed based on the indicators of precision (P), recall (R) and detection frames per second (FPS). The results showed that the measurement accuracy (P) of the EBE-YOLOv4 was 96.25%, the recall rate (F) was 82.72% and the detection speed (FPS) was 64.09F/S. Compared with the original YOLOv4, the precision of detection had no significant change, but the speed increased by 70%, which demonstrated the effectiveness of YOLOv4 lightweight design.

Forest Fire Occurrence Prediction in China Based on Machine Learning Methods

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.

Fuzzy Evaluation Models for Accuracy and Precision Indices

The random variable X is used to represent the normal process containing two important parameters—the process average and the process standard deviation. The variable is transformed using Y = (X − T)/d, where T is the target value and d is the tolerance. The average of Y is then called the accuracy index, and the standard deviation is called the precision index. If only the values of the accuracy index and the process precision index are well controlled, the process quality level as well as the process yield are ensured. Based on this concept, this paper constructed a control chart for the accuracy index and the precision index and derived the confidence intervals of the accuracy index and the precision index using in-control data, as the process was stable. This paper aims to control process quality via monitoring the accuracy and precision of the process. At the same time, fuzzy tests are developed for the indicators of process accuracy and precision to evaluate whether the process quality can reach the k-sigma quality level, as well as offer suggestions about directions of improvement when it fails to reach the k-sigma quality level. Obviously, the model in this paper cannot only evaluate whether the process meets the requirements of the quality level; it can also provide a decision regarding whether the process should be improved. It is very helpful for the enhancement of enterprises’ process capabilities.

Experience of using the evaluations of the repeatability and reproducibility in compa­ ring test methods according to ISO 5725-6 "Accuracy (trueness and preci­ sion) of methods and measurement results. Part 6. Using precision values in practice"

The trueness and precision indicators of two methods — complexometric titration and atomic absorption spectrometry (AAS) — were evaluated to select a method that would provide a higher accuracy of copper determination in a brass coating. In the titrimetric method of copper determination, the brass coating was dissolved in a mixture of ammonia and hydrogen peroxide and titrated with a solution of Trilon В in the presence of xylenol orange indicator, whereas for the atomic absorption method the coating was dissolved in nitric acid and the test solution was measured on an atomic absorption spectrometer. Two laboratories took part in the experiment. The results of measurements and the calculated precision indicators (repeat­ability and reproducibility) for both methods are presented. The intra-laboratory (intermediate precision) and general precision of the methods was compared using the Fisher test, whereas the overall average cop­ per content was compared with the certified value of a standard brass sample in accordance with ISO 5725-6 "Accuracy (trueness and precision) of methods and measurement results. Part 6. Using precision values in practice. " It is shown that the atomic absorption method is the most accurate among two consid­ered methods for determining the mass content of copper in a brass coating, as it has acceptable indicators of precision and trueness.

A Heart Disease Prediction Model Based on Feature Optimization and Smote-Xgboost Algorithm

In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine learning are designed to learn and predict the disease status of patients from a large amount of pathological data. Practice has proved that such a system has the potential to save more lives. Therefore, this paper proposes a new framework for predicting heart disease using the smote-xgboost algorithm. First, we propose a feature selection method based on information gain, which aims to extract key features from the dataset and prevent model overfitting. Second, we use the Smote-Enn algorithm to process unbalanced data, and obtain sample data with roughly the same positive and negative categories. Finally, we test the prediction effect of Xgboost algorithm and five other baseline algorithms on sample data. The results show that our proposed method achieves the best performance in the five indicators of accuracy, precision, recall, F1-score and AUC, and the framework proposed in this paper has significant advantages in heart disease prediction.

Remaining Useful Life Prediction of Ball Screw Under Time-Varying Conditions With Limited Data

Ball screws play a significant role in maintaining transmission accuracy and stability. Accurate prediction of the remaining useful life (RUL) for the ball screw can provide reliable decision-making information for predictive maintenance. However, the RUL prediction of ball screws with the precision indicator under time-varying operating conditions (OCs) is rarely involved. Moreover, obtaining data for the whole life cycle of each OC is impractical due to time and expenditure constraints. To address the aforementioned issues, this article proposes a hybrid approach for RUL prediction under time-varying OCs. First, a time-varying stochastic model considering the precision indicator is established. Then, the data-driven algorithm is proposed to estimate the unknown parameters with limited data. Finally, the effectiveness of the proposed method in prognostics is experimentally validated through the designed platform and compared with the commonly used methods.

A Modified Hybrid Method Based on PSO, GA, and K-Means for Network Anomaly Detection

Data anomaly detection plays a vital role in protecting network security and developing network technology. Aiming at the detection problems of large data volume, complex information, and difficult identification, this paper constructs a modified hybrid anomaly detection (MHAD) method based on the K-means clustering algorithm, particle swarm optimization, and genetic algorithm. First, by designing coding rules and fitness functions, the multiattribute data is effectively clustered, and the inheritance of good attributes is guaranteed. Second, by applying selection, crossover, and mutation operators to particle position and velocity updates, local optima problems are avoided and population diversity is ensured. Finally, the Fisher score expression for data attribute extraction is constructed, which reduces the required sample size and improves the detection efficiency. The experimental results show that the MHAD method has better performance than the K-means clustering algorithm, the support vector machine, decision trees, and other methods in the four indicators of recall, precision, prediction accuracy, and F-measure. The main advantages of the proposed method are that it achieves a balance between global and local search and ensures a high detection rate and a low false positive rate.

An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment.

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.

Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

Protocol matters: A need for standardized procedure in cementochronology

Cementochronology has long been associated in the literature with a high correlation between chronological and estimated age, and low differences between the two ages. The excessive accuracy was rather suspicious, and the method did not even appear in common forensic practice. An important step towards more widespread use of the method is the need to standardize work procedures, including indexes for recording the quality of cementum, preparation of thin sections and the age calculation. In our study, we used the standardized protocol for the preparation of thin sections in a set of Czech modern teeth of known age and sex. In the initial phase, 11.5% of the teeth were discarded due to severe caries in the medial part of the root. In a set of single extractions (55 teeth from 55 individuals), we focused on the detailed results of the age estimation, using precision and accuracy indicators. We also used different dental development data to calculate age, given inconsistencies in the use of eruption / mineralization. In a set of multiple extractions (68 teeth from 22 individuals), intra-individual variability was examined. The result of the application of the standardized protocol is an estimate of age with an absolute inaccuracy of −1.7 years and a relative inaccuracy of 5.4%. Calculation of precision and accuracy in the set of single extractions, however, showed the method’s limitations: the imprecision measuring the variability of cementum increments counts increased with chronological age, as did the inaccuracy. The use of different dental development data did not significantly increase the accuracy of the age estimation results. Intra-individual variability remains poorly understood – in the set of multiple extractions the differences within one individual ranged between 0.9 and 10.8 years.

An Interpretable Feature Selection Based on Particle Swarm Optimization

Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.

Analysis of Children’s Sports Heuristic Teaching Based on Deep Learning

The “pursuit of deep learning” is mentioned among the recent trends driving the key trends driving educational technology in schools. “Deep learning” is widely used as a term, and classroom teaching has begun to focus more and more on deep learning. The heuristic teaching method is gradually accepted and used by educators all over the world with its scientific teaching mode and novel teaching methods. In today’s children’s physical education classroom, the heuristic teaching method has achieved certain results and effects, but in the process of trying, there is still room for development and improvement. Based on the deep learning model, this research will improve the existing heuristic teaching methods, through the experimental research on children's physical education classroom, observe the data results obtained by the deep learning-based children's physical education heuristic teaching, and analyze according to the results, so as to achieve the effect of heuristic teaching. A multilabel classification model ALSTM-LSTM is proposed according to the algorithm adaptation method in the multi-label learning method. The experimental results obtained an accuracy of 95.1%, which is higher than other deep learning models, and also reached the best in the evaluation indicators of precision, recall, and F1 score.

A MODEL FOR MULTI-LAYER DYNAMIC SOCIAL NETWORKS TO DISCOVER INFLUENTIAL GROUPS BASED ON A COMBINATION OF META-HEURISTIC ALGORITHM AND C-MEANS CLUSTERING

Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.

Automatic Extraction of Potential Landslides by Integrating an Optical Remote Sensing Image with an InSAR-Derived Deformation Map

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention.

Multi-Swarm Algorithm for Extreme Learning Machine Optimization.

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

Emerging digital PCR technology in precision medicine

Digital PCR (dPCR) is built on partitioning reagent to the extent that single template molecules are amplified and visualized individually, whereby offers higher precision and other better indicators than the former PCR techniques. Accordingly, dPCR is particular suited for precision medicine applications that require accurate molecular characterization with high sensitivity. This review aims to summarize different applications of dPCR in precision medicine. The state-of-the-art progress of dPCR technique is first introduced, including novel prototype machines and dPCR-integrated biochips. Then the clinical applications based on dPCR technique are briefly described, for instance, detecting biomarkers from tissues and various biopsies components including cell free DNA, circulating tumor cells, extracellular vesicles, and proteins. These emerging dPCR applications have been accepted as auxiliary diagnostic methods in various areas like oncology, infectious disease, and the like. Meanwhile, a usage overview is provided, focusing on successful clinical pilot studies that dPCR is utilized to improve the performances of rare event detection, fine resolution of gene expression analysis, and multiplexing. Finally, some implications and challenges in future research concerning dPCR technique are also discussed.

Research on belt foreign body detection method based on deep learning

Recently, accelerating the improvement in the level of iron ore mining has become extremely important for the sustainable development of the steel industry. Various foreign objects such as steel bars, wood, and plastic pipes easily appear on iron ore belt conveyors, which cause damage to equipment and harm the personal safety of mining workers. Accordingly, a lightweight detection model based on you only look once (YOLO)v3 is proposed in this study. This study first uses the median filter method to preprocess the belt foreign body image to remove the influence of dust and improve the clarity of ore edges. Second, we train the YOLOv3 belt foreign matter detection algorithm based on the selected data set to detect belt foreign matter and evaluate the model based on mean average precision (mAP) and other indicators. Finally, after implementing the sparse training based on the batch normalization (BN) layers, the channel-pruning and layer-pruning strategies are implemented to simplify the YOLOv3 model, followed by parameter fine-tuning. When the accuracy of the model is not affected, our model realizes smaller calculations, faster processing, and a smaller size compared to the original YOLOv3 model. Hence, the model effectively achieves real-time recognition of foreign matter on the ore conveyor belt.

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.