This paper describes a series of experiments in the simulation environment Gazebo aimed at studying the influence of external weather conditions on the automatic landing of an unmanned aerial vehicle (UAV) on a seismi...
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ISBN:
(数字)9783031703003
ISBN:
(纸本)9783031702990;9783031703003
This paper describes a series of experiments in the simulation environment Gazebo aimed at studying the influence of external weather conditions on the automatic landing of an unmanned aerial vehicle (UAV) on a seismic sensor using computer vision and a previously developed control system based on proportional-integral-derivative (PID) and polynomial controllers. As part of the research, methods for modeling external weather conditions were developed and landing experiments were carried out simulating weather conditions such as wind, light, fog, and precipitation, including their combinations. In all experiments, successful landing on the seismic sensor was achieved;during the experiments, landing time and its accuracy were measured. The graphical and statistical analysis of the obtained results revealed the influence of illumination, precipitation and wind on the UAV landing time, and the introduction of wind into the simulation under any other external conditions led to the most significant increase in landing time. At the same time, the study failed to identify a systemic negative influence of external conditions on landing accuracy. 92.4% of pairwise tests for accuracy samples confirmed the hypothesis of no significant differences. The results obtained provide valuable information for further improvement of autonomous automatic landing systems for UAVs without the use of satellite navigation systems.
Artificial intelligence (AI) has become an important means of network anomaly detection and fault root cause analysis (RCA), but most applications are only for a certain segment of the network. In the process of our r...
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The age-old tricks of predicting based on gut intuition have been overruled by mathematics since day one. The study of dispersed data and being able to predict how the data is spread around the median only to show us ...
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In this article, we aim to design an architecture for privacy-preserved credit data and model sharing to guarantee the secure storage and sharing of credit information in a distributed environment. The proposed archit...
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In this article, we aim to design an architecture for privacy-preserved credit data and model sharing to guarantee the secure storage and sharing of credit information in a distributed environment. The proposed architecture optimizes the data privacy by sharing the data model instead of revealing the actual data. This article also proposes an efficient credit data storage mechanism combined with a deletable Bloom filter to guarantee a uniform consensus for the training and computation process. In addition, we propose authority control contract and credit verification contract for the secure certification of credit sharing model results under federated learning. Extensive experimental results and security analysis demonstrate that our proposed credit model sharing system based on federated learning and blockchain is of high accuracy, efficiency, as well as stability. In particular, the findings of this article could alleviate the potential credit crisis under financial pressure that assist to economic recovery after the global COVID-19 pandemic. Our approach has further boosted up the demand for efficient, secure credit models for Industry 4.0.
It is necessary to flexibly analyze and process multi-source heterogeneous data of weak distribution network. It is also of great significance to accurately predict and perceive the fault risk of weak distribution net...
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Mathematical modeling in the study of dynamic processes in biomedicine is currently widely used. The use of mathematical statistics and formal logic methods in the analysis of complex biomedical processes is especiall...
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ISBN:
(数字)9798331520564
ISBN:
(纸本)9798331520571
Mathematical modeling in the study of dynamic processes in biomedicine is currently widely used. The use of mathematical statistics and formal logic methods in the analysis of complex biomedical processes is especially important for increasing the efficiency of biomedical research. Taking into account the established growth of the technical base of research, the development of new technologies and technical means for obtaining and processing statistical biomedical information, the use of mathematical modeling methods is a fairly promising direction in scientific and practical work. In this paper the authors studies the possibility of improving the quality of biomedical processmodeling using the MATLAB software environment. The research was carried out on real statistical material obtained by the authors during a full-scale experiment to study the impact of a complex of external factors on patients. The results obtained in this work can be used in the implementation of scientific and applied research of biomedical processes.
The rapid development of technologies such as the Internet of Things (IoT) has greatly facilitated various aspects of people's lives and profoundly transformed their daily routines and work environments. Sensor de...
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ISBN:
(数字)9798350354621
ISBN:
(纸本)9798350354638
The rapid development of technologies such as the Internet of Things (IoT) has greatly facilitated various aspects of people's lives and profoundly transformed their daily routines and work environments. Sensor devices, a vital component of the IoT, offer a reliable and real-time data source for the advancement of applications like artificial intelligence and the industrial internet. However, there are still numerous unresolved issues in the data collection process of sensor devices, particularly in harsh conditions. Utilizing drone platforms for real-time data collection appears to be a viable approach at present. This paper addresses the optimization and control of energy consumption in unmanned aerial vehicle (UAV) data collection. It comprehensively considers factors such as the distribution of sensor devices, UAV speed control, and sensor energy consumption. By integrating Markov fluid theory, we provide a mathematical expression of the relationship between UAV energy consumption, the distribution of sensor devices, and data collection transmission variables in this scenario.
The proceedings contain 74 papers. The special focus in this conference is on data, Engineering, and Applications. The topics include: Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helido...
ISBN:
(纸本)9789819700363
The proceedings contain 74 papers. The special focus in this conference is on data, Engineering, and Applications. The topics include: Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helidon and Spring Boot’s Metrics;an Efficient Hybrid Model to Summarize the Text Using Transfer Learning;automatic Detection of Learner’s Learning Style;construction of an Intelligent Knowledge-Based System Using Transformer Model;machine Learning-Based Disease Diagnosis Using Body Signals: A Review;finite Difference and Finite Volume 1D Steady-State Heat Conduction Model for Machine Learning Algorithms;Sign Language Detection Through PCANet and SVM;A Novel Surface Roughness Estimation and Optimization Model for Turning process Using RSM-JAYA Method;effective Prediction of Coronary Heart Disease Using Hybrid Machine Learning;feature Extraction Using Levy Distribution-Based Salp Swarm Algorithm;plant Disease Detection Using Machine Learning Approaches: A Review;copy–Move Forgery Detection Algorithm: A Machine Learning-Based Approach to Detect Image Forgery;a Machine Learning-Based Approach to Combat Hate Speech on Social Media;Prediction of SARS-COVID-19 Based on Transfer Machine Learning Techniques Using Lungs CT Scan Images;online Document Identification and Verification Using Machine Learning Model;Mitigating Partial Shading Condition in PV System for MPPT Using Evolutionary Algorithms;road Safety modeling: Safe Road for All;AI-Enabled Road Health Monitoring System for Smart Cities;multi-objective Biofilm Algorithm to Resolve Optimization Problems;comparative analysis of Fake News Identification Using Machine Learning Methods;a Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture;utilizing a Finger Vein in Biometric Authentication Mechanism;local Binary Patterns-Based Retinal Disease Screening.
Real-time monitoring of wind turbine performance degradation can improve the economics and safety of wind farms. Normal operational data can accurately reflect the generation performance of a wind turbine and in the w...
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Real-time monitoring of wind turbine performance degradation can improve the economics and safety of wind farms. Normal operational data can accurately reflect the generation performance of a wind turbine and in the wind-speed coordinate system these normal data constitute the "main power band". This paper invokes a Dirichlet process Gaussian Mixture Model (DPGMM) to cluster operational data in each horizontal power bin, and the number of Gaussian components can be determined automatically. The confidence ellipses of Gaussian components can be used to identify the contour of the main power band which is then used as baseline performance model. In the monitoring phase, Mahalanobis distance is used to judge whether new monitoring data lies outside the contour of main power band and thus should be labeled as degraded operational data. When the proportion of such data exceeds a set value in a sliding window, a wind turbine performance degradation alarm is triggered. Degradation degree and rate can quantitatively measure the severity of performance degradation. For an industrial performance degradation case caused by gearbox oil over temperature, the method proposed timely gives alarm only 12 points (2 h) later than the first degraded operational data appears and is proved to be effective.
This article first introduces the research background, research content, research methods, and research significance of the research on accounting audit risk control based on multi-dimensional data feature analysis. T...
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