Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

Online Intrusion Detection Mechanism Based on Model Migration in Intelligent Pumped Storage Power Stations

With the continuous integration of new energy into the power grid,various new attacks continue to emerge and the feature distributions are constantly changing during the deployment of intelligent pumped storage power stations.The intrusion detection model trained on the old data is hard to effectively identify new attacks,and it is difficult to update the intrusion detection model in time when lacking data.To solve this issue,by using model-based transfer learning methods,in this paper we propose a convolutional neural network(CNN)based transfer online sequential extreme learning machine(TOS-ELM)scheme to enable the online intrusion detection,which is called CNN-TOSELM in this paper.In our proposed scheme,we use pre-trained CNN to extract the characteristics of the target domain data as input,and then build online learning classifier TOS-ELM to transfer the parameter of the ELM classifier of the source domain.Experimental results show the proposed CNNTOSELM scheme can achieve better detection performance and extremely short model update time for intelligent pumped storage power stations.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

Design of Online Vision Detection System for Stator Winding Coil

The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.

Online volume rendering of incrementally accumulated LSCEM images for superficial oral cancer detection

Laser scanning confocal endomicroscope(LSCEM)has emerged as an imaging modality which provides noninvasive,in vivo imaging of biological tissue on a microscopic scale.Scientific visualizations for LSCEM datasets captured by current imaging systems require these datasets to be fully acquired and brought to a separate rendering machine.To extend the features and capabilities of this modality,we propose a system which is capable of performing realtime visualization of LSCEM datasets.Using field-programmable gate arrays,our system performs three tasks in parallel:(1)automated control of dataset acquisition;(2)imaging-rendering system synchronization;and(3)realtime volume rendering of dynamic datasets.Through fusion of LSCEM imaging and volume rendering processes,acquired datasets can be visualized in realtime to provide an immediate perception of the image quality and biological conditions of the subject,further assisting in realtime cancer diagnosis.Subsequently,the imaging procedure can be improved for more accurate diagnosis and reduce the need for repeating the process due to unsatisfactory datasets.

ED-SWE:Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People.In this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets.The proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams automatically.Moreover,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data.In order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely.Finally,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event.Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.

A Novel Chip-based Spectrophotometer for Online Detection

A chip-based spectrophotometer integrated with optical fiber is successfully demonstrated.Grade concentration of lactate solution flowed through the chip to perform an online detection.The response time (100s)and Limit of Detection (LOD, 50mg/L)of the device were measured.This device shows comparable performance with traditional commercial instrument, while greatly decreases the sample requirement per detection and reduces the size of total system,introducing a novel method for real-time detection.

A Review of Machine Learning Techniques in Cyberbullying Detection

Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.

Study of pressure effects on ocean in-situ detection using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) has attracted extensive attention as a new technique for in-situ marine application. In this work, the influence of deep-sea high pressure environment on LIBS signals was investigated by using a compact LIBS-sea system developed by Ocean University of China for the in-situ chemical analysis of seawater. The results from the field measurements show that the liquid pressure has a significant effect on the LIBS signals. Higher peak intensity and larger line broadening were obtained as the pressure increases. By comparing the variations of the temperature and salinity with the LIBS signals, a weak correlation between them can be observed. Under high pressure conditions, the optimal laser energy was higher than that in air environment. When the laser energy exceeded 17 mJ, the effect of laser energy on the signal intensity weakened. The signal intensity decreases gradually at larger delays. The obtained results verified the feasibility of the LIBS technique for the deep-sea in-situ detection, and we hope this technology can contribute to surveying more deep-sea environments such as the hydrothermal vent regions.

Real-Time Spammers Detection Based on Metadata Features with Machine Learning

Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).

基于Online LS-SVM的钢铁件渗碳层深度在线检测

为实现钢铁件渗碳层深度的在线电磁无损检测,提出在线最小二乘支持向量机(Online Least Square Support Vector Machine,Online LS-SVM)的建模方法。Online LS-SVM是以增量学习训练SVM,以减量学习减少样本数,实现小样本估计的训练方法。实验结果表明,Online LS-SVM不仅能实现钢铁件渗碳层深度的在线电磁无损检测,而且具有学习速度快、泛化性能好和对样本依赖程度低的优点。

Online SVM在实时入侵检测中的应用研究

Online支持向量机作为一种新的分类方法可以在异常入侵检测中提供良好的分类效果。根据Online算法对传统支持向量机、Robust支持向量机和One-class支持向量机进行改进,将改进后的算法与原始算法进行比较,然后使用1999DARPA数据作为评估数据。通过实验和比较发现,改进后的支持向量机可以实现在线训练,而且使用更少的支持向量,训练时间也有效缩短,在噪声数据存在的情况下检测正确率和虚警率比未改进前有一定程度的提升。

基于Online LS-SVM的钢铁件淬火硬度在线检测

为了实现钢铁件淬火硬度的在线电磁无损检测,提出了在线最小二乘支持向量机(online least square support vector machine)的建模方法。Online LS-SVM是以增量学习训练SVM,以减量学习减少样本数,实现小样本估计的训练方法。实验结果表明,Online LS-SVM不仅能实现钢铁件淬火硬度的在线电磁无损检测,而且具有学习速度快,泛化性能好,对样本依赖程度低的优点。

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts.Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part.In this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed.Using the proposed method,an average temperature monitoring accuracy of 97%is achieved in various heating patterns.In addition,this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part.This method was experimentally validated during the self-resistance electric heating process,in which the monitoring accuracy reached 93.1%.This method can potentially be applied to automated manufacturing and process control in the composites industry.

Online Judge系统的优化

从Web页面和数据库缓存、服务器架构、多核评测处理规则、前端异步响应、数据表设计、跨平台支持、源代码抄袭检测、测试用例自动生成等方面优化了Online Judge系统,使得评测效率提高的同时减少了服务器数量,节约了运行成本。最后讨论了基于Online Judge系统实现智能优化算法的统一测试平台的方法。

A proposed NMR solution for multi-phase flow fluid detection

In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.

Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.

Acoustic wave detection of laser shock peening

In order to overcome the existing disadvantages of offline laser shock peening detection methods, an online detection method based on acoustic wave signals energy is provided. During the laser shock peening, an acoustic emission sen- sor at a defined position is used to collect the acoustic wave signals that propagate in the air. The acoustic wave signal is sampled, stored, digitally filtered and analyzed by the online laser shock peening detection system. Then the system gets the acoustic wave signal energy to measure the quality of the laser shock peening by establishing the correspondence between the acoustic wave signal energy and the laser pulse energy. The surface residual stresses of the samples are measured by X-ray stress analysis instrument to verify the reliability. The results show that both the surface residual stress and acoustic wave signal energy are increased with the laser pulse energy, and their growth trends are consistent. Finally, the empirical formula between the surface residual stress and the acoustic wave signal energy is established by the cubic equation fitting, which will provide a theoretical basis for the real-time online detection of laser shock peening.

Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.

Design and Develop Online Monitoring and Early-warning System of Crane Structural Stress

This paper proposed an online monitoring and early-warning system of dynamic stress of crane metal structure, and designed this system’s hardware,including sensor unit,data gathering unit,and controlling & processing unit of this sys- tem,and discussed the waterproof protection for resistance strain wafer and scheme of data gathering and transmission of dynamic strain gauge,moreover developed system software of real-time and online monitoring dynamic stress,including data gathering by DLL and data display & processing based on Visual C++.The system applies the dynamic strain gauge to gather the data of the stress,and communicates between PLC control system of crane and upper industrial computer,so that realize the real-time online monitoring and early-warning for crane’s metal structure stress.The test results show this system carry on real time and online monitoring to dynamic stress of loud-bearing metal structure longly and stability,and can give an alarm and overload protection on time.So the system has good practice value.