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.

Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.

Spectral‐spatial sequence characteristics‐based convolutional transformer for hyperspectral change detection

Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel pairs.To solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD task.In the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns.Then,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs.Next,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence dependencies.Specifically,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE.Finally,the binary change map is determined according to the fully‐connected layer.Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.

Coherent change detection of fine traces based on multi-angle SAR observations

Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.

Classification and Spatio-Temporal Change Detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia

Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.

Detecting Climate Change Trend, Size, and Change Point Date on Annual Maximum Time Series Rainfall Data for Warri, Nigeria

The study focused on the detection of indicators of climate change in 24-hourly annual maximum series (AMS) rainfall data collected for 36 years (1982-2017) for Warri Township, using different statistical methods yielded a statistically insignificant positive mild trend. The IMD and MCIMD downscaled model’s time series data respectively produced MK statistics varying from 1.403 to 1.4729, and 1.403 to 1.463 which were less than the critical Z-value of 1.96. Also, the slope magnitude obtained showed a mild increasing trend in variation from 0.0189 to 0.3713, and 0.0175 to 0.5426, with the rate of change in rainfall intensity at 24 hours duration as 0.4536 and 0.42 mm/hr.year (4.536 and 4.2 mm/decade) for the IMD and the MCIMD time series data, respectively. The trend change point date occurred in the year 2000 from the distribution-free CUSUM test with the trend maintaining a significant and steady increase from 2010 to 2015. Thus, this study established the existence of a trend, which is an indication of a changing climate, and satisfied the condition for rainfall Non-stationary intensity-duration-frequency (NS-IDF) modeling required for infrastructural design for combating flooding events.

SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.

A Note on Change Point Detection Using Weighted Least Square

This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are considered. For both cases, the weighted estimators are given and their asymptotic behaviors are studied. It is also described that how the resampling methods like Monte Carlo and bootstrap may be applied to compute the finite sample behavior of estimators.

On-line outlier and change point detection for time series

The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.

Performance assisted enhancement based on change point detection and Kalman filtering

A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.

Change-Point Detection for General Nonparametric Regression Models

A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.

Environmental changes affect picoplanktonic composition in Antarctic Peninsula ponds

Antarctic Peninsula is experiencing one of the largest global warming events worldwide.Shallow water bodies generated by the melting of snow in summer are numerous,and they might act as sentinels of climate change due to their rapid response and ability to integrate catchment information.Shifts in climate can influence the structure of microbial communities which dominate these freshwaters ecosystems.Here,we characterize three ponds at Cierva Point(Antarctic Peninsula)by examining their physico-chemical and morphological characteristics and we explored how different factors modify the structure of the microbial community.We studied the abundance and biomass of heterotrophic bacteria,picocyanobacteria and picoeukaryote algae during January and February of two consecutive summers(2017 and 2018).We found that ponds had different limnological characteristics,due to their location,geomorphological features and presence of the surrounding flora and fauna.Physico-chemical parameters as well as microbial community differed between ponds,months and years.In 2017,most ponds were oligo to mesotrophic states.The larger accumulated rainfall(as a result of environmental changes on the Antarctic Peninsula)during 2018,particularly in February,causes nutrient runoff into water bodies.This affects those ponds with the highest seabird circulation,such as gentoo penguin,increasing eutrophication.As a result,picoplanktonic abundances were higher,and the community structure shifts to a largely heterotrophic bacteria dominated one.These results suggest that these communities could act as sentinels to environmental changes,anticipating a future with mostly hypertrophic ponds.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China

Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas.This study took Gansu Province,China,a typical area with complex terrain and variable climate,as the research subject.Based on Google Earth Engine,we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022,and quantitatively analyzed the main causes of regional differences in surface water area.The findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to 2022.Seasonal surface water area exhibited significant fluctuations,while permanent surface water area showed a steady increase.Notably,terrestrial water storage exhibited a trend of first decreasing and then increasing,correlated with the dynamics of surface water area.Climate change and human activities jointly affected surface hydrological processes,with the impact of climate change being slightly higher than that of human activities.Spatially,climate change affected the'source'of surface water to a greater extent,while human activities tended to affect the'destination'of surface water.Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted.Therefore,we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water'supply-demand'balance strategies.The study not only sheds light on the dynamics of surface water area in Gansu Province,but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.

Trends of Land Use and Land Cover Change in the Savannah Ecological of the Protected Area Reserve Partielle de Dosso, Niger

Information on the dynamics of savannah is important to a country's plan to overcome the problems of uncontrolled development and environmental hazards. Taking the reserve partielle de Dosso, Niger as the case study area, this paper analyzed the long-term land use land cover change from 2002 to 2022. Satellite images were processed by using Google Earth Engine (GEE). Therefore, four major land cover classes were identified based on spectral characteristics of Land sat, namely, built-up, vegetation, cropland, bare land and water. The result revealed that barren and built-up areas increased at the expense of vegetation and water. From the four major land use land cover the large area is covered by vegetation which comprises about 192963.5 hectares followed by cropland and water consisting of 32506.43 and 1596.4 hectares respectively. The built-up area gained substantial area (most) during the study period. The reduction in some of the land cover/uses underlines the dangerous trend of the pressure poised by population growth and the changing functionality. Land cover change is influenced by a variety of societal factors operating on several spatial and temporal levels. The area estimates and spatial distributions of the LULC classes produced from the current study will assist local authorities, managers, and other stakeholders in decision-making and planning regarding forest land cover and uses.

Analysis of Bridge-Bearing Capacity Detection and Evaluation Technology

A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection technology,and the bearing capacity assessment analysis.It is hoped that this analysis can provide a scientific reference for the load-bearing capacity detection and evaluation work in bridge engineering projects,thereby achieving a scientific assessment of the overall load-bearing capacity of the bridge engineering structure.

DM Code Key Point Detection Algorithm Based on CenterNet

Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.

Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.

Detection,Causes and Projection of Climate Change over China:An Overview of Recent Progress

This article summarizes the main results and findings of studies conducted by Chinese scientists in the past five years. It is shown that observed climate change in China bears a strong similarity with the global average. The country-averaged annual mean surface air temperature has increased by 1.1℃ over the past 50 years and 0.5-0.8℃ over the past 100 years, slightly higher than the global temperature increase for the same periods. Northern China and winter have experienced the greatest increases in surface air temperature. Although no significant trend has been found in country-averaged annual precipitation, interdecadal variability and obvious trends on regional scales are detectable, with northwestern China and the mid and lower Yangtze River basin having undergone an obvious increase, and North China a severe drought. Some analyses show that frequency and magnitude of extreme weather and climate events have also undergone significant changes in the past 50 years or so. Studies of the causes of regional climate change through the use of climate models and consideration of various forcings, show that the warming of the last 50 years could possibly be attributed to an increased atmospheric concentration of greenhouse gases, while the temperature change of the first half of the 20th century may be due to solar activity, volcanic eruptions and sea surface temperature change. A significant decline in sunshine duration and solar radiation at the surface in eastern China has been attributed to the increased emission of pollutants. Projections of future climate by models of the NCC （National Climate Center, China Meteorological Administration） and the IAP （Institute of Atmospheric Physics, Chinese Academy of Sciences）, as well as 40 models developed overseas, indicate a potential significant warming in China in the 21st century, with the largest warming set to occur in winter months and in northern China. Under varied emission scenarios, the country-averaged annual mean temperature is projected to increase by 1.5 2.1℃ by 2020, 2.3 3.3℃ by 2050, and by 3.9-6.0℃ by 2100, in comparison to the 30-year average of 1961-1990. Most models project a 10% 12% increase in annual precipitation in China by 2100, with the trend being particularly evident in Northeast and Northwest China, but with parts of central China probably undergoing a drying trend. Large uncertainty exists in the projection of precipitation, and further studies are needed. Furthermore, anthropogenic climate change will probably lead to a weaker winter monsoon and a stronger summer monsoon in eastern Asia.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.