Search results for " CLASSIFICATION"
showing 10 items of 1043 documents
Radiological Assessment of Pediatric Scoliosis Patients
2021
Nosaukums: Radioloģiskais izmeklējums pediatriskiem pacientiem ar skoliozi Autore: Saara Anni Susanna Suomalainen, medicīnas studente Darba vadītājs: Dr. med. Paulis Laizāns un Dr. med. Jānis Upenieks Priekšvēsture : Skolioze ir sānu novirze, kas pārsniedz 10 grādus no normāla mugurkaula, mērot pēc rentgenogrammas, un tāpēc, ka sānu līkne ir saistīta ar skriemeļu rotāciju līknes iekšienē, rodas trīsdimensiju deformācija. Idiopātiskā skolioze bērnu populācijā ir sadalīta trīs vecuma grupās - zīdaiņu, bērnu un pusaudžu. Radiogrāfiskais novērtējums ir galīgās diagnozes pīlārs. Labi uzņemtos rentgenogrammās var novērtēt segmentācijas anomāliju raksturojumu, izliekumu veidu un to elastību, kā ar…
Early detection and classification of bearing faults using support vector machine algorithm
2017
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
X-ray emission from young brown dwarfs in the Orion Nebula Cluster
2005
We use the sensitive X-ray data from the Chandra Orion Ultradeep Project (COUP) to study the X-ray properties of 34 spectroscopically-identified brown dwarfs with near-infrared spectral types between M6 and M9 in the core of the Orion Nebula Cluster. Nine of the 34 objects are clearly detected as X-ray sources. The apparently low detection rate is in many cases related to the substantial extinction of these brown dwarfs; considering only the BDs with $A_V \leq 5$ mag, nearly half of the objects (7 out of 16) are detected in X-rays. Our 10-day long X-ray lightcurves of these objects exhibit strong variability, including numerous flares. While one of the objects was only detected during a sho…
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
2017
Made available in DSpace on 2018-12-11T17:11:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-03-01 Suomen Akatemia Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees repr…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Efficient remote sensing image classification with Gaussian processes and Fourier features
2017
This paper presents an efficient methodology for approximating kernel functions in Gaussian process classification (GPC). Two models are introduced. We first include the standard random Fourier features (RFF) approximation into GPC, which largely improves the computational efficiency and permits large scale remote sensing data classification. In addition, we develop a novel approach which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones using a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery.
SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information
2018
Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately.
SAR Image Classification Combining Structural and Statistical Methods
2011
The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the pr…