The Detailed Doubly Fed Induction Wind Generator for Electromagnetic Transients Calculations and its Initialization in ATP

The Alternative Transients Program (ATP) is one of the most used electromagnetic transient programs due to its powerful modeling capability and versatility. However, it has limitations as regards the automatic initialization of power electronics devices and control systems. To overcome this drawback, a simple methodology is presented in this paper to initialize a detailed model of a doubly fed induction wind generator implemented in ATP. The methodology is based on the automatic initialization of this device and it is divided into two stages. The first one consists of offline calculations to obtain initial steady-state values of certain model variables and, in the second one, these results are used as ATP model parameters. The simulation is started by means of auxiliary switches also included in the model. To validate the methodology, the transient and steady-state behavior of 4 case studies was evaluated. The analysis of these results shows that the steady-state values calculated by ATP for t = 0 are the desired ones and the oscillograms present a steady-state condition. The proposed methodology makes it possible to accurately initialize a detailed DFIG-type generator model in ATP, without the need to sacrifice simulation time to wait for variables to reach a steady state.

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot’s alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi

<p class="Abstrak">Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA , dan yang terakhir segmentasi menggunakan metode level set. Hasil evaluasi menunjukkan bahwa strategi yang diusulkan berhasil melakukan inisialisasi secara otomatis dengan akurasi 73%. Hasil evaluasi segmentasi objek overlap cukup memuaskan dengan rasio misclassification error 0,93% dan relative foreground area error 24%. Dari hasil evaluasi menunjukkan bahwa strategi yang diusulkan dapat melakukan inisialisasi otomatis dengan baik. Inisialisasi yang tepat menghasilkan segmentasi yang baik pada metode level set.</p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Individual teeth segmentation has done a lot of the recent research and obtained good results.</em><em> W</em><em>hen faced with overlapping teeth, this is quite challenging. To separate overlapping teeth, it is necessary to extract the overlapping object first. </em><em>The l</em><em>evel set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial</em><em> </em><em>level set method manually by the user. This research proposes an automatic initialization strategy for the level set method to segment overlapping teeth using Hierarchical Cluster Analysis on dental panoramic radiograph images. The proposed strategy stage consists of preprocessing </em><em>where</em><em> there </em><em>are</em><em> several process</em><em>es</em><em> of enhancement, rotation</em><em>,</em><em> and cropping of the image, Then the automatic initialization process uses the HCA algorithm and the last is segmentation using the level set method. The evaluation results show that the proposed strategy is successful in carrying out automatic initialization with an accuracy of 73%. The results of the overlap object segmentation evaluation are satisfactory with a misclassification error ratio of 0.93% and a relative foreground area error of 24%. The evaluation results show that the proposed strategy can carry out automated initialization well. Proper initialization results can perform good segmentation of the level set method.</em></p><p><em><strong><br /></strong></em></p>

Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

AN AUTOMATIC ICP-BASED 2D-3D REGISTRATION METHOD FOR A HIGH-SPEED BIPLANAR VIDEORADIOGRAPHY IMAGING SYSTEM

High-Speed Biplanar Videoradiography (HSBV) is an X-ray based non-invasive imaging system that can be used to derive dynamic bony translations and rotations. The 2D-3D registration process matches a 3D bone model acquired from magnetic resonance imaging (MRI) or computed tomography (CT) scans with the 2D X-ray image pairs. This study focuses on the registration of MRI data as it can acquire detailed soft tissue contrast that cannot be easily discerned in CT scans. A novel 2D-3D registration method is reported in this paper that is suitable for the MRI-based bone models with high precision and high efficiency. In addition, an automatic initialization procedure with 64 starting poses is established to avoid user intervention in the registration. The method has been tested using the HSBV image sequence of a knee joint during walking. Thirty-five consecutive poses from the sequence were tested for the registration, and 50 non-consecutive poses randomly selected from the sequence were tested for the automatic initialization. The registration precision for each axis was 0.49 to 0.54 mm. For the initialization validation test, 48 over 50 frames were successfully initialized and two failed due to portions of the joint falling outside of the field-of-view of the system. The average time for each initialization is only about 6 min. The improved 2D-3D registration will allow determination of precise 3D kinematic parameters with high efficiency. These kinematic parameters can be used to calculate joint cartilage contact mechanics that provide insight into the mechanical processes and mechanisms of joint degeneration or pathology.

Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter

Vertebrae tracking in videofluoroscopy is a challenging problem because of the low quality ‎of ‎image ‎sequences, like poor image contrast, ambiguous geometry details, and vertebrae rotation. The aim of this article is to tackle this problem by ‎proposing a ‎method for rigid object tracking based on the ‎fragmentation of the tracked object. The proposed method ‎is based on the particle filter using the calculation of the similarity between the ‎respective‏ ‏fragments of ‎objects instead of the whole objects. The similarity measures used are the Jaccard index, the ‎correlation ‎coefficient, and the Bhattacharyya coefficient. The tracking starts with a semi-automatic initialization. ‎The results show that the fragments-based object tracking method outperforms the classical ‎method ‎‎(without fragmentation) for each of the used similarity measures. The results show that the ‎tracking based on the Jaccard index is more stable and outperforms methods based on ‎other similarity ‎measures.‎

SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.