Computing a Compact Spline Representation of the Medial Axis Transform of a 2D Shape

We present a full pipeline for computing the medial axis transform of an arbitrary 2D shape. The instability of the medial axis transform is overcome by a pruning algorithm guided by a user-defined Hausdorff distance threshold. The stable medial axis transform is then approximated by spline curves in 3D to produce a smooth and compact representation. These spline curves are computed by minimizing the approximation error between the input shape and the shape represented by the medial axis transform. Our results on various 2D shapes suggest that our method is practical and effective, and yields faithful and compact representations of medial axis transforms of 2D shapes.Comment: GMP14 (Geometric Modeling and Processing

Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization

In the machine learning domain, research on anomaly detection and localization within image data has garnered significant attention, particularly in practical applications such as industrial defect detection. While existing approaches predominantly rely on Convolutional Neural Networks (CNN) as their backbone network, we propose an innovative method based on the Transformer backbone network. Our approach employs a two-stage incremental learning strategy. In the first stage, we train a Masked Autoencoder (MAE) model exclusively on normal images. Subsequently, in the second stage, we implement pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process enables the model to learn how to repair corrupted regions and classify the state of each pixel. Ultimately, the model produces a pixel reconstruction error matrix and a pixel anomaly probability matrix, which are combined to create an anomaly scoring matrix that effectively identifies abnormal regions. When compared to several state-of-the-art CNN-based techniques, our method demonstrates superior performance on the MVTec AD dataset, achieving an impressive 97.6% AUC

IDENTIFIKASI DAN UPAYA YANG DILAKUKAN GURU DALAM MENGATASI HAMBATAN PELAKSANAAN PEMBELAJARAN RNIPS TERPADU DI MTSN MODEL BANDA ACEH

ABSTRAKKata Kunci: identifikasi, hambatan, pembelajaran, IPS terpaduPembelajaran IPS terpadu merupakan gabungan antara berbagai disiplin ilmu sosial, yang biasanya terdiri atas beberapa mata pelajaran seperti geografi, sosiologi, ekonomi, dan sejarah, maka dalam pelaksanaannya tidak lagi terpisah-pisah melainkan menjadi satu kesatuan. Kurangnya keefektifan guru dalam proses pembelajaran IPS terpadu yang meliputi beberapa disiplin ilmu dengan menggunakan guru tunggal, hal tersebut dikarenakan guru mengajar semua mata pelajaran yang terdapat dalam pembelajaran IPS terpadu. Penelitian ini mengangkat masalah apakah hambatan guru dalam pelaksanaan pembelajaran IPS terpadu di MTsN Model Banda Aceh dan upaya apa yang dilakukan guru untuk mengatasi hambatan pelaksanaan pembelajaran IPS terpadu di MTsN Model Banda Aceh. Tujuan penelitian adalah untuk mengidentifikasi hambatan dalam pelaksanaan pembelajaran IPS terpadu dan untuk mengetahui upaya apa yang dilakukan guru dalam mengatasi hambatan pelaksanaan pembelajaran IPS terpadu di MTsN Model Banda Aceh. Informan dalam penelitian adalah guru IPS terpadu yang berjumlah enam orang. Metode yang digunakan dalam penelitian adalah deskriptif kualitatif, data diperoleh dengan cara wawancara mendalam. Teknik analisis data menekankan penjelasan serta penguraian data melalui cerita tentang peristiwa yang telah diteliti. Hasil analisis data menunjukkan bahwa terdapat kendala yang dialami guru dalam pembelajaran IPS terpadu di MTsN Model Banda Aceh yaitu penguasaan materi, kurangnya waktu bertatap muka dalam pembelajaran, dan kurangnya minat belajar siswa terhadap mata pelajaran IPS. Upaya yang dilakukan guru bidang studi IPS terpadu untuk mengatasi masalah tersebut adalah lebih banyak membaca atau menambah referensi untuk bahan ajar, mencari bahan di internet dan sharing sesama guru mata pelajaran, penggunaan waktu seefektif mungkin, dan memberikan motivasi kepada siswa pada saat pembelajaran berlangsung yaitu dengan menerapkan berbagai macam model pembelajaran.Banda Ace

CCD photometric study of the W UMa-type binary II CMa in the field of Berkeley 33

The CCD photometric data of the EW-type binary, II CMa, which is a contact star in the field of the middle-aged open cluster Berkeley 33, are presented. The complete R light curve was obtained. In the present paper, using the five CCD epochs of light minimum (three of them are calculated from Mazur et al. (1993)'s data and two from our new data), the orbital period P was revised to 0.22919704 days. The complete R light curve was analyzed by using the 2003 version of W-D (Wilson-Devinney) program. It is found that this is a contact system with a mass ratio $q=0.9$ and a contact factor $f=4.1$. The high mass ratio ($q=0.9$) and the low contact factor ($f=4.1$) indicate that the system just evolved into the marginal contact stage

Tunable THz Surface Plasmon Polariton based on Topological Insulator-Layered Superconductor Hybrid Structure

We theoretically investigate the surface plasmon polariton (SPP) at the interface between 3D strong topological insulator (TI) and layered superconductor-magnetic insulator structure. The tunability of SPP through electronic doping can be enhanced when the magnetic permeability of the layered structure becomes higher. When the interface is gapped by superconductivity or perpendicular magnetism, SPP dispersion is further distorted, accompanied by a shift of group velocity and penetration depth. Such a shift of SPP reaches maximum when the magnitude of Fermi level approaches the gap value, and may lead to observable effects. The tunable SPP at the interface between layered superconductor and magnetism materials in proximity to TI surface may provide new insight in the detection of Majorana Fermions.Comment: 6 pages, 4 figure

Optimised Power Error Comparison Strategy for Direct Power Control of the Open-winding Brushless Doubly-Fed Wind Power Generator

This paper presents the conceptual analysis and comparative simulation and experimental evaluation of a novel power error comparison direct power control (PEC-DPC) strategy of the open-winding brushless doubly-fed reluctance generator (OW-BDFRG) for wind energy conversion systems (WECSs). As one of the promising candidates for limited speed range application of pump-alike and wind turbine with partially-rated converter. The emerging OW-BDFRG employed for the proposed PEC-DPC is fed via dual low-cost two-level converters, while the DPC concept is derived from the fundamental dynamic analyses between the calculated and controllable electrical power and flux of the BDFRG with two stators measurable voltage and current. Compared to the traditional two-level and three-level converter systems, the OW-BDFRG requires lower rated capacity of power devices and switching frequency converter, though have more flexible switching mode, higher reliability, redundancy and fault tolerance capability. The performance correctness and effectiveness of the proposed DPC strategy with the selected and optimised switching vector scheme are evaluated and confirmed through computer simulation studies and experimental measurements on a 25 kW generator test rig

Attending Category Disentangled Global Context for Image Classification

In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance. To investigate the capability of the global context, we compare four mathematical models and observe the global context encoded in the category disentangled conditional generative model could give more guidance as "know what is task irrelevant will also know what is relevant". Based on this observation, we define a novel Category Disentangled Global Context (CDGC) and devise a deep network to obtain it. By attending CDGC, the baseline networks could identify the objects of interest more accurately, thus improving the performance. We apply the framework to many different network architectures and compare with the state-of-the-art on four publicly available datasets. Extensive results validate the effectiveness and superiority of our approach. Code will be made public upon paper acceptance.Comment: Under revie