Experimental research and modeling of the parameters of the Quasi-Zenith Satellite System in Ukraine

The results of experimental studies and modeling of the orbital motion of QZSS geosynchronous satellites are presented. The possibility of forecasting the availability of satellites using almanac data obtained from the QZSS system and so on is shownNational Aviation Universit

Unsupervised Activity Segmentation by Joint Representation Learning and Online Clustering

We present a novel approach for unsupervised activity segmentation, which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representation learning and clustering are often performed sequentially. We leverage temporal information in videos by employing temporal optimal transport. In particular, we incorporate a temporal regularization term which preserves the temporal order of the activity into the standard optimal transport module for computing pseudo-label cluster assignments. The temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation. Furthermore, previous methods require storing learned features for the entire dataset before clustering them in an offline manner, whereas our approach processes one mini-batch at a time in an online manner. Extensive evaluations on three public datasets, i.e. 50-Salads, YouTube Instructions, and Breakfast, and our dataset, i.e., Desktop Assembly, show that our approach performs on par or better than previous methods for unsupervised activity segmentation, despite having significantly less memory constraints.Comment: Preprint. Under revie

Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment

This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module which estimates framewise action classes via a transformer encoder. The frame-level prediction module is trained in an unsupervised manner via temporal optimal transport. To exploit segment-level information, we utilize a segment-level prediction module and a frame-to-segment alignment module. The former includes a transformer decoder for estimating video transcripts, while the latter matches frame-level features with segment-level features, yielding permutation-aware segmentation results. Moreover, inspired by temporal optimal transport, we introduce simple-yet-effective pseudo labels for unsupervised training of the above modules. Our experiments on four public datasets, i.e., 50 Salads, YouTube Instructions, Breakfast, and Desktop Assembly show that our approach achieves comparable or better performance than previous methods in unsupervised activity segmentation.Comment: Accepted to WACV 202

STATIC BEARING CAPACITY OF STEEL-PLATE COMPOSITE WALLS

The features of the behavior of steel-plate composite walls for static loads are considered. Based on the analysis of modern technical and regulatory documentation, the rationale for the chosen research topic is given. A review of the literature is performed, and the features of development are noted. A detailed description and features of the experimental structures under study and the materials used are presented. The features of the test are considered, and the test equipment is described. Analytical and numerical calculations of structures for eccentric compression have been performed. The description of the calculation complex and the used models of materials is presented; the description of numerical models, the features of their construction and calculation are given, the results of calculations are presented – stress distributions, deformations, features of cracking. The general types of experimental eccentric compression wall models are presented, the nature of the loss of bearing capacity of experimental structures is described, and a picture of destruction is presented. The analysis of the experimental data obtained and their comparison with analytical and numerical calculations are performed

Learning by Aligning Videos in Time

We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking.Comment: Accepted to CVPR 202