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Энэхүү судалгаагаар бид К-мийнс кластерингд (K-Means Clustering) суурилсан шуугианыг ялгах алгоритмыг танилцуулж байна. Энэ арга нь түүхий өгөгдөл дээр шууд боловсруулалт хийх ба гадаргуун цэгүүдийг шуугиантай болон шуугиангүйгээр ялгаж зөвхөн шуугиантай гадаргуун цэгүүдийн шуугианыг арилгадгаараа бусад аргаас давуу талтай юм. Бидний арга нь гадаргуун цэг бүрийн хөрш цэгийн тоон өөрчлөлт дээр К-мийнс кластеринг хийж шуугиантай гадаргууг ялгах юм. Энэхүү судалгаанд дөрвөн чиглэлд хэмжилт хийн сканерддаг төхөөрөмжөөр үүсгэгдсэн чулуун зэвсгийн цэгэн үүлний хэсэгчилсэн шуугианыг арилгасан ба үр дүнг үзүүлэв.
In this article, we have studied how the technology implementing the "Serverless" architecture is used in modern software development by automating the tasks required for server development technical operations, making them independent of developers and how these technologies can be used in possible situations, the advantages and disadvantages of cloud technology. It also shows how this architectural solution supports the creation of a complex software solution that replaces the current physical and non-physical servers as well as comparative study of pricing and scalability of our testing system using AWS (Amazon Web Services), which provides 33% of the total use of "Serverless" architecture.
Энэхүү судалгааны ажлаар хүүхдийн сэтгэл хөдлөл, хэвийн байдал, инээмсэглэлтэй царайг таних, дүн шинжилгээ хийсэн үр дүнг танилцуулж байна. Энэ танилцуулж аргачлал нь хүүхдийн сэтгэл хөдлөлийн царайнаас илрүүлэхийн тулд дүрс боловсруулалт машин сургалтын аргуудыг хослуулан хэрэглэж, өөрчлөлт ихтэй цөөн өгөгдөл дээр танилт хийх боломжийг эрэлхийлсэн болно. Туршилтын үр дүнгээр 3 төрлийн ангилалд дундажаар 75%-ийн зөв танилттай байна.
Recently, a video of famous politicians and superstars giving certain speeches surfaced online causing severe political and commercial problems. The video, although it seemed authentic, was fake. Therefore, in this study, using 37,936 videos sampled from the deep fake detection challenge (DFDC), we developed an efficient and highly accurate deepfake detection system using EfficientNet with dynamic images. Dynamic image transforms video sequences into one frame instance by conserving spatiotemporal information. The experimental results and comparative analysis indicate that EfficientNet with dynamic image exhibits higher performance than EfficientNet. We also found that dynamic images generated by 20 frames have a higher fake-face detection accuracy than simple images.
Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.
Horizon detection is useful in maritime image processing for various purposes, such as spatial orientation estimation of ship camera and detection of the significant region for post-processing. This paper proposed a novel realtime optimization-based method for detecting the horizon line in maritime images. Traditional methods defined the horizon line detection problem aim to detect the horizon line which perfectly divides the whole image into two regions as the sky and sea. Thus, the complication of traditional methods is the statistical distance metrics of distributions are calculated in the whole image by all combinations of the horizon line parameters. Moreover, traditional methods do not provide realtime processing to detect the horizon line from high-resolution images. To achieve real-time processing on high-resolution images, this study defines the local features of the horizon line using a vanishing line characteristic and the optimization criteria. In addition, the optimization process is improved by combining a genetic algorithm and a coarse-to-fine approach. Our method applied the Singapore marine dataset and the Buoy dataset. To verify the proposed method, our result is compared to the state of the art methods. We confirm the proposed method can accurately detect the horizon line under different scenarios in real-time.
