Samsung

About: Samsung is a company organization based out in Seoul, South Korea. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134067 authors who have published 163691 publications receiving 2057505 citations. The organization is also known as: Samsung Group & Samsung chaebol.

Cyclone dust collecting apparatus for vacuum cleaner

Abstract: A cyclone dust collecting apparatus for a vacuum cleaner is disclosed. The cyclone dust collecting apparatus comprises a cylindrical cyclone body secured to a top of a dust collecting chamber which is provided in a cleaner body and connected to an air drawing path and an air discharging path, the cylindrical cyclone body having an air inlet and an air outlet corresponding to the air drawing path and the air discharging path, respectively, a dirt collecting container removably disposed at a lower portion of the cyclone body for collecting dirt and contaminants centrifuged at the cyclone body, a partition plate disposed between the cyclone body and the dirt collecting container, a first dirt path protruded outwardly from a side of a bottom of the cyclone body for discharging the dust and contaminants centrifuged at the cyclone body into the dirt-collecting container, and a second dirt path protruded outwardly from a side of a top of the dirt collecting container for discharging the dust and contaminants centrifuged at the cyclone body into the dirt collecting container, the first and the second dirt paths corresponding to each other.

Monocular Relative Depth Perception with Web Stereo Data Supervision

Abstract: In this paper we study the problem of monocular relative depth perception in the wild. We introduce a simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and propose a new dataset that consists of diverse images as well as corresponding dense relative depth maps. Further, an improved ranking loss is introduced to deal with imbalanced ordinal relations, enforcing the network to focus on a set of hard pairs. Experimental results demonstrate that our proposed approach not only achieves state-of-the-art accuracy of relative depth perception in the wild, but also benefits other dense per-pixel prediction tasks, e.g., metric depth estimation and semantic segmentation.

Liquid crystal display and driving method thereof

Abstract: An LCD groups pixels in each row into a plurality of blocks, and calculates difference in gray between every two image data applied to a pair of adjacent odd and even pixels in each block including pixels in a row for each of first to third colors. It is determined that a block is first or second dot block depending on a sign of the gray difference when a magnitude of the gray difference between the odd pixel and the even pixel in each pair in the block for at least one color is equal to or larger than a critical value. A current block in a current row and in columns is determined to be a one-dot block when the current block is the first dot block and a previous block in a previous row and in the columns is the second dot block. When the number of the one-dot blocks is a predetermined percentage of the number of the total blocks, it is determined that a one-dot pattern is generated and one-dot inversion of the LCD is changed into another inversion. In this way, a pattern generating flicker is determined and the inversion type is changed for reducing the flicker.

Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data

Abstract: Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.