• YAKHAK HOEJI
• /
• v.44 no.4
• /
• pp.362-370
• /
• 2000

In anesthetized dogs, antidiuretic action of intravenously administered BRL 34915 (10.0~30.0 $\mu$/kg) was blocked by renal denervation, whereas it was not affected by glibenclamide, a selective $K_{ATP}$ blocker, given into renal artery. Diuretic action in ipsilateral kidney produced by intrarenal administration of BRL 34915 was not influenced by renal denervation, but blocked completely by glibenclamide given into the vein. Above results suggest that the antidiuretic action of BRL 34915 is mediated by renal sympathetic nerves and the diuretic action is caused by opening of $K^+$ channel within kidney.

• YAKHAK HOEJI
• /
• v.43 no.5
• /
• pp.665-673
• /
• 1999

This study was performed in order to investigate the effect of ketanserin, a specific antagonist of 5-HT2 receptor, on renal function in dogs. Ketanserin (50.0 and $150.0{\;}\mu\textrm{g}/kg$), when given intravenously, produced antidiuretic action accompanied with the decreased amounts of sodium and potassium excreted in urine (ENa, EK) and the increased reabsorption rates of sodium and potassium in renal tubules (RNa, RK). Ketanserin (50.0 and $50.0{\;}\mu\textrm{g}/kg$), when administered into a renal artery, elicited antidiuretic action in both experimental and control kidney, this time changes of renal function showed the same aspect as when given intravenously. Ketanserin (15.0 and $50.0{\;}\mu\textrm{g}/kg$) injected into the carotid artery exhibited also antidiuretic action and this antidiuretic action was not affected by renal denervation. Above results suggest that ketanserin elicits antidiuretic through central function, this central antidiuretic action is not mediated by renal nerves.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.3024-3026
• /
• 1999

This paper presents a cooperative action controller of a multi-agent system. To achieve an object, i.e. win a game, it is necessary that a robot has its own roles, actions and work with each other. The presented incorporated action controller consists of the role selection, action selection and execution layer. In the first layer, a fuzzy logic controller is used. Each robot selects its own action and makes its own path trajectory in the second layer. In the third layer, each robot performs their own action based on the velocity information which is sent from main computer. Finally, simulation shows that each robot selects proper roles and incorporates actions by the proposed controller.

• The Mathematical Education
• /
• v.53 no.1
• /
• pp.75-92
• /
• 2014

In this paper, we summarize briefly some of the most salient features of Repkina & Zaika's theory of learning action formation levels. We concretize Repkina & Zaika's theory by comparing various points of view of Uoo, Polya, Krutetskii, and Davydov et al. In this study we are able to diagnose students' learning action formation levels in the process of mathematics problem solving. In addition we use interview method to collect various information about students' levels. As a result we suggest data related with each level of learning action formation, and characteristics of students who belong to each level of learning action formation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.5
• /
• pp.2327-2347
• /
• 2018

Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.

• Journal of the Korean Mathematical Society
• /
• v.56 no.1
• /
• pp.53-65
• /
• 2019

Recently, Chung and Lee [2] introduced the notion of topological stability for a finitely generated group action, and proved a group action version of the Walters's stability theorem. In this paper, we introduce the concepts of continuous shadowing and continuous inverse shadowing of a finitely generated group action on a compact metric space X with respect to various classes of admissible pseudo orbits and study the relationships between topological stability and continuous shadowing and continuous inverse shadowing property of group actions. Moreover, we introduce the notion of structural stability for a finitely generated group action, and we prove that an expansive action on a compact manifold is structurally stable if and only if it is continuous inverse shadowing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2148-2161
• /
• 2019

In this paper, we present a real-time cattle action recognition algorithm to detect the estrus phase of cattle from a live video stream. In order to classify cattle movement, specifically, to detect the mounting action, the most observable sign of the estrus phase, a simple yet effective feature description exploiting motion history images (MHI) is designed. By learning the proposed features using the support vector machine framework, various representative cattle actions, such as mounting, walking, tail wagging, and foot stamping, can be recognized robustly in complex scenes. Thanks to low complexity of the proposed action recognition algorithm, multiple cattle in three enclosures can be monitored simultaneously using a single fisheye camera. Through extensive experiments with real video streams, we confirmed that the proposed algorithm outperforms a conventional human action recognition algorithm by 18% in terms of recognition accuracy even with much smaller dimensional feature description.

• East Asian mathematical journal
• /
• v.37 no.2
• /
• pp.245-262
• /
• 2021

This study describes a sixth-grade teacher's professional development journey through action research for improving students' responses in a mathematics class. In the action research, the influence a teacher's questioning tactics would have on students' ability to determine answer reasonability to mathematics problems was investigated. Drawing on qualitative analysis of the teacher's lessons, reflection journal and interviews as well as the classroom students' questionnaires and interviews, this study examines how action research can affect the teacher and the classroom students. The results suggest the popularization of action research among teachers by teacher training and development programs showing the positive changes in the teacher's performance leading to improved student responses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.4
• /
• pp.314-322
• /
• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.327-338
• /
• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.