• Journal of the Chungcheong Mathematical Society
• /
• v.24 no.2
• /
• pp.319-335
• /
• 2011

We classify equivariant topoligical complex vector bundles over real projective plane under a compact Lie group (not necessarily effective) action. It is shown that nonequivariant Chern classes and isotropy representations at (at most) three points are sufficient to classify equivariant vector bundles over real projective plane except one case. To do it, we relate the problem to classification on two-sphere through the covering map because equivariant vector bundles over two-sphere have been already classified.

• Journal of Welding and Joining
• /
• v.10 no.4
• /
• pp.199-212
• /
• 1992

Expert system is a practical application part of the artificial intelligence and can be generally described as a computer-based system designed to simulate the knowledge and reasoning of a human expert, and to make that knowledge conveniently available to other people in a useful way. Expert systems consist of three major components, knowledge base, inference engine and user interface. In this paper, it is aimed to construct a prototype system to design the horizontal-typed pressure vessel. To do this, a representative artificial programming language, Turbo Prolog, was employed, and the knowledge representation was mainly done by the production rule such as "If(condition), than (action)" style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition), then (action)"style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition). then(action)" style and the various table-like data. It was also effective to represent the graphics. Though this expert system is by now small and incomplete, it is possible to expand it to a larger and refined system later.rger and refined system later.

• Journal of Internet Computing and Services
• /
• v.15 no.4
• /
• pp.9-20
• /
• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Archives of design research
• /
• v.17 no.2
• /
• pp.5-14
• /
• 2004

Introducing human capacities of control and sensation which have been overlooked into Human-Computer Interaction(HCI), Ubiquitous computing, Augmented Reality and others have been researched recently. New vision of HCI has embodied in Tangible User Interface(TUI). TUI allows users to grasp and manipulate bits with everyday physical object and architectural surface and also TUI enables user to be aware of background object at the periphery of human perception using ambient display media such of light, sound, airflow and water movement. Tangibles, physical object which constitutes TUI system, is the physical object embodied digital bit. Tangibles is not only input device but also the configuration of computing. To get feedback of computing result, user controls the system with Tangibles as action and the system represents reaction in response to User's action. User appreciates digital representation (sound, graphic information) and physical representation (form, size, location, direction etc.) for reaction. TUI's characters require the consideration about both user's action and system's reaction. Therefore we have to need the method to be concerned about physical object and interaction which can be combined with action, reaction and feedback.

• Proceedings of the Korea Contents Association Conference
• /
• 2004.11a
• /
• pp.558-563
• /
• 2004

We develop a method to simulate diverse actions for a virtual world consisting of numerous interrelated objects. We focus on how to efficiently model those countless actions rather realistic representation of each action. To this end, those actions are structured into hierarchies according to their characteristic parameters so actions can be efficiently added based on inheritance. The first key issue in this model is how to extract commonality among actions and identify their idiosyncrasy. The second issue is how to reify or substantiate inheritance of the actions. We apply the developed model to the walk actions of quadruped, and their simulated results are shown in articulated figures.

• The KIPS Transactions:PartB
• /
• v.9B no.1
• /
• pp.23-28
• /
• 2002

A fundamental problem in building an intelligent agent is that an agent does not understand the meaning of its perception or its action. One reason that an agent cannot understand the world is partially caused by a syntactic approach that converts a semantic feature into a simple string. To solve this problem, Cohen introduces a semantic approach that an agent autonomously learns a meaningful representation of physical schemas, on which some advanced conceptual structures are built, from physically interacting with environment using its own sensors and effectors. However, Cohen does not deal with a meta level of conceptual primitive that makes recognizing a schema possible. We propose that negation is a meta schema that enables an agent to recognize a physical schema. We prove some semantic aspects of negation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1477-1491
• /
• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.5
• /
• pp.1204-1215
• /
• 1998

In this paper, we propose a scenario representing method, a specification language, and a verification technique for OARTS(Object based Approach for Real-Time Systems). As well as the general modeling method(event trace diagram), this study includes a specification language and a verification technique because there was no study about methodological level technique for scenario development as yet. Centering around the synchronization problem of transition of external modules which are the communication interfaces based on the objects, we lay stress on the representation of sequence of external events and internal action transitions. From the results of practical experiences, it has been ascertained that the proposed method reflect well the requirements in the analysis step, and its validity of the representation has been identified by a conceptual verifier. We support that it can serve as an analyzing tool for representing a general real-time scenarios also.

• Journal of Biomedical Engineering Research
• /
• v.26 no.4
• /
• pp.199-205
• /
• 2005

When recording single-unit responses from neural systems, a common problem is the accurate detection of spikes (action potentials) in the presence of competing unwanted (noise) signals. While some sources of noise can be readily dealt with through filtering or 'template subtraction' techniques, other sources present a more difficult problem. In particular, noise components introduced by power supplies, which contain harmonics of the power-line frequency, can be particularly troublesome in that they can mimic the shape of the desired spikes. Thus, standard 'template subtraction' techniques or notch-filtering approaches are not appropriate. In this study, we propose the use of a novel template-subtraction scheme that involves estimating the power-line noise waveform and using cross-correlation techniques to subtract them from the recordings. This technique requires two key steps: (1) cross-correlation analysis of each recorded waveform extracts a robust representation of the power-line noise waveform and (2) a second level of cross-correlation to successfully subtract that representation from each recorded waveform. This paper describes this algorithm and provides examples of its implementation using actual recorded waveforms that are contaminated with these noise signals. An improvement (reduction) in the noise level is reported, as are suggestions for future implementation of this strategy.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1705-1711
• /
• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.