• Korean Journal of Cognitive Science
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• v.18 no.2
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• pp.179-191
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• 2007

In a goal-oriented dialogue, spoken' intentions can be represented by domain actions that consist of pairs of a speech art and a concept sequence. The domain action prediction of user's utterance is useful to correct some errors that occur in a speech recognition process, and the domain action prediction of system's utterance is useful to generate flexible responses. In this paper, we propose a model to predict a domain action of the next utterance using a neural network. The proposed model predicts the next domain action by using a dialogue history vector and a current domain action as inputs of the neural network. In the experiment, the proposed model showed the precision of 80.02% in speech act prediction and the precision of 82.09% in concept sequence prediction.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.259-270
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• 2013

A speaker's intentions can be represented by domain actions (domain-independent speech act and domain-dependent concept sequence pairs). Therefore, it is essential that domain actions be determined when implementing dialogue systems because a dialogue system should determine users' intentions from their utterances and should create counterpart intentions to the users' intentions. In this paper, a neural network model is proposed for classifying a user's domain actions and planning a system's domain actions. An integrated neural network model is proposed for simultaneously determining user and system domain actions using the same framework. The proposed model performed better than previous non-integrated models in an experiment using a goal-oriented dialogue corpus. This result shows that the proposed integration method contributes to improving domain action determination performance.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.65-73
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• 2014

An overall responding security-centered framework is necessary required for infringement accidents, failures, and cyber threats. On the other hand, the correspondence structures of existing administrative, technical, physical security have weakness in a system responding to complex attacks because each step is performed independently. This study will recognize all internal and external users as a potentially threatening element. To perform connectivity analysis regarding an action, an intelligent convergence security framework and road map is suggested. A suggested convergence security framework was constructed to be independent of an automatic framework, such as the conventional single solution for the priority defense system of APT of the latest attack type, which makes continuous reputational attacks to achieve its goals. This study suggested the next generation convergence security framework to have preemptive responses, possibly against an APT attack, consisting of the following five hierarchical layers: domain security, domain connection, action visibility, action control, and convergence correspondence. In the domain, the connection layer suggests a security instruction and direction in the domains of administrative, physical and technical security. The domain security layer has consistency of status information among the security domain. A visibility layer of an intelligent attack action consists of data gathering, comparison and decision cycle. The action control layer is a layer that controls the visibility action. Finally, the convergence corresponding layer suggests a corresponding system of before and after an APT attack. The administrative security domain had a security design based on organization, rule, process, and paper information. The physical security domain is designed to separate into a control layer and facility according to the threats of the control impossible and control possible. Each domain action executes visible and control steps, and is designed to have flexibility regarding security environmental changes. In this study, the framework to address an APT attack and load map will be used as an infrastructure corresponding to the next generation security.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1264-1274
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• 2015

Human action recognition received many interest in the computer vision community. Most of the existing methods focus on either construct robust descriptor from the temporal domain, or computational method to exploit the discriminative power of the descriptor. In this paper we explore the idea of using local action attributes to form an action descriptor, where an action is no longer characterized with the motion changes in the temporal domain but the local semantic description of the action. We propose an novel framework where introduces local action attributes to represent an action for the final human action categorization. The local action attributes are defined for each body part which are independent from the global action. The resulting attribute descriptor is used to jointly model human action to achieve robust performance. In addition, we conduct some study on the impact of using body local and global low-level feature for the aforementioned attributes. Experiments on the KTH dataset and the MV-TJU dataset show that our local action attribute based descriptor improve action recognition performance.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.92-99
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• 2013

As a recent cyber attack has a characteristic that is intellectual, advanced, and complicated attack against precise purpose and specified object, it becomes extremely hard to recognize or respond when accidents happen. Since a scale of damage is very large, a corresponding system about this situation is urgent in national aspect. Existing data center or integration security framework of computer lab is evaluated to be a behind system when it corresponds to cyber attack. Therefore, this study suggests a better sophisticated next generation convergence security framework in order to prevent from attacks based on advanced persistent threat. Suggested next generation convergence security framework is designed to have preemptive responses possibly against APT attack consisting of five hierarchical steps in domain security layer, domain connection layer, action visibility layer, action control layer and convergence correspondence layer. In domain connection layer suggests security instruction and direction in domain of administration, physical and technical security. Domain security layer have consistency of status information among security domain. A visibility layer of Intellectual attack action consists of data gathering, comparison, decision, lifespan cycle. Action visibility layer is a layer to control visibility action. Lastly, convergence correspond layer suggests a corresponding system of before and after APT attack. An introduction of suggested next generation convergence security framework will execute a better improved security control about continuous, intellectual security threat.

• Wind and Structures
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• v.13 no.6
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• pp.487-498
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• 2010

When designing structures to the wind action, the variation of the mean wind velocity and turbulence parameters with the height above the ground must be taken into account. This paper presents the numerical simulation results of atmospheric boundary layer (ABL) airflows, in a numerical domain with no obstacles and with a cubic building. The results of the flow characterization, obtained with the FLUENT CFD code were performed using the ${\kappa}-{\varepsilon}$ turbulence model with the MMK modification. The mean velocity and turbulence intensity profiles in the inflow boundary were defined in accordance with the Eurocode 1.4, for different conditions of aerodynamic roughness. The maintenance of the velocity and turbulence characteristics along the domain were evaluated in an empty domain for uniform incident flow and the ABL Eurocode velocity profiles. The pressure coefficients on a cubic building were calculated using these inflow conditions.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.1-14
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• 2007

In a goal-oriented dialogue, speakers' intentions can be represented by domain actions that consist of pairs of a speech act and a concept sequence. Therefore, if we plan to implement an intelligent dialogue system, it is very important to correctly infer the domain actions from surface utterances. In this paper, we propose a statistical model to determine speech acts and concept sequences using conditional random fields at the same time. To avoid biased learning problems, the proposed model uses low-level linguistic features such as lexicals and parts-of-speech. Then, it filters out uninformative features using the chi-square statistic. In the experiments in a schedule arrangement domain, the proposed system showed good performances (the precision of 93.0% on speech act classification and the precision of 90.2% on concept sequence classification).

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.1
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• pp.58-67
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• 2000

Todays enterprise networks are composed of multiple types of interconnected networks.Enterprise Networks interoperability between these technologies is needed. To enable interworking, it is necessary to be able to map between the relevant object models and to build on this to provide a mechanism to handle protocol conversion on the domain boundaries.In this paper, we describe a gateway between management application in the CORBA domain and agent in the SNMP domain and various integration methods. The main function of the gateway is to dynamically convert the method invocations on object reference in CORBA domain to SNMP messages for MIB entries at remote agents, We also present translation methods from SNMP MIB to CORBA IDL using Direct translation and Abstract translation. JIDM algorithm has no notion of containment and inheritance relationships between object classes and is difficult to understanding between management attribute and SNMP Action attribute. Abstract translation over come these problems.New superclasses define for common attributes and define explicit CORBA method for SNMP Action.It is a methodology for obtaining the CORBA-compliant management agents from already existing SNMP agents.

• Archives of Pharmacal Research
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• v.22 no.4
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• pp.329-334
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• 1999

Insulin stimulates glucose uptake in muscle and adipose cells primarily by recruiting GLUT4 from an intracellular storage pool to the plasma membrane. Dysfunction of this process known as insulin resistance causes hyperglycemia, a hallmark of diabetes and obesity. Thus the understanding of the mechanisms underlying this process at the molecular level may give an insight into the prevention and treatment of these health problems. GLUT4 in rat adipocytes, for example, constantly recycles between the cells surface and an intracellular pool by endocytosis and exocytosis, each of which is regulated by an insulin-sensitive and GLUT4-selective sorting mechanism. Our working hypothesis has been that this sorting mechanism includes a specific interaction of a cytosolic protein with the GLUT4 cytoplasmic domain. Indeed, a synthetic peptide of the C-terminal cytoplasmic domain of GLUT4 induces an insulin-like GLUT4 recruitment when introduced in rat adipocytes. Relevance of these observations to a novel euglycemic drug design is discussed.