• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.96-100
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• 2008

An ontology provides an explicit specification of concepts and relations on information extracted from database or on human knowledge. Using an ontology, The information can be reconstructed according to semantic relations. In this paper. IT-People Event Ontology is constructed using people information extracted from web portals. IT-People Event Ontology represents constant information and time-temporal information on people. A system using this ontology outputs the well-organized reconstructed information on a specific individual in interest, and then the reconstructed information is suitable for users' demand.

• English Language & Literature Teaching
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• v.18 no.1
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• pp.23-43
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• 2012

This study investigates the effects of three different types of instruction, namely form-based, comprehension-based, and production-based on the development of Korean university students' (n=137) comprehension and production of English relative clauses (RCs). The extent of improvements was analyzed by administering pre-and post-tests consisting of two comprehension tests (selecting the right form of RCs and the right picture descriptions) and one production test (combining two sentences). Findings of this study suggest that all three types of instruction increased participants' comprehension and productions of RCs. However, there appeared differential effects by the instruction type. It was found production-based instruction was most effective in promoting comprehension, followed by comprehension-based instruction. Comprehension-based instruction worked best with the development of production, suggesting that the effects of comprehension training did not only work for increasing comprehension skills, but also transfer to production skills. The type or level of tasks employed for each instruction appeared to play an important role in causing such results. Form-based instruction displayed the lowest improvements in both comprehension and production of RCs. A sentence-combination task employed for form-based instruction appear to result in mere explicit rule explanations without chances to notice rules in context or use their knowledge in practice.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.557-575
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• 1999

As the Internet has been used widely in modern firms for gaining competitive advantage in the market, EC (Electronic Commerce) emerged as one of strong alternatives for this purpose. Many researchers and practitioners have proposed a wide variety of EC frameworks that can consider only the structured conditions, but there exists no EC mechanism in which engaged entities can take into account the various unstructured conditions. With the conventional EC framework, the structured EC conditions such as price, quantity, delivery date, etc. can be fully negotiated during the EC process. However, no studies have been conducted on the issue of incorporating those unstructured conditions which are difficult to represent in an explicit form and therefore hard to consider explicitly during the EC process. They are characterized by causal properties. This means that we should have a new EC mechanism which is capable of dealing with causal knowledge. In this sense, we propose a FCM (Fuzzy Cognitive Map)-based simulation framework for EC to resolve the problem of considering the unstructured conditions during the EC process. We experimented our prototype with several illustrative examples and proved that our approach is robust and meaningful.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.537-555
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• 1999

As the Internet has been used widely in modern firms for gaining competitive advantage in the market, EC (Electronic Commerce) emerged as one of strong alternatives for this purpose. Many researchers and practitioners have proposed a wide variety of EC frameworks that can consider only the structured conditions, but there exists no EC mechanism in which engaged entities can take into account the various unstructured conditions. With the conventional EC framework, the structured EC conditions such as price, quantity, delivery date, etc. can be fully negotiated during the EC process. However, no studies have been conducted on the issue of incorporating those unstructured conditions which are difficult to represent in an explicit form and therefore hard to consider explicitly during the EC Process. They are characterized by causal properties. This means that we should have a new EC mechanism which is capable of dealing with causal knowledge. In this sense, we propose a FCM (Fuzzy Cognitive Map)-based simulation framework for EC to resolve the problem of considering the unstructured conditions during the EC process. We experimented our prototype with several illustrative examples and proved that our approach is robust and meaningful.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.236-242
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• 2003

This paper presents a cooperation strategy between a human operator and autonomous robots for an interactive robot soccer game, The interactive robot soccer game has been developed to allow humans to join into the game dynamically and reinforce entertainment characteristics. In order to make these games more interesting, a cooperation strategy between humans and autonomous robots on a team is very important. Strategies can be pre-programmed or learned by robots themselves with learning or evolving algorithms. Since the robot soccer system is hard to model and its environment changes dynamically, it is very difficult to pre-program cooperation strategies between robot agents. Q-learning - one of the most representative reinforcement learning methods - is shown to be effective for solving problems dynamically without explicit knowledge of the system. Therefore, in our research, a Q-learning based learning method has been utilized. Prior to utilizing Q-teaming, state variables describing the game situation and actions' sets of robots have been defined. After the learning process, the human operator could play the game more easily. To evaluate the usefulness of the proposed strategy, some simulations and games have been carried out.

• Structural Engineering and Mechanics
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• v.36 no.3
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• pp.301-319
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• 2010

The paper seeks to explore some aspects of the current state of knowledge on progressive collapse in the technical literature covering blast loads and structural analysis procedure applicable to reinforced concrete (RC) buildings. The paper describes the progressive collapse analysis of a commercial RC building located in the city of Riyadh and subjected to different blast scenarios. A 3-D finite element model of the structure was created using LS-DYNA, which uses explicit time integration algorithms for solution. Blast loads were treated as dynamic pressure-time history curves applied to the exterior elements. The inherent shortcomings of notional member removal have been taken care of in the present paper by simulating the damage of structural elements through the use of solid elements with the provision of element erosion. Effects of erosion and cratering are studied for different scenarios of the blast.

• Journal of Educational Research in Mathematics
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• v.17 no.4
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• pp.437-452
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• 2007

Complementarity, complementary principle and complementary approach have been often used in school mathematics but its meaning has not been obvious. Thus this paper tries to make explicit the meaning by looking around complementary characteristic of mathematical knowledge. First of all, we examines the general meaning of complementarity and Investigate complementary characteristics of mathematical concepts through incommensurability and zeno's paradox. From this, complementary approach to school mathematics is studied. To understand and uncover complementary characteristics of mathematical concepts make it possible for student to have an insight. It is the most important thing that students can have an image of mathematics as a living system rather than as a mechanical application of rules and fragmentary in formations.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.305-313
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• 2005

Spyware is any software which employs a user's Internet connection in the background without their knowledge or explicit permission. The installation of spywares is generally done in a sneaky, misleading or unannounced manner. It does not only compromise the security and privacy of affected users but also be an obstruction to the digital convergence and ubiquitous computing environments. This paper provides a summary of the definition, status, risk analysis, and security controls of the spywares. Furthermore, this paper suggests additional controls which should be considered at an individual, organizational and national perspective.