• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.2
• /
• pp.154-162
• /
• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• The KIPS Transactions:PartB
• /
• v.8B no.5
• /
• pp.485-492
• /
• 2001

Recently, character agents are used as a user-friendly interface. In this paper, we have studied a generic framework for emotional character agents which are designed to infer emotions from diverse personalities, situations, user behaviors and to express them. The method of emotion inference is based on blackboard systems which are used to solve the problems in AI. Because it keeps independence between knowledge sources which are rules of emotions, a blackboard-based inference engine is easy to manage knowledge sources, Blackboard-based systems gave the system flexibility. So we can adapt the engine to various application systems. Each emotional agent monitors user behavior, learns user profile and infers user behavior. And it generates characters emotions according to the user profile. So, in case of same situations, the agent can generate different emotions according to users. We have studied to build an personalized emotional character agent which according to situations and user modeling.

• The KIPS Transactions:PartB
• /
• v.13B no.5 s.108
• /
• pp.569-578
• /
• 2006

The present study implemented AAC(Augmentative and Alternative Communication) system using images that speech defectives can easily understand. In particular, the implementation was focused on the portability and mobility of the AAC system as well as communication system of a more flexible form. For mobility and portability, we implemented a system operable in mobile devices such as PDA so that speech defectives can communicate as food as ordinary People at any Place using the system Moreover, in order to overcome the limitation of storage space for a large volume of image data, we implemented the AAC system in client/server structure in mobile environment. What is more, for more flexible communication, we built an image dictionary by taking verbs as the base and sub-categorizing nouns according to their corresponding verbs, and regularized the types of sentences generated according to the type of verb, centering on verbs that play the most important role in composing a sentence.

• Journal of Information Technology Application
• /
• v.3 no.4
• /
• pp.63-92
• /
• 2001

This paper makes an attempt to look at the process of automatically generating an application software system based on the Entity-Relationship (E-R) model. Basically, the process consists of five steps as follows: First, the designer develops an E-R model of an real-world system. Second, the software generator automatically generates a stereo-type application system. Third, the generator produces database schema and link information between application programs. Fourth, the designer designs the user interface including menu, screen design and so on. Finally, the generator completes the process integrating all the elements of an application system. Five basic program generation rules, three extended rules and a database generation rule have been suggested. By following each rule with the generator, the designer can build an application with an extremely efficient manner compared with traditional approaches. A case study has been included in order to show the applicability of the automated software generation process suggested in this paper. It has been demonstrated from the case study that the idea of applying an automated generator in systems development based upon the E-R model worked well.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.8
• /
• pp.11-18
• /
• 2014

In This paper, we propose the unsupervised learning and fuzzy logic-based coupled data classification method base on ART. The unsupervised learning-based data classification helps improve the grouping technique, but decreases the processing efficiency. However, the data classification requires the decision technique to induce high success rate of data classification with optimal threshold. Therefore it is also necessary to solve the uncertainty of the threshold decision. The proposed method deduces the optimal threshold with the designing of fuzzy parameter and rules. In order to evaluate the proposed method, we design the simulation model with the GPCR(G protein coupled receptor) data in cloud computing environment. Simulation results verify the efficiency of our method with the high recognition rate and low processing time.

• The Korean Journal of Applied Statistics
• /
• v.21 no.3
• /
• pp.361-375
• /
• 2008

We propose proper methods to impute the item nonresponse in 4-8-4 rotation sample survey. We consider nonignorable nonresponse mechanism that can happen when survey deals with sensitive question (e.g. income, labor force). We utilize modeling imputation method based on Bayesian approach to avoid a boundary solution problem. We also estimate a interview time bias using imputed data and calculate cell expectation and marginal probability on fixed time after removing estimated bias. We compare the mean squared errors and bias between maximum likelihood method and Bayesian methods using simulation studies.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.40 no.6
• /
• pp.459-468
• /
• 2003

This paper proposes a novel low-cost and high-speed Reed-Solomon (RS) decoder based on a new degree computationless modified Euclid´s (DCME) algorithm. This architecture has quite low hardware complexity compared with conventional modified Euclid´s (ME) architectures, since it can remove completely the degree computation and comparison circuits. The architecture employing a systolic away requires only the latency of 2t clock cycles to solve the key equation without initial latency. In addition, the DCME architecture using 3t＋2 basic cells has regularity and scalability since it uses only one processing element. The RS decoder has been synthesized using the 0.25${\mu}{\textrm}{m}$. Faraday CMOS standard cell library and operates at 200MHz and its data rate suppots up to 1.6Gbps. For tile (255, 239, 8) RS code, the gate counts of the DCME architecture and the whole RS decoder excluding FIFO memory are only 21,760 and 42,213, respectively. The proposed RS decoder can reduce the total fate count at least 23% and the total latency at least 10% compared with conventional ME architectures.

• Information Systems Review
• /
• v.12 no.2
• /
• pp.89-104
• /
• 2010

Recently, ubiquitous sensor network (USN) has been applied to many areas including environment monitoring. A few studies applied the USN to disaster prevention and emergency management, in particular, aiming to conserve cultural heritage. USN is an useful technology to do online real-time monitoring for the purpose of early detection of the fire which is a critical cause of damage and destruction of cultural heritages. It is necessary to online monitor the cultural heritages that human has a difficulty to access or their external appearance and beauty are important, by using the USN. However, there exists false warning from USN-based monitoring systems without human intervention. In this paper, we presented an alternative to resolve the problem by applying ontology. Our intelligent fire early detection systems for conserving cultural heritages are based on ontology and inference rules, and tested under laboratory environments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.5
• /
• pp.977-984
• /
• 2001

In this paper, Genetic-Fuzzy Algorithm for Inverted Pendulum is presented. This Algorithms is combine Fuzzy logic with the Genetic Algorithm. The Fuzzy Logic Controller is only designed to two inputs and one output. After Fuzzy control rules are determined, Genetic Algorithm is applied to tune the membership functions of these rules. To measure of performance of the designed Genetic-Fuzzy controller, Computer simulation is applied to Inverted Pendulum system. In the simulation, In the case of f[0.3, 0.3] Fuzzy controller is measured that maximum undershoot is $-5.0 \times 10^{-2}[rad]$, maximum undershoot is $3.92\times10^{-2}[rad]$ individually however, Designed algorithm is zero. The Steady state time is approximated that Fuzzy controller is 2.12[sec] and designed algorithm is 1.32[sec]. The result of simulation, Resigned algorithm is showed it's efficient and effectiveness for Inverted Pendulum system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.10
• /
• pp.2396-2402
• /
• 2015

Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.