• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.157-159
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• 2006

ZigBee technology that is observed to radio network of low electric power with latest WPAN's IEEE 802.15 .4 technologies is evaluated as best technology for sensor network and digital home network construction. This need exclusive use application for monitoring, managing, controlling and organizing this nodes. However, these program has weaknesses that is hard to construct flexible system with Internet. This paper will use web server linked with gateway to manage sensor network that is consisted of ZigBee node and design system that manages ZigBee device with relative fast responsibility using AJAX web technology.

• 한국전기전자재료학회논문지
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• 제21권1호
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• pp.73-78
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• 2008

A resistor network model for the horizontal AlInGaN LED was investigated, The parameters of the proposed model are extracted from the test dies and $350{\mu}m$ LED, The center of the P-area is the optimal position of a P-electrode by the simulation using the model. Also the optimal chip size of the LED for the new target current was investigated, Comparing the simulation and fabrication result, the errors for the forward voltage and the light power are average 0,02 V, 8 % respectively, So the proposed resistor network model with the linear forward voltage approximation and the exponential light power model are useful in the simulation for the horizontal AlInGaN LED.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.874-879
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• 1992

It is shown that there exists a nonlinear mappping which transforms features and their changes to the desired camera motion without measurement of the relative distance between the camera and the part, and the nonlinear mapping can eliminate several difficulties encountered when using the inverse of the feature Jacobian as in the usual feature-based visual feedback controls. And instead of analytically deriving the closed form of such a nonlinear mapping, a fuzzy membership function (FMF) based neural network is then proposed to approximate the nonlinear mapping, where the structure of proposed networks is similar to that of radial basis function neural network which is known to be very useful in function approximations. The proposed FMF network is trained to be capable of tracking moving parts in the whole work space along the line of sight. For the effective implementation of proposed IMF networks, an image feature selection processing is investigated, and required fuzzy membership functions are designed. Finally, several numerical examples are illustrated to show the validities of our proposed visual servoing method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.154-157
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• 1999

Photonic Slot Routing(PSR) is a promising approach to solve the fundamental scalability problem of all-optical packet switched WDM networks. In photonic slot routing, packets destined for the same subnetwork are aggregated to form a photonic slot which is jointly routed as a single unit of information through the network. The relative location of the nodes from bridge may cause to fairness problem in the unidirectional WDM ring network based on PSR. As photonic slots from different subnetworks can originate contentions at the bridge, packets may be dropped and retransmitted. Thus we evaluated the performance of PSR based WDM ring network in the point of fairness for each node and slot contentions at the bridge. Simulation results show that the PSR based WDM ring requires a slot access mechanism to guarantee the transmission fairness and efficient switch architecture to resolve slot contention at the bridge.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• 제어로봇시스템학회논문지
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• 제18권8호
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• pp.725-730
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• 2012

In this paper, we propose the localization algorithm for the cooperative behavior of the swarm robots based on WSN (Wireless Sensor Network). The proposed method is as follows: First, we measure positions of the L-bot (Leader robot) and F-bots (Follower robots) by using the APIT (Approximate Point In Triangle) and the RSSI (Received Signal Strength Indication). Second, we measure relative positions of the F-bots against the pre-measured position of the L-bot by using trilateration. Then, to revise a position error caused by noise of the wireless signal, we use the particle filter. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• 대한교통학회:학술대회논문집
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• 대한교통학회 2000년도 제37회 학술발표회논문집
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• pp.89-115
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• 2000

Traffic signal setting policies and traffic assignment procedures are mutually dependent. The combined signal control and traffic assignment problem deals with this interaction. With the total travel time minimization objective, gradient based local search methods are implemented. Deterministic user equilibrium is the selected user route choice rule, Webster's delay curve is the link performance function, and green time per cycle ratios are decision variables. Three implemented solution codes resulting in six variations include intersections operating under multiphase operation with overlapping traffic movements. For reference, the iterative approach is also coded and all codes are tested in four example networks at five demand levels. The results show the numerical gradient estimation procedure performs best although the simplified local searches show reducing the large network computational burden. Demand level as well as network size affects the relative performance of the local and iterative approaches. As demand level becomes higher, (1) in the small network, the local search tends to outperform the iterative search and (2) in the large network, vice versa.

• The Plant Pathology Journal
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• 제38권4호
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• 한국안전학회지
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• 제38권1호
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.