• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.177-180
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• 2000

An algorithm for modeling of yeast fermentation process using genetic-fuzzy algorithm is presented in this work. The algorithm involves developing the fuzzy modeling of the process and model update capability against the system change. The membership functions of state variables and specific rates and the decision table were generated using genetic algorithm. This algorithm could replace the complex mathematical model to simple fuzzy model and cope with the change of process characteristics well.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.372-376
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• 1989

One of the major difficulties with modular approach model of separation process simulation is initial guess problem. Only accurate initial guess make the problem converge and large computer memory and calculating time are required. In this study, we use the initial bottom guess value same as given feed condition and update the value the .theta.method. So we examine;(1)the problem converges using initial guess with large range, (2)computer memory and calculating time are reduced considerably.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제29권1호
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• pp.72-75
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• 2006

Event trees are widely used graphical tool to denote the accident inintiation and escalation to more severe accident. But they have some drawbacks in that they do not have efficient way of updating model parameters and also they can not contain the information about dependency or independency among model parameters. A tool that can cure such drawbacks is an influence diagram. We introduce influence diagrams and explain how to update model parameters and obtain predictive distributions. We show that an event tree can be converted to a statistically equivalent influence diagram, and bayesian prediction can be made more effectively through the use of influence diagrams.

• Journal of Internet Computing and Services
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• 제23권1호
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• Journal of the Korean Institute of Intelligent Systems
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• 제12권6호
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• pp.531-536
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• 2002

In this paper, we have applied Genetic Algorithms(GAs) to Intrusion Detection System(TDS), and then proposed and simulated the misuse detection model firstly. We have implemented with the KBD contest data, and tried to simulated in the same environment. In the experiment, the set of record is regarded as a chromosome, and GAs are used to produce the intrusion patterns. That is, the intrusion rules are generated. We have concentrated on the simulation and analysis of classification among the Data Mining techniques and then the intrusion patterns are produced. The generated rules are represented by intrusion data and classified between abnormal and normal users. The different rules are generated separately from three models "Time Based Traffic Model", "Host Based Traffic Model", and "Content Model". The proposed system has generated the update and adaptive rules automatically and continuously on the misuse detection method which is difficult to update the rule generation. The generated rules are experimented on 430M test data and almost 94.3% of detection rate is shown.3% of detection rate is shown.

• Journal of Broadcast Engineering
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• 제18권1호
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• pp.43-58
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• 2013

Richmedia Service means that interactive media service can provide view with various multimedia elements(such as Video, Audio, Text) at same time. Various Multimedia elements can be serviced by Scene Description technology standards like BIFS(Binary Format for Scenes) and LASeR(Light Application Scene Representation). By providing Scene Component information, richmedia service is available to various multimedia services. so users is available to personalized services fitting temporal and spatial options. In conventional technology, when the scene is changed by user or service, mobile deletes the scene of configuration information and makes new scene of configuration information. this is a very inefficient way. In this paper, Propoesed that by using DOM(Document Object Model) method, to pass only the dynamic configuration part, changes scene method.

• Journal of KIISE:Databases
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• 제30권2호
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• pp.184-196
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• 2003

Mobile transactions updating spatial objects are long transactions that can update independently local objects of mobile clients during disconnection. Validation based protocols that are well known to be appropriate for mobile transactions are required to abort a conflicted transaction in order to keep data consistent. Since abortion leads to cancel of all of tile updates, it is not desirable to use the abortion for resolving conflicts of mobile transactions. In this paper, we propose a reprocessing transaction model to resolve the update conflicts between mobile transactions without aborting them. We also design a mobile transaction server to support the reprocessing transaction and build a prototype of a mobile field system. The reprocessing transaction is a subtransaction of a newly committed mobile transaction and re-executes only conflicted objects with foreign conflicted objects. We also introduce a progressive reprocessing scheme to expose non-conflicted objects of the mobile transaction to other transactions in order to reduce starvation of reprocessing transactions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• 제44권1호
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• pp.113-122
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• 2007

In this paper, we propose a multi-state based hybrid location update scheme, which integrates the time-based and the movement-based methods. In the proposed scheme, a mobile terminal updates its location after n cell boundary crossing and a time interval of T[sec]. We derive an analytical solution for the performance of the hybrid scheme with exponential cell resident time and evaluate it numerically with time-varying random walk mobility model, which we model as multi-states Markov chain. Furthermore, we also evaluate the scheme for arbitrary cell resident times by simulation. From the numerical analysis and the simulation results, we prove that the proposed scheme significantly outperforms the time-based and the movement-based methods, when implemented alone, more accurately adapting to the time-varying user mobility.

• The Journal of the Acoustical Society of Korea
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• 제41권2호
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.