• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.408-417
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• 2021

This paper describes the quality attributes of an integrated simulation software for weapon systems named Advanced distributed simulation environment(AddSIM). AddSIM is developed as a key enabler for Defense Modeling & Simulation(M&S) systems which simulate battlefields and used for battle experiments, analyses, military exercises, training, etc. AddSIM shall provide a standard simulation framework of the next Defense M&S systems. Therefore AddSIM shall satisfy not only functional but also quality requirements such as availability, modifiability, performance, testability, usability, and others. AddSIM consists of operating softwares of hierarchical components including graphical user interface, simulation engines, and support services(natural environment model, math utility, etc.), and separated weapon system models executable on the operating softwares. The relation between software architectures and their quality attributes are summarized from previous works. And the AddSIM architecture and its achievements in the aspect of quality attributes are reviewed.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.440-443
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• 2001

As inter-network traffics grows rapidly, the router systems as a network component becomes to be capable of not only wire-speed packet processing but also plentiful programmability for quality services. A network processor technology is widely used to achieve such capabilities in the high-end router. Although providing two such capabilities, the network processor can't support a deep packet processing at nominal wire-speed. Considering QoS may result in performance degradation of processing packet. In order to achieve foster processing, one chipset of network processor is occasionally not enough. Using more than one urges to consider a problem that is, for instance, an out-of-order delivery of packets. This problem can be serious in some applications such as voice over IP and video services, which assume that packets arrive in order. It is required to develop an effective packet processing mechanism leer using more than one network processors in parallel in one linecard unit of the router system. Simulation analysis is also needed for verifying the mechanism. We propose the packet processing mechanism consisting of more than two NPs in parallel. In this mechanism, we use a load-balancing algorithm that distributes the packet traffic load evenly and keeps the sequence, and then verify the algorithm with simulation analysis. As a simulation tool, we use DEVSim++, which is a DEVS formalism-based hierarchical discrete-event simulation environment developed by KAIST. In this paper, we are going to show not only applicability of the DEVS formalism to hardware modeling and simulation but also predictability of performance of the load balancer when implemented with FPGA.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.875-880
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• 2015

Traditional semiconductor process control has been performed through statistical process control techniques in a constant process-recipe conditions. However, the complexity of the interior of the etching apparatus plasma physics, quantitative modeling of process conditions due to the many difficult features constraints apply simple SISO control scheme. The introduction of the Advanced Process Control (APC) as a way to overcome the limits has been using the APC process control methodology run-to-run, wafer-to-wafer, or the yield of the semiconductor manufacturing process to the real-time process control, performance, it is possible to improve production. In addition, it is possible to establish a hierarchical structure of the process control made by the process control unit and associated algorithms and etching apparatus, the process unit, the overall process. In this study, the research focused on the methodology and monitoring improvements in performance needed to consider the process management of future developments in the semiconductor manufacturing process in accordance with the age of the APC analysis in real applications of the semiconductor manufacturing process and process fault diagnosis and control techniques in progress.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.433-441
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• 2003

We propose a new resource reservation scheme called MC-HMRSVP(Mobile clustering based-Hierarchical mobile ReSource reserVation Protcol) that is an extension of HMRSVP[3,4]. MC-HMRSVP always establishes a virtual cluster called Mobile Cluster, which includes its immediately adjacent MA(Mobile Agent) for passive reservation as well as the current MA for active reservation to which MH(Mobile Host) belongs. Our scheme also establishes the MC regardless of intra/inter region movement by GMA(Gateway Mobile Agent) function when a MH moves. To provide a general formulation on analyzing the performance in terms of reservation cost, we also model the resource reservation cost by using a simple recursive equation. Then, we show that our scheme decreases the reservation cost in comparison with the existing HMRSVP extentions.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.12
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• pp.529-538
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• 2002

Conventional method to reduce the handoff call blocking probability(PBH) in mobile communication system is to reserve a predetermined number of channels only for handoff calls. To determine the number of reserved channels, an optimization problem, which is generally computationally heavily involved, must be solved. In this Paper, we propose a call admission control (CAC) scheme that can be used to reduce the PBH without reserving channels in advance. For this, we define a new measure, gain, which depends on the state of the system upon the arrival of a new call. The proposed CAC decision rule relies on the gain computed when a new call arrives. SRN, an extended stochastic Petri nets, provides compact modeling facilities for system analysis can be calculated performance index by appropriate reward to the model. In this Paper, we develop SRN models which can perform the CAC with gain. The SRN models are 2 level hierarchical models. The upper layer models are the structure state model representing the CAC and channel allocation methods considering QoS with multimedia traffic The lower layer model Is to compute the gain under the state of the upper layer models.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.9 s.339
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• pp.29-40
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• 2005

In this paper, we propose a Cluster-based Multicast Routing (CMR) suitable for mobile ad-hoc networks. The main features that our proposed method introduces are the following: a) mobility-based clustering and group based hierarchical structure in order to effectively support stability and scalability, b) group based mesh structure and forwarding tree concepts in order to support the robustness of the mesh topologies which provides limited redundancy and the efficiency of tree forwarding simultaneously, and c) combination of proactive and reactive concepts which provide low route acquisition delay and low overhead. The performance evaluation of the proposed protocol is achieved via modeling and simulation. The corresponding results demonstrate the Proposed multicast protocol's efficiency in terms of packet delivery ratio, scalability, control overhead, end-to-end delay, as a function of mobility, multicast group size, and number of senders.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.53-71
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• 2024

Generative Artificial Intelligence (AI) has become the focus of societal attention due to its wide range of applications and profound impact. This paper constructs a comprehensive theoretical model based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), integrating variables such as Personal Innovativeness and Perceived Risk to study the key factors influencing enterprises' adoption of Generative AI. We employed Structural Equation Modeling (SEM) to verify the hypothesized paths and used the Bootstrapping method to test the mediating effect of Behavioral Intention. Additionally, we explored the moderating effect of Perceived Risk through Hierarchical Regression Analysis. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Price Value, and Personal Innovativeness have significant positive impacts on Behavioral Intention. Behavioral Intention plays a significant mediating role between these factors and Use Behavior, while Perceived Risk negatively moderates the relationship between Behavioral Intention and Use Behavior. This study provides theoretical and empirical support for how enterprises can effectively adopt Generative AI, offering important practical implications.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.