• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.291-296
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• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.609-618
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• 2014

Information system audit, by checking overall matters about constructing and managing information system, has to contribute to improvement of information system's quality and improving performance of projects. For this, an auditor has to present objective corroborative facts which back up result of audit and ways of improvement, but in reality, general(especially businessmen's) cognition is that audit is biased by way too subjective opinions. Local experience and theoretical research until now propose that tools of automating audit will be an active means of systematically collecting and proposing these objective evidences of audit. This research not only verified that in the field of audit, phenomenon of technology application can be explained and predicted by applying TAM, but it also contributed in extending theoretical base on information technology and audit by distinguishing several characteristics which appear in the process of the model's application and analysis.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.9
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• pp.1126-1134
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• 1999

최근 다양한 멀티미디어 서비스를 지원하기 위한 통신망 관리와 서비스 관리가 통합된 개방형 정보 통신망의 구조가 요구되고 있다. 이러한 구조를 기반으로, TINA-C(Tele-communications Information Networking Architecture-Consortium)에서는 분산 환경에서 분산 어플리케이션 객체들에 대한 복잡한 서비스 및 관리 인터페이스들을 간결하도록 객체그룹의 개념을 정의하고 있다. 이러한 환경 속에서 트레이딩 서비스는 통신 서비스를 제공하는 객체그룹간의 상호작용을 지원하는데 매우 중요하다.따라서, 본 논문에서는 객체그룹간의 상호접속을 지원할 수 있는 트레이딩 기능과 이들 트레이더들간의 연동을 위한 트레이더 연합 모델을 제시하고자 한다. 이를 위해 우리는 트레이더의 중개자로서 Cooperator를 설계하여 기존의 트레이더와 연동시켰다. 이러한 결과로서 우리의 새로운 트레이더 연합 모델에서 Cooperator는 객체그룹간의 상호접속에서 객체들의 접속 권한의 체크 기능과 기존의 트레이더 연합모델의 문제점인 트레이더들간의 단 방향 연결문제를 보완하여 양방향 연결 기능을 갖도록 하였다. 끝으로, 이러한 해결 과정을 보이기 위해 트레이더와 Cooperator들로 이루어진 본 연합모델에서 분산 객체그룹간의 상호접속 절차과정과 사건 추적 다이어그램을 보였다.Abstract Recently, the open networking architecture is required to support various multimedia services as integrated functions of network management and service management. Based on this architecture, TINA-C defines an object group concept for simplifying complex management and service interfaces, when distributed application is executed in distributed environments. Within the support environment the trading service is an important of the interacting object groups which provide a telecommunication service.Hence, we suggest the trader federation model for supporting interconnections between object groups and among existing traders by using the cooperator we designed, as an intermediator among traders. Our cooperator has functions for checking access rights of objects in object groups, and for providing bidirectional linkage among traders. Up to now, the existing trader federation models have a single directional linkage for interactions among traders. Finally, we showed the interface procedure and the event trace diagram of distributed object groups using our model consisted of traders and the cooperators.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.306-317
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• 2007

We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At run-time, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30~100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm [2] and observed about two times improvement.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.432-443
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• 2004

As the XML data has been widely used in the Internet, it is necessary to store and retrieve the XML data by using DBMSs. However, relational DBMSs suffer from the model difference between graph structure of the XML data and table forms in relational databases. We propose an ORDBMS-based DTD-dependent XML data management system Xing. Xing stores XML data in a DTD-dependent form in an object database. Since the object database schema has a graph structure and supports multi-valued attributes, mapping from an XML data model and queries into an object data model and OQLs is a simple problem. For rapid storing of large quantities of the XML data, we use SAX parser with customized Xing-tree which requires a small memory space compared with the DOM-tree. Xing also returns the query result in an XML document form. We have implemented the Xing system on top of UniSQL object-relational DBMS for the validity checking and performance comparison. For XML genome data from GenBank, and experimental evaluation shows that Xing can provide significant performance improvement (maximum 10 times) compared with the relational approach.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.79-84
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• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1471-1476
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• 2021

Flood damage can cause floods or tsunamis, which can result in enormous loss of life and property. In this regard, damage can be reduced by making a quick evacuation decision through flood prediction, and many studies are underway in this field to predict floods using time series data. In this paper, we propose a CNN-based time series prediction model. A CNN-based water level prediction model was implemented using the river level and precipitation, and the performance was confirmed by comparing it with the LSTM and GRU models, which are often used for time series prediction. In addition, by checking the performance difference according to the size of the input data, it was possible to find the points to be supplemented, and it was confirmed that better performance than LSTM and GRU could be obtained. Through this, it is thought that it can be utilized as an initial study for flood prediction.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Food Science of Animal Resources
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• v.42 no.2
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• pp.280-294
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• 2022

This study aimed to evaluate the effectiveness of propolis extract as a natural preservative for livestock products in term of chemical and microbiological characteristics by meta-analysis. The stages carried out in this study were identification, selection, checking suitability, and the resulting selected articles were used in the meta-analysis. The selection results obtained a total of 22 selected journal articles consisting of 9 articles for analysis of the antimicrobial activity of propolis extract and 13 articles for analysis of the chemical and mirobiological characteristics of livestock products. The articles were obtained from electronic databases, namely Science Direct and Google Scholar. The model used in this study is the random-effect model involving two groups, control and experimental. Heterogeneity and effect size values were carried out in this study using Hedge's obtained through openMEE software. Forest plot tests and data validation on publication bias was obtained using Kendall's test throught JASP 0.14.1 software. The results showed that there is a significant relationship between propolis extract with the results of the antimicrobial activity (p<0.05). In addition, the results of the application of propolis extract on the livestock products for the test microbes and the value of thiobarbituric acid reactive substances (TBARs) showed significant results (p<0.05). Conclusion based on the random-effect model on the effectiveness of antimicrobial activity of propolis extract and their apllication as a natural preservative of the chemical and microbiological characteristics of livestock products is valid by Kendall's test (p>0.05). Propolis in this case effectively used as natural preservatives in livestock products.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.186-191
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• 2019

Introduction: In aged people, stroke incidence is increased. But standardized experimental animal protocol study for the research of stroke therapy is rare. There is little report on the success rate of cerebral artery occlusion model using standardized Nylon thread length of precise thread end-size controlled. Method: In this study, the operator intended the occlusion of middle cerebral artery (MCA) using $0.18{\pm}0.02mm$ end 5-0 Nylon thread. Middle cerebral artery occlusion was induced for 60min under isoflurane anesthesia. After 60min, the operator removed the Nylon thread and reperfusion was induced for 23hrs. The mice was killed 23hrs after reperfusion and infarction area of brain was confirmed by 1.5% TTC (2,3,5-tryphenyl tetrazolium chloride) staining. Results: According to end size and insert length of Nylon thread, Middle cerebral artery occlusion (n=50), internal carotid artery occlusion (n= 14), distal middle cerebral artery occlusion (n= 36), anterior cerebral artery (n= 1) were induced. And no infarction (n= 50) was observed. Conclusion: According to weight of mice, the operator induced reversible cerebral artery occlusion model by different insert length (30.0~36.9g : 9.0mm, 37.0~40.0g : 9.5mm) of Nylon thread. Success of cerebral artery occlusion model was confirmed by checking infarction area using TTC staining. The success rate (66.9%, 101/151) of reversible cerebral artery occlusion model in the mouse and the operational conditions are shown.