• ETRI Journal
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• v.25 no.2
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• pp.140-143
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• 2003

In this paper, we present a probabilistic approach to determining whether extracted facial features from a video sequence are appropriate for creating a 3D face model. In our approach, the distance between two feature points selected from the MPEG-4 facial object is defined as a random variable for each node of a probability network. To avoid generating an unnatural or non-realistic 3D face model, automatically extracted 2D facial features from a video sequence are fed into the proposed probabilistic network before a corresponding 3D face model is built. Simulation results show that the proposed probabilistic network can be used as a quality control agent to verify the correctness of extracted facial features.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.220-227
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• 2006

In this paper, we present a novel approach to analyze the throughput of direct-sequence spread spectrum multiple access (DS/SSMA) unslotted ALOHA system. In the unslotted system, the departure rate of interfering transmissions is proportional to the number of current interferers that can be regarded as the system state. In order to model this state-dependency, we introduce a two-dimensional state transition model that describes the state transition of the system. This model provides a more rigorous analysis tool for the DS/SSMA unslotted ALOHA systems with both fixed and variable packet lengths. Numerical results reveal that this analysis yields an accurate system performance that coincides with the simulation results. Throughout the analysis we have discovered that the state-dependency of the departure rate causes interference averaging effect in the unslotted system and that this effect yields a higher throughput for the unslotted system than for the slotted system when supported by a strong channel coding.

• Steel and Composite Structures
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• v.6 no.2
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• pp.123-138
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• 2006

This paper proposes a model for the analysis of the construction sequences of steel-concrete composite decks in which the slab is cast-in-situ for segments. The model accounts for early age shrinkage, such as thermal and endogenous shrinkage, drying shrinkage, tensile creep effects and the complex sequences of loading due to pouring of the different slab segments. The evolution of the structure is caught by suitably defining the constitutive relationships of the concrete and the steel reinforcements. The numerical solution is obtained by means of a step-by-step procedure and the finite element method. The proposed model is then applied to a composite deck in order to show its potential.

• Journal of Welding and Joining
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• v.19 no.1
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• pp.104-111
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• 2001

This study presents an analysis method for heat flow and deformation of sheet metal laser welding. A heat source model for 2-dimensional heat flow analysis of laser welding process was suggested in this paper. To investigate the availability of the heat source model, the analysis results were compared and estimated with the results of previous researches. We could get a good agreement between the results of numerical analysis and experiments in the temperature distribution of weldment. Due to the characteristics of welding process, some kinds of deformations are usually generated in a welded structure. Generally, the degree of deformation is dependent on the welding sequence constraints as well as input power Therefore, in this paper we evaluate the deformation of gas pressure vessel according to the welding sequence and input power. In the analysis of weld deformation, 2-dimensional thermo-elasto-plastic analysis was performed for the gas pressure vessel by using a commercial FE program package.

• The Journal of Advanced Prosthodontics
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• v.12 no.5
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• pp.299-306
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• 2020

PURPOSE. The aim of this study was to evaluate the accuracy of six recently introduced intraoral scanners (IOSs) for single crown preparations isolated from the complete arch, and to determine the effect of scanning sequence on accuracy. MATERIALS AND METHODS. A complete arch with right and left canine preparations for single crowns was used as a study model. The reference dataset was obtained by scanning the complete arch using a highly accurate industrial scanner (ATOS Core 80, GOM GmbH). Six different IOSs (Trios, iTero, Planmeca Emerald, Cerec Omnicam, Primescan, and Virtuo Vivo) were used to scan the model ten times each. The scans performed with each IOS were divided into two groups, based on whether the scanning sequence started from the right or left quadrant (n=5). The accuracy of digital impression was evaluated using three-dimensional analyzing software (Geomagic Studio 12, 3D Systems). The Kruskal Wallis and Mann- Whitney U statistical tests for trueness analysis and the One-way ANOVA test for precision analysis were performed (α=.05). RESULTS. The trueness and precision values were the lowest with the Primescan (25 and 10 ㎛), followed by Trios (40.5 and 11 ㎛), Omnicam (41.5 ㎛ and 18 ㎛), Virtuo Vivo (52 and 37 ㎛), iTero (70 and 12 ㎛) and Emerald (73.5 and 60 ㎛). Regarding trueness, iTero showed more deviation when scanning started from the right (P=.009). CONCLUSION. The accuracy of digital impressions varied depending on the IOS and scanning sequence used. Primescan had the highest accuracy, while Emerald showed the most deviation in accuracy for single crown preparations.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.83-94
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• 1991

This study is concerned with the scheduling problem for mixed-model assembly lines in Just-In-Time(JIT) production systems. The most important goal of the scheduling for the mixed-model assembly line in JIT production systems is to keep a constant rate of usage for every part used by the systems. In this study, we develop two heuristic algorithms able to keep a constant rate of usage for every part used by the systems in the single-level and the multi-level. In the single-level, the new algorithm generates sequence schedule by backward tracking and prevents the destruction of sequence schedule which is the weakest point of Miltenburg's algorithms. The new algorithm gives better results in total variations than the Miltenburg's algorithms. In the multi-level, the new algorithm extends the concept of the single-level algorithm and shows more efficient results in total variations than Miltenburg and Sinnamon's algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.209-216
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• 1997

A feature is well known as a medium to integrate CAD, CAPP and CAM systems. For a part drawing including both simple geometry and compound geometry, a process plan such as the selection of process, machine tool, cutting tool etc. normally needs simple geometry data and non-geometry data of the feature as the input. However, a extended process plan such as the generation of process sequence, operation sequence, jig & fixture, NC program etc. necessarily needs the compound geometry data as well as the simple geometry data and non-geometry data. In this paper, we propose a feature data model according to the result of analyzing necessary data, including the compound geometry data, the simple geometry data and the non-geometry data. Also, an example of the feature data model in milling process planning is described.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.306-312
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• 2008

In a goal-oriented dialogue, speaker's intention can be approximated by a semantic structure that consists of a pair of a speech act and a concept sequence. Therefore, it is very important to correctly identify the semantic structure of an utterance for implementing an intelligent dialogue system. In this paper, we propose a model to efficiently analyze the semantic structures based on an active teaming method. To reduce the burdens of high-level linguistic analysis, the proposed model only uses morphological features and previous semantic structures as input features. To improve the precisions of semantic structure analysis, the proposed model adopts CRFs(Conditional Random Fields), which show high performances in natural language processing, as an underlying statistical model. In the experiments in a schedule arrangement domain, we found that the proposed model shows similar performances(92.4% in speech act analysis and 89.8% in concept sequence analysis) to the previous models although it uses about a third of training data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.95-104
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• 2012

Pattern recognition using ensemble classifiers is composed of strong classifier which consists of many weak classifiers. In this paper, we used feature extraction to organize strong classifier using static camera sequence. The strong classifier is made of weak classifiers which considers environmental factors. So the strong classifier overcomes environmental effect. Proposed method uses binary foreground image by frame difference method and the boosting is used to train crowdedness model and recognize crowdedness using features. Combination of weak classifiers makes strong ensemble classifier. The classifier could make use of potential features from the environment such as shadow and reflection. We tested the proposed system with road sequence and subway platform sequence which are included in "AVSS 2007" sequence. The result shows good accuracy and efficiency on complex environment.