• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.154-160
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• 2006

Instantaneous reaction and intelligence are required for autonomous mobile robots to achieve multiple goals in the unpredictable and dynamic environments. Design of the appropriate control architecture and clear definitions of systems are needed to construct and control these robots. This research proposes the hybrid deliberative/reactive control architecture which consists of three layers and uses the method of software structure design. The highest layer, Deliberative Layer makes the overall run-time schedule for navigation and/or manipulation, and the middle layer, Task Execution Layer carries out various missions. The lowest layer, Reactive Layer enables a robot to react rapidly in the dynamic environment and controls the mechanical devices concurrently. This paper proposes independent system supervisors called Manager to reuse the modules so that the Manager supports common use of the system and multi-processing tasks. It is shown that the mobile robot based on the proposed control scheme can perform the basic navigation and cope with the dynamic obstacles reasonably well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.322-325
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• 2001

This paper is focused on the development of the DFX system which can support the task that designer or project teams analyse and evaluate the feasibility of the product concept quickly, and then improve the problem of the product and process design before the manufacture in the M-T-O (make-to-order) environments. In this paper, we importantly treated three points for the effective development of the DFX system. The first, we proposed the procedure and architecture of the DFX system which fit with the M-T-O environments. The second, we designed and implemented the DFX prototype system on the WWW, in order to allow the multi-accessing and multi-processing regardless of time and position. The last, we described the benefits of the Web-based DFX system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12B
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• pp.1081-1090
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• 2003

NGI(Next Generation Internet) is characterized by QoS(Quality of Service) and high speed transmission. Recently, DiffServ and MPLS become most influential NGI architecture. To guarantee end-to-end QoS, it is essential for NGI to interwork MPLS with DiffServ. Here, MPLS WG(Working Group) in IETF(Internet Engineering Task Force) proposed the method of E-LSP(EXP inferred-PSC LSPs) and L-LSP(Label-Only-Inferred PSC LSPs), but both of them have serious problems to satisfy perfect interworking. In this paper, we proposed an extended E-LSP architecture that supports Perfect DiffServ class and experimental function in MPLS such as ECN(Explicit Congestion Notification) capability. We verify that the proposed E-LSP architecture improves QoS in NGI by using ns2 simulator.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.720-725
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• 2008

This paper proposes a system architecture for USN with a service robot to provide more active assisted living services for elderly persons by monitoring their mental and physical well-being with USN environments at home, hospital, or silver town. Sensors embedded in USN are used to detect preventive measures for chronic disease. Logged data are transferred to main controller of a service robot via wireless channel in which the analysis of data is performed. For the purpose of handling emergency situations, it needs real-time processing on gathering variety sensor data, routing algorithms for sensor nodes to a moving sink node and processing of logged data. This paper realized multi-hop sensor network to detect user movements with biometric data transmission and performed algorithms on Xenomai, a real-time embedded Linux. To leverage active sensing, a mobile robot is used of which task was implemented with a priority to process urgent data came from the sink-node. This software architecture is anticipated to integrate sensing, communication and computing with real-time manner. In order to verify the usefulness of a proposed system, the performance of data transferring and processing on a real-time OS with non real-time OS is also evaluated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.249-254
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• 2009

The today's decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge's, decision making tools and possibly expertise's pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.5
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• pp.388-399
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• 2016

A motion analysis of an underwater towed cable is a complex task due to its nonlinear nature of the problem. The major source of the nonlinearity of the underwater cable analysis is that the motion of the cable involves large rigid-body motion. This large rigid-body motion makes difficult to use standard displacement-based finite element method. In this paper, the authors apply recently developed nodal position-based finite element method which can deal with the geometric nonlinearity due to the large rigid-body motion. In order to enhance the stability of the large-scale nonlinear cable motion simulation, an efficient time-integration scheme is proposed, namely predictor/multi-corrector Newmark scheme. Three different predictors are introduced, and the best predictor in terms of stability and robustness for impulsive cable motion analysis is proposed. As a result, the nonlinear motion of underwater cable is predicted in a very efficient manner compared to the classical finite element of finite difference methods. The efficacy of the method is demonstrated with several test cases, involving static and dynamic motion of a single cable element, and also under water towed cable composed of multiple cable elements.