• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.161-167
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• 2012

Portable devices such as smartphones with built-in wireless LAN to the prevalence of anyone using. But the wireless Internet connection and positioning services are limited to high-quality wireless service, they may not be available. Thus, wireless LAN infrared sensor in the same way as with angry alternative way wireless capabilities of the application automatically identify the location of the Sensor application as an alternative method is proposed. Thus, wireless LAN, such as infrared sensors and other alternzative methods of wireless features in a way where the application can recognize and automatically recognize the sensor application as an alternative method is proposed. Sensor is signals between wireless LAN and access points using the sensitivity, WLAN antenna with omni-directional signal output operation of the sensor is assumed to be recognize this by putting a direction to obtain through the proposed algorithm, Sensors such as photo-coupler without direct recognition sensor, wireless LAN and access points, the same function as the connection between the sensitivity to perform its function was to utilizing.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.27-33
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• 2011

In this paper, we propose an efficient on-line handwritten digit recognition base on Convex-Concave curves feature which is extracted by a chain code sequence using Smith-Waterman alignment algorithm. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Convex-Concave curves feature extraction. This feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a Smith-Waterman alignment algorithm, which in turn classifies it as one of the nine digits. In comparison with backpropagation neural network, Smith-Waterman alignment has the more outstanding performance.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.9-16
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• 2019

The goal of this study is to propose an efficient model for recognizing and classifying tree images to measure the accuracy that can be applied to smart devices during class. From the 2009 revised textbook to the 2015 revised textbook, the learning objective to the fourth-grade science textbook of elementary schools was added to the plant recognition utilizing smart devices. In this study, we compared the recognition rates of trees before and after retraining using a pre-trained inception V3 model, which is the support of the Google Inception V3. In terms of tree recognition, it can distinguish several features, including shapes, bark, leaves, flowers, and fruits that may lead to the recognition rate. Furthermore, if all the leaves of trees may fall during winter, it may challenge to identify the type of tree, as only the bark of the tree will remain some leaves. Therefore, the effective tree classification model is presented through the combination of the images by tree type and the method of combining the model for the accuracy of each tree type. I hope that this model will apply to smart devices used in educational settings.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.25-32
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• 2013

Due to increasing competition of globalization and fast technological improvements the appropriate method for evaluating and selecting IS-personnel is one of the key factors for an organization's success. Personnel selection is a multi-criteria decision-making (MCDM) problem which consists of both qualitative and quantitative metrics. Although many articles have discussed various knowledge and skills IS personnel should possess, no specific model for IS personnel selection and evaluation, to our knowledge, has been published up to now. After reviewing the IS personnel's important characteristics, we propose an approach for categorizing the IS personnel based on their skills, ability, and knowledge during evaluation and selection process. Our proposed approach is derived from a model of neural network algorithm. We have adapted and implemented the fuzzy ART algorithm with Jaccard choice function. The result of an illustrative numerical example is proposed to demonstrate the easiness and effectiveness of our approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.924-942
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• 2020

The facial expression is diverse and various among persons due to the impact of the psychology factor. Whilst the facial action is comparatively steady because of the fixedness of the anatomic structure. Therefore, to improve performance of the action unit recognition will facilitate the facial expression recognition and provide profound basis for the mental state analysis, etc. However, it still a challenge job and recognition accuracy rate is limited, because the muscle movements around the face are tiny and the facial actions are not obvious accordingly. Taking account of the moving of muscles impact each other when person express their emotion, we propose to make full use of co-occurrence relationship among action units (AUs) in this paper. Considering the dynamic characteristic of AUs as well, we adopt the 3D Convolutional Neural Network(3DCNN) as base framework and proposed to recognize multiple action units around brows, nose and mouth specially contributing in the emotion expression with putting their co-occurrence relationships as constrain. The experiments have been conducted on a typical public dataset CASME and its variant CASME2 dataset. The experiment results show that our proposed AU co-occurrence constraint 3DCNN based AU recognition approach outperforms current approaches and demonstrate the effectiveness of taking use of AUs relationship in AU recognition.