• Journal of the Korean Home Economics Association
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• v.49 no.6
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• pp.13-22
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• 2011

This study investigated the influence of product attribute on initial trust and purchase intention of sensor-based smart clothing. Questionnaires were administered to 256 university students in Daegu, Korea. The results were as follows: First, there were 5 factor solutions in product attribute of smart clothing; comfortableness, health usefulness, management easiness, safety, and aesthetic appearance. Second, there were significant effects on initial trust in heath usefulness, management easiness and safety. Third, there were significant effects on purchase intention in health usefulness, management easiness and aesthetic appearance. Fourth, there was a significant effect on purchase intention in initial trust.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.21-31
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• 2013

A ciphertext policy-attribute based encryption(CP-ABE) scheme can be used to realize access control mechanism without a trusted server. We propose an attribute-based access control mechanism by incorporating a CP-ABE scheme to ensure only authorized users can access the sensitive data. The idea of CP-ABE is to include access control policy in the ciphertexts, in which they can only be decrypted if a user possesses attributes that pass through the ciphertext's access structure. In this paper, we prove a secure CP-ABE scheme where the policy can be expressed in non-monotonic access structures. We further compare the performance of our scheme with the existing CP-ABE schemes.

• Smart Media Journal
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• v.5 no.2
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• pp.84-89
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• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3108-3120
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• 2019

Point cloud is widely used in 3D applications due to the recent advancement of 3D data acquisition technology. Polygonal mesh-based compression has been dominant since it can replace many points sharing a surface with a set of vertices with mesh structure. Recent point cloud-based applications demand more point-based interactivity, which makes point cloud compression (PCC) becomes more attractive than 3D mesh compression. Interestingly, an exploration activity has been started to explore the feasibility of PCC standard in MPEG. In this paper, a new color attribute compression method is presented for point cloud data. The proposed method utilizes the spatial redundancy among color attribute data to construct a color palette. The color palette is constructed by using K-means clustering method and each color data in point cloud is represented by the index of its similar color in palette. To further improve the compression efficiency, the spatial redundancy between the indices of neighboring colors is also removed by marking them using a flag bit. Experimental results show that the proposed method achieves a better improvement of RD performance compared with that of the MPEG PCC reference software.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.131-138
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• 2022

In this paper, we propose an FA-RBAC (FA-RBAC) model based on flexible properties. This model is assigned attribute-role-centric, making it easy to manage objects, as efficient as access control, and as the network environment changes, it can provide flexible access control. In addition, fine-grained permissions and simple access control can be achieved while balancing the advantages and disadvantages of the RBAC and ABAC models, reducing the number of access control rules by combining static attribute-based roles and dynamic attribute-based rules, and verifying the validity and performance benefits of the proposed model through comparison analysis and simulation.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.727-732
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• 2007

Most of previous work on indexing and searching time series focused on the similarity matching and retrieval of one-attribute time series. However, multimedia databases such as music, video need to handle the similarity search in multi-attribute time series. The limitation of the current similarity models for multi-attribute sequences is that there is no consideration for attributes' sequences. The multi-attribute sequences are composed of several attributes' sequences. Since the users may want to find the similar patterns considering attributes's sequences, it is more appropriate to consider the similarity between two multi-attribute sequences in the viewpoint of attributes' sequences. In this paper, we propose the similarity search method based on attributes's sequences in multi-attribute time series databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. In addition, we give preliminary experimental results to show the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1404-1423
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• 2015

In smart grid, electricity consumption data may be handed over to a third party for various purposes. While government regulations and industry compliance prevent utility companies from improper or illegal sharing of their customers' electricity consumption data, there are some scenarios where it can be very useful. For example, it allows the consumers' data to be shared among various energy resources so the energy resources are able to analyze the data and adjust their operation to the actual power demand. However, it is crucial to protect sensitive electricity consumption data during the sharing process. In this paper, we propose a fine-grained access control scheme (FAC) with efficient attribute revocation and policy updating in smart grid. Specifically, by introducing the concept of Third-party Auditor (TPA), the proposed FAC achieves efficient attribute revocation. Also, we design an efficient policy updating algorithm by outsourcing the computational task to a cloud server. Moreover, we give security analysis and conduct experiments to demonstrate that the FAC is both secure and efficient compared with existing ABE-based approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3685-3707
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• 2021

In recent years, facial attribute editing has been successfully used to effectively change face images of various attributes based on generative adversarial networks and encoder-decoder models. However, existing models have a limitation in that they may change an unintended part in the process of changing an attribute or may generate an unnatural result. In this paper, we propose a model that improves the learning of the attention mask by adding a spatial attention mechanism based on the unified selective transfer network (referred to as STGAN) using semi-supervised learning. The proposed model can edit multiple attributes while preserving details independent of the attributes being edited. This study makes two main contributions to the literature. First, we propose an encoder-decoder model structure that learns and edits multiple facial attributes and suppresses distortion using an attention mask. Second, we define guide masks and propose a method and an objective function that use the guide masks for multiple facial attribute editing through semi-supervised learning. Through qualitative and quantitative evaluations of the experimental results, the proposed method was proven to yield improved results that preserve the image details by suppressing unintended changes than existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.76-78
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• 2014

Naive Bayes (NB, for shortly) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting.