• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.1
• /
• pp.55-60
• /
• 2020

In this paper, we propose an access control system using cryptography as a method to protect personal data in public blockchain. The proposed system is designed to encrypt data according to the access policy, store it in the blockchain, and decrypt only the person who satisfy the access policy. In order to improve performance and scalability, an encryption mechanism is implemented outside the blockchain. Therefore, data access performance could be preserved while cryptographic operations executed Furthermore it can also improve the scalability by adding new access control modules while preserving the current configuration of blockchain network. The encryption scheme is based on the attribute-based encryption (ABE). However, unlike the traditional ABE, the "retention period", is incorporated into the access structure to ensure the right to be forgotten. In addition, symmetric key cryptograpic algorithms are used for the performance of ABE. We implemented the proposed system in a public blockchain and conducted the performance evaluation.

• Journal of Global Scholars of Marketing Science
• /
• v.13
• /
• pp.89-111
• /
• 2004

The authors address the effectiveness of the composite brand extension In the context of international brand alliance. In composite brand extension, four combinations of 4 domestic brands and one internationally well-known brand as header and modifier brands are used as the brand names for four experimental products. The results of analyses reveal that (1) degrees of brand attribute transferability between header and composite brands, and (2) the impact of header and modifier brands on a composite brand appear to be decreasing when the distance of product categories between header and modifier brands are farther. In addition, the authors demonstrate that the fitness between constituent brands and composite brands tends to have more influences on consumers' evaluation of a composite brand than does the complementarity when the distance of product categories between header and modifier brands are farther. Some implications and future research directions are also discussed.

• Journal of Korea Multimedia Society
• /
• v.9 no.9
• /
• pp.1231-1246
• /
• 2006

HTML is a format for rendering Web documents visually and uses tables to present a relational information. Since HTML has limits in terms of information processing and management by a computer, it is important to transform HTML tables into XML documents, which is able to represent logical structure information. As a prerequisite for extracting information from the Web, this paper presents an efficient method for extracting logical structures from HTML tables and transforming them into XML documents. The proposed method consists of two phases: Area segmentation and structure analysis. The area segmentation step removes noisy areas and extracts attribute and value areas through visual and semantic coherency checkup. The hierarchical structure between attribute and value areas are analyzed and transformed into XML representations using a proposed table model. Experimental results with 1,180 HTML tables show that the proposed method performs better than the conventional method, resulting in an average precision of 86.7%.

• The KIPS Transactions:PartD
• /
• v.11D no.3
• /
• pp.731-740
• /
• 2004

In this paper we propose a fast negotiation agent system that guarantees the reciprocity of the attendants in a bilateral negotiation on the e-commerce. The proposednegotiation agent system exploits the incremental learning method based on an artificial neural network in generating a counter-offer and is trained by the previous offer that has been rejected by the other party. During a negotiation, the software agents on behalf of a buyer and a seller negotiate each other by considering the multi-attributes of a product. The experimental results show that the proposed negotiation system achieves better agreements than other negotiation agent systems that are operated under the realistic and practical environment. Furthermore, the proposed system carries out negotiations about twenty times faster than the previous negotiation systems on the average.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.235-242
• /
• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3836-3854
• /
• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.2
• /
• pp.55-63
• /
• 2014

Fractures and wrinkles are two major defects frequently found in the sheet metal forming process. The process has several noise factors that cannot be ignored when determining the optimal process conditions. Therefore, without any countermeasures against noise, attempts to reduce defects through optimal design methods have often led to failure. In this study, a new and robust design methodology that can reduce the possibility of formation of fractures and wrinkles is presented using decision-making theory. A two-attribute value function is presented to form the design metric for the sheet metal forming process. A modified complex method is adopted to isolate the optimal robust design variables. One of the major limitations of the traditional robust design methodology, which is based on an orthogonal array experiment, is that the values of the optimal design variables have to coincide with one of the experimental levels. As this restriction is eliminated in the complex method, a better solution can be expected. The procedure of the proposed method is illustrated through a robust design of the sheet metal forming process of a side member of an automobile body.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.3076-3092
• /
• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Korean Journal of Geomatics
• /
• v.1 no.1
• /
• pp.43-49
• /
• 2001

Although many kinds of stereo matching method have been developed in the field of computer vision and photogrammetry, wrong matches are not easy to avoid. This paper presents a new method to reduce wrong matches after matching, and experimental results are reported. The main idea is to analyze the histogram of the image attribute differences between each pair of image patches matched. Typical image attributes of image patch are the mean and the standard deviation of gray value for each image patch, but there could be other kinds of image attributes. Another idea is to check relative position among potential matches. This paper proposes to use Gaussian blunder filter to detect the suspicious pair of candidate match in relative position among neighboring candidate matches. If the suspicious candidate matches in image attribute difference or relative position are suppressed, then many wrong matches are removed, but minimizing the suppression of good matches. The proposed method is easy to implement, and also has potential to be applied as post processing after image matching for many kinds of matching methods such as area based matching, feature matching, relaxation matching, dynamic programming, and multi-channel image matching. Results show that the proposed method produces fewer wrong matches than before.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.7
• /
• pp.881-886
• /
• 2007

Fuzzy inference has the advantage which can process the ambiguous knowledge. However the associated attributes of fuzzy rules are difficult to determine useful and important rules because the redundant attribute of rules is more than enough. In this paper, we propose a method to minimize the number of rules and preserve the accuracy of inference results by using fuzzy relative cardinality after removing unnecessary attributes from rough set. From the experimental results, we can see the fact that the proposed method provides better results (e.g the number of rules) than those of general rough set with the redundant attributes.