• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2336-2344
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• 2008

In this paper, muscular activity and muscle fatigue of FDI(first dorsal interosseous muscle) and thenar muscle of hand was analyzed with surface EMG signal based on four kinds of attitudes(grip, tip, key and palmar) to measure grip strength and pinch strength after hand operation and rehabilitation treatment. The normative data are needed to interpret evaluation data to assess a patient's ability to return to labor environment. The preceding researchers proposed the standard data only by studying on maximum grip strength and the maximum pinch strength followed by each attitude of subjects' hands. But in this study, the muscle activity and muscle fatigue were considered under the various attitude to propose normative data. As a results, the muscle fatigue may be used only for presentation of normative data in labor environment.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.63-69
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• 2001

In this paper, we present an interactive 3D character modeling and animation including a data management tool for editing the animation. It includes an animation editor for changing animation sequences according to the modified structure of 3D object in the object structure editor. The animation tool has the feature that it can produce motion data independently of any modeling tool including our modeling tool. Differently from conventional 3D graphics tools that model objects based on geometrically calculated data, our tool models 3D geometric and animation data by approximating to the real object using 2D image interactively. There are some applications that do not need precise representation, but an easier way to obtain an approximated model looking similar to the real object. Our tool is appropriate for such applications. This paper has focused on the data management for enhancing the automatin and convenience when editing a motion or when mapping a motion to the other character.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2198-2222
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• 2019

The cloud storage service becomes a rising trend based on the cloud computing, which promotes the remote data integrity auditing a hot topic. Some research can audit the integrity and correctness of user data and solve the problem of user privacy leakage. However, these schemes cannot use fewer data blocks to achieve better auditing results. In this paper, we figure out that the random sampling used in most auditing schemes is not well apply to the problem of cloud service provider (CSP) deleting the data that users rarely use, and we adopt the probability proportionate to size sampling (PPS) to handle such situation. A new scheme named improving audit efficiency of remote data for cloud storage is designed. The proposed scheme supports the public auditing with fewer data blocks and constrains the server's malicious behavior to extend the auditing cycle. Compared with the relevant schemes, the experimental results show that the proposed scheme is more effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.619-634
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• 2019

In the field of traceability systems, researchers focus on applications in the agricultural food traceability and scanning commodities. The purposes of this paper, however, is to propose an efficient and reliable traceability system that can be applied to all kinds of commodities. Currently, most traceability systems store data in a central server, which is unreliable when the system is under attack or if the administrator tampers with the data for personal interests. Therefore, it is necessary to design a system that can eliminate these threats. In this paper, we propose a decentralized and non-reversible traceability system for storing commodity data. This system depends on blockchain technology, which organizes data in the form of chains without a central server. This chain-style storage mechanism can prevent malicious modifications. In addition, some strategies are adopted to reduce the storage pressure and response time when the system has stored all kinds of commodity data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1523-1545
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• 2019

Cloud computing is now a widespread and economical option when data owners need to outsource or share their data. Designing secure and efficient data access control mechanism is one of the most challenging issues in cloud storage service. Anonymous broadcast encryption is a promising solution for its advantages in the respects of computation cost and communication overload. We bring forward an efficient anonymous identity-based broadcast encryption construction combined its application to the data access control mechanism in cloud storage service. The lengths for public parameters, user private key and ciphertext in the proposed scheme are all constant. Compared with the existing schemes, in terms of encrypting and decrypting computation cost, the construction of our scheme is more efficient. Furthermore, the proposed scheme is proved to achieve adaptive security against chosen-ciphertext attack adversaries in the standard model. Therefore, the proposed scheme is feasible for the system of data access control in cloud storage service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4624-4644
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• 2019

With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important. Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques. However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data. They cannot use the data information fully. In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem. First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly. Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation. The experiments demonstrate that our framework has an excellent performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3348-3364
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• 2021

Magic matrix-based data hiding schemes are applied to transmit secret information through open communication channels safely. With the development of various magic matrices, some higher dimensional magic matrices are proposed for improving the security level. However, with the limitation of computing resource and the requirement of real time processing, these higher dimensional magic matrix-based methods are not advantageous. Hence, a kind of data hiding scheme based on a single or a group of multi-dimensional flexible magic matrices is proposed in this paper, whose magic matrix can be expanded to higher dimensional ones with less computing resource. Furthermore, an adaptive mechanism is proposed to reduce the embedding distortion. Adapting to the secret data, the magic matrix with least distortion is chosen to embed the data and a marker bit is exploited to record the choice. Experimental results confirm that the proposed scheme hides data with high security and a better visual quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3406-3418
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• 2022

Reversible data hiding is an algorithm that has ability to extract the secret data and to restore the marked image to its original version after data extracting. However, some previous schemes offered the low image quality of marked images. To solve this shortcoming, a new reversible data hiding scheme based on pixel value ordering and edge detection mechanism is proposed. In our proposed scheme, the edge image is constructed to divide all pixels into the smooth regions and rough regions. Then, the pixels in the smooth regions are separated into non overlapping blocks. Then, by taking advantages of the high correlation of current pixels and their adjacent pixels in the smooth regions, PVO algorithm is applied for embedding secret data to maintain the minimum distortion. The experimental results showed that our proposed scheme obtained the larger embedding capacity. Moreover, the greater image quality of marked images are achieved by the proposed scheme than that other previous schemes while the high EC is embedded.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.938-957
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• 2023

The limited computing power of sensor nodes in wireless sensor networks (WSNs) and data tampering during wireless transmission are two important issues. In this paper, we propose a scheme for independent individual authentication of WSNs data based on digital watermarking technology. Digital watermarking suits well for WSNs, owing to its lower computational cost. The proposed scheme uses independent individual to generate a digital watermark and embeds the watermark in current data item. Moreover, a sink node extracts the watermark in single data and compares it with the generated watermark, thereby achieving integrity verification of data. Inherently, individual validation differs from the grouping-level validation, and avoids the lack of grouping robustness. The improved performance of individual integrity verification based on proposed scheme is validated through experimental analysis. Lastly, compared to other state-of-the-art schemes, our proposed scheme significantly reduces the false negative rate by an average of 5%, the false positive rate by an average of 80% of data verification, and increases the correct verification rate by 50% on average.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.