• Journal of Soil and Groundwater Environment
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• v.17 no.1
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• pp.47-54
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• 2012

When quicklime is added into soil for various purposes, abrupt changes in soil chemistry may affect essential ecological functions played by indigenous bacterial communities in soil. The magnitude of influence was estimated by observing changes in abundance and diversity of soil bacteria after quicklime treatment. When several soil samples were treated up to 20% (w/w) quicklime, plate count of viable cells ranged $10^2{\sim}10^3$ CFU $g^{-1}$, showing a reduction of more than $10^4$ times from viable counts of the untreated sample. Diversity of the bacterial isolates that survived after quicklime treatment was analyzed by conducting $GTG_5$ rep-PCR fingerprinting. There were only two types of fingerprints common to both 5% and 20% quicklime samples, implying that bacteria surviving at different strength of quicklime treatment differed depending on their tolerance to quicklime-treated condition. Isolates surviving the quicklime treatments were further characterized by Gram staining and endospore staining. All isolates were found to be Gram positive bacteria, and 85.4% of them displayed endospores state. In conclusion, most bacteria surviving quicklime treatment appear to be endospores. This finding suggests that most of ecological functions of bacteria in soil are lost with quicklime treatment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.522-539
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• 2021

In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.904-919
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• 2019

Many security systems rely solely on solutions based on Artificial Intelligence, which are weak in nature. These security solutions can be easily manipulated by malicious users who can gain unlawful access. Some security systems suggest using fingerprint-based solutions, but they can be easily deceived by copying fingerprints with clay. Image-based security is undoubtedly easy to manipulate, but it is also a solution that does not require any special training on the part of the user. In this paper, we propose a multi-factor security framework that operates in a three-step process to authenticate the user. The motivation of the research lies in utilizing commonly available and inexpensive devices such as onsite CCTV cameras and smartphone camera and providing fully secure user authentication. We have used technologies such as Argon2 for hashing image features and physically unclonable identification for secure device-server communication. We also discuss the methodological workflow of the proposed multi-factor authentication framework. In addition, we present the service scenario of the proposed model. Finally, we analyze qualitatively the proposed model and compare it with state-of-the-art methods to evaluate the usability of the model in real-world applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.594-596
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• 2019

In this paper, we propose an indoor logistic tracking system that identifies the location and inventory of the logistics in the room based on fingerprints. Through this, we constructed the actual infrastructure of the logistics center and designed and implemented the logistics management system. The proposed system collects the signal strength through the location terminal and generates the signal map to locate the goods. The location terminal is composed of a UHF RFID reader and a wireless LAN card, reads the peripheral RFID signal and the signal of the wireless AP, and transmits it to the web server. This allows the user to communicate with the server through the smartphone app and get information and location of nearby items.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.1-7
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• 2020

Over the past few years, Wi-Fi signal based indoor positioning system (IPS) has been researched extensively because of its low expenses of infrastructure deployment. There are two major aspects of location-related information contained in Wi-Fi signals. One is channel state information (CSI), and one is received signal strength indicator (RSSI). Compared to the RSSI, the CSI has been widely utilized because it is able to reveal fine-grained information related to locations. However, the conventional IPS that employs a single access point (AP) does not exhibit decent performance especially in the environment of non-line-of-sight (NLOS) situations due to the reliability degeneration of signals caused by multipath fading effect. In order to address this problem, in this paper, we propose a novel method that utilizes multiple APs instead of a single AP to enhance the robustness of the IPS. In our proposed method, a hybrid neural network is applied to the CSIs collected from multiple APs. By relying more on the fingerprint constructed by the CSI collected from an AP that is less affected by the NLOS, we find that the performance of the IPS is significantly improved.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.151-155
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• 2006

In 2005, Yong and Lee proposed a buyer-seller fingerprinting protocol using symmetric and commutative encryptions. They claimed that their protocol was practical and anonymous since they used symmetric and commutative encryptions. However, an attacker can get the content embedded with one or more honest buyers' fingerprints using man-in-the-middle attack. In this letter, we point out the weakness and propose methods for improving to their protocol.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.653-662
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• 2022

Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.591-608
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• 2023

With the emergence of advanced encryption technologies such as Quantum Cryptography and Full Disk Encryption, an era of strengthening information security has begun. Users respond positively to the advancement of privacy-enhancing technology, on the other hand, investigative agencies have difficulty unveiling the actual truth as they fail to decrypt devices. In particular, unlike past ciphers, encryption methods using biometric information such as fingerprints, iris, and faces have become common and have faced technical limitations in collecting digital evidence. Accordingly, normative solutions have emerged as a major issue. The United States enacted the CLOUD Act with the legal mechanism of 'Contempt of court' and in 2016, the United Kingdom substantiated the Compelled Decryption through the Investigatory Powers Act (IPA). However, it is difficult to enforce Compelled Decryption on individuals in Korea because Korean is highly sensitive to personal information. Therefore, in this paper, we sought a method of introducing a Compelled Decryption that does not contradict the people's legal sentiment through a perception survey of 95 people on the Compelled Decryption. We tried to compare and review the Budapest Convention with major overseas laws such as the United States and the United Kingdom, and to suggest a direction of legislation acceptable to the people in ways to minimize infringement of privacy. We hope that this study will be an effective legal response plan for law enforcement agencies that can normatively overcome the technical limitations of decoding.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.81-88
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• 2009

Fingerprinting scheme is a technique which supports the copyright protection to track redistributors of digital content using cryptographic techniques. These schemes enable the original merchant to identify the original buyer of the digital data by embedding fingerprints into digital contents. Asymmetric property of fingerprinting schemes is important to keep the buyer's privacy. In this paper, we propose a symmetric encryption based fingerprinting protocol without trusted third party. Our scheme enables the reduction of computational costs for the encryption using symmetric key encryption scheme. Since a trusted third party doesn't take part in making the fingerprint of each buyer, the protocol doesn't need to control the trusted third party and it is more secure against collusion attack.