• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.86-91
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• 2019

Block chains began by recognizing the danger of centralized control of financial systems after the 2008 financial crisis. From the beginning of 2008, blockchain has been developing a lot of attention and technology, starting with a tremendous innovation called cryptographic destruction based on technology, a block chain called Bitcoin. However, the vulnerability has also been discovered over time and the damage has been repeated. But in the fourth industry of the 21st century, many advances and technologies based on them are expected to emerge. Therefore, this paper proposes new analytical transaction protocols for vulnerabilities in cryptographic currency transactions. The proposed transaction protocol is a way to authenticate once again at the end of the transaction and the transaction is concluded with this authentication. It presents countermeasures by adding security elements between users to the transaction process rather than internal security to transaction vulnerabilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.45-50
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• 2021

In this paper, we propose the development of a smart space security device that can be opened and closed remotely using IoT. Existing space security devices can control opening and closing by breaking hardware or only using button devices or replicated keys. The recent COVID-19 crisis has created several applications for non-contact devices. In this study, we propose the development of a small space security device that has the function of unlocking through an app without touching the device. By transferring the control authority to a smartphone, device that cannot be opened or closed by only operating hardware at the user's option. It is convenient and hygienic because it can be opened and closed using an app without touching the locking device. Multiple security is possible because security can be released using an app after user authentication by fingerprint recognition and pattern input on a smartphone. If the user wishes, after using the app security, the security is released by directly touching a button installed in the safe or space or opening it with a key. In addition, by adding an inactive function to the app, it is designed so that the door of the safe cannot be opened when the key is lost or the small safe is lost. This study is expected to be able to effectively expand the security system by applying variously to objects that require security.