• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.268-275
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• 2020

A lightweight hardware design of self-timed ring based true random number generator (TRNG) suitable for information security applications is described. To reduce hardware complexity of TRNG, an entropy extractor with feedback structure was proposed, which minimizes the number of ring stages. The number of ring stages of the FSTR-TRNG was determined to be a multiple of eleven, taking into account operating clock frequency and entropy extraction circuit, and the ratio of tokens to bubbles was determined to operate in evenly-spaced mode. The hardware operation of FSTR-TRNG was verified by FPGA implementation. A set of statistical randomness tests defined by NIST 800-22 were performed by extracting 20 million bits of binary sequences generated by FSTR-TRNG, and all of the fifteen test items were found to meet the criteria. The FSTR-TRNG occupied 46 slices of Spartan-6 FPGA device, and it was implemented with about 2,500 gate equivalents (GEs) when synthesized in 180 nm CMOS standard cell library.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.81-93
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• 2006

A file system is an evidence resource of cyber crime in computer forensics. Therefore the methods of recovering the file system and searching important information have been offered. However, the methods for finding a malicious fie in free blocks or slack spaces have not been suggested. In this paper, we propose an investigation method to find a maliciously executable fragmented file. After estimating if a file is executable with a machine code rate, we conclude it could be malicious by comparing a similarity of instruction sequences. To examine instruction sequences, we also propose a method of profiling malicious files using file and a method of comparing the continued scores. As the results, we could exactly pick out the malicious execution files, such as buffer overflow attack program, at fitting threshold level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1609-1617
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• 2019

Described in this paper is a design of hardware accelerator for implementing public-key cryptographic protocols (PKCPs) based on Elliptic Curve Cryptography (ECC) and RSA. It supports five elliptic curves (ECs) over GF(p) and three key lengths of RSA that are defined by NIST standard. It was designed to support four point operations over ECs and six modular arithmetic operations, making it suitable for hardware implementation of ECC- and RSA-based PKCPs. In order to achieve small-area implementation, a finite field arithmetic circuit was designed with 32-bit data-path, and it adopted word-based Montgomery multiplication algorithm, the Jacobian coordinate system for EC point operations, and the Fermat's little theorem for modular multiplicative inverse. The hardware operation was verified with FPGA device by implementing EC-DH key exchange protocol and RSA operations. It occupied 20,800 gate equivalents and 28 kbits of RAM at 50 MHz clock frequency with 180-nm CMOS cell library, and 1,503 slices and 2 BRAMs in Virtex-5 FPGA device.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.979-987
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• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.335-351
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• 2021

Purpose: An underlying factor for the failure of several clinical trials of anti-epidermal growth factor receptor (EGFR) therapies is the lack of an effective method to identify patients who overexpress EGFR protein. The quantitative dot blot method (QDB) was used to measure EGFR protein levels objectively, absolutely, and quantitatively. Its feasibility was evaluated for the prognosis of overall survival (OS) of patients with gastric cancer. Materials and Methods: Slices of 2×5 ㎛ from formalin-fixed paraffin-embedded gastric cancer specimens were used to extract total tissue lysates for QDB measurement. Absolutely quantitated EGFR protein levels were used for the Kaplan-Meier OS analysis. Results: EGFR protein levels ranged from 0 to 772.6 pmol/g (n=246) for all gastric cancer patients. A poor correlation was observed between quantitated EGFR levels and immunohistochemistry scores with ρ=0.024 and P=0.717 in Spearman's correlation analysis. EGFR was identified as an independent negative prognostic biomarker for gastric cancer patients only through absolute quantitation, with a hazard ratio of 1.92 (95% confidence interval, 1.05-3.53; P=0.034) in multivariate Cox regression OS analysis. A cutoff of 208 pmol/g was proposed to stratify patients with a 3-year survival probability of 44% for patients with EGFR levels above the cutoff versus 68% for those below the cutoff based on Kaplan-Meier OS analysis (log rank test, P=0.002). Conclusions: A QDB-based assay was developed for gastric cancer specimens to measure EGFR protein levels absolutely, quantitatively, and objectively. This assay should facilitate clinical trials aimed at evaluation of anti-EGFR therapies retrospectively and prospectively for gastric cancer.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2564-2571
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• 2022

SiPMs are generally coupled into whole columns in gamma energy spectrum measurement, but the relationship between the distribution of whole SiPM columns and the energy resolution of the measured energy spectra is rarely reported. In this work, ∅ 3 × 3 inch NaI scintillator is placed on an 8 × 8 SiPM array, and the energy resolution of the ¹³⁷Cs peak at 662 keV corresponding to the γ-ray is selected as a reference. Each SiPM is switched to explore the influence of the number of SiPM arrays, distribution position, and reflective layer on the energy resolution of SiPMs. Results show that without coupling, the energy resolution is greatly improved when the number of SiPMs ranges from 4 to 32. However, after 32 slices (the area covered by SiPMs relative to the scintillator reaches 25.9%), the improvement in energy resolution and total pulse count is not obvious. In addition, the position of SiPMs relative to the scintillator does not exert much impact on the energy resolution. Results also indicate that by adding a reflective film (ESR), the energy resolution of the tested group increases by 10.38% on average. This work can provide a reference for the design and application of miniaturized SiPM gamma spectrometers.

• Journal of Periodontal and Implant Science
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• v.52 no.1
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• pp.54-64
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• 2022

Purpose: This in vitro study was conducted to evaluate the effects of different debridement techniques and conditioning procedures on root surface morphology and blood clot stabilization. Methods: Two debridement techniques (curette [CU] vs. high-speed ultrasound [US]) and 2 conditioning procedures (ethylenediaminetetraacetic acid [EDTA] and phosphoric acid [PA]) were used for the study. Seven experimental groups were tested on root surfaces: 1) no treatment (C); 2) CU; 3) US; 4) CU+EDTA; 5) US+EDTA; 6) CU+PA; and 7) US+PA. Three specimens per group were observed under scanning electron microscopy (SEM) for surface characterization. Additional root slices received a blood drop, and clot formation was graded according to the blood element adhesion index by a single operator. Data were statistically analyzed, using a threshold of P<0.05 for statistical significance. Results: The C group displayed the most irregular surface among the tested groups with the complete absence of blood traces. The highest frequency of blood component adhesion was shown in the CU+EDTA group (P<0.05), while no differences were detected between the CU, US+EDTA, and CU+PA groups (P<0.05), which performed better than the US and US+PA groups (P<0.05). Conclusions: In this SEM analysis, EDTA and conventional manual scaling were the most efficient procedures for enhancing smear layer removal, collagen fiber exposure, and clot stabilization on the root surface. This technique is imperative in periodontal healing and regenerative procedures.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2003.11a
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• pp.62-62
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• 2003

It is now convincing that free radical generation is involved in the pathophy siological mechanisms of ischemic stroke, particularly in ischemia-reperfusion injury. The present study, therefore, examined neuroprotective effect of aloesin isolated from Aloe vera, which was known to have antioxidative activity, in a rat model of transient focal cerebral ischemia. Transient focal cerebral ischemia was induced by occlusion of middle cerebral artery for 2 hr with a silicone-coated 4-0 nylon monofilament in male Sprague-Dawley rats under isoflurane anesthesia Aloesin (1, 3, 10, 30 and 50 mg/kg/injection) was administered intravenously 3 times at 0.5, 2 and 4 hr after onset of ischemia. Neurological score was measured 24 hr after onset of ischemia immediately before sacrifice. Seven serial coronal slices of the brain were stained with 2,3,5-triphenyltetrazolium chloride and infarct size was measured using a computerized image analyzer. Treatment with the close of 1 or 50 mg/kg did not significantly reduce infarct volume compared with the saline vehicle-treated control group. However, treatments with the closes of 3 and 10 mg/kg significantly reduced both infarct volume and edema by approximately 47% compared with the control group, producing remarkable behavioral recovery effect. Treatment with the close of 30 mg/kg also significantly reduced infarct volume to a lesser extent by approximately 33% compared with the control group, but produced similar degree of behavioral recovery effect. In addition, general pharmacological studies showed that aloesin was a quite safe compound. The results suggest that aloesin can serve as a lead chemical for the development of neuroprotective agents by providing neuroprotection against focal ischemic neuronal injury.

• Journal of the Korean Geotechnical Society
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• v.22 no.11
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• pp.101-111
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• 2006

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of them are connected to the variability of soil properties involved in the analysis. In this paper, a numerical procedure of probabilistic analysis of slope stability is presented based on Spencer's method of slices. The deterministic analysis is extended to a probabilistic approach that accounts fur the uncertainties and spatial variation of the soil parameters. The procedure is based on the first-order reliability method to compute the Hasofer-Lind reliability index and Monte-Carlo Simulation. A probabilistic stability assessment was performed to obtain the variation of failure probability with the variation of soil parameters in homogeneous and layered slopes as an example. The examples give insight into the application of uncertainty treatment to the slope stability and show the impact of the spatial variability of soil properties on the outcome of a probabilistic assessment.