• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.316-321
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• 2019

In this paper, by applying the constant current principle we develop an impedance measurement system which can measure the high precision impedance of various electric materials by using microprocessor. This measurement system board has an interface device for acquiring digital data from an external device including an impedance measuring device, and system software is also developed by a firmware program executed on such an embedded board. It can measure the high precision impedance of a specific band with 1/32768 precision by using 15-bit ADC(analog to digital converter) and calculate it to the five digits to the right of the decimal point(fraction part). Data is transmitted through a USB interface of a general computer and a measuring device to manage digital data. An impedance measurement system equipped with a communication function capable of a more general and easy-to-use interface than other equipment is developed and verified.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

• Archives of Plastic Surgery
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• v.48 no.1
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• pp.98-106
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• 2021

Background Hand fractures can be treated using various operative or nonoperative methods. When an operative technique utilizing fixation is performed, early postoperative mobilization has been advocated. We implemented a protocol involving controlled active exercise in the early postoperative period and analyzed the outcomes. Methods Patients who were diagnosed with proximal phalangeal or metacarpal fractures of the second to fifth digits were included (n=37). Minimally invasive open reduction and internal fixation procedures were performed. At 3 weeks postoperatively, controlled active exercise was initiated, with stress applied against the direction of axial loading. The exercise involved pain-free active traction in three positions (supination, neutral, and pronation) between 3 and 5 weeks postoperatively. Postoperative radiographs and range of motion (ROM) in the interphalangeal and metacarpophalangeal joints were analyzed. Results Significant improvements in ROM were found between 6 and 12 weeks for both proximal phalangeal and metacarpal fractures (P<0.05). At 12 weeks, 26 patients achieved a total ROM of more than 230° in the affected finger. Postoperative radiographic images demonstrated union of the affected proximal phalangeal and metacarpal bones at a 20-week postoperative follow-up. Conclusions Minimally invasive open reduction and internal fixation minimized periosteal and peritendinous dissection in hand fractures. Controlled active exercise utilizing pain-free active traction in three different positions resulted in early functional exercise with an acceptable ROM.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.27-34
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• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

• Journal of Veterinary Clinics
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• v.40 no.3
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• pp.215-220
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• 2023

A 3-month-old Korean native cattle (Hanwoo) calf with difficulty taking normal posture and an inability to rise was referred for a definite diagnosis and active treatment, including surgery. The calf had a history of an accident in which both hind limbs were trapped in a barn structure. After admission, a "rabbit leg" posture was observed, a typical sign of gastrocnemius muscle rupture, and both digits were knuckled downward like they were trying to grip the ground. This was considered to be a result of the superficial digital flexor not rupturing but only the gastrocnemius muscle rupturing. Physical examination revealed laceration of the metatarsus and firmness behind both stifle joints which were presumed to be the sites of gastrocnemius muscle rupture. Skeletal abnormalities, including fractures, were ruled out by radiography. Based on these findings, the patient was diagnosed with bilateral gastrocnemius muscle rupture, and surgery was performed to reconnect the head of the ruptured muscle. Because the rupture occurred perpendicular to the muscle direction, the locking loop technique, a method of suturing severed tendons, was used to reduce the tension. After surgery, the cast was used to prevent further injuries and promote voluntary rehabilitation. Follow-up was completed, with the calf showing normal posture and gait 112 days after surgery. This is the first case report in the Republic of Korea describing the successful diagnosis and treatment of bilateral gastrocnemius muscle rupture in a calf.

• Korean Journal of Culture and Social Issue
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• v.11 no.4
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• pp.83-95
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• 2005

The purpose of the present study was to investigate the relationship between spatial-erceptual ability and several aspects of driving-related situation awareness(in particular, recognition and prediction). Video clips of real driving were used in both recognition and prediction tasks, and the digit calculation task during driving the simulator was required as the integration task of recognition and prediction. The results showed that the subjects of higher spatial-perceptual ability performed better in recognition task, especially in terms of sensitivity measured in d'(as signal detection theory), prediction task, and digits calculation performance than those of lower spatial-perceptual ability.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.88-95
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• 2006

In this Paper. a method for improving the performance of automatic speech recognition (ASR) system for conversational speech is proposed. which mainly focuses on increasing the robustness against the rapidly speaking utterances. The proposed method doesn't require an additional speech recognition task to represent speaking rate quantitatively. Energy distribution for special bands is employed to detect the vowel regions, the number of vowels Per unit second is then computed as speaking rate. To improve the Performance for fast speech. in the pervious methods. a sequence of the feature vectors is expanded by a given scaling factor, which is computed by a ratio between the standard phoneme duration and the measured one. However, in the method proposed herein. utterances are classified by their speaking rates. and the scaling factor is determined individually for each class. In this procedure, a maximum likelihood criterion is employed. By the results from the ASR experiments devised for the 10-digits mobile phone number. it is confirmed that the overall error rate was reduced by $17.8\%$ when the proposed method is employed

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.17-28
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• 2024

As listed as one of the most important requirements for Post-Quantum Cryptography standardization process by National Institute of Standards and Technology, the resistance to various side-channel attacks is considered very critical in deploying cryptosystems in practice. In fact, cryptosystems can easily be broken by side-channel attacks, even though they are considered to be secure in the mathematical point of view. The timing attack(TA) and the simple power analysis attack(SPA) are such side-channel attack methods which can reveal sensitive information by analyzing the timing behavior or the power consumption pattern of cryptographic operations. Thus, appropriate measures against such attacks must carefully be considered in the early stage of cryptosystem's implementation process. The Montgomery multiplier is a commonly used and classical gadget in implementing big-number-based cryptosystems including RSA and ECC. And, as recently proposed as an alternative of building blocks for implementing post quantum cryptography such as lattice-based cryptography, the big-number multiplier including the Montgomery multiplier still plays a role in modern cryptography. However, in spite of its effectiveness and wide-adoption, the multiplier is known to be vulnerable to TA and SPA. And this paper proposes a new countermeasure for the Montgomery multiplier against TA and SPA. Briefly speaking, the new measure first represents a multiplication operand without 0 digits, so the resulting multiplication operation behaves in a very regular manner. Also, the new algorithm removes the extra final reduction (which is intrinsic to the modular multiplication) to make the resulting multiplier more timing-independent. Consequently, the resulting multiplier operates in constant time so that it totally removes any TA and SPA vulnerabilities. Since the proposed method can process multi bits at a time, implementers can also trade-off the performance with the resource usage to get desirable implementation characteristics.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.33-41
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• 2024

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.