• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.25-32
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• 2005

The spread of mobile devices, PDAs and sensors has enabled the construction of ubiquitous computing environments, transforming regular physical spaces into 'Smart space' augmented with intelligence and enhanced with services. However, the deployment of this computing paradigm in real-life is disturbed by poor security, particularly, the lack of proper authentication and authorization techniques. Also, it is very important not only to find security measures but also to preserve user privacy in ubiquitous computing environments. In this Paper, we propose efficient user authentication and authorization model with anonymity for the privacy-preserving for ubiquitous computing environments. Our model is suitable for distributed environments with the computational constrained devices by using MAC-based anonymous certificate and security association token instead of using Public key encryption technique. And our Proposed Protocol is better than Kerberos system in sense of cryptographic computation processing.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.169-174
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• 2020

Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.1-15
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• 2007

Recently, semantic web technology, represented by ontology building, is being combined with web services technology, creating 'Semantic Web Services' as a new promising field in information retrieval research. Accordingly, many brokering and matchmaking agents are being developed and used in the field. However, literature review revealed that most models do not take QoS(Quality of Services) into consideration. In this study, a QoS-augmented matchmaking algorithm is developed based on service availability, response time, maximum transaction amount, reliability, accessibility and price as critical QoS items. A prototype for Intelligent Semantic Web Services System is developed using publicly available data. Performance test was conducted and reported at the end.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.345-350
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• 2018

CNN (convolution neural network) has become the most popular artificial intelligence technique and shows remarkable performance in image classification task. In this paper, we propose a CNN-based classification method for various street images as well as a method of evaluating the safety score for the street. The proposed method consists of learning four types of street images using CNN and classifying input street images using the learned CNN model followed by evaluating the safety score. During the learning process, four types of street images are collected and augmented, and then CNN learning is performed. It is shown that learned CNN model classifies input images correctly and the safety scores are evaluated quantitatively by combining the probabilities of different street types.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Smart Media Journal
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• v.10 no.2
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• pp.30-37
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• 2021

Recently, 3D contents used in various fields have been attracting people's attention due to the development of virtual reality and augmented reality technology. In order to produce 3D contents, it is necessary to model the objects as vertices. However, high-quality modeling is time-consuming and costly. In order to convert a 2D character into a 3D model, it is necessary to express it as line drawings through feature line extraction. The extraction of consistent line drawings from 2D cartoon cartoons is difficult because the styles and techniques differ depending on the designer who produces them. Therefore, it is necessary to extract the line drawings that show the geometrical characteristics well in 2D cartoon shapes of various styles. This study proposes a method of automatically extracting line drawings. The 2D Cartoon shading image and line drawings are learned by using adversarial network model, which is artificial intelligence technology and outputs 2D cartoon artwork of various styles. Experimental results show the proposed method in this research can be obtained as a result of the line drawings representing the geometric characteristics when a 2D cartoon painting as input.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.75-86
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• 2020

With the advent of the 4.0 era of logistics due to the Fourth Industrial Revolution, infrastructures have been built to receive the same services online and offline. Logistics services affected by logistics 4.0 and IT technology are rapidly changing. Logistics services are developing using technologies such as big data, artificial intelligence, blockchain, Internet of things, and augmented reality. The convergence of logistics services and various IT new technologies is accelerating, and the development of data management solution technology has led to the emergence of electronic cargo waybill to replace paper cargo waybill. The electronic waybill was developed to supplement paper waybill that lack economical and safety. However, the electronic waybill that appeared to complement the paper waybill are also in need of complementation in terms of efficiency and reliability. New research is needed to ensure that electronic cargo waybill gain the trust of users and are actively utilized. To solve this problem, electronic cargo waybill that combine blockchain technology are being developed. This study aims to improve the reliability, operational efficiency and safety of blockchain electronic cargo waybill. The purpose of this study is to analyze the blockchain-based electronic cargo waybill system and to derive evaluation indicators for system supplementation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.195-200
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• 2021

With the development of transportation and mobiles demand for tourism travel is increasing and related industries are also developing significantly. The combination of augmented reality and tourism contents one of the areas of digital media technology, is also actively being studied, and artificial intelligence is already combined with the tourism industry in various directions, enriching tourists' travel experiences. In this paper, we propose a system that scans miniature models produced by reducing tourist areas, finds the relevant tourist sites based on models learned using deep learning in advance, and provides relevant information and 3D models as AR services. Because model learning and object detection are carried out using YOLOv3 neural networks, one of various deep learning neural networks, object detection can be performed at a fast rate to provide real-time service.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.212-220
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• 2021

The objectives of this research were to identify the digital competencies required for information science specialists at Saudi universities and to examine whether there existed conspicuous differences in the standpoint of these specialists due to years of work experience with regard to the importance of these competencies. A descriptive analytical method was used to accomplish these objectives while extracting the required digital competency list and ascertaining its importance. The research sample comprised 24 experts in the field of information science from several universities in the Kingdom of Saudi Arabia. The participants in the sample were asked to complete a questionnaire prepared to acquire the pertinent data in the period between January 5, 2021 and January 20, 2021. The results reveal that the digital competencies required for information science specialists at Saudi universities encompass general features such as the ability to use computer, Internet, Web2, Web3, and smartphone applications, digital learning resource development, data processing (big data) and its sharing via the Internet, system analysis, dealing with multiple electronic indexing applications and learning management systems and its features, using electronic bibliographic control tools, artificial intelligence tools, cybersecurity system maintenance, ability to comprehend and use different programming languages, simulation, and augmented reality applications, and knowledge and skills for 3D printing. Furthermore, no statistically significant differences were observed between the mean ranks of scores of specialists with less than 10 years of practical experience and those with practical experience of 10 years or more with regard to conferring importance to digital competencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.897-906
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• 2022

In this paper, we propose a novel MAC protocol based on the harmonic transmission of ACK, called HAT-LoRa, for improving the reliability and the utilization in multiple gateway LoRa Networks. LoRa is basically vulnerable to collision due to the primitive pure ALOHA-like MAC. Whereas data frame delivery can be guaranteed by the transparent bridge of multiple receiving gateways, ACK is still transmitted by a single gateway in LoRa Network. HAT-LoRa provides the augmented reception opportunity of ACK via the simultaneous transmissions of identical ACK in multiple spreading factors. The proposed method reduces the expected transmission times of ACK double gateway environment as well as single gateway environment, by 55 and 60% in maximum, by 35% and 40% in average, in a single- and double-gateway environment, respectively. Especially, it outperforms under the environment where the distance between end device and gateways are similar to each other.