• Journal of Communications and Networks
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• v.1 no.3
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• pp.201-212
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• 1999

This paper studies the rapid design and implementation of the medium access control(MAC) and related interface circuits for 802.11 wireless LANs based on the field programmed gate ar-ray(FPGA) technology. Our design is thus aimed to support both the distributed coordination function (DCF) and the point coordination function(PCF) with the aid of FPGA technology. Further-more, in an infrastructure network, some stations may serve as the access points (APs) which may function like a learning bridge. This paper will also discuss how to design for such application. The hardware of the MAC and interface may at least consist of three major parts: wireless transmission and reception processes and in-terface, host(bus) interface, and the interface to the distributed system (optional). Through the increasing popularity of FPGA de-sign, this paper presents how Complex Programmable Logic De-vices(CPLD) can be utilized for speedy design of prototypes. It also demonstrates that there is much room for low-cost hardware prototype design to accelerate the processing speed of the MAC control function and for field testing.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.814-819
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• 2004

Soft engineering, based on symbiotic coexistence of human, machines and environment, is a new engineering field to explore the proper technology and the proper way of engineering. To explore soft engineering intents easily, various robot projects at Pusan National University conducted are presented. Thought experiment, interactive e-leaning, rapid prototyping engineering, biomimciry, tangibility, and ubiquity are concepts to be explored. Thought experiments projects are organized and performed, which include robot assembly game, Turing test, and robotics in science fiction. "Junk robot project" and "ubiquitous Pusan National University (u-PNU) project" have been organized. Also, bug robot project, interactive robot project, and interactive emotional robot projects are introduced. Weekly science fiction films are shown and discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.255-265
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• 2022

Educational programs for students with intellectual disabilities have undergone drastic changes in pursuit of the general curriculum. Accordingly, teachers in various fields, including mathematics, strive to find effective methods that enhance learning. The objective of this systematic review is to examine the field of teaching mathematics to students with intellectual disabilities to investigate relevant effective teaching strategies and required teaching skills. To achieve this goal, studies published during the period 2018-2021 were reviewed. Findings indicate the inclusion of nine studies that met the inclusion criteria out of 55 studies. The included studies found that the system of least prompts (SLP) in conjunction with feedback and error correction, and schema-based instruction are generally the most effective strategies in teaching mathematical skills to students with intellectual disabilities. Addition is the most targeted skill, followed by subtraction and algebra problem solving. The least targeted skills are multiplication, recognition of geometric shapes, calculating price after discount, rapid recognition of numbers, and rapid problem solving. The paper provides recommendations and suggests venues of future research.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.859-872
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• 2004

With the rapid advance of computer and communication techonology, the recent trend of education environment is edveloping in the ubiquitous learning (u-learning) direction that learners select and organize the contents, time and order of learning by themselves. Since the amount of education information through the internet is increasing rapidly and it is managed in document in an effective way is necessary. The document clustering is integrated documents to subject by classifying a set of documents through their similarity among them. Accordingly, the document clustering can be used in exploring and searching a document and it can increased accuracy of search. This paper proposes an efficient incremental clustering method for a set of documents increase gradually. The incremental document clustering algorithm assigns a set of new documents to the legacy clusters which have been identified in advance. In addition, to improve the correctness of the clustering, removing the stop words can be proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2101-2121
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• 2020

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos' repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users' interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users' interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.7-18
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• 2013

These days, the teaching and learning system has been increasing for the rapid advancement of the information technologies. We can access education systems of good quality anytime, anywhere and we can use the individually personalized teaching and learning system depending on developing the wireless communication technology and the multimedia processing technology. The more the various systems develop, the more software security systems become important. There are a lot kind of fault analysis methods to evaluate software security systems. However, the only assessment method to evaluate software security system is not enough to analysis properly on account of the various types and characteristic of software systems by progressing information technology. Therefore, this paper proposes an integrative method of Fault Tree Analysis (FTA) and Fault Modes and Effect Analysis(FMEA) to evaluate the security of e-teaching and learning system as an illustration.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.237-242
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• 2021

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.