• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.796-800
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• 2006

In this paper, we developed an e-loaming contents for C programming language using Linux and open source software, not using commercial integrated development tool like Microsoft's Visual Studio. In most programming language courses, students study or practice the programming language by editing source code compiling and running the executable code by commercial software like Visual Studio which installed on each PC. This way of learning has some difficulties in total cost of purchasing software and using other PCs which donot have proper software installed. To overcome this situation and enable loaming anywhere, with any device, at anytime, we propose a way of utilizing Linux and open source software in Web-based learning environment. In this environment students can input their source code on the form of broweser and get the result instantly from the server.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.553-556
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• 2022

이상지질혈증의 발병에 대한 조기 진단 및 관리하는 것은 중요한 문제이다. 이상지질혈증의 진단은 혈액계측 정보 중에서 네 가지 LDL, HDL, TG, 그리고 TC를 이용하여 진단하며, 이상지질혈증 관리를 위해서는 LDL을 추정하는 것이 중요하다. 본 논문에서는 나이, 성별, 그리고 BMI와 같은 신체계측 정보를 학습하여 LDL-콜레스테롤을 예측하기 위한 준지도학습(Semi-supervised learning) 기반 기계학습 방법을 제안한다. 제안 방법은 얕은 학습(Shallow Learning)기반의 MLP(Multi-Layer Perceptron)을 이용하고, 이상지질혈증 진단인자간의 상관관계를 고려하여 신체계측 정보로 예측된 HDL, TG, 그리고 TC을 이용하여 일반적인 기계학습을 이용한 예측방법의 정확도를 개선한다. 즉, 제안방법은 신체계측 정보를 이용하여 혈액계측 정보의 LDL, HDL, TG, 그리고 TC을 각각 예측하고, 신체계측에 혈액계측의 예측 정보를 추가하여 학습한 준지도학습 기반 얕은 네트워크를 설계한다. 실험결과, HDL, TG, 그리고 TC의 혈액예측 정보를 이용한 준지도학습 기반 LDL 예측 정확도는 71.4%로 신체계측 정보만을 이용한 예측 방법의 67.0% 보다 약 4.4% 개선할 수 있음을 확인한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.901-910
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• 2023

In universities, programming language-based thinking and software education for non-majors are being implemented to cultivate creative and convergent talent capable of leading the digital convergence era in line with the Fourth Industrial Revolution. However, learners face difficulties in acquiring the unfamiliar syntax and programming languages. The purpose of this study is to propose a software education model to alleviate the challenges faced by non-major students during the learning process. By introducing algorithm techniques and diagram techniques based on programming language thinking and using the EZMKER kit as an instructional model, this study aims to overcome the lack of learning about programming languages and syntax. Consequently, a structured software education model has been designed and implemented as a top-down system learning model.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.466-471
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• 2024

Recently, the Machine Learning based Artificial Intelligence has introduced in aviation field. In most cases, safety assurance of aviation software is achieved by applying RTCA DO-178C or DO-278A or similar standards. These standards were developed for and are well-suited to software that has inherent deterministic properties and explainability. Considering the characteristics of AI software based on ML, it is not feasible to assure the integrity of those new aviation systems using traditional software assurance standards mentioned above. In this paper, we research the certification framework that is newly suggested by EASA to deal with the aviation system including ML AI functions, and discuss what should the Korean authority and related industries prepare to cope with this issue.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.27-37
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• 2018

In this paper, we propose a design and development method of an elevator simulator that can be used as an embedded application layer software learning tool and a teaching and learning method using it. The simulator provides students with an environment to implement the operating principle and control method of the elevator system in the application layer excluding the issues of hardware and embedded OS layer. This allows students to have a reactive and real-time embedded application development experience. In addition, we present a four-week embedded application software training course with hands-on exercises that add step-by-step functionality using a simulator. As a result of training for actual students, we obtained 83.3 points of learning achievement score and proved that the curriculum has a significant effect on embedded application learning.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.674-679
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• 2017

As the importance of the 4th industrial revolution and ICT technology increased, it became a software centered society. Existing software training was limited to the composition of the learning environment, and a lot of costs were incurred early. In order to solve these problems, a learning method using a web compiler was developed. The web compiler supports various software languages and shows compilation results to the user via the web. However, Web compilers that support artificial intelligence technology are missing. In this paper, we designed and implemented a tensor flow based web compiler, Google's artificial intelligence library. We implemented a system for learning artificial intelligence by building a meteorJS based web server, implementing tensor flow and tensor flow serving, Python Jupyter on a nodeJS based server. It is expected that it can be utilized as a tool for learning artificial intelligence in software centered society.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.171-174
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• 2003

In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.