• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.65-74
• /
• 2021

Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1302-1309
• /
• 2021

Recently, the Korean Government announced the Korean New Deal as a national development strategy to overcome the economic recession from the pandemic crisis and lead the global action against structural changes. In the Korean New Deal, the Green New Deal related with the energy aims to achieve net-zero emissions and accelerates the transition towards a low-carbon and green economy. To this end, the government plans to promote an increased use of renewable energy in the society at large. This paper introduces a binary power generation using unused low-grade thermal energy to accelerate the transition towards a low-carbon and green economy and examines a control system based on Neural Network which is capable maintenance at low-cost by an unmanned automated operation in actual power generation environment. It is expected that the realization of binary power generation accelerates introduction of renewable energy along with solar and wind power.

• Electronics and Telecommunications Trends
• /
• v.38 no.1
• /
• pp.55-64
• /
• 2023

Recently, preparations for 6G have led to the increasing interest in integrated or hybrid communication networks considering low-orbit satellite communication networks with terrestrial mobile communication networks. In addition, the demand for frequency allocation for new mobile services from low-orbit small satellites to provide global internet of things (IoT) services is increasing. The operation of such satellites and terrestrial mobile communication networks may inevitably cause interference in adjacent bands and the same band frequency between satellites and terrestrial systems. Focusing on the results of the recent ITU-R WP4C meeting, this study introduces the current status of frequency sharing and interference issues between satellites and terrestrial systems, and frequency allocation issues for new mobile satellite operations. Coexistence and compatibility studies with terrestrial IMT in L band and 2.6 GHz band, operated by Inmassat and India, respectively, and a new frequency allocation study (WRC-23 AI 1.18) are carried out to reflect satellite IoT demand. For the L band, technical requirements have been developed for emission from IMT devices at 1,492 MHz to 1,518 MHz to bands above 1,518 MHz. Related studies in the 2 GHz and 2.6 GHz bands are not discussed due to lack of contributions at the recent meeting. In particular, concerning the WRC-23 agenda 1.18 study on the new frequency allocation method of narrowband mobile satellite work in the Region 1 candidate band 2,010 MHz to 2,025 MHz, Region 2 candidate bands 1,695 MHz to 1,710 MHz, 3,300 MHz to 3,315 MHz, and 3,385 MHz to 3,400 MHz, ITU-R results show no new frequency allocation to narrow mobile satellite services. Given the expected various collaborations between satellites and the terrestrial component are in the future, interference issues between terrestrial IMT and mobile satellite services are similarly expected to continuously increase. Therefore, participation in related studies at ITU-R WP4C and active response to protect terrestrial IMT are necessary to protect domestic radio resources and secure additional frequencies reflecting satellite service use plans.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.375-381
• /
• 2023

In an increasingly hyperconnected battlefield, the reliance on battlefield networks and AI-based autonomous weapons systems creates uncertainty and raises ethical concerns. This article explores the responsibility gap in operating autonomous weapons systems, using Robert Sparrow's theory. By analyzing Sparrow's arguments, we propose overcoming the responsibility gap in lethal autonomous weapon systems (LAWS). Our objective is to establish a framework of responsibility that aligns with the evolving battlefield, promoting the development and use of responsible weapon systems.

• Smart Media Journal
• /
• v.12 no.1
• /
• pp.24-31
• /
• 2023

Meta-learning is metacognition that instantly distinguishes between knowing and unknown. It is a learning method that adapts and solves new problems by self-learning with a small amount of data.A few-shot learning method is a type of meta-learning method that accurately predicts query data even with a very small support set. In this study, we propose a method to solve the limitations of the prototype created with the mean-point vector of each class. For this purpose, we use the few-shot learning method that created the prototype used in the few-shot learning method as the median prototype. For quantitative evaluation, a handwriting recognition dataset and mini-Imagenet dataset were used and compared with the existing method. Through the experimental results, it was confirmed that the performance was improved compared to the existing method.

• Journal of Practical Engineering Education
• /
• v.15 no.1
• /
• pp.31-38
• /
• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.877-884
• /
• 2023

In this study, when another vehicle is parked in a designated space where a personal vehicle can park and a defined personal use time, an ultrasonic object recognition sensor is used to determine vehicle entry, and a camera sensor recognizes a license plate. If the vehicle is not recognized by the individual vehicle owner, the "private parking lot operation block" of the application server receives the individual phone number based on the National Police Agency's Vehicle Number Information Inquiry Open API. Afterwards, when parking is processed, the non-right holder receives the approval of the parking right holder, parks for the recognized time, and deposits the parking fee into the public account of the city hall. Through this study, it was possible to find an operation processing method that can most effectively manage parking in the city center in a private parking space recognized by the city hall.

• Convergence Security Journal
• /
• v.23 no.5
• /
• pp.57-64
• /
• 2023

In this study, we analyzed how to steal the target model without training data. Input data is generated using the generative model, and a similar model is created by defining a loss function so that the predicted values of the target model and the similar model are close to each other. At this time, the target model has a process of learning so that the similar model is similar to it by gradient descent using the logit (logic) value of each class for the input data. The tensorflow machine learning library was used as an experimental environment, and CIFAR10 and SVHN were used as datasets. A similar model was created using the ResNet model as a target model. As a result of the experiment, it was found that the model stealing method generated a similar model with an accuracy of 86.18% for CIFAR10 and 96.02% for SVHN, producing similar predicted values to the target model. In addition, considerations on the model stealing method, military use, and limitations were also analyzed.

• Convergence Security Journal
• /
• v.24 no.3
• /
• pp.153-163
• /
• 2024

Despite the increasing hacking threats and security threats as IT technology advances and many companies adopt security solutions, cyberattacks and threats still persist for years. APT attack is a technique of selecting a specific target and continuing to attack. The threat of an APT attack uses all possible means through the electronic network to perform APT for years. Zero-day attacks, malicious code distribution, and social engineering techniques are performed, and some of them directly invade companies. These techniques have been in effect since 2000, and are similarly used in voice phishing, especially for social engineering techniques. Therefore, it is necessary to study countermeasures against APT attacks. This study analyzes the attack cases of Korean-based APT groups in Korea and suggests the correct method of using intelligence to analyze APT attack groups.

• The Journal of the Korea Contents Association
• /
• v.20 no.2
• /
• pp.240-247
• /
• 2020

There have been many efforts to understand the trends of IT environments that have been rapidly changed. In a view point of management, it needs to prepare the social systems in advance by using Big-data these days. This research is for the implementation of Issue Analysis System for the Big-data based on Artificial Intelligence. This paper aims to confirm the possibility of new technology for Big-data processing through the proposed Issue Analysis System using. We propose a technique for semantic reasoning and pattern analysis based on the AI and show the proposed method is feasible to handle the Big-data. We want to verify that the proposed method can be useful in dealing with Big-data by applying latest security issues into the system. The experiments show the potentials for the proposed method to use it as a base technology for dealing with Big-data for various purposes.