• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.191-199
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• 2023

The purpose of this study is to examine the impact of ChatGpt's quality on users' satisfaction and intention to continuous use it. For this purpose, a survey was conducted targeting college students in the Busan and Gyeongnam regions, and responses from a total of 155 people were verified using the SPSS 28.0 program. As a result of the study, reliability and stability among ChatGPT quality factors were found to have a positive effect on satisfaction with use and intention to continuous use. Satisfaction with the use of ChatGPT was found to have a positive effect on intention to continuous use.. Satisfaction with use was found to have a positive mediating effect between the reliability and stability of ChatGPT quality and intention to continous use it. As a result of this study, we aim to contribute to suggesting educational and policy directions necessary to promote the use of ChatGPT by presenting factors that affect users' intention to continuous use ChatGPT among the qualities of ChatGPT.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.447-452
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• 2024

As the remarkable achievements of generative artificial intelligence technology become increasingly visible, the issue of 'controllability' in artificial intelligence is emerging as a prominent global keyword. This comes at a time when existential threats, such as the possibility of machines dominating humans, are being raised. Accordingly, this study aims to establish the groundwork for shaping a social public sphere by closely examining the concept of control, the current status, and the global landscape of artificial intelligence. It seeks to address the innovative changes anticipated in future society, with artificial intelligence technology at its core. The study aims to derive implications for preparing countermeasures against social problems and unpredictable variables that may arise from the evolution of artificial intelligence technology. It also aims to present guidelines and strategic insights for the establishment of government regulations. Furthermore, the study seeks to uncover implications for the formation of social public discourse.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.123-131
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• 2024

The dispute between SK and Netflix, which has been going on for more than three years, ended in the direction of dropping the lawsuit and establishing a cooperative relationship between the two companies. However, as Internet traffic usage is likely to increase further in the future due to digital transformation and activation of generated AI, conflicts between domestic mobile carriers and global CP operators over network usage fees can arise at any time. In this study, the issues of the dispute related to network usage fees that occurred between SK and Netflix were examined, and different implications were drawn for each issue. The cost and scope of network usage considerations are an issue that must be determined entirely by negotiations between operators. However, if a dispute occurs between operators, user damage such as speed delays may occur, so it is necessary to prepare a policy alternative. As the domestic media industry has grown cooperatively with global CPs, it is considered important to form a reciprocal relationship between domestic mobile telecommunication operators and global CP operators regarding network usage fees in the future.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.981-986
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• 2020

The Internet of Things refers to a space-of-things connection network configured to allow things with built-in sensors and communication functions to interact with people and other things, regardless of the restriction of place or time.IoT is a network developed for the purpose of services for human convenience, but the scope of its use is expanding across industries such as power transmission, energy management, and factory automation. However, the communication protocol of IoT, MQTT, is a lightweight message transmission protocol based on the push technology and has a security vulnerability, and this suggests that there are risks such as personal information infringement or industrial information leakage. To solve this problem, we designed a synchronous MQTT security channel that creates a secure channel by using the characteristic that different chaotic dynamical systems are synchronized with arbitrary values in the lightweight message transmission MQTT protocol. The communication channel we designed is a method of transmitting information to the noise channel by using characteristics such as random number similarity of chaotic signals, sensitivity to initial value, and reproducibility of signals. The encryption method synchronized with the proposed key value is a method optimized for the lightweight message transmission protocol, and if applied to the MQTT of IoT, it is believed to be effective in creating a secure channel.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.10
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• pp.1251-1257
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• 2008

For the first 42 days, we made rats obese by feeding them potato starch instead of corn starch and after that we fed them transformed potato starch by 4 groups for 70 days. The 4 groups are GPS group, SPS group, EZ group and H40 groups and each were fed normal potato, small potato, enzyme treated potato, and acid treated potato starches, respectively. We determined body weight and feeding efficiency, lipid profiles in serum, lipid peroxidation in tissues and redox antioxidant system as GSH and GP-x in vivo. As a result, there was no difference in the increment of body weight in the groups of GPS, EZ and H40. Therefore EZ group showed lower body weight increment than other groups. While GPS group and SPS group did not show significant difference in blood glucose, cholesterol level, LDL-cholesterol and TC, and their measured values were lower than those of EZ and H40 groups. No significant difference was found in HDL-cholesterol level except for GPS group. Furthermore, when calculating atherogenic index (AI) by HDL-cholesterol and TC contents, H40 group showed higher measured value than other groups. When measuring the lipid peroxidation in serum, kidney and liver tissues, the serum lipid peroxidation in H40 group was higher than others. In the tissue of liver and kidney, EZ and H40 groups showed significantly lower contents than others. The content of GSH showed different tendency in each tissue, but the measured value of GP-x activity was lower in SPS group.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.