• Resources Recycling
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• v.9 no.6
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• pp.45-52
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• 2000

Generally, the residues of EAF's dusts treated by reduction process at high temperature are disposed. If the residues can be recycled as iron sources of EAF by upgrading their iron contents, it can be expected to reduce the amounts of disposed wastes and the environmental impacts. Reduction of EAF's dusts mixed with millscale was carried out in rotary hearth furnace to upgrade iron contents of reduction residues. Dusts should be reduced rapidly to protect from reoxidation of reduced iron residue which can be reoxidized at high temperature. In our experimental conditions, optimum reduction time was about 40min. and iron contents of the residues were increased with increasing mixing ratio of millscale and upgrade to 85% at 50%wt mixing ratio. Zinc and lead contents in residues were about 3% and 0.5% respectively. The residues reduced rapidly must be recycled in EAF because heavy metal elements in the residues can be extracted easily and contaminate air and water.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.255-263
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• 2015

In this paper, we suggest a plan to take advantage of the SNS data to proactively identify the information on crime risk factor and to prevent crime. Recently, SNS(Social Network Service) data have been used to build a proactive prevention system in a variety of fields. However, when users are collecting SNS data with simple keyword, the result is contain a large amount of unrelated data. It may possibly accuracy decreases and lead to confusion in the data analysis. So we present a method that can be efficiently extracted by improving the search accuracy through text mining analysis of SNS data.

• The Journal of the Korean dental association
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• v.57 no.9
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• pp.514-522
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• 2019

In accordance with Article 33(8) of the Korean Medical Law, it is stated that a medical person cannot open or operate a medical institution by borrowing the name of another medical person. However, the publicity of medical care is threatened by the recent illegal network dental clinics. The purpose of this study is to investigate the actual condition of illegal network dentistry and to analyze the cases and to find out the reason why the prohibition of double opening & operating of medical institution. As a result, the illegal network dental clinics treated less health care insurance treatment such as dental caries and periodontal treatment than general dental hospitals. In contrast, the rate of implementation of illegal network dentistry was high in endodontics treatment and extraction, which could lead to uninsured treatments such as crowns and implants. As a result of Supreme Court precedent analysis, it is concluded that illegal act is not only the opening of a medical institution by borrowing the name of other medical personnel, but also the duplicated operation which has the authority to make decision about management matters of medical institutions. The results of the patient's case survey also showed that excessive dental treatment due to such as dental staff incentive system. In conclusion, the illegal network dental clinics not only threatens the oral health of the public, but also causes leakage of health insurance premiums. In other words, the ban on opening and operating the multiple medical institution should be strictly applied as a strong protection device for protecting the patient in dental case.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.3
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• pp.237-244
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• 2021

Background: This study aimed to analyze Twitter users' emotional tendencies regarding oral surgery procedures before and after the coronavirus disease 2019 (COVID-19) pandemic worldwide. Methods: Tweets posted in English before and after the COVID-19 pandemic were included in the study. Popular tweets in 2019 were searched using the keywords "tooth removal", "tooth extraction", "dental pain", "wisdom tooth", "wisdom teeth", "oral surgery", "oral surgeon", and "OMFS". In 2020, another search was conducted by adding the words "COVID" and "corona" to the abovementioned keywords. Emotions underlying the tweets were analyzed using CrystalFeel - Multidimensional Emotion Analysis. In this analysis, we focused on four emotions: fear, anger, sadness, and joy. Results: A total of 1240 tweets, which were posted before and after the COVID-19 pandemic, were analyzed. There was a statistically significant difference between the emotions' distribution before and after the pandemic (p < 0.001). While the sense of joy decreased after the pandemic, anger and fear increased. There was a statistically significant difference between the emotional valence distributions before and after the pandemic (p < 0.001). While a negative emotion intensity was noted in 52.9% of the messages before the pandemic, it was observed in 74.3% of the messages after the pandemic. A positive emotional intensity was observed in 29.8% of the messages before the pandemic, but was seen in 10.7% of the messages after the pandemic. Conclusion: Infectious diseases, such as COVID-19, may lead to mental, emotional, and behavioral changes in people. Unpredictability, uncertainty, disease severity, misinformation, and social isolation may further increase dental anxiety and fear among people.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.999-1005
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• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.4
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• pp.229-236
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• 2023

Maxillary bone defects may follow surgical treatment of benign and malignant tumors, trauma, and infection. Palatal defects often lead to problems with swallowing and pronunciation from the leakage of air into the nasal cavity and sinus. Obturators have been commonly used to solve these problems, but long-term use of the device may cause irritation of the oral mucosa or damage to the abutment teeth. Utilizing implants in the edentulous area for the fabrication of the obturators has gained attention. This case report describes a patient, who had undergone partial resection of the maxilla due to adenocarcinoma, in need of a new obturator after losing abutment teeth after long-term use of the previous obturator. Implants were placed in strategic locations, and an implant-retained maxillary obturator was fabricated, showing satisfactory results in the rehabilitation of multiple aspects, including palatal defect, masticatory function, swallowing, pronunciation, and aesthetics.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.