• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.229-232
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• 2022

Recently, production of offshore fisheries in Korea has been decreasing. Since production of offshore fisheries in 2016 fell below 1 million tons for the first time in 44 years, it has not recovered and has been decreasing. In order to cope with such a decrease in fishery resources, the TAC (total allowable catch) system is implemented internationally for fisheries resource management. Since 1999, South Korea has introduced the TAC system to perform resource management. In this paper, we propose an artificial intelligence-based fish classification and weight estimation system that can be used to investigate fishery resources of land observers essential for the implementation of the TAC system. The system consists of an app and a cloud server that automatically measures the body size and height of fish and takes photos using a terminal equipped with a lidar sensor. In the cloud server, fish classification is performed using a CNN-based efficientnet model and the weight of fish is predicted using automatically measured body length and body height information. Using this system, it is possible to improve the existing method in which the land observer manually writes after measuring the tape measure and weight in the stomach market.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.983-991
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• 2019

This study aims to analyze movies which contain various stories according to the size of their shots. To achieve this, it is needed to classify dataset according to the shot size, such as extreme close-up shots, close-up shots, medium shots, full shots, and long shots. However, a typical video storytelling is mainly composed of close-up shots, medium shots, full shots, and long shots, it is not an easy task to construct an appropriate dataset for extreme close-up shots. To solve this, we propose an image cropping method based on the region of interest (ROI) detection. In this paper, we use the face detection and saliency detection to estimate the ROI. By cropping the ROI of close-up images, we generate extreme close-up images. The dataset which is enriched by proposed method is utilized to construct a model for classifying shots based on its size. The study can help to analyze the emotional changes of characters in video stories and to predict how the composition of the story changes over time. If AI is used more actively in the future in entertainment fields, it is expected to affect the automatic adjustment and creation of characters, dialogue, and image editing.

• Convergence Security Journal
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• v.17 no.2
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• pp.153-158
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• 2017

The robot imitates humans and recognizes the external environment and judges the situation. The robot is a machine that operates autonomously. Robots are divided into manufacturing robots and service robots. Service robots are classified as professional service robots and personal service robots. Because of the intensified competition of productivity in manufacturing industries, rising safety issues, low birth rate and aging, the robots industry is emerging. Recently, the robot industry is a complex of advanced technology fields, and it is attracting attention as a new industry where innovation potential and growth potential are promising. IT, BT, and NT related elements are fused and implemented, and the ripple effect is very large. Due to changes in social structure and life patterns, social interest in life extension and health is increasing. There is much interest in the medical field. Now the artificial intelligence (AI) industry is growing rapidly. It is necessary to secure global competitiveness through strengthening cooperation between large and small companies. We must combine R&D investment capability and marketing capability, which are advantages of large corporations, and robotic technology. We need to establish a cooperative model and secure global competitiveness through M&A.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.51-56
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• 2022

With the development of industrialization, the amount of food and waste is rapidly increasing. Accordingly, the government is aware of the seriousness and is making efforts in various ways to reduce it. As a part of that, the volume-based food system was introduced, and although there were several trials and errors at the beginning of the introduction, it shows a reduction effect of 20 to 30%. These results suggest that the volume-based food system is being established. However, the waste is caused by foreign substances in the process of recycling resources by collecting them from the 1st collection to the 2nd collection process. Therefore, in this study, to solve these problems fundamentally, artificial intelligence is applied to classify foreign substances and improve them. Due to the nature of food waste, there is a limit to obtaining many images, so we compare several models based on CNNs and classify them as abnormal data, that is, CNN-based models are trained on various types of foreign substances, and then models with high accuracy are selected. We intend to prepare improvement measures for maintenance, such as manpower input to protect equipment and classify foreign substances by applying it.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.