Zarraoa, N.;Tajdine, A.;Caro, J.;Alcantarilla, I.;Porras, D.
Proceedings of the Korean Institute of Navigation and Port Research Conference
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v.1
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pp.27-31
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2006
GNSS Services and Applications are today in permanent evolution in all the market sectors. This evolution comprises: ${\bullet}$ New constellations and systems, being GALILEO probably the most relevant example, but not the only one, as other regions of the world also dwell into developing their own elements (e.g. the Chinese Beidou system). ${\bullet}$ Modernisation of existing systems, as is the case of GPS and GLONASS ${\bullet}$ New Augmentation services, WAAS, EGNOS, MSAS, GRAS, GAGAN, and many initiatives from other regions of the world ${\bullet}$ Safety of Life services based on the provision of integrity and reliability of the navigation solutions through SBAS and GBAS systems, for aeronautical or maritime applications ${\bullet}$ New Professional applications, based on the unprecedented accuracies and integrity of the positioning and timing solutions of the new navigation systems with examples in science (geodesy, geophysics), Civil engineering (surveying, construction works), Transportation (fleet management, road tolling) and many others. ${\bullet}$ New Mass-market applications based on cheap and simple GNSS receivers providing accurate (meterlevel) solutions for daily personal navigation and information needs. Being on top of this evolving market requires an active participation on the key elements that drive the GNSS development. Early access to the new GNSS signals and services and appropriate testing facilities are critical to be able to reach a good market position in time before the next evolution, and this is usually accessible only to the large system developers as the US, Europe or Japan. Jumping into this league of GNSS developers requires a large investment and a significant development of technology, which may not be at range for all regions of the world. Bearing in mind this situation, MAGIC appears as a concept initiated by a small region within Europe with the purpose of fostering and supporting the development of advanced applications for the new services that can be enabled by the advent of SBAS systems and GALILEO. MAGIC is a low cost platform based on the application of technology developed within the EGNOS project (the SBAS system in Europe), which encompasses the capacity of providing real time EGNOS and, in the near future, GALILEO-like integrity services. MAGIC is designed to be a testing platform for safety of life and liability critical applications, as well as a provider of operational services for the transport or professional sectors in its region of application. This paper will present in detail the MAGIC concept, the status of development of the system within the Madrid region in Spain, the results of the first on-field demonstrations and the immediate plans for deployment and expansion into a complete SBAS+GALILEO regional augmentation system.
HanJoo Lee;Minkyu Jee;Hakdong Kim;Taeheul Jun;Cheongwon Kim
Journal of Broadcast Engineering
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v.28
no.3
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pp.285-292
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2023
Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.
KIPS Transactions on Software and Data Engineering
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v.12
no.4
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pp.149-158
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2023
As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.
KIPS Transactions on Software and Data Engineering
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v.12
no.4
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pp.159-172
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2023
In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.
KIPS Transactions on Software and Data Engineering
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v.12
no.4
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pp.173-178
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2023
As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.
Ye-Young Kim;Su-Hyun Jeong;So-Hyun Park;Young-Ho Park
KIPS Transactions on Software and Data Engineering
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v.12
no.4
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pp.189-198
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2023
As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.
KIPS Transactions on Software and Data Engineering
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v.12
no.3
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pp.141-148
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2023
Accurate overlay metrology is essential to achieve high yields of semiconductor products. Overlay metrology performance is greatly affected by overlay target design and measurement method. Therefore, in order to improve the performance of the overlay target, measurement methods applicable to various targets are required. In this study, we propose a new algorithm that can measure image-based overlay. The proposed measurement algorithm can estimate the sub-pixel position by using a motion vector. The motion vector may estimate the position of the sub-pixel unit by applying a quadratic equation model through polynomial expansion using pixels in the selected region. The measurement method using the motion vector can calculate the stacking error in all directions at once, unlike the existing correlation coefficient-based measurement method that calculates the stacking error on the X-axis and the Y-axis, respectively. Therefore, more accurate overlay measurement is possible by reflecting the relationship between the X-axis and the Y-axis. However, since the amount of computation is increased compared to the existing correlation coefficient-based algorithm, more computation time may be required. The purpose of this study is not to present an algorithm improved over the existing method, but to suggest a direction for a new measurement method. Through the experimental results, it was confirmed that measurement results similar to those of the existing method could be obtained.
KIPS Transactions on Software and Data Engineering
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v.12
no.3
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pp.133-140
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2023
The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.
Excessive earth pressure is one of the major mechanical factors in the deformation and damage of Cut-and-Cover Tunnel lining in shallow tunnels and portals of mountain tunnels (Kim, 2000). Excessive earth pressure may be attributed to insufficient compaction and consolidation of backfill material due to self-weight, precipitation and vibration caused by traffic (Komiya et al., 2000; Taylor et al., 1984; Yoo, 1997). Even though there were a lot of tests performed to determine the earth pressure acting on the tunnel lining, unfortunately there were almost no case histories of studies performed to determine remedial measures that reduce differential settlement and excessive earth pressure. In this study the installation of geotextile mat was selected to reduce the differential settlement and excessive earth pressure acting on the cut-and-cover tunnel lining. In order to determine settlement and earth pressure reduction effect (reinforcement effect) of geotextile mat reinforcement, laboratory tunnel model tests were performed. This study was limited to the modeling of rigid circular cut-and-cover tunnel constructed at a depth of $1.0D\sim1.5D$ in loose sandy ground and subjected to a vibration frequency of 100 Hz. Model tests with varying soil cover, mat reinforcement scheme and slope roughness were performed to determine the most effective mat reinforcement scheme. Slope roughness was adjusted by attaching sandpaper #100, #400 and acetate on the cut slope surface. Mat reinforcement effect of each mat reinforcement scheme were presented by the comparison of earth pressure obtained from the unreinforced and mat reinforced model tests. Soil settlement reduction was analyzed and presented using the Picture Analysis Method (Park, 2003).
Downhole seismic method is widely used for obtaining shear wave velocity profile of a site because it is simple and economical. Determining accurate travel time of shear wave is very important to obtain reliable result in downhole seismic method. In this paper, comparison study of various travel time determination methods was performed. Numerical study and model chamber test were performed for effective comparison study. Signal traces were acquired by performing downhole test at each numerical simulation and soil box test. Travel time data for each signal traces were determined by using six different methods and Vs profiles were evaluated. Comparing travel time data and Vs profiles with the reference value, the first arrival picking method proved to be ambiguous and unreliable. Other methods also did not always provide accurate results and the magnitude of error was dependent on the signal to noise ratio. Cross-correlation method proved to be the most adequate method for the field application and it was verified additionally with field data.
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