• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1901-1906
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• 2010

This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3587-3594
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• 2022

A distributed high-speed data acquisition and monitoring system for the RF negative ion source at Huazhong University of Science and Technology (HUST) is developed, which consists of data acquisition, data forwarding and data processing. Firstly, the data acquisition modules sample physical signals at high speed and upload the sampling data with corresponding absolute-time labels over UDP, which builds the time correlation among different signals. And a special data packet format is proposed for the data upload, which is convenient for packing or parsing a fixed-length packet, especially when the span of the time labels in a packet crosses an absolute second. The data forwarding modules then receive the UDP messages and distribute their data packets to the real-time display module and the data storage modules by PUB/SUB-pattern message queue of ZeroMQ. As for the data storage, a scheme combining the file server and MySQL database is adopted to increase the storage rate and facilitate the data query. The test results show that the loss rate of the data packets is within the range of 0-5% and the storage rate is higher than 20 Mbps, both acceptable for the HUST RF negative ion source.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.315-333
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• 2021

Due to the Global Navigation Satellite System (GNSS) modernization, recently launched GNSS satellites transmit signals at various frequency bands such as L1, L2 and L5. Considering the Korean Positioning System (KPS) signal and other GNSS augmentation signals in the future, there is a high probability of applying more complex communication techniques to the new GNSS signals. For the reason, GNSS receivers based on flexible Software Defined Radio (SDR) concept needs to be developed to evaluate various experimental communication techniques by accessing each signal processing module in detail. This paper proposes a novel SDR-based A-GNSS receiver capable of processing multi-GNSS/RNSS signals at multi-frequency bands. Due to the modular structure, the proposed receiver has high flexibility and expandability. For real-time implementation, A-GNSS server software is designed to provide immediate delivery of satellite ephemeris data on demand. Due to the sampling bandwidth limitation of RF front-ends, multiple SDRs are considered to process the multi-GNSS/RNSS multi-frequency signals simultaneously. To avoid the overflow problem of sampled RF data, an efficient memory buffer management strategy was considered. To collect and process the multi-GNSS/RNSS multi-frequency signals in real-time, the proposed SDR A-GNSS receiver utilizes multiple threads implemented on a CPU and multiple NVIDIA CUDA GPGPUs for parallel processing. To evaluate the performance of the proposed SDR A-GNSS receiver, several experiments were performed with field collected data. By the experiments, it was shown that A-GNSS requirements can be satisfied sufficiently utilizing only milliseconds samples. The continuous signal tracking performance was also confirmed with the hundreds of milliseconds data for multi-GNSS/RNSS multi-frequency signals and with the ten-seconds data for multi-GNSS/RNSS single-frequency signals.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.257-266
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• 2007

The research on red tide is generally in progress through field work, such as the naked eye and sampling. It was difficult to forecast exactly the course, from appearance of red tide to disappearance. with the established ways of investigation and analysis. Accordingly it is need to analyze environmental factors in time and space, the appearance of red tide and the path of its migration by more objective and scientific methods. In this study, GIS is applied to analyse the space character of red tide and the interpolation of IDW(Inverse Distance Weight) is applied to assume the density distribution of red tide after gather data by using Arc/Info. After IDW interpolation, the sea area occurred over 1,000 cells/ml of red tide density is extracted with CON and SUM Function of Grid Module, and the density of the sea area is accumulated daily. As a result of this study, the distribution condition of red tide is found timely and spacially by applying GIS to the sea area of red tide, the results indicated that the spatial density and the cumulative frequency about the origin of red tide using GIS, the sea area demonstrated that the maximum density and the maximum frequency varied significantly over the Nammyun of Namhae-Is. with the maximum frequency being 49 times. accordingly if data about the areas of red tide will occur from the present are accumulated, the shifting route of red tide occurrence and extinction can be predicted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3628-3633
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• 2012

This paper proposes a model of the real time monitoring system based on Ubiquitous Sensor Network (USN) for the detection and prediction of landslides. For this purpose, the real time monitoring system with tilting sensor and USN was set up and the performance was conducted. The performance was accomplished by conducting both field examinations and the experimental evaluation of the monitoring system. The results of this study show that the angle $0^{\circ}$, $-10^{\circ}$, $-20^{\circ}$ and $0{\sim}-30^{\circ}$ of sensor position detected by the sensor module coincide with the data measured from USN monitoring system by giving a sampling time 100[msec]. Consequently, the proposed model of the real time monitoring system with tilting sensor based on USN will be widely used as a monitoring system in the exposure to dangerous landslide regions.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.112-122
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• 2021

The goal of this study is to explore ways to restructure Convergence Elective Courses in science in preparation for the high school credit system, ahead of the 2022 revised science curriculum. This study started from the problem that the 2015 revised science curriculum has not guaranteed science subject choice for students with non-science/engineering career aptitudes. To this end, a survey was conducted by randomly sampling high schools across the country. A total of 1,738 students responded to the questionnaire of 3 science elective courses such as Science History, Life & Science, Convergence Science. In addition, in-depth interviews with 12 science teachers were conducted to examine the field operation of these three courses, which will be classified and revised as Convergence Elective subjects in the 2022 revised curriculum. According to the results of the study, high school students perceive these three courses as science literacy courses, and find these difficult to learn due to lack of personal interest, and difficulties in content itself. The reason students choose these three courses is mainly because they have aptitude for science, or these courses have connection with their desired career path. Teachers explained that students mainly choose Life & Science, and both teachers and students avoid Science History because the course content is difficult. Based on the research results, we suggested ways to restructure Convergence Electives for the 2022 revised curriculum including developing convergence electives composed of interdisciplinary convergence core concepts with high content accessibility, developing convergence electives with core concepts related to AI or advanced science, developing module-based courses, and supporting professional development of teachers who will teach interdisciplinary convergence electives.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.395-405
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• 2018

Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.