• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• Journal of the korean society of oceanography
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• v.37 no.3
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• pp.154-159
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• 2002

Satellite remote sensing technology for ocean observation has evolved considerably in these last twenty years. Ocean color is one of the most important parameters of ocean satellite measurements. This paper describes a remote sensing of ocean color data project - Asian I-Lac Project; it also introduces several case studies using satellite images in the Asian waters. The Asian waters are related to about 30 Asian countries, representing about 60% of the world population. The project aims at generating long-term time series images (planned for 10 years from 1996 to 2006) by combining several ocean color satellite data, i.e., ADEOS-I OCTS and SeaWiFS, and some other sensors. Some typical parameters that could be measured include Chlorophyll- a (Chl-a), Colored Dissolved Organic Matter (CDOM), and Suspended Material (SSM). Reprocessed OCTS images display spatial variation of Chl-a, CDOM, and SSM in the Asian waters; a short term variability of phytoplankton blooms was observed in the Gulf of Oman in November 1996 by analyzing OCTS and NOAA sea surface temperature (SST)； Chl-a concentrations derived from OCTS and SeaWiFS have also been evaluated in coastal areas of the Taiwan Strait, the Gulf of Thailand, the northeast Arabian Sea, and the Japan Sea. The data system provides scientists with capability of testing or developing ocean color algorithms, and transferring images for their research. We have also analyzed availability of OCTS images. The results demonstrate the potential of long-term time series of satellite ocean color data for research in marine biology, and ocean studies. The case studies show multiple applications of satellite images on monitoring of coastal environments in the Asian Waters.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.920-930
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• 2019

To figure out the impact of debt financing on the profits of industrial enterprises, it starts with calculating the first differences against the logarithms of the cost profit ratios and the debt asset ratios of Chinese industrial enterprises during 179 months from 2002 to 2016; next, it runs the cointegration test and afterwards the regression test to analyze the obtained first differences, and still next uses the Simulink software to get the regularity of those changes. It finds out that there is not only a long-term stable relationship between the enterprises' profits and debts, but also a steady time series trend within a short term. The profit rate positively correlates to the debt asset ratio, and profit for the current term positively correlates to the profit for the previous term. It indicates that properly raised debts can help increase the profit rate of the industrial enterprises, and a higher previous profit level can help improve the current profit level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.654-659
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• 2018

In this study, continuous long-term observation is implemented with an ocean radar. Ocean radar conducts remote observation (combined) with ground-based radars, which enable a series of simultaneous observations of an extensive range of the coast with high frequency. An ocean radar for continuous long-term observation is operated at Samcheok on the east coast of Korea. Samcheok experienced tsunami damage in recent years and is the location of a nuclear power plant. In order to examine the reliability of the ocean radar, a pressure-type wave gauge, ultrasonic wave gauge, and ocean buoy are installed for the purpose of data comparison and verification. The ocean radar used in this study is an array-type HF-RADAR named WERA (WavE RAdar). The analysis of the data obtained from continuous long-term observations showed that the radar observations were in agreement with more than 90% of the wave data collected within a 25 km range from the center of two sites. Less than 1% of the entire observation data was unmeasured by the time series analysis. As a result of comparing the radar data with the direct observations made by the wave gauge, it was inferred that the RMS deviation is less than 20cm and the correlation coefficient was in the range of 0.84 ~ 0.87. Moreover, supported by such observations, a comprehensive monitoring system is being developed to provide the public with real-time reports on waves and currents via the internet.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3866-3873
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• 2000

In this paper, we investigates the long-term memory and the Correct answer rate of the foreign exchange data (Yen/Dollar) that is one of economic time series, There are many cases where two kinds of fractal dimensions exist in time series generated from dynamical systems such as AR models that are typical models having a short terrr memory, The sample interval separating from these two dimensions are denoted by kcrossover. Let the fractal dimension be $D_1$ in K < $k^{crossover}$,and $D_2$ in K > $k^{crossover}$ from the statistics mode. In usual, Statistic models have dimensions D1 and D2 such that $D_1$ < $D_2$ and $D_2\cong2$ But it showed a result contrary to this in the real time series such as NIKKEL The exchange data that is one of real time series have relation of $D_1$ > $D_2$ When the interval between data increases, the correlation between data increases, which is quite a peculiar phenomenon, We predict exchange data by neural networks, We confirm that $\beta$ obrained from prediction errors and D calculated from time series data precisely satisfy the relationship $\beta$ = 2-2D which is provided from a non-linear model having fractal dimension, And We identified that the difference of fractal dimension appeaed in the Correct answer rate.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.99-107
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• 2021

We live in a myriad of data. Various data are created in all situations in which we work, and we discover the meaning of data through big data technology. Many efforts are underway to find meaningful data. This paper introduces an analysis technique that enables humans to make better choices through the trend and prediction of time series data as a principal component analysis technique. Principal component analysis constructs covariance through the input data and presents eigenvectors and eigenvalues that can infer the direction of the data. The proposed method computes a reference axis in a time series data set having a similar directionality. It predicts the directionality of data in the next section through the angle between the directionality of each time series data constituting the data set and the reference axis. In this paper, we compare and verify the accuracy of the proposed algorithm with LSTM (Long Short-Term Memory) through cryptocurrency trends. As a result of comparative verification, the proposed method recorded relatively few transactions and high returns(112%) compared to LSTM in data with high volatility. It can mean that the signal was analyzed and predicted relatively accurately, and it is expected that better results can be derived through a more accurate threshold setting.