• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.19-28
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• 2022

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Steel and Composite Structures
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• v.46 no.3
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• Nuclear Engineering and Technology
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• v.4 no.3
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• pp.214-222
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• 1972

In the optic,11 grade LiF crystal, the electron traps corresponding to the thermoluminescence(abbreviated to TL) glow peak develop as irradiation dose is increased. Originally the electron trap of the crystal has two levels but as the dose reaches to the order of 10$^4$rontgen, it attains five levels as observed in the TL glow curves. The five trap depths are determined from the glow peak temperatures for two different heating rates, $\theta$=6.6$^{\circ}C$/sec and 3.4$^{\circ}C$/sec. The electron trap depths have the following values E$_1$=0.79 eV, E$_2$=0.93 eV, E$_3$=1.02 eV, E$_4$=1.35 eV, E$_{5}$=1.69eV. The special feature of thermoluminescence of optical grade LiF is that the traps, except E$_1$and E$_2$corresponding to 12$0^{\circ}C$ glow peak and 15$0^{\circ}C$ glow peak for $\theta$=6.6$^{\circ}C$/sec, have severe thermal instability, namely E$_3$, E$_4$and E$_{5}$ levels disappear during bleaching process. These defects in the optical grade LiF crystal seem annealed out during the course of TL measurement. The fresh or long time unused LiF(Mg) crystal shows only two glow peaks at 17$0^{\circ}C$ and 23$0^{\circ}C$ for $\theta$=6.6$^{\circ}C$/sec, but upon sensitization with r-ray irradiation, it converts to the six glow peak state. The four electron traps, E$_1$, E$_2$, E$_3$, and E$_{6}$ created by r-ray irradiation and corresponding to the glow peaks at T=10$0^{\circ}C$ 13$0^{\circ}C$, 15$0^{\circ}C$ and 29$0^{\circ}C$ are stable and not easily annealed out thermally, The sensitization essentially required to LiF(Mg) dosimeter is to give the crystal the stable six levels in the electron trap. In optical grade LiF, the plot between logarithm of total TL output versus logarithm of r-ray dose gives more supra-linear feature than that of LiF(Mg). However, if one takes the height of 12$0^{\circ}C$ glow peak(S=6.6$^{\circ}C$/sec), instead of the total TL output, the curve becomes close to that of LiF(Mg).

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.175-185
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• 2020

Three-dimensional registration is a process of matching data with or without a coordinate system to a reference coordinate system, which is used in various fields such as the absolute orientation of photogrammetry and data combining for producing precise road maps. Three-dimensional registration is divided into a method using points and a method using linear features. In the case of using points, it is difficult to find the same conjugate point when having different spatial resolutions. On the other hand, the use of linear feature has the advantage that the three-dimensional registration is possible by using not only the case where the spatial resolution is different but also the conjugate linear feature that is not the same starting point and ending point in point cloud type data. In this study, we proposed a method to determine the scale and the three-dimensional translation after determining the three-dimensional rotation angle between two data using quaternion to perform three-dimensional registration using linear features. For the verification of the proposed method, three-dimensional registration was performed using the linear features constructed an indoor and the linear features acquired through the terrestrial mobile mapping system in an outdoor environment. The experimental results showed that the mean square root error was 0.001054m and 0.000936m, respectively, when the scale was fixed and if not fixed, using indoor data. The results of the three-dimensional transformation in the 500m section using outdoor data showed that the mean square root error was 0.09412m when the six linear features were used, and the accuracy for producing precision maps was satisfied. In addition, in the experiment where the number of linear features was changed, it was found that nine linear features were sufficient for high-precision 3D transformation through almost no change in the root mean square error even when nine linear features or more linear features were used.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.553-568
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• 2015

This paper focuses on the geographical characteristics and the spatial distributions and patterns of the road names in the new address system for which all the 183 road names of Cheongju City has been used. All 183 road names in Cheongju City and their textural information are analyzed and classified into four main categories and six divisions as sub-category. Each type is mapped and its spatial patterns are discussed in order to identify the interaction between the road name and the geographical characteristics of each type. From the discussion stated in the paper, it can be inferred that the road name is not only a representative place name in an area, but also presents an important geographical feature reflecting the toponymy of the cultural and historical backgrounds of an area. Therefore, it is necessary to recognize that for road naming, various aspects such as geographical backgrounds and characteristics should be considered. These are directly related to the publicity and utilization of the road names to the public who is still unfamiliar with the new address system to be used. Finally, various geographical topics and approaches such as toponymy and spatial analysis are proposed for further geographical research, which will contribute to the extent of geographical research scopes.

• Cartoon and Animation Studies
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• s.33
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• pp.363-382
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• 2013

Up until about ten years ago, the use of typography played only an auxiliary role on broadcast television programs, primarily by transmitting information in order to facilitate a basic understanding of content. Recently, however, kinetic typography has become an important component in broadcast production. In fact, kinetic typography has developed into a visual language and a means of artistic expression, one that is increasingly used in the production of entertainment programs on television. This paper analyzes six aspects of kinetic typography: manner of development, location, intent, expressive techniques, color and font selection. Particular attention is placed on their use in three highly rated television entertainment programs: "2 Days & 1 Night", "Running Man", and "Infinite Challenge". The development way consists of the technique : starts off with cut and ends with cut. While, other techniques show conversation and situation representation using Z axis : zoom-in, zoom-out in , X axis : pan in <2 Days & 1 Night>. and Y axis : tilt in . Typographic design elements, expression technique, color, font are shown up according to the feature of each program. The resulting analysis suggests new ways for motion arts designers and the broadcast media to use kinetic typography in the development of television programs.

• The Journal of Engineering Geology
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• v.7 no.2
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• pp.151-159
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• 1997

Since healed microcracks in quartz grain of grantic rocks within the same mass have identical preferred orientations, the oreintations of granitic cores may be determined if the distinctive feature of healed microcracks can be used.In this study, the possibility of determining orientations of granitic cores using healed microcrack orientations were examined using samples from the borehole drilled to 200 m in depth at the Hongcheon. Eight sections whose core recoveries are 100% were selected. Two to six samples were collected in each section and orientations of healed microcracks in each sample were measured. Healed microcracks in samples from each section show almost identical orientations. The error range for sections with only one preferred orientations is within $\pm$5$^{\circ}$, indicating that correct orientations of core can be determined. However, orientations of cores in sections which have 2 or more healed microcrack orientations should be determined using orientations as well as distribution of peaks of orientations. The error range for this case is lager than former one and is within $\pm$15$^{\circ}$. The orientations of joint which is very impontant factor for designing tunnel and slope stability can be determined using healed microcrack orientation in cores.