• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.5
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• pp.535-543
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• 2008

Purpose: The objective of this study was to compare the bonding characteristics of a new self-adhesive resin cement to dentin, which does not require bonding and conditioning procedure of the tooth surface, and conventional resin cement. The effect of phosphoric acid etching prior to application of self-adhesive resin cement on the shear bond strength was also evaluated. Material and methods: Fortyfive non-carious human adult molars extracted within 6 months were embedded in chemically cured acrylic resin. The teeth were ground with a series of SiC-papers ending with 800 grit until the flat dentin surfaces of the teeth were exposed. The teeth were randomly divided into 3 experimental groups. In group 1, self-adhesive resin cement, RelyX Unicem (3M ESPE, Seefeld, Germany) was bonded without any conditioning of teeth. In group 2, RelyX Unicem was bonded to teeth after phosphoric acid etching. For group 3, Syntac Primer (Ivoclar Vivadent AG, Schaan, Liechtenstein) was applied to the teeth before Syntac adhesive (Ivoclar Vivadent AG, Schaan, Liechtenstein) and Helibond (Ivoclar Vivadent AG, Schaan, Liechtenstein) followed by conventional resin cement, Variolink II (Ivoclar Vivadent AG, Schaan, Liechtenstein). To make a shear bond strength test model, a plastic tuble (3 mm diameter, 3 mm height) was applied to the dentin surfaces at a right angle and filled it with respective resin cement, and light-polymerized for 40 seconds. All the specimens were stored in distilled water at $37^{\circ}C$ for 24 hours before test. Universal Testing Machine (Z020, Zwick, Ulm, Germany) at a cross head speed of 1 mm/min was used to evaluate the shear bond strength. The failure sites were inspected under a magnifier and Scanning Electron Microscope. The data was analyzed with One way ANOVA and Scheffe test at ${\alpha}$= 0.05. Results: (1) The shear bond strengths to dentin of RelyX Unicem was not significantly different from those of Variolink II/Syntac. (2) Phosphoric acid etching lowered the shear bond strength of RelyX Unicem significantly. (3) Most of RelyX Unicem and Variolink II showed mixed fractures, while all the specimens of RelyX Unicem with phosphoric acid etching demonstrated adhesive failure between dentin and resin cement. Conclusion: Shear bond strength to dentin of self-adhesive resin cement is not significantly different from conventional resin cement, and phosphoric acid etching decrease the shear bond strength to dentin of self-adhesive resin cement.

• The Korean Journal of Physiology
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• v.3 no.2
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• pp.9-16
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• 1969

It is well known that a number of -SH and -SS containing substances afford a certain measure of protection against radiation effects in many biological systems, and it is conceivable that inherent -SH levels in Ehrlich ascites tumour (ELD)cells may be of decisive improtance with respect to the development of cellular radiation injury. So far, little effort has been directed to elucidate the changes in levels of different -SH and -SS groups in ELD cells when the tumour-bearing whole animal was subjected to the sublethal dose of X-irradiation. The present study was designed to bring some lights in the possible changes of and relationship between various sulfhydryl levels, such as P-SH, NP-SH and NP-SS, as well as the content of protein and cell volume of ELD cells, after subjecting the ELD mice to 1,200 r of X-irradiation. The animals used in this experiment were all mixed bred mice of $20{\sim}25\;gm$ in body weight (approximately 2 months old) irrespective of sex. 12 mice in one experiment were inoculated intraperitoneally with 0.2 ml of ascites tumour cells $(2{\times}10^6\;cells)$, and on the 7th day of the tumour growth, they were X-irradiated with 1,200 r, using the conventional X-ray machine under the following conditions: 200 Kv at 15 mA, 0.5 mm Cu filter, target-skin distance: 50 cm. Radiation dose was measured with the the Philip integrating dosimeter. At 24, 36, 48 and 60 hours after the X-irradiation, the mice were killed by cervical dislocation, and the tumours were taken out. Freshly withdrawn ascites tumours were placed in ice, and immediately the cell concentration was measured with the Coulter Cell Counter (Model B), and the hematocrit of the tumour cells were also determined. Cell volume was thus calculated by the cell concentration and hematocrit value. P-SH content of ELD cells was measured potentiometrically according to the method of Calcutt & Doxey, and NP-SH and NP-SS contents were measured spectrophotometrically by the method described by Ellman. Protein content of ELD cells was determined with the Folin phenol reagent by Lowry et al. Altogether, 48 experimental mice were used, and 12 mice with the only exception of X-irradiation were used as the control. Results obtained indicate that the contents of all the cellular sulfhydryl groups as well as cell volume and protein content of the ELD cells increase significantly as time progresses after the sub-lethal X-ray dose of 1,200 r was given and that all the increase is in a lineal fashion. The regression lines of the relative values, (i. e., taking each control value as 1) of all the values obtained, and the regression lines of cell volume, protein and NP-SH are identical, whereas those of NP-SS and P-SH appear to be widely seperated. However, the difference of those two lines (NP-SS & P-SH) were found to be not significant statistically (p>0.05). Therefore, it can be concluded from the above results that all the values examined increase in a lineal fashion with no statistically significant difference among them. Also, with the radiation dose of 1,200 r, the ELD cell becomes enlarged and swollen progressively up to 60 hours post-irradiation and it becomes more than two times of the original normal size at 60 hours after the irradiation, and up to this stage, it seems apparent that the cell division has been slow due to the X-irradiation applied in this experiment. It is well understandable that the contents of NP-SH, NP-SS, P-SH and protein of the ELD cells increase in parallel with the increase of the cell volume by the X-ray does used, but it also seems interesting to note that all the cellular substances tested show no appreciable difference in the pattern of increase.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.144-149
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• 2018

The eggs are incubated for 18 days through the generator and incubated in the developing incubator. During the developmental period, the weight loss of the fetus is correlated with the ventricular formation, and the proper ventricular formation is also associated with the healthy embryonic hatching and the egg hatching rate. However, in the incubator period of the domestic hatchery, it is a reality to acquire the resultant side by the Iranian standard weight measurement with the experience of the hatchery and the person concerned and the development period without the apparatus for measuring the present weight. As a result, prevalence of early mortality, hunger and illness during hatching are frequent. Monitoring the reduction of weaning weight is crucial to obtaining chick quality and hatching performance with weight changes within the development machine. Water loss is different depending on the size of eggs, egg shell, and elder group. We can expect to increase the hatching rate by measuring the weight change in real time and optimizing the ventilation change accordingly. There is a need to develop a real-time measurement system that can control 10 to 13% reduction of the total weight during hatching. The system through this study is a way to check the one - time directly when moving the existing egg, and it is impossible to control the measurement of the fetal water evaporation within the development period. Unlike systems that do not affect the hatching rate, four load cells are connected in parallel on the Arduino sketch board and the AT-command command is used to connect the mobile phone and computer in real time. The communication speed of Bluetooth was set to 15200 to match the communication speed of Arduino and Hyper-terminal program. The real - time monitoring system was designed to visually check the change of the weight of the fetus in the artificial incubator. In this way, we aimed to improve the hatching rate and health condition of the hatching eggs.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• The Journal of Korean Society for Radiation Therapy
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• v.12 no.1
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• pp.91-104
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• 2000

Recently linear accelerator in radiation therapy in asymmetric field has been easily used since the improvement and capability of asymmetrical field adjustment attached to the machine. It has been thought there have been some significant errors in dose calculation when asymmetrical radiation fields have been utilized in practice of radiation treatments if the fundamental data for dose calculation have been measured in symmetrical standard fields. This study investigated how much the measured data of dose distributions and their isodose curves are different between in asymmetrical and symmetrical standard fields, and how much there difference affect the error in dose calculation in conventional method measured in symmetrical standard field. The distributions of radiation dose were measured by photon diode detector in the water phantom (RFA-300P, Scanditronix, Sweden) as tissue equivalent material on utilization of 6 MV linear accelerator with source surface distance (SSD) 1000 mm. The photon diode detector has the velocity of 1 mm per second from water surface to 250 mm depth in the field size of $40mm{\times}40mm\;to\;250mm{\times}250mm\;symmetric\;field\;and\;40mm{\times}20mm\;to\;250mm{\times}125mm$ asymmetrical fields. The measurements of percent depth dose (PDD) and subsequent plotting of their isodose curves were performed from water surface to 250mm dmm from Y-center axis in $100mm{\times}50mm$ field in order to absence the variability of depth dose according to increasing field sizes and their affects to plotted isodose curves. The difference of PDD between symmetric and asymmetric field was maximum $4.1\%\;decrease\;in\;40mm{\times}20mm\;field,\;maximum\;6.6\%\;decrease\;in\;100mm{\times}50mm\;and\;maximum\;10.2\%\;decrease\;200mm{\times}100mm$, the larger decrease difference of PDD as the greater field size and as greater the depth, The difference of PDD between asymmetrical field and equivalent square field showed maximum $2.4\%\;decrease\;in\;60mm{\times}30mm\;field,\;maximum\;4.8\%\;decrease\;in\;150mm{\times}75mm\;and\;maximum\;6.1\%\;decrease\;in\;250mm{\times}125mm$, and the larger decreased differenced PDD as the greater field size and as greater the depth, these differences of PDD were out of $5\%$ of dose calculation as defined by international Commission on radiation unit and Measurements(ICRU). In the dose distribution of asymmetrical field (half beam) the plotted isodose curves were observed to have deviations by decreased PDD as greater as the blocking of the beam moved closer to the central axis, and as the asymmetrical field increased by moving the block 10 mm keeping away from the central axis, the PDD increased and plotted isodose curves were gradually more flattened, due to reduced amount of the primary beam and the fraction of low energy soft radiations by passing thougepth in asymmetrical field by moving independent jaw each 10 h beam flattening filter. As asymmetrical radiation field as half beam radiation technique is used, the radiation dosimetry calculated in utilizing the fundamental data which measured in standard symmetrical field should be converted on bases of nearly measured data in asymmetrical field, measured beam data flies of various asymmetrical field in various energy and be necessary in each institution.

• The korean journal of orthodontics
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• v.28 no.5 s.70
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• pp.803-812
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• 1998

The purpose of this study was to evaluate the shear bond strength and failure mode of ceramic brackets according to the surface treatment of porcelain. Sixty Porcelain samples were randomly divided into six groups of ten samples. Then they were treated as follows: Group 1(silane only), Group 2(etching+silane), Group 3(stone+silane), Group 4(sandblasting+silane), Group 5(stone +etching+silane), Group 6(sandblasting+etching+silane) After surface treatment of porcelain, sixty Transcend 6000 brackets were bonded to the prepared porcelain surface and they were stored in $37^{\circ}C$ saline for 24 hours. An Instron universal testing machine was used to test the shear bond strength of ceramic brackets to porcelain. After debonding, bases of ceramic brackets and porcelain surfaces were examined under scanning electron microscope(SEM) to determine failure mode. Statistical analysis of the data was carried out with one-way ANOVA and Duncan's multiple range test. The results were as follows : 1. The shear bond strength of surface-treated groups 2 to 6 was higher than that of only silane-treated group 1, and there was statistical significance. (P<0.05) 2. There was no significant difference among the groups 3 to 6. (P>0.05) 3. The shear bond strength of etching-surface treated group 2 was significantly lower than those of sandblasting-surface treated group 4, complex surface treated group 5 and group 6. 4. According to the scanning electromicroscopic images, the surface roughness of sandblasting-surface treated group 4 was less than those of the group 5 and 6, but there was no significant difference in the shear bond strength. (P>0.05) As a conclusion we can have a clinically adequate bond strength when an application of silane is done after the treatment of porcelain surface with more than one way to bond ceramic bracket on the porcelain. Also, it is considered that the sandblasting and application of silane is effective for the simplication and convenience of the treatment.

• International Journal of Ocean System Engineering
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• v.2 no.3
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• pp.185-194
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• 2012

A Macroscopic combination of two or more distinct materials is commonly referred to as a "Composite Material", having been designed mechanically and chemically superior in function and characteristic than its individual constituent materials. Composite materials are used not only for aerospace and military, but also heavily used in boat/ship building and general composite industries which we are seeing increasingly more. Regardless of the various applications for composite materials, the industry is still limited and requires better fabrication technology and methodology in order to expand and grow. An example of this is that the majority of fabrication facilities nearby still use an antiquated wet lay-up process where fabrication still requires manual hand labor in a 3D environment impeding productivity of composite product design advancement. As an expert in the advanced composites field, I have developed fabrication skills with the use of machinery based on my past composite experience. In autumn 2011, the Korea government confirmed to fund my project. It is the development of a composite sanding machine. I began development of this semi-robotic prototype beginning in 2009. It has possibilities of replacing or augmenting the exhaustive and difficult jobs performed by human hands, such as sanding, grinding, blasting, and polishing in most often, very awkward conditions, and is also will boost productivity, improve surface quality, cut abrasive costs, eliminate vibration injuries, and protect workers from exposure to dust and airborne contamination. Ease of control and operation of the equipment in or outside of the sanding room is a key benefit to end-users. It will prove to be much more economical than normal robotics and minimize errors that commonly occur in factories. The key components and their technologies are a 360 degree rotational shoulder and a wrist that is controlled under PLC controller and joystick manual mode. Development on both of the key modules is complete and are now operational. The Korean government fund boosted my development and I expect to complete full scale development no later than 3rd quarter 2012. Even with the advantages of composite materials, there is still the need to repair or to maintain composite products with a higher level of technology. I have learned many composite repair skills on composite airframe since many composite fabrication skills including repair, requires training for non aerospace applications. The wind energy market is now requiring much larger blades in order to generate more electrical energy for wind farms. One single blade is commonly 50 meters or longer now. When a wind blade becomes damaged from external forces, on-site repair is required on the columns even under strong wind and freezing temperature conditions. In order to correctly obtain polymerization, the repair must be performed on the damaged area within a very limited time. The use of pre-impregnated glass fabric and heating silicone pad and a hot bonder acting precise heating control are surely required.