• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

• KDI Journal of Economic Policy
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• v.46 no.3
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• pp.49-66
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• 2024

This study empirically investigates how the exports of downstream products to the US change the imports of their upstream products from China during the US-China tariff war. To accomplish this, we use province-level trade data in Vietnam, known to be a country that increased its exports to the US market in place of China, i.e., known to enjoy a trade diversion in the US market. The use of regional trade data enables us to capture the input-output linkages more precisely. Specifically, focusing on the trade in general and electrical machinery industries from January of 2019 to December of 2023, we regress imports of upstream products from China on exports of their downstream products to the US, finding that the rise of exports of downstream products to the US significantly increases imports of their upstream products from China. On the other hand, the rise in these products does not significantly increase the imports of upstream products from Japan, Korea, and Taiwan. Furthermore, the input-output linkage between exports to the US and imports from China was found to be greater in provinces with better business environments in terms of entry costs, transparency in public services, and public support to businesses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.141-146
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• 2018

Recently, location recognition technology has attracted much attention, especially for locating people or objects in an indoor environment without being influenced by the surrounding environment GPS technology is widely used as a method of recognizing the position of an object or a person. GPS is a very efficient, but it does not allow the positions of objects or people indoors to be determined. RFID is a technology that identifies the location information of a tagged object or person using radio frequency information. In this study, an RFID system is constructed and the position is measured using tags. At this time, an error occurs between the actual and measured positions. To overcome this problem, a neural network is trained using the measured and actual position data to reduce the error. In this case, since the number of read tags is not constant, they are not suitable as input values for training the neural network, so the neural network is trained by converting them into center-of-gravity inputs and median value inputs. This allows the position error to be reduce by the neural network. In addition, different numbers of trained data are used, viz. 50, 100, 200 and 300, and the correlation between the number of data input values and the error is checked. When the training is performed using the neural network, the errors of the center-of-gravity input and median value input are compared. It was found that the greater the number of trained data, the lower the error, and that the error is lower when the median value input is used than when the center-of-gravity input is used.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.136-141
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• 2019

In this paper, we describe the design of a small size data acquisition system with STM32 processor based on Cortex-M4. The system is used for the sensor devices to collect raw data on production lines at factory and send them to the server computer in real time. Also the system is designed to easily acquisite various kinds of data collected from various sensors with the digital signal input unit, the analog signal input unit, the digital signal output unit and the analog signal output unit This small data acquisition system will contribute to the improvement of the quality of precision products in the industrial field by collecting various data in real time and transmitting data at high speed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.157-172
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• 2020

This study attempts to analyze trade flows between Vietnam and China in order to understand the mutual influence of bilateral trade relations. China is a country with the world's leading economic potential. China and Vietnam are neighboring countries sharing a border of 1,281 km. Trade relations between the two countries are a necessity and, with a right policy, are beneficial to both. Vietnam has a trade deficit with China. This situation is exacerbated by the continuing rise in the gap. Vietnam trade deficit from China was USD12.5 billion in 2010, increasing to USD24 billion in 2018. Data are extracted from the 2015 national input-output tables of Vietnam and China as well as Vietnam Household Living Standard Survey statistics. The research identified 36 sectors of bilateral input-output trade between Vietnam and China. A bilateral output-input model is applied to analyze how final demand and use of input in the production of this country induces output and value added of the other country. The results show that China benefits more from Vietnam's production and consumption than Vietnam does. Vietnam's inter-sector structure does not stimulate domestic production due to the absence of supporting products as inputs in the production process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.261-267
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• 2001

This paper presents the architecture of multiple-queue manager for input-queued switch, which has arbitration latency, and the design of the chip. The proposed architecture of multiple-queue manager provides wire-speed routing with a pipelined buffer management, and the tolerance of requests and grants data transmission latency between the input queue manager and central arbiter using a new request control method, which is based on a high-speed shifter. The multiple-input-queue manager has been implemented in a field programmable gate array chip, which provides OC-48c port speed. It enhances the maximum throughput of the input queuing switch up to 98.6% with 128-cell shared input buffer in 16$\times$16 switch size.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.10
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• pp.32-39
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• 2009

Most mobile devices provide limited input interfaces in order to maximize the mobility and the portability. In this paper, the author proposes a small cubic-shaped tangible input interface which tracks the location, the direction, and the velocity using MEMS sensor technology to overcome the physical limitations of the poor input devices in mobile computing environments. As the preliminary phase for implementing the proposed tangible input interface, the prototype design and implementation methods are described in this paper. Various experiments such as menu manipulation, 3-dimensional contents control, and sensor data visualization have been performed in order to verify the validity of the proposed interface. The proposed tangible device enables direct and intuitive manipulation. It is obvious that the mobile computing will be more widespread and various kinds of new contents will emerge in near future. The proposed interface can be successfully employed for the new contents services that cannot be easily implemented because of the limitation of current input devices. It is also obvious that this kind of interface will be a critical component for future mobile communication environments. The proposed tangible interface will be further improved to be applied to various contents manipulation including 2D/3D games.