• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.4
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• pp.23-30
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• 2011

Fillers have been used for papermaking in order to enhance the optical properties, to improve sheet formation, printability and dimensional stability and to reduce the furnish cost. However, filler particles in paper interfere with fiber-fiber bonding, resulting in decreased paper strength. In order to increase filler content in paper without sacrificing too much paper strength, dual addition technology of fillers was investigated. As a first step, the effects of thick stock addition of fillers on paper properties and papermaking process were elucidated. It was shown that thick stock addition of fillers could increase paper strength at a given filler content. No significant adverse effects on formation, drainage and filler retention were observed. However, bulk of paper was reduced with thick stock addition of fillers, which shall be resolved with regulating other factors such as the mixing ratio of pulps and type of fillers.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.1-12
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• 2019

On the Internet age, the news is generated and distributed not only by traditional news media, but also by a variety of online news media, news platforms, content websites/content farms, and social media. Since it is an easy task to create and distribute news, some of these news reports may contain fake or false facts. In the end, the cyberspace is full of fake or false messages. People may wonder if these fake news actually influence our decision making. In this paper, we discussed a real case of fake news. In this case, a Taiwanese company used some fake news, advertorial news, and news placement to manipulate or influence its stock price and trade volume. We collected all news for the case company during a period of four years and five months (from January 2013 to May 2017). We analyzed the relationship between published news and stock price. Based on the analysis results, we conclude that we should not ignore the influence of news placement and fake business news on the stock price.

• Journal of Contemporary Eastern Asia
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• v.20 no.2
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• pp.63-84
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• 2021

The present study was conducted to understand the turmoil effects of COVID-19 pandemic on the Malaysian stock market during the different periods of the Movement Control Order (MCO). The present study was based on the secondary data extracted from the DataStream and Bloomberg from 2nd January 2020 to 29th May 2020 to evaluate the effects of COVID-19 pandemic on the Malaysian stock market. The findings suggested that during the different periods of the Movement Control Order (MCO) from the 1st January to 29th May 2020, the COVID-19 pandemic adversely affected the performance of KLCI index and all sectoral indices. The weakest performance indices were energy, property, and finance while the least affected indices were healthcare, technology, telecommunications, and media. This paper provides a review of the impacts of COVID-19 pandemic on the Malaysian stock market throughout the different periods of MCO.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.375-381
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• 2021

Underpricing signifies that IPO share prices do not reflect the fundamental value of the listed company. Corporate governance plays an essential role in IPOs where the board of directors, the independent board of directors, and the board of supervisors are significant elements of accurate share pricing. The study investigates the underpricing phenomena and short-term performance of the IPO companies during the listing process in the Ho Chi Minh Stock Exchange (HOSE). The work outcomes illustrate the role of the corporate organizational structure in the period of the IPO process that may attract potential investors. The hypothesis testing is conducted with a multiple regression model including 100 observations from enterprises doing IPO listed on HOSE. The study results generate signals for the investors and regulators that the board of directors holds a strong negative influence on the underpricing process. Secondly, the level of the independent board of directors and stock exchange in itself has no significant impact on the underpricing process. Underpricing is one of the many anomalies of the stock exchanges that provide wrong signals for the market participants. Identifying stock prices that reflect their intrinsic value is an ongoing debate among scholars, investors, and other market participants.

• The Journal of Economics, Marketing and Management
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• v.7 no.2
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• pp.1-5
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• 2019

Purpose - Along with the rise of foreign investments in the Korean stock market, there has been a variety of studies on their influence. The conflicting findings on the question of information asymmetry of foreign investors among existing literatures appear to be a result of mixture of research method problems, what information is defined as being comparable, individual business levels, or the entire stock market. This paper empirically investigates what factors contribute to foreign investments in firms in the Korean stock market. Research design, data, and Methodology - Samples are constructed by manufacturing firms listed on the stock market of Korea as well as those who settle accounts in December from 2001 to 2018. Financial institutions are excluded from the sample as their accounting procedures, governance and regulations differ. This study adopted the panel regression model to assess the sample construction including yearly and cross-sectional data. Result - This paper find that firms' R&D, dividends, size give significant positive impact to foreign investment, whereas debt gives significant negative impact to foreign investment. This relationship does not change when the samples are divided before and after the 2008 global financial crisis. Conclusion - This results support the literatures that foreign investors favor firms lowering their information asymmetry.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.5
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• pp.50-58
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• 2014

The PRT(Personal Rapid Transit) system is highly interested to meet a need for demand-responsive transport service and increasing demands of traffic in Korea recently. And it is being spotlighted as an eco-friendly transportation system. For these reasons, researches on the PRT system are actively undergoing in Korea. In this study, we evaluated the static structural and fatigue strengths based on ASCE-APM standards and ERRI B 12/RP 17 by means of FE simulation. We also evaluate the running stability by multi-body dynamic analyses and the rollover safety by a theoretical static stability factor according to the road modeling scenarios for the PRT system. From the results of this study, we confirmed the durability and running stability of the Korean PRT under development.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Journal of Forest and Environmental Science
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• v.25 no.3
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• pp.167-175
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• 2009

An exploratory study was conducted in Juri Forest Range-2, a proposed biodiversity conservation area of Bangladesh to explore the present growing stock of tree, regeneration condition and status of non-timber forest products (NTFPs). This conservation area contains both natural and artificial plantation was selected by using multistage random sampling method. For determination of plot size and sampling methods, the quadrate size ($10m{\times}10m$) for tree stock measurement, ($2m{\times}2m$) for regeneration survey, ($20m{\times}20m$) for NTFPs survey was determined. Regarding tree stock survey, 14 species under eight families were found where Tectona grandis shows average number of stem/ha was 624 and basal area/ha was (10.36 $m^2/ha$) followed by Acacia auriculiformis (0.2 $m^2/ha$ and 637 stem/ha), Gmelina arborea (0.2 $m^2/ha$ and 600 stem/ha). In regeneration survey, 14 species were found belonging to 9 families where Alstonia scholaris shows highest (3,750) seedling per hectare. Regarding NTFPs, bamboo and cane are the most common resources. In last ten years, the total timber output was 1,28,596.14 cubic feet and total amount of revenue was 4,64,434 US$. The vacant area is 1,335.5 acre which contains 14% of total area. If this vacant area is planted with suitable species and take proper steps for appropriate management of this species it will be a good biologically diversified area.

• Horticultural Science & Technology
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• v.35 no.4
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• pp.439-445
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• 2017

Transplant production in a plant factory with artificial lighting provides several benefits; (1) rapid and uniform transplant production, (2) high production rate per unit area, and (3) production of disease free transplants production. To improve the growth of runner plants when strawberry transplants are produced in a plant factory, we conducted two experiments to investigate (1) the effect of different light intensity for stock and runner plants on the growth of runner plants, and (2) the effect of different container volume for runner plants on their growth. When the stock and runner plants were grown under nine different light conditions composed of three different light intensities (100, 200, and $400{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ PPF) for each stock and runner plants, increasing the light intensity for stock plants promoted the growth of runner plants, however, the growth of runner plants was not enhanced by increasing the light intensity for runner plants under same light intensity condition for stock plants. We also cultivated runner plants using plug trays with four different container volumes (21, 34, 73, and 150 mL) for 20 days after placing the stock plants, and found that using plug trays with lager container volume did not enhance the growth of runner plants. These results indicate that providing optimal condition for stock plants, rather than the runner plants, is more important for increasing the growth of the runner plants and that the efficiency of strawberry transplant production in a plant factory can be improved by decreasing light intensity or container volume for runner plants.