• Journal of the Korea Convergence Society
• /
• v.13 no.5
• /
• pp.219-228
• /
• 2022

In order to improve audit quality, it is essential to understand the occurrence of disagreement between auditors and managers, and this study aims to analyze the impact of Business Strategies on audit risk and accounting audit delay. To this end, we conducted an empirical analysis using sample 2,910 firm-year data from 2018 to 2020 of KOSPI-listed and KOSDAQ-listed companies. The results of the empirical analysis of this study are as follows. First, compared to the companies of defender type, prospectors can expand audit procedures for new products, R&D costs, and intangible assets, and increase audit delays due to disagreement between managers and auditors. Second, compared to KOSPI-listed companies, the prospectors in KOSDAQ are more likely to have lower financial reporting quality, which further increases audit delays. The results of this study analyzed whether a company's Business Strategy affects the possibility of disagreement between an auditor and a company, and verified whether there is a difference in the audit report lag by stock market. The results of this study show that auditors' strong duty of care is needed for the companies of prospector type with high audit risk, and it is meaningful to present reinforced audit systems and specific guidelines for the companies of prospector type through the definition of prospector type. It also enables the expansion of research to identify the relationship between non-financial factors and audit risks that make up the companies of prospector type.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.227-227
• /
• 2015

The Wairarapa Valley occupies a predominantly rural area in the lower North Island of New Zealand. It supports a mix of intensive farming (dairy), dry stock farming (sheep and beef cattle) and horticulture (including wine grapes). The valley floor is traversed by the Ruamahanga River, the largest river in the Wellington region with a total catchment area of 3,430 km2. Environmental, cultural and recreational values associated with this Ruamahanga River are very high. The alluvial gravel and sand aquifers of the Wairarapa Valley, support productive groundwater aquifers at depths of up to 100 metres below ground while the Ruamahanga River and its tributaries present a further source of water for users. Water is allocated to users via resource consents by Greater Wellington Regional Council (GWRC). With intensifying land use, demand from the surface and groundwater resources of the Wairarapa Valley has increased substantially in recent times and careful management is needed to ensure values are maintained. This paper describes the approach being taken to manage water resources in the Wairarapa Valley and redefine appropriate limits of sustainable water use. There are three key parts: Quantifying the groundwater resource. A FEFLOW numerical groundwater flow model was developed by GWRC. This modelling phase provided a much improved understanding of aquifer recharge and abstraction processes. It also began to reveal the extent of hydraulic connection between aquifer and river systems and the importance of moving towards an integrated (conjunctive) approach to allocating water. Development of a conjunctive management framework. The FEFLOW model was used to quantify the stream flow depletion impacts of a range of groundwater abstraction scenarios. From this, three abstraction categories (A, B and C) that describe diminishing degrees of hydraulic connection between ground and surface water resources were mapped in 3 dimensions across the Valley. Interim allocation limits have been defined for each of 17 discrete management units within the valley based on both local scale aquifer recharge and stream flow depletion criteria but also cumulative impacts at the valley-wide scale. These allocation limits are to be further refined into agreed final limits through a community-led decision making process. Community involvement in the limit setting process. Historically in New Zealand, limits for sustainable resource use have been established primarily on the basis of 'hard science' and the decision making process has been driven by regional councils. Community involvement in limit setting processes has been through consultation rather than active participation. Recent legislation in the form of a National Policy Statement on Freshwater Management (2011) is reforming this approach. In particular, collaborative consensus-based decision making with active engagement from stakeholders is now expected. With this in mind, a committee of Wairarapa local people with a wide range of backgrounds was established in 2014. The role of this committee is to make final recommendations about resource use limits (including allocation of water) that reflect the aspirations of the communities they represent. To assist the committee in taking a holistic view it is intended that the existing numerical groundwater flow models will be coupled with with surface flow, contaminant transport, biological and economic models. This will provide the basis for assessing the likely outcomes of a range of future land use and resource limit scenarios.

• Journal of Animal Environmental Science
• /
• v.8 no.3
• /
• pp.199-208
• /
• 2002

This paper introduces the method to make management network about milking cow farm tasks. The object of this research was to design of biological measuring system and managing network system in a livestock farm. This auto-management system provides informations about individual cows' temperature, conductivity of milk and weight for efficient management of feeding, and milking works by a micro-processor and RS -485 type serial COM. ports. And measured bio-data which are basic informations for remote raising management are saved to user PC by serial communication between the PLC and user PC. Milking cow farm is divided into three working place to each measurement work and feed. The first working place is milking station which has two thermometers, a conduct meter and a scale set. The second working place is feeding station, and the third place is cattle cage. These are combined by network system and the PLC which is used to drive network and sub-modules. Sub-modules have a micro-process to control the sensor and to interface with network. The PLC which drive network and control sequence has two serial communication port to be linked with user PC for sending the measured data and for receiving data. Above all, in this study tells the sequence operating method by the driving scenario of breeding milk cow for livestock auto-management using the PLC and network system.

• The Journal of the Petrological Society of Korea
• /
• v.26 no.3
• /
• pp.235-254
• /
• 2017

We investigate the geological history that formed geology and landscapes of the Juwangsan National Park and its surrounding areas. The Juwangsan area is composed of Precambrian gneisses, Paleozoic metasedimentary rocks, Permian to Triassic plutonic rocks, Early Mesozoic sedimentary rocks, Late Mesozoic plutonic and volcanic rocks, Cenozoic Tertiary rhyolites and Quaternary taluses. The Precambrian gneisses and Paleozoic metasedimentary rocks of the Ryeongnam massif occurs as xenolithes and roof-pendents in the Permian to Triassic Yeongdeok and Cheongsong plutonic rocks, which were formed as the Songrim orogeny by magmatic intrusions occurring in a subduction environment under the northeastern and western parts of the area before a continental collision between Sino-Korean and South China lands. The Cheongsong plutonic rocks were intruded by the Late Triassic granodiorite, which include to be metamorphosed as an orthogneiss. The granodiorite includes geosites of orbicular structure and mineral spring. During the Cretaceous, the Gyeongsang Basin and Gyeongsang arc were formed by a subduction of the Izanagi plate below East Asia continent in the southeastern Korean Peninsula. The Gyeongsang Basin was developed to separate into Yeongyang and Cheongsong subbasins, in which deposited Dongwach/Hupyeongdong Formation, Gasongdong/Jeomgok Formation, and Dogyedong/Sagok Formation in turn. There was intercalated by the Daejeonsa Basalt in the upper part of Dogyedong Formation in Juwangsan entrance. During the Late Cretaceous 75~77 Ma, the Bunam granitoid stock, which consists of various lithofacies in southwestern part, was made by a plutonism that was mixing to have an injection of mafic magma into felsic magma. During the latest Cretaceous, the volcanic rocks were made by several volcanisms from ubiquitous andesitic and rhyolitic magmas, and stratigraphically consist of Ipbong Andesite derived from Dalsan, Jipum Volcanics from Jipum, Naeyeonsan Tuff from Cheongha, Juwangsan Tuff from Dalsan, Neogudong Formation and Muposan Tuff. Especially the Juwangsan Tuff includes many beautiful cliffs, cayon, caves and falls because of vertical columnar joints by cooling in the dense welding zone. During the Cenozoic Tertiary, rhyolite intrusions formed lacolith, stocks and dykes in many sites. Especially many rhyolite dykes make a radial Cheongsong dyke swarm, of which spherulitic rhyolite dykes have various floral patterns. During the Quaternary, some taluses have been developed down the cliffs of Jungtaesan lacolith and Muposan Tuff.

• The Korean Journal of Pesticide Science
• /
• v.18 no.4
• /
• pp.376-382
• /
• 2014

The soluble loess-sulfur mixture allowed standing to remove insoluble component materials for five weeks after manufacturing. We decreased the pH level of soluble loess-sulfur mixture at pH 1.0 modified with decreasing 25% sodium hydroxide than original content. The pH ranges of soluble loess-sulfur mixture solutions were adjusted to pH 5.0-pH 11.0 (pH 1 unit) with brown rice vinegar (pH 2.8). The pH of original loess-sulfur mixture was about pH 13 and damaged the foliar parts and young leaves of tomato after twice application. These stock solutions can be diluted 500:1 with tap water to make a 0.05% working solution and were sprayed two times with 7 days interval to the leaf and stem of tomato, which were spontaneously infected with E. cichoracearum. Control efficacy of powdery mildew ranged from 85% to 90% at 7 days after first application. After second application, each loess-sulfur mixture solutions adjusted pH level significantly suppressed the powdery mildew disease in tomato. Consequently, loess-sulfur complex adjusted pH level with brown rice vinegar was suggested to be low in acute toxicity at all different pH values and suggested to use an agent for control of tomato powdery mildew in organic farming.

• Journal of Korea Technology Innovation Society
• /
• v.15 no.3
• /
• pp.649-674
• /
• 2012

The purpose of this study is to investigate the effects of public R&D subsidies on private R&D. We have analyzed rationales for the public R&D subsidy from different perspectives. On the basis of literature review, a two step research model is constructed: participation phase (when firms benefit from public subsidies) and decision phase (when firms make decision on additional R&D investments). Using propensity score matching(PSM) method, we compare the potential outcome of the treated group to a matched controlled group of non-subsidized firms. The data used in this paper was collected from various sources. The Korean Innovation Survey 2008(manufacturing sector) is a main source of data. Financial data such as revenue, asset and capital stock, and number of employees were supplemented from the Nice Information Service KIS Value database. The R&D survey, conducted by MEST(Ministry of Education, Science and Technology) each year, was also used for the R&D expenditures of the manufacturing firms. This study comes up with the following empirical results. First, a firm's innovation capability, financial constraints, and sector appear to influence the selection of firms who were benefited from government's financial supports for R&D. Second, empirical results show that public R&D funding complements private investment on average and appear to have perpetual effects on the following year. Finally, sectoral difference in the effect of public subsidies on firms' R&D investment was confirmed. In addition, SMEs show more positive effects than large firms.

• Knowledge Management Research
• /
• v.22 no.4
• /
• pp.119-133
• /
• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Korean Journal of Construction Engineering and Management
• /
• v.25 no.5
• /
• pp.41-54
• /
• 2024

In this study, we compare and analyze the performance of three inventory management policies for raw material inventory management in a Precast Concrete production plant: Fixed Order Quantity Policy (FOQP), Fixed Order Interval Policy (FOIP), and (s, S) Ordering Policy (sSOP). In order to make more realistic conclusion, we developed and utilized the ARENA simulation model, a performance evaluation tool that considers the variance of raw material demand and supply for the entire production process in a PC production plant using multiple raw materials. For the three policies, reorder point, order quantity, target level, and order interval parameters were initialized by using Economic Order Quantity (EOQ) model and then optimized through OptQuest. As a result of optimization, inventory management costs were reduced by an average of 97.28% compared to the EOQ model that does not consider the variance of demand and supply. After setting three influencing factors, Project Occurrence Cycle (POC), Raw Material Lead-time (RML), and Unit Stock-out Cost (USC), a performance evaluation was conducted for the three policies. As a result of evaluation, the inventory management costs of FOQP and sSOP, which determine order intervals by considering inventory levels by real-time or daily, were 30.6% and 27.9% lower than FOIP with fixed order intervals respectively. In addition, inventory management costs were affected by RML and USC factors excluding POC, but the differences were 2.17% and 2.09% respectively, which were not large due to the optimization of parameters for responding the variance of raw material demand and supply.

• Journal of Korean Society of Forest Science
• /
• v.102 no.3
• /
• pp.365-371
• /
• 2013

Pine mushroom (Tricholoma matsutake) is one of the most profitable forest products in Korea. We postulated a hypothesis that a high rate of returns to labor input could make the harvest of pine mushroom off the optimum level. In the view of developing a sustainable production strategy for pine mushroom producers, production of pine mushroom collectors and pine mushroom growth function were estimated using maximum entropy method. Annual pine mushroom production and labor input were the data used in the estimation of production function of pine mushroom collectors and pine mushroom growth function. The level of sustainable maximum production derived from the estimated function. The production function estimated shows that production of pine mushroom is affected more by the resource of pine mushroom stocked in the forests than by labor that households put in forestry business. The production function of mushroom collectors and the estimated growth function indicate that pine mushroom harvests for the period of 2005-2011 did not reach the potential level of maximum sustainable production. Therefore, we suggest that pine mushroom harvest should be controlled until the resource stock of pine mushroom in the forests increases to the level of maximum sustainable production.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.105-129
• /
• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.