• Korea Trade Review
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• v.43 no.5
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• pp.25-45
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• 2018

This paper estimates the export losses of the Korean domestic automobile industry due to US trade pressure and its economic ripple effects. Using the HS 6 digit tariff and export data from 2010 to 2017, this paper estimates the tariff elasticity of Korea's US automobile exports against a US tariff increase by applying the Poisson Pseudo maximum likelihood estimation method. After estimating Korea's export losses to the US in three trade pressure scenarios, we estimate its impact on Korean domestic production, value-added and job creation by applying the tariff impact accumulation model based on the industry input-output analysis. Empirical results show that the impact of 25% global tariff by the US on the Korean domestic economy is estimated to result in $30.8 billion in export losses for the five years from 2019 to 2023, about 300 thousand job losses, 88.0 trillion in production inducement losses, and 24.0 trillion in value-added inducement losses. The impacts of withdrawal of the automobile tariff concession are estimated at $4.27 billion export losses and 41.7 thousand job losses. A 15% tariff rate on automobile parts for 3 years is estimated to result in $1.93 billion export losses and 18.7 thousand job losses.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.5
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• pp.367-377
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• 2016

The purpose of the projects for strengthening the Service Business Competitiveness, which had been sponsored by the Ministry of Trade, Industry and Energy, and managed by the NIPA, is to support for combining the whole business process of the SMEs with the business model considering the scientific aspects of the services, to enhance the productivity of them and to add the values of their activities. 5 organizations are selected in 2014, and 4 in 2015 as leading organizations for these projects. This study analyzed the efficiency of these projects using DEA. Throughout the analysis of the prior researches, this study used the amount of government-sponsored money as the input variable, and the number of new customer business, the sales revenue, and the number of new employment as the output variables. And the result of this analysis showed that the decision making unit 12, 15, and 21 was efficient. And from this study, we found out two more performance indicators such as, the number of new employment and the amount of sales revenue, besides the number of new customer businesses.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.373-385
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• 2009

A new indexing methodology so called KTH-index was developed to quantitatively evaluate a potential level for tunnel collapse hazard, which has been successfully applied to tunnel construction sites to date. In this study, an attempt is made to apply this methodology for validating an outcome of tunnel design by checking the variation of KTH-index along longitudinal tunnel section. In this KTH-index simulation, it is the most important to determine the input factors reasonably. The design factor and construction condition are set up based on the designed outcome. Uncertain ground conditions are arranged based on borehole test and electro-resistivity survey data. Two scenarios for ground conditions, best and worst scenarios, are set up. From this simulation, it is shown that this methodology could be successfully applied for providing quantitative validity of a tunnel design and also potential hazard factors which should be carefully monitored in construction stage. The hazard factors would affect sensitively the hazard level of the tunnel site under consideration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• The Journal of Bigdata
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• v.6 no.2
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• pp.151-159
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• 2021

Construction of a blockchain network needs a cumbersome and time consuming activity. To overcome these limitations, global IT companies such as Microsoft are providing cloud-based blockchain services. In this paper, we propose a blockchain-based construction and management tool that enables blockchain developers, blockchain operators, and enterprises to deploy blockchain more comfortably in their infrastructure. This tool is implemented using Hyperledger Fabric, one of the famous private blockchain platforms, and Ansible, an open-source IT automation engine that supports network-wide deployment. Instead of complex and repetitive text commands, the tool provides a user-friendly web dashboard interface that allows users to seamlessly set up, deploy and interact with a blockchain network. With this proposed solution, blockchain developers, operators, and blockchain researchers can more easily build blockchain infrastructure, saving time and cost. To verify the usefulness and convenience of the proposed tool, a blockchain network that conducts electronic voting was built and tested. The construction of a blockchain network, which consists of writing more than 10 setting files and executing commands over hundreds of lines, can be replaced with simple input and click operations in the graphical user interface, saving user convenience and time. The proposed blockchain tool will be used to build trust data infrastructure in various fields such as food safety supply chain construction in the future.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.67-77
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• 2021

This study is a study on the methodology of establishing an innovation strategy centering on the customer experience, which is essential in order to transform the existing collection and preservation-centered mutual aid company service into a visitor-centered service. To this end, we conducted literature research on environmental changes in the funeral industry from the perspective of service science and the significance and value of customer experiences within them, good customer experiences and bad customer experiences from the perspective of customer experience management. A study was conducted to present and prove a specific model. The customer experience-oriented innovation strategy of the funeral industry means to search for various alternatives that can reach the target state from the present state, focusing on the customer, and select the most appropriate transformation plan among them. As an effect of application, it was found that it is a source of differentiation by generating positive emotions to customers, and that customer experience data is highly helpful in making important decisions for the actual resource input of the parent company. This innovation model was presented, and its value was firstly proved by analyzing the difference from the existing evaluation method. Finally, as a result of analyzing the causal relationship through regression analysis using the customer experience measurement procedure, customer experience diagnosis/evaluation, customer experience innovation strategy, and cooperative company's performance as variables, the relationship proved to be significant.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.901-908
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• 2006

The structural integrity of containment building in Nuclear Power Plants has to be verified by the ISI(In Service Inspection) because there are some variations on the structural behavior of it due to the change of the physical properties of concrete and tendon with the lapse of time. In this study, the program 'SAPONC-CANDU' which can monitor and analyze the structural behavior of the containment building with grouted tendon (CANDU-type, 'Wolsong unit-2, 3, and 4' in Korea) was developed. This program is based on the algorithm which can calculate the prediction values of the quantities of strain variation for the vibrating-wire strain gauges embedded into the concrete of the containment building under temperature and time dependent factors which are creep, shrinkage, and prestressing force. The readings of the strain gauges are used as input data for the operation of the program. And it finally provides graphically a prediction value, line and band of the quantity of strain variation for the respective strain gauges, therefore, it is thought that the site engineers are able to assess the structural integrity of the domestic containment building with grouted tendon with easy using this program.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.159-167
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• 2010

The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.