• Journal of Korea Port Economic Association
• /
• v.25 no.4
• /
• pp.183-202
• /
• 2009

This study estimates the port waiting cost of international trade ports in Korea by an opportunity cost approach. In the next step, we present a method to assess the levels of port services by the average waiting cost of ships derived from the results of the first step. Because the port waiting cost reflects the social cost, it is difficult to use as a service indicator even though it is the decision support information for a particular port facility expansion. The percentages of waiting ships and time also are insufficient indicators to reflect only the quantitative aspects by the time. However, the average waiting cost of ships in this study can be utilized as a service indicator to reflect waiting time and the loss of economic value simultaneously. It is also very useful information for a shipper and a carrier to select a port. Based on the average waiting cost of ships in 2007, it is analyzed in order of lowest service ports sequentially such as Pyeongtaek-Dangjin, Pohang, Donghae, and Samcheonpo. It is different from the sequential order of ports by the port waiting cost such as Pohang, Incheon, Gwangyang, Pyeongtak-Dangjin, and Ulsan. The port waiting cost is to a port authority as a key indicator what the average waiting cost of ships is to a port user as a useful indicator to evaluate the levels of port services.

• International Commerce and Information Review
• /
• v.14 no.4
• /
• pp.109-130
• /
• 2012

In recent years, the volume of global fair trade has been increased rapidly. As the quality of life improves, consumers are increasingly concerned about fairness and environment and show positive attitudes toward ethical products. Fair trade is a social movement that aims to set fair prices for products, alleviate poverty, and assist producers marginalized by the traditional economic model. The study attempt to investigate empirically fair trade products purchase intention, so consumers attitude toward fair trade product purchase, normative belief, fairness restoration expectation, ethical responsibility are defined and their impacts on the purchase intention were analyzed. The statistical method used to test the hypotheses was multi-regression using SPSS 18 for window. The results of this study are follows. Purchase attitudes toward fair trade products and ethical responsibility had a significant effect on the purchase intention. The Effectiveness of the ethical responsibility had greater than that of the purchase attitudes. The result of the empirical study provides important implications for the fair trade related organizations and businesses.

• Journal of Digital Convergence
• /
• v.15 no.7
• /
• pp.147-157
• /
• 2017

Recently, ICT Retail Convergence(IRC) has been rapidly increasing to improve consumer satisfaction and consumer experience. In this paper, we aim to diagnose IRC from consumers' point of view by reviewing the present status and value of IRC according to consumer purchase decision making process. Based on the previous studies in retail industry, we classified IRC into 4 types: Experience-specific tech(Virtual Reality and Augmented Reality); Information-specific tech(Artificial Intelligence and Big Data); Location-based tech(Radio Frequency Identification and Beacon); Payment-related tech(Fin-tech and Biometrics). Next, we found that there is a difference in value provided to consumers according to the type of technology, analysing the value by consumer purchase decision making process. This study can be useful to introduce IRC for improving consumer satisfaction as well as ICT and Retail. Also, it can be basic data for future technology studies with a consumer perspective.

• Journal of Intelligence and Information Systems
• /
• v.16 no.1
• /
• pp.93-116
• /
• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• The KIPS Transactions:PartA
• /
• v.18A no.2
• /
• pp.45-52
• /
• 2011

In the past, a patient went to the room where an ultrasound image diagnosis device was set, and then he or she was examined by a doctor. However, currently a doctor can go and examine the patient with a handheld ultrasound device who stays in a room. However, it was implemented with only fundamental functions, and can not meet the high performance required by the focusing algorithm of ultrasound beam which determines the quality of ultrasound image. In addition, low energy consumption was satisfied for the mobile ultrasound device. To satisfy these requirements, this paper proposes a high-performance and low-power single instruction, multiple data (SIMD) based multi-core processor that supports a representative beamforming algorithm out of several focusing methods of mobile ultrasound image signals. The proposed SIMD multi-core processor, which consists of 16 processing elements (PEs), satisfies the high-performance required by the beamforming algorithm by exploiting considerable data-level parallelism inherent in the echo image data of ultrasound. Experimental results showed that the proposed multi-core processor outperforms a commercial high-performance processor, TI DSP C6416, in terms of execution time (15.8 times better), energy efficiency (6.9 times better), and area efficiency (10 times better).

• Journal of the Society of Disaster Information
• /
• v.17 no.2
• /
• pp.254-265
• /
• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.202-217
• /
• 2018

Urban land use changes by human activities affect spatial configuration of urban areas and their surrounding ecosystems. Although it is necessary to identify patterns of urban land use changes and to simulate future changes for sustainable urban management, simulation of land use changes is still challenging due to their uncertainty and complexity. Cellular automata model is widely used to simulate urban land use changes based on cell-based approaches. However, cell-based models can not reflect features of actual land use changes and tend to simulate fragmented patterns. To solve these problems, object-based cellular automata models are developed, which simulate land use changes by land patches. This study simulate future land use changes in Hanam city using an object-based cellular automata model. Figure of merit of the model is 24.1%, which assess accuracy of the simulation results. When a baseline scenario was applied, urban decreased by 16.4% while agriculture land increased by 9.0% and grass increased by 19.3% in a simulation result of 2038 years. In an urban development scenario, urban increased by 22.4% and agriculture land decreased by 26.1% while forest and grass did not have significant changes. In a natural conservation scenario, urban decreased by 29.5% and agriculture land decreased by 8.8% while each forest and grass increased by 6% and 42.8%. The model can be useful to simulate realistic urban land use change effectively, and then, applied as a decision support tool for spatial planning.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.887-894
• /
• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• Management & Information Systems Review
• /
• v.38 no.1
• /
• pp.141-153
• /
• 2019

This study attempts to analyze the role of price discovery and the dynamic interdependence between KOSPI200 Index and KODEX Leverage(KODEX inverse), which are Korea's representative ETFs, using the vector error correction model. For the empirical analysis, one minute data of KODEX leverage, KODEX inverse and KOSPI200 index from April 10, 2018 to July 10, 2018 were used. The main results of the empirical analysis are as follows. First, between KODEX Leverage and KOSPI200 index, we found evidence that KODEX leverage plays a dominant role in price discovery. In addition, the KOSPI200 index is superior to price discovery between KODEX inverse and KOSPI200 index. Second, the KOSPI200 index has a relatively strong dependence on KODEX leverage, which is consistent with the KODEX leverage index playing a dominant role in price discovery compared to the KOSPI200 index. On the other hand, KOSPI200 index has a dependency on KODEX inverse index, but it is weaker than KODEX leverage index. These results are expected to be useful information for investors in capital markets.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.105-112
• /
• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.