• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.167-177
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• 2004

The Hub-and-spoke strategy is widely used in the field of transportation. According to containerization and the development of transshipment technology, it is also introduced into European rail freight transportation. The objective of this article is to develop a service network design model for rail freight transportation based on the Hub-and-spoke strategy and efficient algorithms that can be applied to large-scale network. Although this model is for strategic decision, it includes not only general operational cost but also time-delay cost. The non-linearity of objective function due to time-delay factor is transformed into linearity by establishing train service variables by frequency. To solve large scale problem, this model used a heuristic method based on decomposition and three newly-developed algorithms. The new algorithms were examined with respect to four test problems base on the actual network of European rail freight and discussed the accuracy of solutions and the efficiency of proposed algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.323-334
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• 2023

The submerged floating tunnel (SFT) infrastructure has been regarded as an emerging technology that efficiently and safely connects land and islands. The SFT route problem is an essential part of the SFT planning and design phase, with significant impacts on the surrounding environment. This study aims to develop an optimization model considering transportation and structure factors. The SFT routing problem was optimized based on two objective functions, i.e., minimizing total travel time and cumulative strains, using NSGA-II. The proposed model was applied to the section from Mokpo to Jeju Island using road network and wave observation data. As a result of the proposed model, a Pareto optimum curve was obtained, showing a negative correlation between the total travel time and cumulative strain. Based on the inflection points on the Pareto optimum curve, four optimal SFT routes were selected and compared to identify the pros and cons. The travel time savings of the four selected alternatives were estimated to range from 9.9% to 10.5% compared to the non-implemented scenario. In terms of demand, there was a substantial shift in the number of travel and freight trips from airways to railways and roadways. Cumulative strain, calculated based on SFT distance, support structure, and wave energy, was found to be low when the route passed through small islands. The proposed model helps decision-making in the planning and design phases of SFT projects, ultimately contributing to the progress of a safe, efficient, and sustainable SFT infrastructure.

• ETRI Journal
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• v.36 no.3
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• pp.418-428
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• 2014

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

• Journal of the Korean Society for Railway
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• v.13 no.6
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• pp.565-569
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• 2010

Sleepers perform bearing rails and are underneath rails. Therefore, the current and voltage of rails are related to the resistance of sleepers. In case that the resistance of sleepers are low, operation problems of relays in tr ack circuits are occur because of flowing leakage current through sleepers. So the condition that the track circuit is always occupied by a train is kept. Currently, the creosote has been used in wood sleepers due to prevention against putrefaction. After a long time, the material is changeable to the chemistry material bases on carbon dioxide or carbon. So, the insulation resistance of wood sleepers is lower than the initial insulation resistance. In case of effecting wood sleepers as conductors, amplitude of the current and voltage on rails is decreased. This phenomenon causes that a train does not receive signals. In this paper, four-network model on the track circuit including the insulation resistance of sleepers is suggested. Also, the standard value of the resistance in straight section is proposed in the wood sleeper.

• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Journal of Distribution Science
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• v.12 no.6
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• pp.81-88
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• 2014

Purpose - Entrepreneurship promotion is emerging as an important economic growth agenda. However, in Korea, entrepreneurship has weakened because of the collapse of the venture bubbles of the 2000s and the global economic recession in 2008, which have induced the business community to choose stability over risk. The Korean government has been implementing several support projects to inspire and promote youth entrepreneurship through various means including financial assistance; however, the perpetuation rate of young entrepreneurship is still low as compared to advanced economies such as the US and EU. This case study focuses on the Youth Start-Up Business Support Program of the Small & Medium Business Corporation, and explores practical alternatives. Further, it aims to suggest managerial factors and a conceptual model for change management factors affecting the business performance creation of a startup company, based on the Small and medium Business Corporation's young venture startup fund. Research design, data, and methodology - Many studies examine the current progress and issues of startup firms, for example, a lack of systematic cultivation of entrepreneurship and startup business training, lack of commercialization funding for youth startup businesses, lack of mentoring, and inadequate infrastructure. From prior research, we address four factors, namely, personal managerial capabilities, innovative business model, sufficient cash flow, and social network, affecting startup companies' business performance. This study involved a sample survey of 200 young entrepreneurs to investigate casual relations between the four factors and business performance. A regression analysis was used to verify the hypotheses. Results - First, in relation to differences in the founder's personal characteristics, age, sales amount, and number of employees significantly impact business performance. Second, regarding the causal relation between the four factors for creating business performance, an innovative business model and social networking have supported the hypotheses, revealing that the more that a start-up founder has an innovative business model and social networking, the more the start-up firms are likely to have better performance (e.g., sales volume, employment, ROE, ROI, etc.). Although the founder's competency and sufficient cash flow have no significant relationship with business performance, the mean value was higher performance for high founder's competency and sufficient cash flow. Conclusions - This study provides basic data on policy support strategies of the Small and Medium Business Corporation, to help young entrepreneurs achieve their start-up business goals. It shows that young entrepreneurship startup firms should strive to explore ideas to satisfy customers' needs, and that changes in customer value and the continuous innovation of business model differentiation are required to actively respond to change management. Moreover, at the infant startup stage, they should activate social network programs to share information, thereby offsetting resource scarcity and managing business risk. Further, the establishment of a long-term vision and the implementation of training programs in related specific fields should be supported to strengthen founders' personal capabilities.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.