• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1535-1545
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• 2022

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.49-58
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• 2009

Top-down approach in the intelligent robot research has focused on the single object intelligence however, it has two weaknesses. One is that has a high cost and a long spending time of sensing, calculating and communications. The other is the difficulty of responding to react changes in the unpredictable environment. we propose the collective intelligence algorithm based on Bottom-up approach for improving these weaknesses and the applied agent model and verify by simulation. The Modified Flocking Algorithm proposed in this research is the algorithm which is modified version of the concept of the Flocking (Craig Reynolds) which is used to model the flocks, herds, and schools in the graphics or games, and simplified the operation of conventional Flocking algorithm to make it easy to apply for the number of group robots. We modeled the Boid agent and verified possibility collectivization of the Modified Flocking Algorithm by simulation. And We validated by the actual multiple mobile robot experiment.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.