• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.1
• /
• pp.1-9
• /
• 2019

In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.

• Korea Trade Review
• /
• v.44 no.3
• /
• pp.203-226
• /
• 2019

This study incorporates the structural relationships between leadership styles (LS), affect climate (AC), and organizational performance (OP) in Korean export manufacturing companies with three or more overseas subsidiaries. A theoretical model is suggested with the following empirical results. First, the positive effect of engaging leadership (EL) on organizational citizenship behavior (OCB), as well as of engaging leadership, involving leadership (IL), and goal-oriented leadership (GL) on team performance (TP), proved to be significant. Second, both engaging leadership and goal-oriented leadership have significant positive effects on optimism, while involving leadership has significant negative effects on pessimism. Third, only optimism has a positive (+) effect on OCB and TP. The mediating effects were proved to be significant in two paths: one in EL->optimism->OCB and the other in EL->optimism->TP. Finally, in responding to rapid changes in the external environment of exporting companies, the engaging leadership is a key source of organizational performance by forming a favorable affect climate. Therefore, top management should recognize the role of team leaders and strengthen their leadership training. In addition, it was confirmed that leaders with emotional intelligence that can respond to the affects of members play a more important role in forming an optimistic climate in Korea export manufacturing enterprises with foreign subsidiaries.

• Journal of Information Technology Services
• /
• v.20 no.2
• /
• pp.91-109
• /
• 2021

Recently, social and economic environment has been rapidly changed. In particular, the development of IT technology accelerated the introduction of databases, communication networks, information processing and analyzing systems, making the use of such information and communication technology an essential factor for corporate management innovation. This change also affected the accounting areas. The purpose of this study is to document changes in accounting areas due to the adoption of IT technologies in the era of technology and information, to define the required accounting professions in this era, and to present the efficient educational methodologies for training such accounting experts. An accounting expert suitable for the era of technology and information means an accounting profession not only with basic accounting knowledge, competence, independency, reliability, communication skills, and flexible interpersonal skills, but also with IT skills, data utilization and analysis skills, the understanding big data and artificial intelligence, and blockchain-based accounting information systems. In order to educate future accounting experts, the accounting curriculum should be reorganized to strengthen the IT capabilities, and it should provide a wide variety of learning opportunities. It is also important to provide a practical level of education through industry and academic cooperation. Distance learning, web-based learning, discussion-type classes, TBL, PBL, and flipped-learnings will be suitable for accounting education methodologies to foster future accounting experts. This study is meaningful because it can motivate to consider accounting educational system and curriculum to enhance IT capabilities.

• International Journal of High-Rise Buildings
• /
• v.9 no.4
• /
• pp.351-360
• /
• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• Journal of Information Technology Services
• /
• v.20 no.1
• /
• pp.55-65
• /
• 2021

For smart defense innovation, the key is to apply state-of-the-art technologies of the Fourth Industrial Revolution to national defense. In order to apply state-of-the-art technology to the defense sector, we need to apply and develop technologies to analyze and respond to uncertain future operational environments. To this end, our military is investing a lot of time and effort. To understand future operational environment analysis and to apply and develop our military, we explored the perspectives of operational environment analysis in major countries and studied specific cases of U.S. troops with systematic analysis functions. The U.S. Army has established a cooperative system to analyze future operational environment under the leadership of the Education Command and operates the organization organically. It also utilizes the collective intelligence of expert groups in various fields by utilizing the MSC, and it is time for the Korean military to take the lead in keeping with the era of transformation. To that end, the organization of the U.S. Education Command should be benchmarked and the Korean Future Operation Environment Analysis Organization should be established and operated. Through this study, we have developed an understanding of the future operational environment analysis system of the U.S. Army and presented a plan to apply the ROK MND.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.1114-1121
• /
• 2021

In this paper, we implement a recipe recommendation system using ingredient harmonization analysis based on recipe data. The proposed system receives an image of a food ingredient purchase receipt to recommend ingredients and recipes to the user. Moreover, it performs preprocessing of the receipt images and text extraction using the OCR algorithm. The proposed system can recommend recipes based on the combined data of ingredients. It collects recipe data to calculate the combination for each food ingredient and extracts the food ingredients of the collected recipe as training data. And then, it acquires vector data by learning with a natural language processing algorithm. Moreover, it can recommend recipes based on ingredients with high similarity. Also, the proposed system can recommend recipes using replaceable ingredients to improve the accuracy of the result through preprocessing and postprocessing. For our evaluation, we created a random input dataset to evaluate the proposed recipe recommendation system's performance and calculated the accuracy for each algorithm. As a result of performance evaluation, the accuracy of the Word2Vec algorithm was the highest.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1057-1070
• /
• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Journal of the Korean Society of Visualization
• /
• v.20 no.2
• /
• pp.78-85
• /
• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.45-55
• /
• 2022

Recently, large-scale language models (LPLM) have been shown state-of-the-art performances in various tasks of natural language processing including intent classification. However, fine-tuning LPLM requires much computational cost for training and inference which is not appropriate for dialog system. In this paper, we propose compressed intent classification model for multi-agent in low-resource like CPU. Our method consists of two stages. First, we trained sentence encoder from LPLM then compressed it through knowledge distillation. Second, we trained agent-specific adapter for intent classification. The results of three intent classification datasets show that our method achieved 98% of the accuracy of LPLM with only 21% size of it.

• Journal of the Korean Wood Science and Technology
• /
• v.50 no.6
• /
• pp.490-498
• /
• 2022

The current study aimed to verify the image segmentation ability of rays in tangential thin sections of conifers using artificial intelligence technology. The applied model was Mask region-based convolutional neural network (Mask R-CNN) and softwoods (viz. Picea jezoensis, Larix gmelinii, Abies nephrolepis, Abies koreana, Ginkgo biloba, Taxus cuspidata, Cryptomeria japonica, Cedrus deodara, Pinus koraiensis) were selected for the study. To take digital pictures, thin sections of thickness 10-15 ㎛ were cut using a microtome, and then stained using a 1:1 mixture of 0.5% astra blue and 1% safranin. In the digital images, rays were selected as detection objects, and Computer Vision Annotation Tool was used to annotate the rays in the training images taken from the tangential sections of the woods. The performance of the Mask R-CNN applied to select rays was as high as 0.837 mean average precision and saving the time more than half of that required for Ground Truth. During the image analysis process, however, division of the rays into two or more rays occurred. This caused some errors in the measurement of the ray height. To improve the image processing algorithms, further work on combining the fragments of a ray into one ray segment, and increasing the precision of the boundary between rays and the neighboring tissues is required.