• Journal of Platform Technology
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• v.9 no.4
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• pp.90-96
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• 2021

Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.61-84
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• 2022

With the recent application of deep learning to Natural Language Processing (NLP), the performance of NLP has improved significantly and NLP is emerging as a core competency of organizations. However, when encountering NLP use cases that are sporadically reported through various online and offline channels, it is often difficult to come up with a big picture of how to understand and interpret them or how to connect them to business. This study presents a framework for systematically analyzing NLP use cases, considering the characteristics of NLP techniques applicable to almost all industries and business functions, environmental changes in the era of the Fourth Industrial Revolution, and the effectiveness of adopting NLP reflecting all business functional areas. Through solving research questions based on the framework, the usefulness of it is validated. First, by accumulating NLP use cases and pivoting them around the business function dimension, we derive how NLP techniques are used in each business functional area. Next, by synthesizing related surveys and reports to the accumulated use cases, we draw implications for each business function and major NLP techniques. This work promotes the creation of innovative business scenarios and provides multilateral implications for the adoption of NLP by systematically viewing NLP techniques, industries, and business functional areas. The use case analysis framework proposed in this study presents a new perspective for research on new technology use cases. It also helps explore strategies that can dramatically improve organizational performance through a holistic approach that encompasses all business functional areas.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.19-26
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• 2022

This paper presents a pre-processing method to facilitate the container's BIC-code recognition. We propose a network that can find ROI(Region Of Interests) containing a BIC-code region and estimate a homography matrix for warping. Taking the structure of STN(Spatial Transformer Networks), the proposed network consists of next 3 steps, ROI detection, homography matrix estimation, and warping using the homography estimated in the previous step. It contributes to improving the accuracy of BIC-code recognition by estimating ROI and matrix using the proposed network and correcting perspective distortion of ROI using the estimated matrix. For performance evaluation, five evaluators evaluated the output image as a perfect score of 5 and received an average of 4.25 points, and when visually checked, 224 out of 312 photos are accurately and perfectly corrected, containing ROI.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• Smart Media Journal
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• v.11 no.5
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• pp.26-37
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• 2022

In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.225-232
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• 2022

Recent development in information and communication technology has been beneficial to many, but at the same time, malicious attack attempts are also increasing through vulnerabilities in new programs. Among malicious attacks, malware operate in various ways and is distributed to people in new ways every time, and to solve this malware, it is necessary to quickly analyze and provide defense techniques. If new malware can be classified into the same type of malware, malware has similar behavioral characteristics, so they can provide defense techniques for new malware using analyzed malware. Therefore, there is a need for a solution to this because the method of accurately and quickly classifying malware and the number of data may not be uniform for each family of analyzed malware. This paper proposes a system that combines image preprocessing and ensemble techniques to increase accuracy in imbalanced data.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1861-1869
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• 2022

Recent climate change can increase the frequency and damage of wildfires worldwide. It can also lead to the deterioration of the forest ecosystem and increase casualties and economic loss. Satellite-based indices for forest damage can facilitate an objective and rapid examination of burned areas and help analyze inaccessible places like North Korea. In this letter, we conducted a detection of burned areas in North Korea using the traditional Normalized Burn Ratio (NBR), the Normalized Difference Vegetation Index (NDVI) to represent vegetation vitality, and the Fire Burn Index (FBI) and Forest Withering Index (FWI) that were recently developed. Also, we suggested a strategy for the satellite-based detection of burned areas in the Korean Peninsula as a result of comparing the four indices. Future work requires the examination of small-size wildfires and the applicability of deep learning technologies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.