• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• The Journal of Bigdata
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• v.8 no.2
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• pp.177-187
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• 2023

Semi-supervised learning (SSL) is an effective approach to training models using a small amount of labeled data and a larger amount of unlabeled data. However, many papers in the field use a fixed threshold when applying pseudo-labels without considering the feature-wise differences among images of different classes. In this paper, we propose a SSL method for synthetic aperture radar (SAR) image classification that applies different thresholds for each class instead of using a single fixed threshold for all classes. We propose a threshold learning module into the model, considering the differences in feature distributions among classes, to dynamically learn thresholds for each class. We compare the application of a SSL SAR image classification method using different thresholds and examined the advantages of employing class-specific thresholds.

• Journal of Nutrition and Health
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• v.54 no.5
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• pp.560-572
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• 2021

Purpose: In this study, the serving size of home meal replacement (HMR)-soups (Guk, Tang) and stews (Jjigae) available in the Korean market was investigated, and an evaluation of the nutrition per serving was conducted based on the nutrition labeling. Methods: The market research was conducted from March to August 2021 on products sold on the internet, convenience stores, supermarkets, and hypermarkets. A total of 370 products were investigated and classified into 3 types: Guk (n = 129), Tang (n = 132), and Jjigae (n = 109). Results: An analysis of the survey revealed that 72.9% of Guk, 71.2% of Tang, and 79.8% of Jjigae had labels with servings per container, and 89.2% of Guk, 91.7% of Tang, and 99.1% of Jjigae had labels with nutrition facts. The nutritional evaluation per serving of Guk, Tang, and Jjigae was conducted for 259 products (87 Guk, 86 Tang, and 86 Jjigae) having labels containing both the servings per container and nutrition facts. The average serving size of Tang was 367.6 g, which was significantly higher than Guk (325.3 g) and Jjigae (305.1 g) (p < 0.001). The calorie content of Jjigae (171.4 kcal) and Tang (162.3 kcal) was significantly higher than Guk (90.8 kcal) (p < 0.001), and the protein content was the highest in Tang (16.3 g) (p < 0.001). The sodium content per serving of Jjigae (1,479.0 mg) was significantly higher than Guk (1,073.3 mg) and Tang (959.8 mg) (p < 0.001). The percent daily value per serving of all three types was less than 10% on average for calories and 15-30% for protein, whereas for sodium showed an average of around 50% (48-74%). Conclusion: The serving size and nutritional value per serving of the HMR-soups and stews found in this study can be used as basic data to establish the reference serving size.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.355-372
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• 2021

Purpose: This study aimed to provide baseline data for establishing a sugar reduction policy at coffee shops by analyzing the factors that affect a coffee shop user's perception and behavioral intention of reducing sugar intake. Methods: An online survey was conducted involving 1,274 Daegu citizens aged 19-49 years, who had visited coffee shops within the last month. Results: When visiting a coffee shop, the purchase of sweet drinks was higher in the younger age group, and the addition of syrup or sugar was higher in the older age group. Of the total respondents, 42.1% were aware that some coffee shops accommodate reduced sugar requests, 57.9% perceived the need to reduce sugar in coffee shop beverages and 22.3% had purchased beverages intending to reduce their sugar intake. In addition, 59.7% knew about sugar nutrition labeling, and 68.8% perceived the need for nutrition labeling for sugar. When purchasing beverages, 35.6% checked the nutrition labeling, and 77.2% purchased alternative drinks when the sugar content was high. Guiding the choice of sweetness levels in coffee shop orders was seen to have the highest effectiveness and intention to reduce sugar intake. Moreover, the perceived need to reduce sugar intake had the most positive effect on the behavioral intention to reduce sugars in coffee shop beverages (β = 0.558, p < 0.001). Conclusion: Although the overall awareness and practice of reducing the sugar intake in coffee shop users were low, the behavioral intention to reduce sugars was positive, and this was most affected by the perception of the need to reduce sugars. Therefore, there is a need for differentiated education and promotion for each age group for recognizing the necessity and outlining methods for reducing sugar intake. Furthermore, coffee shops should reflect customer's sugar reduction needs.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.96-106
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• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.