• International journal of advanced smart convergence
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• v.12 no.4
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• pp.35-43
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• 2023

This paper emphasizes that information received in disaster situations can lead to disparities in the effectiveness of communication, potentially causing damage. As a result, there is a growing demand for disaster and safety information among citizens. A user-centered disaster and safety information application service is designed to address the rapid dissemination of disaster and safety-related information, bridge information gaps, and alleviate anxiety. Through the Open API (Open Application Programming Interface), we can obtain clear information about the weather, air quality, and guidelines for disaster-related actions. Using chatbots, we can provide users with information and support decision-making based on their queries and choices, utilizing cloud APIs, public data portal open APIs, and solution knowledge bases. Additionally, through Mashup techniques with the Google Maps API and Twitter API, we can extract various disaster-related information, such as the time and location of disaster occurrences, update this information in the disaster database, and share it with users.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.768-791
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• 2023

Effective brand management using images has been a challenge for the brand managers. The brand identity-brand image alignment on the social media is an important yet mostly-overlooked phenomenon. We proposed a scalable Google Cloud Vision-based approach for measuring the alignment between brand identity and brand image, and understanding the brand positions. We analyzed 3247 images of 13 leading Indian brands on Instagram. Images containing wordy announcements by the firms are in stark contrast with the relatively more emotive images by the users. It leads to a noticeable disconnect between the brand identity and brand image. Also, the private sector brands do not always outperform the public sector brands in branding efforts. By offering practical guidance on how to measure and reduce the misalignment, this study paved a feasible path towards better visual branding on Instagram.

• Journal of Platform Technology
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• v.6 no.1
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• pp.17-23
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• 2018

Taxi's queue for entering into taxi stand makes traffic jam and congestion around taxi stand. If we make that taxi waits in another place around taxi stand, and can approach to taxi stand when it gets in its turn, these traffic jam and congestion around taxi stand can be reduced. In this paper, we propose entry sequence operating system for taxi stand to reduce traffic jam and congestion around taxi stand. In this system, taxi driver can request his sequence number, and the system issues sequence number to driver. When it is time to approach to taxi stand due to issued sequence number, the proposed system notifies to taxi driver via taxi terminal. Taxi getting the proposed service can wait in another place around taxi stand, and can approach to taxi stand after receiving notify for approaching. Therefore, the proposed system in this paper can reduce traffic jam and congestion around taxi stand because it can reduce or get rid of taxi's queue around taxi stand. We implement the taxi stand approach sequence management system proposed in this paper for taxi stand installed in Chungju-Si, Chungbuk. We use Google Cloud service and Android platform for implementing.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.43-53
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• 2019

In recent years, there have been many cases of damage to indoor air hazardous materials, and major damage due to the lack of quick action. In this regard, the system is intended to establish for sending push messages to the user's mobile when the concentration of hazardous substances is exceeded. This system extracts data with IoT system such as Arduino and Raspberry Pi and then constructs database through MongoDB and MySQL in cloud computing system. The database is imported through the application server using NodeJS and sent to the application for visualization. Also, when receiving signals about a dangerous situation in IoT system, push message is sent using Google FCM library. Mobile application is developed using Android Web view, and page to enter Web view is developed using HTML5 (HTML, Javascript CSS). The application of this system enables real-time monitoring of indoor air-dangerous substances. In addition, real-time information on indoor/outdoor detection location and concentration can be sent to the user's mobile in case of a risk situation, which can be expected to help the user respond quickly.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.71-80
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• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.370-372
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• 2021

According to the Clinical Research Center for Dementia, 840,000 Koreans aged 65 or older had dementia patients, with a prevalence rate of 10.39%. The prevalence rate is one in 10 elderly people, but difficult for families to take care of them all day. Judged that possible to manage the conditions and schedules of elderly people living alone by utilizing AI speaker system where schedule management is stored. This paper implements modules for AI schedulers in patients with dementia. Configured to link AWS, a remote IOT, inside the raspberry pi, and to output the schedule to speakers using a calendar from Google API. Through this study, judged that ease of scheduling will help manage and schedule dementia patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.740-743
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• 2012

The initial role of the operating system acts as an intermediary between the computer and the user, and, hardware and process management, and the convenience of your computer system is to use. Of these operating systems as well as servers and personal computers, smartphones and tablet mounted on mobile devices such as mobile operating system was born. Mobile Operating System has been expanded a TV or Car Area that built into a simple embedded operating system, is emergence of a variety of devices, cloud services, combined with the desire of users due to the high built-in simple embedded operating system that was working on a TV or a car is expanding to the area. The reason for the emergence of a variety of devices, cloud services, combined with the desire of users is high. In this paper, the mobile operating system, N-Screen, Smart TV to find out about and through the analysis of the major smart TV, the future Find out about trends in the mobile operating system.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.454-460
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• 2020

Artificial intelligence assistants that provide speech recognition operate through cloud-based voice recognition with high accuracy. In cloud-based speech recognition, Wake-Up-Word (WUW) detection plays an important role in activating devices on standby. In this paper, we compare the performance of Convolutional Neural Network (CNN)-based WUW detection models for mobile devices by using Google's speech commands dataset, using the spectrogram and mel-frequency cepstral coefficient features as inputs. The CNN models used in this paper are multi-layer perceptron, general convolutional neural network, VGG16, VGG19, ResNet50, ResNet101, ResNet152, MobileNet. We also propose network that reduces the model size to 1/25 while maintaining the performance of MobileNet is also proposed.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.