• Journal of the Korea Institute of Construction Safety
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• v.5 no.1
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• pp.1-8
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• 2023

The purpose of this study is to suggest a way to solve the excessive paperwork of safety managers in domestic construction sites, and to suggest a work efficiency plan that can shorten the time required to prevent safety accidents. First, a function to automatically generate a safety document and find the necessary data is applied using the RPA program. The second is document creation using mobile devices. After safety training, use the Moleil app to keep the training log. Third, to prevent omission of essential safety and health documents, the automatic warning function is activated according to the RPA submission time and sent to the person in charge by e-mail or text. Fourth, the function to find the latest data with high accuracy and speed through 'Google Cloud Search', a search function, was applied.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.989-998
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• 2023

As diseases are already increasing due to aging population and changes in eating habits, interest in digital healthcare is increasing. According to the 2020 Elderly Status Survey, 84% of South Koreans aged 65 and older suffer from at least one chronic disease, and the number and duration of drugs taken by the elderly are also increasing. Effective management that enhances compliance of chronic disease patients can prevent the onset of complications, thereby averting progression to severe illnesses. Thus, a proper medication-taking habit is crucial. This paper proposes a medication information retrieval system using OCR technology. By leveraging Google Cloud Vision API, the system detects and recognizes the names of medicines. Once recognized, the medication name is searched in a database to provide users with medication information and medication schedule management services. By providing accurate medication information through the search, it is possible to induce changes in medication methods and habits. By eliminating the inconvenience of direct input through OCR technology, we anticipate enhancing user convenience by promptly delivering information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.331-341
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• 2019

With the popularity of sensor-rich environments, smartphones have become one of the major platforms for obtaining and sharing information. Since it is difficult to utilize GNSS (Global Navigation Satellite System) inside the area with many buildings, the localization of smartphone in this case is considered as a challenging task. To resolve problem of localization using smartphone a four step image-based localization method and procedure is proposed. To improve the localization accuracy of smartphone datasets, MMS (Mobile Mapping System) and Google Street View were utilized. In our approach first, the searching for candidate matching image is performed by the query image of smartphone's using GNSS observation. Second, the SURF (Speed-Up Robust Features) image matching between the smartphone image and reference dataset is done and the wrong matching points are eliminated. Third, the geometric transformation is performed using the matching points with 2D affine transformation. Finally, the smartphone location and attitude estimation are done by PnP (Perspective-n-Point) algorithm. The location of smartphone GNSS observation is improved from the original 10.204m to a mean error of 3.575m. The attitude estimation is lower than 25 degrees from the 92.4% of the adjsuted images with an average of 5.1973 degrees.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1047-1059
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• 2022

With the development of cloud computing technology, container technology that provides services based on a virtual environment is also developing. Container orchestration technology is a key element for cloud services, and it has become an important core technology for building, deploying, and testing large-scale containers with automation. Originally designed by Google and now managed by the Linux Foundation, Kubernetes is one of the container orchestrations and has become the de facto standard. However, despite the increasing use of Kubernetes in container orchestration, the number of incidents due to security vulnerabilities is also increasing. Therefore, in this paper, we study the vulnerabilities of Kubernetes and propose a security policy that can consider security from the initial development or design stage through threat analysis. In particular, we intend to present a specific security guide by classifying security threats by applying STRIDE threat modeling.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.2
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• pp.93-106
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• 2013

Cloud computing services have became an hot issues in business fields while the IT-related global company such as Amazon, MS, and Google took part in. The cloud computing makes it possible to share in the form of outsourcing of hardware and software through the Internet, to build a distributed computing environment via multiple terminals, and to reduce the cost using open source. Such a cloud computing services have not shown any significant growth in Korea yet compared to other countries. Therefore, in this study focusing on public cloud computing services, it is intended to analyze the determinants of public cloud computing services using the technology model. Thus, for users using public cloud services, we conducted a questionnaire survey from the beginning of January 2013 to the end of February 2013. And we derived a research model based on the Technology Acceptance Model(TAM). As a result of this study, it shows that personal aspects significantly impacts on the perceived usability, service aspects, systems aspects, and the intent of the technology acceptances. System aspects significantly effect on the intent of technology acceptances. Perceived usability significantly effects on the service aspects and the systems aspects. The main factors effecting on the intent of the technology acceptances are system aspects and perceived usability in order. And it shows that the personal aspects decreases the intent of the technology acceptances.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.67-76
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• 2023

COVID-19, which occurred in 2019, has a strong contagious power, has serious symptoms of infection and after-effects, and death in severe cases depending on the underlying disease and symptoms. As COVID-19 is highly contagious, in Korea, screening clinics have been set up across the country to determine whether or not to be positive for COVID-19 and isolate the infected to prevent the spread of COVID-19. However, there are cases where COVID-19 test applicants flock to screening clinics and cannot receive tests due to longer waiting times, and there is a risk that secondary infections may occur in the atmosphere. In this study, the reservation and notification system can be applied from the existing screening care system to solve spatial constraints, reducing waiting time with screening appointments, and solving population bottlenecks to screening clinics. Taking the COVID-19 pandemic as an experience, we propose a system that can present directions in future pandemic situations. To process real-time data, we use Google's Firebase to use Realtime Database in the cloud environment. Because a real-time database is used, users can check the status of screening clinics in real time through the app, make reservations, and receive notifications about test reservations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3999-4005
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• 2013

We forecast that the scale of mobile market will be large scale according to the twenty million times of smart phone's user is arrived. The type of software development is being transferred into mobile-based App. or cloud computing-based service from web-based software. Currently both Google's android and Apple's IOS are known for two prominent platforms. Existing modeling techniques cannot be applied into the case of mobile application which is executed those platforms. Therefore, we suggest a modeling technique that is customized existing modeling techniques into android-based software development. That is, we propose an analysis and design techniques which are needed into android-based mobile smart environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.670-686
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• 2017

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.249-254
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• 2011

A user desktop service can be made available on internet or local area network with the help of virtualization and cloud technologies. The service is usually called a virtual desktop or a desktop cloud. However, a user interface is limited to I/O capabilities of a user's mobile terminal. In order to enhance a user interface on a remote virtual desktop, it is important to connect full-featured I/O devices which are founded locally. Our previous work called SoD (System-on- Demand) has proposed a technique to associate local full-featured I/O devices with a remote virtual desktop in Xen. On the technique, it is required to install a SoD client agent in a user's mobile terminal for connecting a remote virtual desktop. In this paper, we propose a new framework called Web-SoD that does not require any explicit installation to make SoD service available. The SoD client agent is provided by the web technology so that the agent can be installed transparently, and the platform independency is also achieved. Due to insufficient network socket performance of current web technologies, we extend Native Client (NaCl) proposed by Google to support a network functionality by modifying a NaCl library and a service runtime. With conducted experiment, we show that the network extension supports a full socket functionality over the compromised overhead on the web environment.