• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.23-28
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• 2018

In this work, we developed a platform that can monitor status and manage events of factory workplaces by providing events and data collected from various types of multi-touch technology based sensors installed in the workplace. By using the image recognition technology, faces of the people in the factory workplace are recognized and the customized contents for each worker are provided, and security of contents is enhanced by the authenticating an individual worker through face recognition. Contents control function through gesture recognition is constructed, so that workers can easily search documents. Also, it is possible to provide contents for workers by implementing face recognition function in mobile devices. The result of this work can be used to improve workplace safety, convenience of workers, contents security and can be utilized as a base technology for future smart factory construction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.45-50
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• 2017

Micro-service Architecture is a service architecture optimized for large-scale distributed systems such as real-time realistic broadcasting systems, so that are fiercely adopted by Global leading service platform vendors such as Netflix and Twitter due to the merit of horizontal performance scalability enabling the scale-out technique. In addition, micro-service architecture makes it possible to execute image processing and real-time data analysis using an asynchronous-based processing that are difficult to handle in Web API such as REST. In this paper, an event stream processing framework applicable to asynchronous based micro services is proposed in the sense that the accountability of event processing order is not guaranteed in the events such as IoT sensor data analysis or cloud-based image editing because these are the situations where the real-time media editing generates multiple event streams and asynchronous processes in the platform.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.15-21
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• 2020

In order to utilize big data in general industrial sites, the structured big data collected from facilities, processes, and environments of industrial sites must first be processed and stored, and in the case of unstructured data, it must be stored as unstructured data or converted into structured data and stored in a database. In this paper, we study a method of collecting big data based on open IoT standards that can converge and utilize measurement information, environmental information of industrial sites to collect big data. The platform for collecting big data proposed in this paper is capable of collecting, processing, and storing big data at industrial sites to process real-time sensing information. For processing and analyzing data according to the purpose of the stored industrial, various big data technologies also can be applied.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.307-312
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• 2023

North Korea has already been using underground space for military purposes for decades, and is currently developing it as a key base for operating asymmetric forces. Accordingly, the special operations teams need fighting methods, weapon systems, and organizational structures to carry out subterranean operations. This paper presents an unmanned system platform for subterranean operations that combines tilt-rotor type drones, high-tech sensors, communication repeaters, and small robots, and a system that can be operated by special operation teams. Based on this, the survivability of the special operations teams can be strengthened and operational utility can be maximized. Afterwards, if Special Warfare Command collects collective intelligence based on the ideas related to subterranean operations presented in this paper and further develops these, it will be possible to drive subterranean operations doctrines, weapon systems, and organizational structures optimized for the battlefield on the Korean Theater of Operations in the near future.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.97-111
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• 2024

Developing countries and small local governments face financial constraints, limiting the adoption of their own bus information systems. However, despite poor social infrastructure and low income levels, developing countries have a high smartphone penetration rate, and the distribution and usage of online content and social media are widespread. Smartphones, equipped with GPS sensors, cameras, and other location-based information collection capabilities, can replace expensive on-site terminals. This study aims to replace expensive on-site terminals with smartphones, develop a center system based on cloud servers, and establish an extensible Open BIS (Bus Information System) service and platform that can be applied anywhere. The goal is to formulate a business model in the process.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.11-18
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• 2008

This study is implementation of attitude control system(ACS - Attitude Control System). for a multi legs robot. This study designs H/W of Inertial Measurement Unit (IMU) and attitude control algorithm S/W. Compare performance with Mtx and MTx in order to verify action performance of this system after implementation, and will verify a system integrated IMU of a multi-legs robot. ACS uses Gyro and an accelerometer and an earth magnetism sensor, and it is a system controlling a roll, pitch angle attitude of an object. Generally, low price MEMS is difficult to calculate a correct situation of an object as an error occurs severely the Inertial sensor. This study implements IMU in order to develop ACS as use MEMS, accelerometer, Gyro sensor and earth magnetism sensor. Design algorithm each a roll, pitch, yaw attitude guaranteeing regular performance, and do poling in a system as include an attitude calculation program in an IMU system implemented. Mixed output of Gyro and an accelerometer, and recompensed a roll, pitch angle, and loaded in this study on a target platform in order to implement the ACS which guaranteed performance more than a continuously regular level, and operated by real time, and did porting, and verified.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.225-234
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• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.13-30
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• 1997

Recent improvements of satellite remote sensing sensors which are represented by hyperspectral imaging sensors and high spatial resolution sensors provide a large amount of data, typically several hundred megabytes per one scene. Moreover, increasing information exchange via internet and information super-highway requires the developments of more active service systems for processing and analysing of remote sensing data in order to provide value-added products. In this sense, an advanced satellite data processing system is being developed to achive high performance in computing speed and efficieney in processing a huge volume of data, and to make possible network computing and easy improving, upgrading and managing of systems. JAVA internet programming language provides several advantages for developing software such as object-oriented programming, multi-threading and robust memory managent. Using these features, a satellite data processing system named as GeoPixel has been developing using JAVA language. The GeoPixel adopted newly developed techniques including object-pipe connect method between each process and multi-threading structure. In other words, this system has characteristics such as independent operating platform and efficient data processing by handling a huge volume of remote sensing data with robustness. In the evaluation of data processing capability, the satisfactory results were shown in utilizing computer resources(CPU and Memory) and processing speeds.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.