• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.101-108
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• 2021

This paper builds a real-time home security system based on the OneNet cloud platform to control the status of the house through a smartphone. The system consists of a local part and a cloud part. The local part has I/O devices, router and Raspberry Pi (RPi) that collects and monitors sensor data and sends the data to the cloud, and the Flask web server is implemented on a Rasberry Pi. When a user is at home, the user can access the Flask web server to obtain the data directly. The cloud part is OneNet in China Mobile, which provides remote access service. The hybrid App is designed to provide the interaction between users and the home security system in the smartphone, and the EDP and RTSP protocol is implemented to transmit data and video stream. Experimental results show that users can receive sensor data and warning text message through the smartphone and monitor, and control home status through OneNet cloud.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2331-2340
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• 2015

In this paper, we propose a social-based collaboration systems for business process management in the mobile cloud. This XMDR-DAI is to provide services for data sharing and exchange between local systems that operate independently in a cloud environment, take charge of a social-based collaborative business process management. Social-based collaborative business processes are handled in a collision among a structure such as unit conversion, meaning conflict, and the schema mapping data resulting from the inner query. The conflict was resolved by mapping process which takes in each XMDR-DAI.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.205-213
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• 2024

As the Restaurant industry is growing rapidly. The demand for an effortless POS (Point Of Sale) system which can make management easy is increasing. So, the purpose of this study is to digitalise the growing industry of restaurants and its consumers by utilizing cross-platform development. Crossplatform development frameworks provide great opportunities to solve the issues of handling ubiquitous devices with minimum efforts to reduce the cost and increase the stability, accessibility of the end consumers. By availing those opportunities, an Integrated POS system with cross platform is proposed. This integrated cross-platform POS system is originally designed for a single restaurant managed by its own private cloud server. This research solves the 2 major problems. One of them is the accessibility of the system on modern devices without even writing platform-specific code with the help of cross-platform development. This included web, mobile, desktops & tablets at the same time with the same codebase. Second one is handling data consistency with ubiquitous devices with the help of cloud infrastructure to make data safe and consistent more than ever. In the Development of this system Dart will be used as the primary programming language for cross-platform development. On the Cloud server system apache will be used as the web server and PHP as server side language. System will be using MySQL as the database server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.496-499
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• 2016

The smartfarm is a leader in the Field of environmental monitoring in agriculture. By the use of wireless remote systems, monitoring applications of the smartfarm are able to provide vital information to the farmer wherever he may be. Absentee farmers are finding the ease of viewing the application graphs on their mobile phone is providing them with peace of mind. We design system and technical requirements of service for managing and operating smart-farm based on cloud technology. It describes requirements of cloud technology for monitoring, controlling, managing, and operating cloud-based smart farm. Smart farm system and service with cloud platform contains 3 interfaces and 3 services. In addition, smart-farm using cloud platform could have several cases so it should be established and managed in varying way depending on cultivars, its size and type. This paper will focus the industry's attention on the importance of Open/Standard Cloud platform thereby stimulating the smartfarm in agriculture.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.458-470
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• 2022

As the 4th industrial revolution progresses, the application of several cutting-edge technologies such as the Internet of Things, big data, and mixed reality (MR) in relation to autonomous ships is being considered in the maritime logistics field. The aim of this study was to apply the concept of a digital twin model based on Human Machine Interaction (HMI) including a digital twin model and the role of an operator to a ship. The role of the digital twin is divided into information provision, support, decision, and implementation. The role of the operator is divided into operation, decision-making, supervision, and standby. The system constituting the ship was investigated. The digital twin system that could be applied to the ship was also investigated. The cloud-based digital twin system architecture that could apply investigated applications was divided into ship data collection (part 1), cloud system (part 2), analysis system/ application (part 3), and MR/mobile system (part 4). A Mixed Reality device HoloLens was used as an HMI equipment to perform a simulation test of a digital twin system of an 8 m battery-based electric propulsion ship.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.165-170
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• 2021

Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.49-55
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• 2019

An open office or a shared office is emerging as the emphasis on the collaborative and communicative work environments is increasing. In the past, the user maintained the space, but the maintenance of indoor space became difficult because there is no fixed user. Indoor space information can be collected using the ICBM framework system. The facility management can achieve this with data. Therefore, this study proposed a framework based on ICBM (Internet of Things, Cloud, Big Data, and Mobile) for verifying the possibility of a smart facility management system for indoor space. IoT (Internet of Things) technology was used to measure the indoor temperature, humidity, occupancy, and brightness continuously, and provided the data to Web API via WiFi. Data acquired automatically via IoT, existing maintenance data, and spatial information were integrated through the Cloud. Big data collected by sensors were processed as meaningful spatial information for maintenance. Indoor space information and maintenance information can be delivered to the manager through the mobile. Based on the collected data, room occupancy recognition is limited due to a range of ultrasonic wave sensors. On the other hand, brightness represents the space conditions. The difference between lighting on/off, weekday and weekend can be shown. The temperature data and the relative humidity data were collected steadily to evaluate the comfort.

• Smart Media Journal
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• v.8 no.3
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• pp.88-94
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• 2019

Livestock disease is a very important issue in the livestock industry because if livestock disease is not responded quickly enough, its damage can be devastating. To solve the issues involving the occurrence of livestock disease, it is necessary to diagnose in advance the status of livestock disease and develop systematic and scientific livestock feeding technologies. However, there is a lack of domestic studies on such technologies in Korea. This paper, therefore, proposes Livestock Disease Forecasting and Livestock Farm Integrated Control System using Cloud Computing to quickly manage livestock disease. The proposed system collects a variety of livestock data from wireless sensor networks and application. Moreover, it saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system. This provides livestock disease forecasting and livestock farm integrated controlling service through MapReduce Model-based parallel data processing. Lastly, it also provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.298-302
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• 2011

In this paper, we propose an efficient encryption algorithm for ensuring data privacy and security for cloud computing in mobile environments. As part of the evaluation of the proposed algorithm, we have implemented the algorithm in a PC environment and compared with the well-known encryption algorithm of the Data Encryption Standard (DES). The conventional DES algorithm is hard to maintain privacy, due to the fact that its initial and final permutation are known to the network To prevent this critical weakness, a triple DES algorithm has been reported, but it has a disadvantage of long encryption time. In this study, we propose random interleaving algorithm that uses the permutation table for improving privacy further. The proposed algorithm is found to run faster than the triple DES algorithm and also offers improved security in a wireless communication system.