• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.151-155
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• 2017

With the rapid growth of the up-to-date wireless network and Internet technologies, huge and various types of things around us are connected to the Internet and build the hyper-connected society, and lots of smart applications using these technologies are actively developed recently. IoT connects human, things, space, and data with various types of networks to construct the hyper-connected network that can create, collect, share and appling realtime information. Furthermore, most of the smart applications are concentrated on the service that can collect and store realtime contexts using various sensors and cloud technology, and provide intelligence by making inferences and decisions from them nowadays. In this paper, we design a smart application that can accurately control and process the current state of the specific context in realtime by using the state-of-the-art ICT techniques such as various sensors and cloud technologies on the IoT based mobile computing environment.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.240-246
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• 2013

Airtouch technology to integrate the system in the way of information delivery devices, touch screen DID this study is to develop new ways of information delivery systems. Airtouch technology to design and implement a system that can be utilized to view the college campus announcements, education, information, and employment information, and store the remote operation and sharing content, the development of cloud services to sync content via smart technology implementation fusion DID systemto develop. Packs USB interface kinek because you may be used in connection with the information appliances, and low-cost product by leveraging the Kinect sensor, Airtouch technology implementation. Types of input devices paper Airtouch technology systems, the user's hand gestures alone can interact with information appliances, smart fusion system developed by DID by tracking the user's hand movements to manipulate the mouse pointer, and information through the user's hand gestures to command the unit so that you can make. Airtouch technology smart fusion DID system technology utilizing a ripple effect on other industries, such as the online education industry, advertising, information industry increases. Also, replace the existing interface device with the versatility of a wide range of technologies, usability is an infinite expansion.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.385-387
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• 2005

Polarimetric measurements of the sky radiation by the PSR-I000, which is the multi-spectral polarimeter developed by the Opt Research Corporation and has the same wavelength regions (443nm, 490nm, 565nm, 670nm, 765nm and 865nm) as the ADEOSII/POLDER sensor, have been carried out at the ground station in Kanazawa city, Japan from March to May. First of all, the wavelength dependency of degrees of polarization is examined and it is shown that degrees of polarization measured under the hazy dust cloud are lower than those measured in the clear sky and decrease as the wavelength increases. Next, a new method for estimating optical properties, such as the optical thickness, the number size distribution and the refractive index, of the Asian dust and the ground reflectance from degrees of polarization measured by PSR-I000 is described. Finally, this method is applied to polarization data acquired on April 15,2002. As a result, it is shown that our estimation algorithm provides a good result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4385-4399
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• 2021

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

• Management & Information Systems Review
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• v.36 no.1
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• pp.273-290
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• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.348-353
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• 2020

This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.