• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.449-452
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• 2007

An intelligent robot with RSSI based indoor location estimation function was designed and implemented. A wireless sensor node was attached to the robot to received the location data from the indoor location estimation function. Spartan III was used as the main control device in the mobile robot. The current location data collected from the indoor location estimation system was transferred to the mobile robot and server through Zigbee/IEEE 802.15.4 wireless communication of the sensor node. Once the location data is received, the sensor node senses the direction of the robot head and directs the robot to move to its destination. Indoor location estimation intelligent robot is able to move efficiently and actively to the user appointed location by implementing the proposed obstacles avoidance algorithm. This system is able to monitor real-time environmental data and location of the robot using PC program. Indoor location estimation intelligent robot also can be controlled by executing the instructions sent from the PC program.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.38-48
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• 2017

Link attributes, such as speed, occupancy, and flow, are essential factors for transportation planning and operation. It is, therefore, one of the most important decision-making problems in intelligent transport system (ITS) to determine the optimal location of a sensor for collecting the information on link attributes. This paper aims to develop a model to determine the optimal location of a sensor to minimize the variability of traffic information on whole networks. To achieve this, a network sensor location model (NSLM) is developed to reflect discrete characteristics of data collection. The variability indices of traffic information are calculated based on the summation of diagonal elements of the variance-covariance matrix. To assess the applicability of the developed model, speed data collected from the dedicated short range communication (DSRC) systems were used in Daegu metropolitan area. The developed model in this study contributes to the enhancement of investment efficiency and the improvement of information accuracy in intelligent transport system (ITS).

• International conference on construction engineering and project management
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• 2020.12a
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• pp.510-519
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• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.375-378
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• 2007

This paper describes indoor location estimation intelligent robot. It is loaded indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks. Spartan III(Xilinx, U.S.A.) is used as a main control device in the mobile robot and the current direction data is collected in the indoor location estimation system. The data is transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1195-1200
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• 2007

This paper describes indoor location estimation intelligent robot. Indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks were implemented in the robot. Spartan III(Xilinx, U.S.A.) was used as a main control device in the mobile robot and the current direction data was collected in the indoor location estimation system. The data was transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.63-69
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• 2016

In this paper, we proposed the location tracking system based on MS-Based/Assisted(Mobile Station-Based and Assisted) location trigger service model with context-awareness for the intelligent location tracking of moving objects. It provides the proper resulting value that matches the context of users through the analysis about the situation of the user, physical environment, computing resource and the existing information on user input. In order to provide real-time data, we proposed the location tracking system which realizes the intelligent information such as the expecting arrival time and passing the specific area of the moving object by adopting the location trigger. So, it derives to minimize the costs of communication for the mobile object tracking applications. The proposed location tracking system based on context-awareness can be used for realtime monitoring, intelligent alarm/action, setting up of the optimized moving path, dynamic adjustment of strategies and policies. So it has the advantage to develop the application system which is aimed at optimization of the object tracking and movement.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.241-243
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• 2017

이동 단말기에서 실시간 데이터 제공을 위하여 대부분의 객체 위치 추적 시스템은 GPS 기반의 추적 기법을 사용하고 있으나, 본 논문에서는 위치 트리거 모델을 제안하여 객체의 이동 위치에 따른 시점과 위치 특성과 같은 지능적 정보를 통한 효율적 저비용의 위치 추적 기법을 제시한다. 본 논문에서 제안하는 위치 트리거 모델은 객체 정보의 흐름에 대한 실시간 모니터링과 예외상황 발생 시 지능화된 경고/조치, 최적화된 이동 경로 수립 및 계획의 동적/지능적 재조정을 위한 객체추적 및 이동의 최적화를 목표로 하는 시스템을 구성하기 위해 사용될 수 있다.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.954-956
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• 1999

In the case of using sensor in the industrial control systems, the location of sensor is not close to the system which utilizes the sensor data. Two main functions of intelligent sensor are data processing and communication. In this paper, we will show that the developed result of intelligent sensor, which process the sensor data inside of the sensor module, except for the communication function. For this, we refered to the Profibus and Fieldbus Foundation standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.