• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.367-372
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• 2018

This paper proposed an algorithm that detects boundary nodes effectively in wireless sensor network. A boundary node is a sensor that lies on the border of network holes or the outer boundary of wireless sensor network. Proposed algorithm detects boundary nodes using only the position information of sensors. In addition, to improve detect performance, sensor computes the overlap area of nearest sensor first. Simulation is performed to validate the process of the proposed algorithm. In Simulation, several obstacles are placed and varying number of sensors in the range of 500~1500 are deployed in the area in order to reflect real world. The simulation results shows that proposed algorithm detects boundary nodes effectively that are located on the border of holes and the outer boundary of wireless sensor network.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.107-109
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• 2004

In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile information. The proposed system focuses on improving recognition performance of 3D object. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse these informations. Tactual signals are obtained from the reaction force by the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of teaming iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though visual information has a defect. The experimental results show that the proposed system can improve recognition rate and reduce learning time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme of 3D object.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.8
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• pp.147-154
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• 2002

In this paper, a path navigation algorithm through use of a sensor platform is proposed. The sensor platform is composed of two electric motors which make panning and tilting motions. An algorithm for computing a real path and an obstacle length is developed by using a scanning method that controls rotation of the sensors on the platform. An Autonomous Robot Vehicle(ARV) can perceive the given path by adapting this algorithm. A sensor scanning method is applied to the sensor platform for using small numbers of sensor. The path navigation algorithm is composed of two parts. One is to perceive a path pattern, the other is used to avoid an obstacle. An optimal controller is designed for tracking the reference path which is generated by perceiving the path pattern. The ARV is operated using the optimal controller and the path navigation algorithm. Based on the results of actual experiments, this algorithm for an ARV proved sufficient for path navigation by small number of sensors and for a low cost controller by using the sensor platform with a scanning method.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.19-29
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• 2022

Natural user interface (NUI) is used for the natural motion interface without using a specific device or tool like a mouse, keyboards, and pens. Recently, as non-contact sensor-based interaction technologies for recognizing human motion, gestures, voice, and gaze have been actively studied, an environment has been prepared that can provide more diverse contents based on various interaction methods compared to existing methods. However, as the number of sensors device is rapidly increasing, the system using a lot of sensors can suffer from a lack of computational resources. To address this problem, we proposed a real-time multi-device control system for natural interactive platform. In the proposed system, we classified two types of devices as the HC devices such as high-end commercial sensor and the LC devices such astraditional monitoring sensor with low-cost. we adopt each device manager to control efficiently. we demonstrate a proposed system works properly with user behavior such as gestures, motions, gazes, and voices.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.30-35
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• 2019

Inertial measurement units (IMUs) are widely used for wearable motion-capturing systems in the fields of biomechanics and robotics. When the IMUs are combined with optical motion sensors (hereafter, OPTs) for their complementary capabilities, it is necessary to align the coordinate system orientations between the IMU and OPT. In this study, we compare the application of two coordinate transformation-based orientation alignment methods between two coordinate systems. The first method (M1) applies angular velocity coordinate transformation, while the other method (M2) applies gyroscopic angle coordinate transformation. In M1 and M2, the angular velocities and angles, respectively, are acquired during random movement for a least-square algorithm to determine the alignment matrix between the two coordinate systems. The performance of each method is evaluated under various conditions according to the type of motion during measurement, number of data points, amount of noise, and the alignment matrix. The results show that M1 is free from drift errors, while drift errors are present in most cases where M2 is applied. Thus, this study indicates that M1 has a far superior performance than M2 for the alignment of IMU and OPT coordinate systems for motion analysis.

• Spatial Information Research
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• v.19 no.3
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• pp.53-62
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• 2011

Spatial information services in indoor space are an im portant application area of GIS as in outdoor space. Unlike in outdoor space, a position in indoor space is specified by a symbolic code such as room number, rather than coordinate. Therefore tracking in indoor space is no longer a prediction of coordinates but a symbolic estimation on the current position of a moving object. In this paper, we propose a framework for tracking moving objects in indoor symbolic space with RFID sensors. First, we introduce the concepts of indoor symbolic space and tracking in indoor symbolic space, and define the accessibility graph for trackable indoor symbolic space. Second, we propose a deployment method of RFID readers and a construction algorithm of accessibility graph for trackable indoor symbolic space. Third, a tracking method is proposed for moving objects in symbolic indoor space with RFID sensors. Finally, we present an implementation exmaple and the result of experiment with real data to validate the proposed method.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.43-52
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• 1998

In this paper, we have investigated the effects of geographic information on the performance of multiple ground target tracking system using multiple sensors. Geographic information is utilized in two cases: association and masking target measurement. Virtually no improvement is observed to the overall performance of tracking system when we applied mobility to the association procedure. Masking target measurement based on mobility produces desirable result that the number of false tracks is reduced. Since geographic information can be regarded as an additional sensor in sensor fusion paradigm, careful usage is required.

• Journal of Sensor Science and Technology
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• v.26 no.4
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• pp.286-291
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• 2017

With regards to heading estimation using gyroscope and magnetometer signals, magnetic disturbance added in the magnetometer signals is a main degradation factor in the estimation accuracy. Although there are a number of existing mechanisms that may properly compensate for the magnetic disturbances, they are designed to react only to the magnetic disturbances, but not to the time derivative of disturbances. Note that the sensors may experience abrupt changes in the magnetic disturbances, particularly for ambulatory applications. This paper proposes a magnetic disturbance model-embedded heading estimation filter for time-varying magnetic environments. The proposed magnetic disturbance model is based on a first-order Markov chain with a conditional switching technique depending on the time derivative of disturbances. Once a high amount of derivative is detected, the corrupted magnetometer signals are discarded to protect the filter from them. In our experimental results, the averaged heading error of tests was $1.46^{\circ}$, while that of the original approach without switching was $5.75^{\circ}$.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.