• Journal of KIISE:Computing Practices and Letters
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• v.9 no.1
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• pp.33-46
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• 2003

A Home Location Register(HLR) database system manages each subscriber's location information, which continuously changes in a cellular network. For this purpose, the HLR database system provides table management, index management, and backup management facilities. In this thesis, we propose using a two-level index method for the mobile directory number(MDN) as a suitable method and a chained bucket hashing method for the electronic serial number(ESN). Both the MDN and the ESN are used as keys in the HLR database system. We also propose an efficient backup method that takes into account the characteristics of HLR database transactions. The retrieval speed and the memory usage of the two-level index method are better than those of the R-tree index method. The insertion and deletion overhead of the chained bucket hashing method is less than that of the modified linear hashing method. In the proposed backup method, we use two kinds of dirty flags in order to solve the performance degradation problem caused by frequent registration-location operations. For a million subscribers, proposed techniques support reduction of memory size(more than 62%), directory operations (2500,000 times), and backup operations(more than 80%) compared with current techniques.

• Convergence Security Journal
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• v.12 no.1
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• pp.3-8
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• 2012

MANET composed wireless nodes without fixed infrastructure provides high flexibility, but it has weak disadvantage to various attack. It has big weakness to DDoS attack because every node perform packet forwarding especially. In this paper, packet transmission information control technique is proposed to reduce damage of DDoS attack in MANET and search location of attacker when DDoS attacks occur. Hierarchical structure using gateway node is adopted for protect a target of attack in this study. Gateway node in cluster is included like destination nodes surely when source nodes route path to destination nodes and it protects destination nodes. We confirmed efficiency by comparing proposed method in this study with CUSUM and measured the quantity consumed memory of cluster head to evaluate efficiency of information control using to location tracing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5042-5048
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• 2010

In ubiquitous environments, many continuous query processing techniques make use of operator network and sharing methods on continuous data stream generated from various sensors. Since similar continuous queries with the location information intensively occur in specific regions, we suggest a new operator sharing method based on grid partition for the spatial continuous query processing for location-based applications. Due to the proposed method shares moving objects by the given grid cell without sharing spatial operators individually, our approach can not only share spatial operators including similar conditions, but also increase the query processing performance and the utilization of memory by reducing the frequency of use of spatial operators.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.161-166
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• 2011

We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.10a
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• pp.124-127
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• 2000

The finite element analyses of the wrinkling initiation and growth in the sheet metal forming process provide the detailed information about the wrinkling behavior of sheet metal. The direct analyses of the wrinkling initiation and growth, however, bring about a little difficulty in complex industrial problems because it needs large memory size and long computation time. For the description of wrinkling growth, the mesh elements should be sufficiently small and the size of finite element matrix becomes large. In the static implicit finite element method therefore, the direct analysis of wrinkling growth in a complex sheet metal forming process is rather difficult. From the industrial viewpoint of tooling design, the readily available information of possibility and location of wrinkling is sometimes more preferable to the detailed time-consuming information. In the present study, therefore, the wrinkling factor that shows locations and relative possibility of wrinkling initiation is proposed as a convenient tool of relative wrinkling estimation based on the energy criterion. The location and relative possibility of wrinkling initiation are predicted by calculating the wrinkling factor in various sheet metal forming processes such as cylindrical cup deep drawing, spherical cup deep drawing, and elliptical cup deep drawing. The wrinkling factor is also implemented in the analysis of the door inner stamping process to predict wrinkling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.723-743
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• 2016

Indoor positioning is considered an enabler for a variety of applications, the demand for an indoor positioning service has also been accelerated. That is because that people spend most of their time indoor environment. Meanwhile, the smartphone integrated powerful camera is an efficient platform for navigation and positioning. However, for high accuracy indoor positioning by using a smartphone, there are two constraints that includes: (1) limited computational and memory resources of smartphone; (2) users' moving in large buildings. To address those issues, this paper uses the TC-OFDM for calculating the coarse positioning information includes horizontal and altitude information for assisting smartphone camera-based positioning. Moreover, a unified representation model of image features under variety of scenarios whose name is FAST-SURF is established for computing the fine location. Finally, an optimization marginalized particle filter is proposed for fusing the positioning information from TC-OFDM and images. The experimental result shows that the wide location detection accuracy is 0.823 m (1σ) at horizontal and 0.5 m at vertical. Comparing to the WiFi-based and ibeacon-based positioning methods, our method is powerful while being easy to be deployed and optimized.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.12
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• pp.1105-1111
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• 2016

In modern warfare, various target tracking radars are used for target location tracking. So, the importance of EA radar jamming technique which disrupt enemy target tracking radar in oder to neutralize tararget location tracking has increased. VGPO/I jamming is a base technique of EA(Electronic Attack), it is possible to operate to pulse-Doppler radar. In this papar, we develop VGPO/I jamming technique that can apply to phase sampled DRFM by using phase information and verifiy through simulations.

• Journal of IKEEE
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• v.19 no.2
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• pp.160-167
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• 2015

We present an experimental implementation of the inexpensive microwave security sensor that can detect both static and slowly moving objects in cluttered environment. The prototype consists of a frequency-modulated continuous wave radar sensor, control board or computer and software. The prototype was tested in a cluttered indoor environment. In case of intrusion or change of environment the sensor will give an alarm, determine the location of new object, change in its location and can detect a slowly moving target. To make a low-cost unit we use commercially available automotive radar and own signal processing techniques for object detection and tracking. The intruder detection is based on a comparison between current 'image' in memory and 'no-intrusion' reference image. The main challenge is to develop a reliable technique for detection of a relatively low-magnitude object signals hidden in multipath clutter echo signals. Various experimental measurements and computations have shown the feasibility and performance of the system.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.165-172
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• 2010

Sensor network can be applied in many areas in our life, and security is very important in applying sensor network. For secure sensor communication, pairwise key establishment between sensor nodes is essential. In this paper, we cluster the network field in hexagonal shapes and preassign three different kinds of key information for each sensor according to its expected location. We adopt overlapped key string pool concept for our clustered network architecture and every node uses the part of sub-strings for setting up pairwise keys with all neighboring nodes in its own cluster and from different clusters according to respective position with small amount of information. Our proposal decreases the memory requirement and increases security level efficiently.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.