• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.19-24
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• 2009

When a dynamic method is applied at the time of occurrence of an client's connection request for most of the online gaming server engine, the gaming server is processing a session connection, it's initialization. Yet, such a method will cause a lot of loading and bottle-neck in the gaming server at the same time. Therefore we propose the object polling scheme to minimizes a memory fragmentation and loading of the initialization on the client using a static allocation method for an efficient On-lin gaming serverin this paper. We implement the gaming server applying to our proposed scheme, and we show an improvement in our proposed scheme by performance analysis in this paper.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.43-49
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• 2007

The implementation of client polling as a weak cache coherence mechanism has two major drawbacks: Firstly, the cache may return a stale copy if the object is changed in the origin server while the cached copy is considered valid. Secondly, the cache can invalidate a cached copy that is still valid in the server. Therefore, we propose a multilevel pre-fetching (MLP) in conjunction with the client polling to refine these drawbacks. MLP is introduced to improve the level of freshness among the cached objects. The simulation results presented in this paper show that the proposed MLP significantly minimizes the number of stale objects and reduces the invalidation messages sent out to the server, i.e., increase the cache HIT rate.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.89-103
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• 2006

Location-Based Services(LBS) has been varied and expanded rapidly in local and overseas markets due to technology developments and expanded applications of wireless internet. Location Based Alert Service(LBA) capable of automatically furnishing data when entering or outing a specific location is expected to become one of the most important services in LBS. For LBA operation, it is essential to periodically get location information about moving object. However, this can cause a serious system load because system should continuously and largely receive location information of many moving objects. Existing and current methods for location polling of moving object are not suitable for an efficient location acquisition and a search structure required for LBA. In this study, to acquire large-scaled location information for LBA, a MDR moving object location polling algorithm will be suggested to reduce unnecessary location information and decrease system load by using mobility patterns of moving object.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.22-32
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• 2010

Location-based services have been expanded rapidly in local and overseas markets due to technological advances and increasing applications of wireless internet. Various researches have been made to manage efficiently the location information of moving objects. A basic location-based alert service provides alerting messages automatically when either entering or leaving a specific location and it is expected to become one of the most important location-based services. Location-based alert services require a location polling method to acquire current locations for a large number of moving objects. However, a simple periodical location polling method causes severe system overload because a system should keep updating location information of the moving objects ceaselessly. Most location polling algorithms for location-based alerting services are not suitable for mobile users with dynamic and unsteady moving patterns. In this paper, we propose an improved location polling algorithm for location-based alerting services to reduce the amount of location information acquisition and therefore, to decrease the system load. Various experiments show that the proposed algorithm outperforms other algorithms.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1266-1276
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• 2003

Multimedia information containing voice and image is transmitted on Internet, which is ten times or hundred times larger than ordinary information. Analysis types for network management in this environment consist of a real time analysis, a basic analysis and an intensive analysis. The intensive analysis is useful for gathering the trend information of specific objects periodically for certain period in order to monitor network status. When SNMP is applied to collect the trend information of intensive analysis, it brings on the increase of network load, the delay of response time and the decrease of data collection accuracy since an agent responds to manager's every polling. In this paper, an efficient SNMP is proposed and implemented to add time variables in the existing SNMP PDU. It minimizes unnecessary traffic in the intensive analysis between manager and agent, and collects trend information more accurately. The results of experiments show that it has compatibility with the existing SNMP, decreases the amount of network traffic greatly and increases the accuracy of data collection.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.