• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.71-76
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• 2022

Buffer cache bridges the performance gap between memory and storage, but its effectiveness is limited due to periodic flush, performed to prevent data loss in smartphones. This paper shows that selective flushing technique with small persistent memory can reduce the flushing overhead of smartphone buffer cache significantly. This is due to our I/O analysis of smartphone applications in that a certain hot data account for most of file writes, while a large proportion of file data incurs single-writes. The proposed selective flushing policy performs flushing to persistent memory for frequently updated data, and storage flushing is performed only for single-write data. This eliminates storage write traffic and also improves the space efficiency of persistent memory. Simulations with popular smartphone application I/O traces show that the proposed policy reduces write traffic to storage by 24.8% on average and up to 37.8%.

• Journal of ILASS-Korea
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• v.29 no.2
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• pp.53-59
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• 2024

Cars are one of the main causes of air pollution in large cities, and 34.6% of domestic air pollution emissions come from mobile sources, of which cars account for 69.6%. In particular, the importance of nitrogen oxides (NOx) and particulate matter (PM), which are major pollutants in diesel vehicles, is increasing due to their high contribution to emissions. Therefore, in this study, the problem of natural regeneration caused by low exhaust gas temperature during low speed and low load operation was solved by applying a complex regeneration DPF that is not affected by temperature conditions to large diesel vehicles with higher driving time and engine displacement than small and medium-sized vehicles. And the feasibility of application to large diesel vehicles was reviewed by measuring the emission reduction efficiency. As a result of the reduction efficiency test on the actual vehicle durability product, PM showed a reduction efficiency of 84% to 86%, and the reduction efficiency of gaseous substances showed a high reduction efficiency of over 90%. The actual vehicle applicability test was completed with three driving patterns: village bus vehicle, police car, and road-going construction equipment vehicle, and no device problems occurred until the end of the test. Both load and no-load smoke measurement results showed a smoke reduction efficiency of over 96%.

• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.237-246
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• 2012

Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.133-142
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• 2009

Recently, with the advance of wireless internet and the frequent use of mobile devices, demand for LBS(Location Based Service) is increasing, and research is required on spatial indexes for the storage and maintenance of spatial data to provide efficient LBS in mobile device environments. In addition, the use of flash memory as an auxiliary storage device is increasing in order to store large spatial data in a mobile terminal with small storage space. However, the application of existing spatial indexes to flash-memory lowers index performance due to the frequent updates of nodes. To solve this problem, research is being conducted on flash-memory based spatial indexes, but the efficiency of such spatial indexes is lowered by low utilization of buffer and flash-memory space. Accordingly, in order to solve problems in existing flash-memory based spatial indexes, this paper proposed FR-Tree (Flash-Memory based R-Tree) that uses the node compression technique and the delayed write operation technique. The node compression technique of FR-Tree increased the utilization of flash-memory space by compressing MBR(Minimum Bounding Rectangle) of spatial data using relative coordinates and MBR size. And, the delayed write operation technique reduced the number of write operations in flash memory by storing spatial data in the buffer temporarily and reflecting them in flash memory at once instead of reflecting the insert, update and delete of spatial data in flash-memory for each operation. Especially, the utilization of buffer space was enhanced by preventing the redundant storage of the same spatial data in the buffer. Finally, we perform ed various performance evaluations and proved the superiority of FR-Tree to the existing spatial indexes.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.593-603
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• 2010

Most spatial data acquisition systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly reduced by employing an omni-directional camera that is capable of acquiring images in every direction. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by extending the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images. The proposed mathematical model includes three main parts; observation equations based on the collinearity equations newly derived for omni-directional images, stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four different types of mathematical models. With the type where only GCPs are used as the constraints, the proposed BBA can provide the most accurate results, ${\pm}5cm$ of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.1
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• pp.72-85
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• 2012

Today, although most bluetooth hands-free devices within a vehicle provide telephone service functions such as voice communication, caller id display and SMS message display and so on, they do not provide a function that displays Internet-based text data. We need to develop a scheme that displays the internet-based text data including existing hands-free function because the request for using the Internet service is increasing within a vehicle recently. The proposed bluetooth device application includes advanced function such as SNS message arrival notification, the message display function and we chose Android as the implementation mobile platform giving consideration to the fact that most SNS applications operate on Android and the platform is easily embedded into small embedded device. Smartphone or tablet PC connected with the proposed bluetooth device is an Android-based device and we designed a form of Android app for the function implementation of the devices. When the audio-text gateway app receives SNS text data, it extracts title and sender information from the message header information in a form of text data and sends them via ACL (Asynchronous Connection-Oriented) link to the bluetooth device showing the data on the screen. Android-based bluetooth devices are not possible to play voice through speaker because the bluetooth hands-free or headset profile ported within Android platform normally only includes audio gateway's function. The proposed bluetooth device application, therefore, applies the streaming scheme that sends data via ACL link instead of the way that sending them via SCO (Synchronous Connection-Oriented) link.

• Journal of Fashion Business
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• v.14 no.3
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• pp.35-51
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• 2010

This study aims at providing data for establishing a marketing strategy which can enhance the competitiveness of Korea domestic SPA(Specialty Store Retailer of Private Label Apparel) Brands by suggesting countermeasure strategy through the observation and analysis for SPA Brands, under the current circumstance in which the systematic and scholastic discussion for the matter, is lack, despite the diastrophism in fashion industry is prospected according to the rapid growth of Global SPA Brands. For this purpose, the characteristic and current status of Global SPA Brands is examined, and the main cause of growth is analyzed by approaching to their marketing characteristic, in this study. In relation with this situation, this study suggests the provisions as below, which are drawn from the analysis on Global SPA Brands' marketing strategy, so that Korea domestic SPA Brands could achieve successive performance under fierce competition. First, to be a competitive SPA Brands a business should be able to supply products with frequent product turnover by an interval level of one week or so, the existent product planning by seasons, as a business obtains various swift informations on consumers' demand with R&D center foundation. Secondly, SPA Brands should establish a strategy that a business can create high net profit by inventory management which enables lowering inventory ratio remarkably, and a strategy for innovative product supply by small quantity batch production, along with founding a high technological logistics system. Third, SPA Brands should establish a strategy for primary cost reduction by overseas dispersed outsourcing in order to enable diverse product development and rational price setting. Fourth, fashion marketers should establish also a strategy for communication by which brand image can be delivered effectively, by firming the brand identity and by informing product characteristic and customer service totally, with the method of VMD and flagship store. Additionary, fashion marketers also should establish a strategy by developing mobile application which can provide brand image and diverse other fashion related information.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.653-658
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• 2010

In this paper, we propose a word spacing system which can be performed with just a small memory. We focus on significant memory reduction while maintaining the performance of the system as much as the latest studies. Our proposed method is based on the theory of Hidden Markov Model. We use only probability information not adding any rule information. Two types of features are employed: 1) the first features are the spacing patterns dependent on each individual syllable and 2) the second features are the values of transition probability between the two syllable-patterns. In our experiment using only the first type of features, we achieved a high accuracy of more than 91% while reducing the memory by 53% compared with other systems developed for mobile application. When we used both types of features, we achieved an outstanding accuracy of more than 94% while reducing the memory by 76% compared with other system which employs bigram syllables as its features.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.1
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• pp.31-42
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• 2017

Thumbnail, the small sized graphic file such as JPEG or GIF, serves to help the users to be recognized as a rapidly helps to make it easier recognize while browsing the large sized graphic file. Gallery application, which is installed in a later version of the 4.4.x(Kitkat) Android smartphone records the generated time of graphic file in thumbnail metadata. Thumbnail can be used to draw up the timeline of user action about user's action such as creation, modification, deletion with original graphic file analysis. Also, take advantage of the features thumbnails are stored sequentially in a single thumbcache file, even if the thumbcache is deleted, we can restore the thumbnails. This paper illustrates the feature of thumbnail created by Android OS basic gallery app and methods for utilization in digital forensics.