• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.89-98
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• 2010

In current digital broadcasting systems, GEM(Globally Executable MHP)-based middlewares such as MHP(Multimedia Home Platform), OCAP(OpenCable Application Platform), ACAP(Advanced Common Application Platform) are the norm. Despite much of the common characteristics shared, such as MPEG-2 and DSM-CC(Digital Storage Media-Command and Control) protocols, the information and data structures they need are slightly different, which results in incompatibility issues. In this paper, in line with an effort to develop an integrated DTV middleware, we propose a general-purpose, reconfigurable DSM-CC architecture for supporting various standard GEM-based middlewares without code modifications. First, we identify DSM-CC components that are common and thus can be shared by all GEM-based middlewares. Next, the system is provided with middleware-specific information and data structures in the form of XML. Since the XML information can be parsed dynamically at run time, it can be interchanged either statically or dynamically for a specific target middleware. As for the performance issues, the response time and usage frequency of DSM-CC module highly contribute to the performance of STB(Set-Top-Box). In this paper, we also propose an efficient application cache management strategy and evaluate its performance. The performance result has shown that the cache strategy reflecting user preferences greatly helps to reduce response time for executing application.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Journal of Korea Water Resources Association
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• v.43 no.7
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• pp.667-679
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• 2010

The distributed hydrologic model has been considerably improved due to rapid development of computer hardware technology as well as the increased accessibility and the applicability of hydro-geologic information using GIS. It has been acknowledged that physically-based distributed hydrologic model require significant amounts of data for their calibration, so its application at ungauged catchments is very limited. In this regard, this study was intended to develop a distributed hydrologic model (S-RAT) that is mainly based on conceptually grid-based water balance model. The proposed model shows advantages as a new distributed rainfall-runoff model in terms of their simplicity and model performance. Another advantage of the proposed model is to effectively assess spatio-temporal variation for the entire runoff process. In addition, S-RAT does not rely on any commercial GIS pre-processing tools because a built-in GIS pre-processing module was developed and included in the model. Through the application to the two pilot basins, it was found that S-RAT model has temporal and spatial transferability of parameters and also S-RAT model can be effectively used as a radar data-driven rainfall-runoff model.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.539-550
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• 2003

As the service of mobile internet has been expended, student users are increase. The computers have been widely used in a education field as the teaching tool by improvement of the multimedia contents processing and user interface. The English learning using the computers in the restricted education environment provides motivations and effective learning to learners, but still have some problem such as teaching and evaluating without consideration for differences of individual levels. In order to solve the problems and take the advantages, we propose the intelligent tutoring system for english learning with mobile technology. Overcoming limitations of the mobile environment and using proper treacher's roles,. We have applied the conventional estimation method of the intellectual learner level for students. Also, we have proposed the diagnostic function in order to determine the method of teaching-learing and item disposition that each leaner prefers. Then we have designed and implemented the expert module, providing the feedback for teaching, of the intelligent turoring system for mobile english learning. This system will be able to support the interaction between teachers and students and replace some roles of teacher in the mobile english learning.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.2
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• pp.165-182
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• 2001

In this paper, we describe a design and implementation of the Wireless Markup Language(WML) compiler to deploy wireless Internet services effectively. The WML compiler translates textual WML decks into binary ones in order to reduce the traffic on wireless links that have relatively low bandwidth to wireline links and mitigate the processing overhead of WML decks on, wireless terminals that have relatively low processing power to fixed workstations. In addition, it takes over the overhead of eXtensible Markup Language(XML) well-formedness and validation processes. The WML compiler consists of the lexical analyzer and parser modules. The granunar for the WML parser module is LALR(1) context-free grammar that is designed based on XML 1.0 and WML 1.2 DTD(Document Type Definition) with the consideration of the Wireless Application Protocol Binary XML grammar. The grammar description is converted into a C program to parse that grammar by using parser generator. Even though the tags in WML will be extended or WML DTD will be upgraded, this approach has the advantage of flexibility because the program is generated by modifying just the changed parts. We have verified the functionality of the WML compiler by using a WML decompiler in the public domain and by using the Nokia WAP Toolkit as a WAP client. To measurethe compressibility gain of the WML compiler, we have tested a large number of textual WML decks and obtained a maximum 85 %. As the effect of compression is reduced when the portion of general textual strings increases relative to one of the tags and attributes in a WML deck, an extended encoding method might be needed for specific applications such as compiling of the WML decks to which the Hyper Text Markup Language document is translated dynamically.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.66-76
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• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.97-109
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• 2019

Degree days have been determined using temperature data measured at nearby weather stations to a site of interest to produce information for supporting decision-making on agricultural production. Alternatively, the data products of Moderate Resolution Imaging Spectroradiometer (MODIS) can be used for estimation of degree days in a given region, e.g., Korean Peninsula. The objective of this study was to develop a simple tool for processing the MODIS product for estimating cooling degree days (CDD), which would help assessment of heat stress conditions for a crop as well as energy requirement for greenhouses. A set of scripts written in R was implemented to obtain temperature profile data for the region of interest. These scripts had functionalities for processing spatial data, which include reprojection, mosaicking, and cropping. A module to extract air temperature at the surface pressure level was also developed using R extension packages such as rgdal and RcppArmadillo. Random forest (RF) models, which estimate mean temperature and CDD with a different set of MODIS data, were trained at 34 sites in South Korea during 2009 - 2018. Then, the values of CDD were calculated over Korean peninsula during the same period using those RF models. It was found that the CDD estimates using the MODIS data explained >74% of the variation in the CDD measurements at the weather stations in North Korea as well as South Korea. These results indicate that temperature data derived from the MODIS atmospheric products would be useful for reliable estimation of CDD. Our results also suggest that the MODIS data can be used for preparation of weather input data for other temperature-based agro-ecological models such as growing degree days or chill units.