• Management & Information Systems Review
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• v.33 no.1
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• pp.99-118
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• 2014

This study is a product launch strategy Preannouncing companies associated with temporal distance( in the near future/ distant future) and prior knowledge level(high knowledge/ low knowledge), the memory - based judgments(Global product judgment/ Discrete product judgment) and the purchase intention appears, the difference between the empirical verification of what was discriminatory. The study, first, Preannouncing main effect of temporal distance on judgments remember the difference between the purchase intention and consistent global product judgment is more discrete product judgment were higher awareness, purchase intention is higher. Second, Preannouncing high level of product knowledge in global product judgment showed that compared to discrete product judgment. In addition, low levels of knowledge than a discrete product judgment that global product judgment and purchase intention shown that a high level of consumer knowledge through systematic information processing and the road leads to higher purchase attitude. Third, Preannouncing according to the temporal distance and level of knowledge about the interaction effect results in the near future in terms of the high level of knowledge consumers global product judgment was higher than the discrete product judgment. On the other hand, a low level of knowledge of conditions in the distant future, consumers are more discrete product judgment recognized global product judgment showed that high.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.363-367
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• 1999

As the subscription rate to both Internet services and wireless communications services increases rapidly, traffic in the future wireless communications will be dominated by Internet applications. Current research for wireless technology is focused to the multimedia data transmission mainly for the personal computer or notebook whose display which has large display and abundant memory. But in the near future, Internet access request from the handphone or PDA device which is already spreaded in the world will be large. Special handset terminals which has small display monitor, low capacity memory and poor processing power has to be managed differently from torrent wireless communications protocols. So, WAP Forum is organized with wireless handset manufacturers in the world and the standardization process for wireless application is being actively developed. In this paper, basic model and architecture of WAP is introduced and adaptation requirements for change from HTML documents to WML formats are proposed. Also, compression ratio gained in the transform from the existing web documents to WML is shown.

• Journal of The Korean Astronomical Society
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• v.49 no.4
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• pp.157-162
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• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.1
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• pp.1-10
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• 2002

A space-time concatenated convolutional and differential coding scheme is employed in a multiuser direct-sequence code-division multiple-access(DS-CDMA) system. The system consists of single-user detectors (SUD), which are used to suppress multiple-access interference(MAI) with no requirement of other users' spreading codes, timing, or phase information. The space-time differential code, treated as a convolutional code of code rate 1 and memory 1, does not sacrifice the coding efficiency and has the least number of states. In addition, it brings a diversity gain through the space-time processing with a simple decoding process. The iterative process exchanges information between the differential decoder and the convolutional decoder. Numerical results show that this space-time concatenated coding scheme provides better performance and more flexibility than conventional convolutional codes in DS-CDMA systems, even in the sense of similar complexity Further study shows that the performance of this coding scheme applying to DS-CDMA systems with SUDs improves by increasing the processing gain or the number of taps of the interference suppression filter, and degrades for higher near-far interfering power or additional near-far interfering users.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1215-1221
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• 2017

SeNtinel's Application Platform (SNAP) is an open source software developed by the European Space Agency and consists of several toolboxes that process data from Sentinel satellite series, including SAR (Synthetic Aperture Radar) and optical satellites. Among them, S1TBX (Sentinel-1 ToolBoX)is mainly used to process Sentinel-1A/BSAR images and interferometric techniques. It provides flowchart processing method such as Graph Builder, and has convenient functions including automatic downloading of DEM (Digital Elevation Model) and image mosaicking. Therefore, if computer memory is sufficient, InSAR (Interferometric SAR) and DInSAR (Differential InSAR) perform smoothly and are widely used recently in the world through rapid upgrades. S1TBX also includes existing SAR data processing functions, and since version 5, the processing capability of KOMPSAT-5 has been added. This paper shows an example of processing the interference technique of KOMPSAT-5 SAR image using S1TBX of SNAP. In the open mine of Tavan Tolgoi in Mongolia, the difference between DEM obtained in KOMPSAT-5 in 2015 and SRTM 1sec DEM obtained in 2000 was analyzed. It was found that the maximum depth of 130 meters was excavated and the height of the accumulated ore is over 70 meters during 15 years. Tidal and topographic InSAR signals were observed in the glacier area near Jangbogo Antarctic Research Station, but SNAP was not able to treat it due to orbit error and DEM error. In addition, several DInSAR images were made in the Iraqi desert region, but many lines appearing in systematic errors were found on coherence images. Stacking for StaMPS application was not possible due to orbit error or program bug. It is expected that SNAP can resolve the problem owing to a surge in users and a very fast upgrade of the software.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.974-983
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• 2014

The commercialization of ITS(Intelligent Transport System) is in sight including V2V(Vehicle-toVehicle) communication and analysis of related electromagnetic circumstances is essential process in relevant legislation. However analysis including numbers of vehicles have electrically large environment which leads to a lack of computational resources. In this letter, we suggest superposition method which require much less computational resources by subgrouping environment and using post-processing of results. Suggested method approximate original result by superpositioning of analysis which include scatterers near source, observation point. This letter also presented guideline of method and example for comparison with full analysis result.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.662-672
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• 2017

Recently, with the development of mobile devices and near field communication, mobile P2P networks have been actively studied to improve the limits of the existing centralized processing system. A peer has limited components such as batteries, memory and storage spaces in mobile P2P networks. The trust of a peer should be discriminated in order to share reliable contents in mobile P2P networks. In this paper, we propose a trust discrimination scheme considering limited resources in mobile P2P environments. The proposed scheme discriminates the trust of a peer by direct rating values using the rating information of the peer and indirect rating values by the other peers. The recent update time is included in the rating information. The proposed scheme reduces the redundant rating information by comparing the recent update times of the rating information. It is shown through performance evaluation that the proposed scheme reduces the number of messages and improves the accuracy of trust over the existing scheme.