• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.3
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• pp.23-30
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• 2010

Compression techniques can be applied to large-scale fingerprint systems to store or transmit fingerprint data efficiently. In this paper, we investigate the effects of FBI WSQ fingerprint image compression on the performance of a fingerprint verification system using multiple linear regressions. We propose a maximum compression using fingerprint image quality score. Based on the experiments, we can confirm that the proposed approach can compress the fingerprint images up to 3 times more than the fixed compression ratio without significant degradation of the verification accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.368-376
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• 2021

With the recent development of deep learning technology, autonomous things technology is attracting attention, and DNNs are widely used in embedded systems such as drones and autonomous vehicles. Embedded systems that can perform large-scale operations and process multiple DNNs for high recognition accuracy without relying on the cloud are being released. DNNs with various levels of priority exist within these systems. DNNs related to the safety-critical applications of autonomous vehicles have the highest priority, and they must be handled first. In this paper, we propose a priority-based scheduling framework for DNNs when multiple DNNs are executed simultaneously. Even if a low-priority DNN is being executed first, a high-priority DNN can preempt it, guaranteeing the fast response characteristics of safety-critical applications of autonomous vehicles. As a result of checking through extensive experiments, the performance improved by up to 76.6% in the actual commercial board.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.71-76
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• 2022

Round-robin is widely used for the scheduling of large-scale parallel workloads in the computing units of GPGPU. Round-robin is easy to implement by sequentially allocating tasks to each computing unit, but the load balance between computing units is not well achieved in multi-workload environments like cloud. In this paper, we propose a new thread block scheduling policy to resolve this situation. The proposed policy manages thread blocks generated by various GPGPU workloads with multiple queues based on their computation loads and tries to maximize the resource utilization of each computing unit by selecting a thread block from the queue that can maximally utilize the remaining resources, thereby inducing load balance between computing units. Through simulation experiments under various load environments, we show that the proposed policy improves the GPGPU performance by 24.8% on average compared to Round-robin.

• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.15-21
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• 2022

The purpose of this study is to investigate the sedimentation area and runout distance in the downstream when debris flow occurred on a mountain slope through an experimental performance. Super typhoons and torrential rains caused by climate change cause large-scale debris flow disasters in the downstream areas of mountainous areas, mainly where sediments are deposited and flowed downstream. To analyze the characteristics of the sediment deposited downstream, the disposition area and runout distance were investigated through experiments in the case of a straight channel and channel with berm, respectively. As experimental conditions, changes in sediment volume concentration and channel slope, and channel with or without berm, reduction rates in sedimentation area and runout distance were investigated. In the straight channel, the steeper the channel slope and the lower the sedimentation concentration, the sedimentation area and runout distnace were increased. In a channel with berm, the runout distance and sediment area increased as the slope became steeper and the sediment area decreased.

• Clean Technology
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• v.15 no.4
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• pp.287-294
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• 2009

In order to separate the fuel-grade DME from the product of a direct DME synthesise reaction, containing 19~20% of DME, an absorption column and a purification column were employed. In the DME absorption column, the flow rate of the methanol required to recover more than 99% of DME at 50 bar was estimated by the correlation obtained from the lab-scale experiments. In the DME purification column, the maximum DME recovery of 98.2% could be obtained even from the side stream at the 3rd stage above the feed stage, since the feed stream originated from the product of the absorption column had already contained a large amount of DME (20~30 mol%) and only a small amount of light products such as $CO_2$ and $N_2$ (5~10 mol%).

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.3-3
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• 2021

하천의 식생은 생태서식처 제공, 강턱이나 하안 경사의 안정, 영양염류나 오염물 차단에 의한 수질 개선 등의 다양한 생태서비스를 제공하는 반면, 과도하고 편중된 식생 분포로 인해 흐름저항이 증가하고 이로 인한 홍수위 상승과 홍수피해 위험이 가중될 수 있다. 기후변화 영향과 토지이용 변화에 따른 수리수문 특성 변화와 유역으로부터 영양염류의 유입은 하천내 식생이 과도하게 퍼지는 직접적인 원인이 될 수 있다. 과도한 식생분포로 인한 흐름저항과 홍수위 상승을 평가하기 위해 식생하도의 흐름저항과 관련된 매개변수의 다양성과 복잡성에 따른 해석상의 한계에도 불구하고 이에 대한 이론적 해석과 수학모형의 개발, 실험실 수로와 하천현장조사, 수치모형을 이용한 예측 등의 다양한 연구가 수행되고 있다. 본 연구에서는 소규모 실험실 수로에서 단순화된 식생 구조와 배열이 적용되는 한계성과 상류에서 유입되는 유량과 유속 등의 수리조건을 통제할 수 없는 현장실험의 한계성을 보완하고자 목본류 형태의 식생패치를 실규모 하천실험 수로에 조성하여 통제된 수리조건에서의 식생하도의 구간 흐름저항을 추정하기 위한 계측 실험을 수행하였다. 실규모 실험에서는 피에조미터 형식의 정밀 압력식 센서를 이용하여 수위와 수면경사를 계측하였으며, 실험구간의 접근 수로에서 ADCP(Acoustic Doppler Current Profiler)를 이용하여 유입 유량에 대한 정보를 수집하였다. 전체 수로 길이 약 600 m, 하폭 11 m, 깊이 2 m 규모의 실규모 하도 일부 80 m 완경사 구간에 식생패치를 설치하고 구간 수면경사를 실측함으로써 식생하도의 흐름저항을 직접 산정하였다. 이러한 실규모 식생하도의 실험을 통해 식생패치의 밀도, 횡적 차단면적, 구간 차단부피, 목본 식생의 물리적 특성, 식생패치의 배열 등의 다양한 식생분포 조건에 따른 수리조건별 흐름저항 값의 변화를 분석할 수 있다. 식생하도에 대한 정확한 흐름저항 추정과 홍수위 변화에 대한 해석은 하천 식생의 생태서비스와 홍수방어의 하천 치수효과의 최적 솔루션을 제시하기 위한 하도관리의 다양한 방안을 제시하는데 필수적인 정보로 활용될 수 있다.

• Convergence Security Journal
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• v.22 no.5
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• pp.145-154
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• 2022

Recently, the widespread proliferation and high sophistication of botnets are having serious consequences not only for enterprises and users, but also for cyber warfare between countries. Therefore, research to detect botnets is steadily progressing. However, the DGA-based botnet has a high detection rate with the existing signature and statistics-based technology, but also has a high limit in the false positive rate. Therefore, in this paper, we propose a detection model using text-based n-gram to detect DGA-based botnets. Through the proposed model, the detection rate, which is the limit of the existing detection technology, can be increased and the false positive rate can also be minimized. Through experiments on large-scale domain datasets and normal domains used in various DGA botnets, it was confirmed that the performance was superior to that of the existing model. It was confirmed that the false positive rate of the proposed model is less than 2 to 4%, and the overall detection accuracy and F1 score are both 97.5%. As such, it is expected that the detection and response capabilities of DGA-based botnets will be improved through the model proposed in this paper.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.19-19
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• 2020

The Cassava(Manihot esculenta Crantz) is a tropical root crop, originally from Amazonia, that provides the staple food of an estimated 800 million people worldwide. It belongs to the family Euphorbiaceae which also includes rubber (Hevea brasiliensis) and castor bean (Ricinus communis). Among tropical crops, rice, sugarcane, maize and cassava are the most important sources of calories for human consumption. Problems in the propagation of cassava are virus diseases and low rates of seed germination. So we tried to optimize protocols for mass production of somatic embryo amenable to large-scale vegetative propagation of Cassava. After in vitro eight-week culture of leaves of Cassava, the medium which contained the 2,4-D, BAP and IBA showed the highest callus induction rate, embryogenesis callus formation rate and somatic embryo formation in Cassava culture. In the medium with GA₃ and myo-inositol, shoots were most vigorously regenerated from somatic embryos of Cassava. Our experiments confirmed that in vitro growth and multiplication of plantlets could depend on its reaction to the different medium composition, and this micropropagation techniques could be a useful system for healthy and vigorous plant production.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.97-102
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• 2023

This study is to provide improvement of tester for IoT system, which has recently become diversified and large-scale and It is about a method to improve the packet processing performance of the tester and securing flexibility in traffic protocol creation and operation. The purpose of this study is to design a OSS DPDK-based high-speed IoT network performance test system, which pre-verifies and measures the performance of data traffic transmission in an increasingly sophisticated high-capacity IoT network system. The basic structure of the high-speed IoT performance tester was designed using a DPDK-based traffic generator, the expected effect was suggested to traffic modeling and packet generation capability when the system was applied through experiments

• Smart Media Journal
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• v.13 no.4
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• pp.23-32
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• 2024

Large-scale fires and their consequential damages are becoming increasingly common, but confidence in fire detection systems is waning. Recently, widely-used chemical fire detectors frequently generate lots of false alarms, while video-based deep learning fire detection is hampered by its time-consuming and expensive nature. To tackle these issues, this study proposes a fire detection model utilizing an autoencoder approach. The objective is to minimize false alarms while achieving swift and precise fire detection. The proposed model, employing an autoencoder methodology, can exclusively learn from normal data without the need for fire-related data, thus enhancing its adaptability to diverse environments. By amalgamating data from five distinct sensors, it facilitates rapid and accurate fire detection. Through experiments with various hyperparameter combinations, the proposed model demonstrated that out of 14 scenarios, only one encountered false alarm issues. Experimental results underscore its potential to curtail fire-related losses and bolster the reliability of fire detection systems.