• The Transactions of the Korea Information Processing Society
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• v.4 no.5
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• pp.1319-1326
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• 1997

The Internet is a very convenient neteork, but not always a So.many orvanizations often require administrative security policies when they want to connect with ther organizations thtough a pubic network.A firewall is one way of protecting an internal network from the untrusted extemal network with a mechanism for selectively permiting or blocking traffic between the intermal network and the extermal network. The firewall also provides some limited Intemet access such as Ftp.Telet, etc.Usually , through a firewall system, X applications running on the extermal network may not access an X display on the intermal network.Thus, X applications run-ning on the extemal network are not supported through the usual firewall.In this paper, we propose a method of suppoting X application programs effedtively dffectvely through the firewall, overcoming this restriction.

• 중국학논총
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• no.71
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• pp.41-59
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• 2021

With the progress of social economy and science and technology, network media technology has developed rapidly, China has ushered in the network information age, and the network buzzwords emerged to reflect the interaction and influence between language and society. The network buzzwords of "X+ ren "indirectly show the social psychology and value orientation of modern people with their unique structural characteristics, semantic connotation and cultural deposits, and so on. Based on this, we have conducted a multi-angle investigation on the network buzzwords "X+ ren". This paper first analyzes the structure types and syntactic functions of the lexical model of "X+ ren ", then makes a semantic analysis of the lexical model of "X+ Ren ", and finally investigates the causes and influences of the popularity of "X+ ren ". Through the investigation, we believe that "X+ ren "will continue to grow, and "X+ ren" will continue to attract the attention of the academic community.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.04a
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• pp.119-124
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• 1986

This paper describes the development of the KORNET that may be regarded as the latest public packet-swiched computer communication network. The KORNET project included the development of the network management center (NMC), a network concentrator. For the KORNET we use the virtual circuit(VC) method, a distributed adaptive routing algorithm, and a dynamic buffer management algorithm. The NMC acts as a nerve center of the network, performing such function as network monitoring, subscriber and network management and routing management, etc. As for the NNP and NC hardware, we have implemented them with the 16-bit multitask/multiprocessor technology using MC68000 microprocessors. Softwares have been developed using C language is required for real time processing. All the network protocols we have developed comply completely with the latest CCITT recommendations including X.25, X,3 , X.28 and X.29.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.165-173
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• 2021

The purpose of this study was to provide directions to help enter and expand long-distance routes by analyzing the characteristics of AirAsia X's network construction with Malaysia Airlines. To this end, long-distance route data was extracted from the OAG Schedule Analyzer and the network was analyzed on a two-period basis using SNA. Since AirAsia X's entry into long-range routes, Malaysia Airlines has steadily reduced its routes across the entire region. On the other hand, it is analyzed that AirAsia X is building an expanded network by increasing its network in Northeast Asia instead of ultra-long range routes. Studies have shown that LCCs also have potential growth in the long-distance route market of less than 7,000 km. The results of this study may help LCC establish a long-distance market entry and network deployment strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.918-926
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• 2001

In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1529-1534
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• 2018

We used in-situ synchrotron X-ray scattering to investigate phase transformations of octahedral tilted monoclinic $SrRuO_3$ (MSRO) and tetragonal SRO (TSRO) thin films on $SrTiO_3$ (STO) substrates. The octahedral tilted MSRO thin films were highly crystalline and the monoclinic distortion angle was $0.45^{\circ}$. The phase transition temperature from the MSRO to TSRO phase occurred at approximately $200^{\circ}C$ as a second order transition. Conversely, no phase transformations of the TSRO thin film occurred within the range from RT to $250^{\circ}C$. The octahedral $RuO_6$ rotation was strongly affected by the phase transformation in the SRO thin films.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.489-490
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• 2023

기계 학습에서 데이터 및 기능은 기계 학습의 상한을 결정한다.이러한 기능은 산업 생산에서 과도한 데이터 양과 유형으로 인해 상당한 추가 비용이 발생할 수 있다. 따라서 적절한 특징 처리 방법이 매우 중요해졌다. 대부분의 기존 특징 처리 방법은 특징 엔지니어링을 기능 검색 문제, 즉 모델 성능을 최적화할 수 있는 기능 변환 작업을 검색하는 것으로 추상화한다. 그러나 자동 특징 엔지니어링의 경우 검색량과 변환 조합의 수가 매우 많기 때문에 요인 분해 기반 모델을 사용하여 벡터 곱셈을 통해 상호 작용을 측정하면 조합 특징의 패턴을 자동으로 학습하는 방법이 특히 효율적이다. xDeepFM 은 명확한 방식으로 특징적인 상호작용을 생성하도록 설계된 새로운 Compressed Interaction Network (CIN)를 제안한다. 여기에 제시된 Low-rank Compressed Interaction Network(LRCIN )은 xDeepFM 접근 방식에서 CIN 네트워크의 단순화된 개선을 기반으로 하며 xDeepFM 에 주의 메커니즘을 추가하여 보다 정확하게 예측된다. 실험 결과에 따르면 모델은 계산 복잡성을 단순화할 뿐만 아니라 예측 정확도도 다른 모델보다 훨씬 우수한다.