• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.179-188
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• 2014

Due to the tremendous amount and its rapid increase of network traffic, the performance of network equipments are becoming an important issue. Network filtering is one of primary functions affecting the performance of the network equipment such as a firewall or a load balancer to process the packet. In this paper, we propose a cache based tri method to improve the performance of the existing tri method of searching for network filtering. When several packets are exchanged at a time between a server and a client, the tri method repeats the same search procedure for network filtering. However, the proposed method can avoid unnecessary repetition of search procedure by exploiting cache so that the performance of network filtering can be improved. We performed network filtering experiments for the existing method and the proposed method. Experimental results showed that the proposed method could process more packets up to 790,000 per second than the existing method. When the size of cache list is 11, the proposed method showed the most outstanding performance improvement (18.08%) with respect to memory usage increase (7.75%).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.643-650
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• 2008

If existing system need to expand security part, the security was established after paying much cost, processing of complicated installation and being patient with inconvenience at user's view because of closed structure. In this thesis, those defects could be overcome by using open security tools and constructing security server, which is firewall of 'bastion' form including proxy server, certification server and so on. Also each security object host comes to decide acceptance or denial where each packet comes from, then determines security level each hosts. Precisely it is possible choosing the packets from bastion host or following at the other policies. Although an intruder enter into inside directly, it is constructed safely because encryption algorithm is applied at communication with security object host. This thesis suggests more flexible, independent and open security system, which improves existing security through systematic linkage between system security and network security.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.632-637
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• 2019

Recent researches on autonomous driving of vehicles are becoming very active, and it is a trend to assist safe driving and improve driver's convenience. Autonomous vehicles are required to combine artificial intelligence, image recognition capability, and Internet communication between objects. Because mobile telecommunication networks have limitations in their processing, they can be easily implemented and scale using an easily expandable Wi-Fi network. We propose a wireless design method to construct such a vehicle control network. We propose the arrangement of AP and the software configuration method to minimize loss of data transmission / reception of mobile terminal. Through the design of the proposed network system, the communication performance of the moving vehicle can be dramatically increased. We also verify the packet structure of GPS, video, voice, and data communication that can be used for the vehicle through experiments on the movement of various terminal devices. This wireless design technology can be extended to various general purpose wireless networks such as 2.4GHz, 5GHz and 10GHz Wi-Fi. It is also possible to link wireless intelligent road network with autonomous driving.

• The Korean Journal of Emergency Medical Services
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• v.11 no.3
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• pp.93-109
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• 2007

The pre-hospital care in site transportation care and site care will be divided, it will follow in site or evacuation it will enforce it will can evacuate in condition of the emergency patient of like this at the initial stage and emergency care from inside fire fighting helicopter back transfer means and the manpower security of the specialty emergency necessary personnel(nursing and 1st EMT's) as the medical treatment agency and modernization of first aid equipment necessity inside American securing and fire fighting helicopter and specialty first aid packet won about lower the emergency care which is appropriate cannot become accomplished are the actual condition in total lack of emergency care equipment. Consequently craving augmentation, in order to be adapted with the handling kind transfer whose specialty and is appropriate and present time of rapid increase and the citizen of emergency demand by fire fighting helicopter simplicity transfer compared to it is a condition where the countermeasure preparation is earnest. Must expand emergency care equipment first even in fire fighting helicopter and 1st EMT's which it follows in him become arrangement and quickly the execution and specialty temporary disposal(ALS) must be enforced a temporary disposal and must buy the life which is. Also it gets by experience a helicopter induction outline, a radio communication method and the patient helicopter on-board hour attention point back various attention fact back with the body and when where it stands but accurately there must be it will be able to induce the helicopter. Also every manuals anger it does a helicopter transfer method and the emergency care method back and that all processes must do fixed form anger, it becomes feed. Also it related with a helicopter transfer even from the relationship agency many research to lead, difference of the advanced foreign nation and the maximum it is the actual condition where the medical emergency system construction which it reduces is earnestly demanded. Also with emergency structure(crane) it confronts to an aviation transfer even from the establishment college and education it leads intensively and 1st EMT's of the good quality which relates with an aviation structure expects is cultivate at all.

• Journal of KIISE:Information Networking
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• v.30 no.2
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• pp.282-292
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• 2003

This paper proposes an efficient data structure for forwarding IPv4 and IPv6 packets at the gigabit speed in backbone routers. The LPM(Longest Prefix Matching) search becomes a bottleneck of routers' performance since the LPM complexity grows in proportion to the forwarding table size and the address length. To speed up the forwarding process, this paper introduces a data structure named BMT(Bit-Map Tie) to minimize the frequent main memory accesses. All the necessary search computations in BMT are done over a small index table stored at cache. To build the small index table from the tie representation of the forwarding table, BMT represents a link pointer to the child node and a node pointer to the corresponding entry in the forwarding table with one bit respectively. To improve the poor performance of the conventional tries when their height becomes higher due to the increase of the address length, BMT adopts a binary search algorithm for determining the appropriate level of tries to start. The simulation experiments show that BMT compacts the IPv4 backbone routers' forwarding table into a small one less than 512-kbyte and achieves the average speed of 250ns/packet on Pentium II processors, which is almost the same performance as the fastest conventional lookup algorithms.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.271-280
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• 1999

인공신경망을 시계열예측에 적용하는 경우에 고려되어야 할 문제중, 특히 모형에 적합한 입력변수의 생성이 중요시되고 있는데, 이러한 분야는 인공신경망의 모형생성과정에서 입력변수에 대한 전처리기법으로써 다양하게 제시되어 왔다. 가장 최근의 입력변수 전처리기법으로써 제시되고 있는 신호처리기법은 전통적 주기분할처리방법인 푸리에변환기법(Fourier transforms)을 비롯하여 이를 확장시킨 개념인 웨이블릿변환기법(wavelet transforms) 등으로 대별될 수 있다. 이는 기본적으로 시계열이 다수의 주기(cycle)들로 구성된 상이한 시계열들의 집합이라는 가정에서 출발하고 있다. 전통적으로 이러한 시계열은 전기 또는 전자공학에서 주파수영역분할, 즉 고주파 및 저주파수를 분할하기 위한 기법에 적용되어 왔다. 그러나, 최근에는 이러한 연구가 다양한 분야에 활발하게 응용되기 시작하였으며, 그 중의 대표적인 예가 바로 경영분야의 재무시계열에 대한 분석이다. 전통적으로 재무시계열은 장, 단기의사결정을 가진 시장참여자들간의 거래특성이 시계열에 각기 달리 가격으로 반영되기 때문에 이러한 상이한 집단들의 고요한 거래움직임으로 말미암아 예를 들어, 주식시장이 프랙탈구조를 가지고 있다고 보기도 한다. 이처럼 재무시계열은 다양한 사회현상의 집합체라고 볼 수 있으며, 그만큼 예측모형을 구축하는데 어려움이 따른다. 본 연구는 이러한 시계열의 주기적 특성에 기반을 둔 신호처리분석으로서 기존의 시계열로부터 노이즈를 줄여 주면서 보다 의미있는 정보로 변환시켜줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망의 모형결합을 통해 기존연구과는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서는 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이브릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다. 기존의 주기분할방법론은 모형개발자입장에서 여러 가지 통계기준치중에서 최적의 기준치를 합리적으로 선택해야 하는 문제가 추가적으로 발생하며, 본 연구에서는 이상의 제반 문제들을 개선시키기 위해 통합방법론으로서 기존의 인공신경망모형을 구조적으로 확장시켰다. 이 모형에서 기존의 입력층 이전단계에 새로운 층이 정의된다. 이렇게 해서 생성된 새로운 통합모형은 기존모형에서 생성되는 기본적인 학습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실증사례 분석결과는 일일 환율예측문제를 적용하였을 경우, 기존의 방법론보다 더 나운 예측성과를 타나내었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.9
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• pp.381-392
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• 2013

During the last decade, the multitude of advances attained in terminal computers, along with the introduction of mobile hand-held devices, and the deployment of high speed networks have led to a recent surge of interest in Quality of Service (QoS) for video applications. The main difficulty is that mobile devices experience disparate channel conditions, which results in different rates and patterns of packet loss. One way of making more efficient use of network resources in video services over wireless channels with heterogeneous characteristics to heterogeneous types of mobile device is to use a scalable video coding (SVC). An SVC divides a video stream into a base layer and a single or multiple enhancement layers. We have to ensure that the base layer of the video stream is successfully received and decoded by the subscribers, because it provides the basis for the subsequent decoding of the enhancement layer(s). At the same time, a system should be designed so that the enhancement layer(s) can be successfully decoded by as many users as possible, so that the average QoS is as high as possible. To accommodate these characteristics, we propose an efficient transmission scheme which incorporates SVC-aware dual-channel repetition to improve the perceived quality of services. We repeat the base-layer data over two channels, with different characteristics, to exploit transmission diversity. On the other hand, those channels are utilized to increase the data rate of enhancement layer data. This arrangement reduces service disruption under poor channel conditions by protecting the data that is more important to video decoding. Simulations show that our scheme safeguards the important packets and improves perceived video quality at a mobile device.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.975-983
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• 1999

최근 네트워크의 속도가 빨라지고 멀티미디어 데이터를 다루기 위한 기술들이 개발됨에 따라 많은 멀티미디어 응용 프로그램들이 인터넷에 등장하고 있다. 그러나 이들 응용프로그램들은 수신자에게 전송되는 영상.음성의 품질이 낮기 때문에 기대만큼 빠르게 확산되지 못하고 있다. 영상.음성의 품질이 낮은 이유는 현재 인터넷이 실시간 응용프로그램이 요구하는 만큼 빠르고 신뢰성 있게 데이터를 전송할 수 없기 때문이다. 현재 인터넷의 내부구조를 바꾸지 않고 품질을 높이기 위해 많은 연구들이 진행되고 있는데 그 중 하나는 동적으로 변화하는 인터넷의 상태에 맞게 멀티캐스트 트래픽의 전송율을 조절하는 종단간의 흐름제어이다. 본 논문은 기존의 흐름제어 기법인 IVS와 RLM의 성능을 개선시키기 위한 두 가지 흐름제어 기법을 소개한다. IVS는 송신자가 주기적으로 측정된 네트워크 상태에 따라 전송율을 일정하게 조절한다. 송신자가 하나의 데이타 스트림을 생성하는 IVS와는 달리 RLM에서는 송신자가 계층적 코딩에 의하여 생성된 여러개의 데이타 스트림을 전송하고 각 수신자는 자신의 네트워크 상태에 맞게 데이타 스트림을 선택하는 기법이다. 그러나 IVS는 송신자가 전송율을 일정하게 증가시키고, RLM은 각자의 네트워크 상태를 고려하지 않고 임의의 시간에 하나 이상의 데이타 스트림을 받기 때문에 성능을 저하시킬 수 있다. 본 논문에서는 TCP-like IVS와 Adaptive RLM이라는 두 가지 새로운 기법을 소개한다. TCP-like IVS는 송신자가 전송율을 동적으로 결정하고, Adaptive RLM은 하나 이상의 데이타 스트림을 받기 위해 적당한 시간을 선택할 수 있다. 본 논문에서는 시뮬레이션을 통해 여러 가지 네트워크 구조에서 두 가지 방식이 기존의 방식에 비하여 더욱 높은 대역폭 이용율과 10~20% 정도 적은 패킷손실율을 이룬다는 것을 보여준다.Abstract Nowadays, many multimedia applications for the Internet are introduced as the network gets faster and many techniques manipulating multimedia data are developed. These multimedia applications, however, do not spread widely and are not fast as expected at their introduction time due to the poor quality of image and voice delivered at receivers. The poor quality is mainly attributed to that the current Internet can not carry data as fast and reliably as the real-time applications require. To improve the quality without modifying the internal structure of the current Internet, many researches are conducted. One of them is an end-to-end flow control of multicast traffic adapting the sending rate to the dynamically varying Internet state. This paper proposes two flow-control techniques which can improve the performance of the two conventional techniques; IVS and RLM. IVS statically adjusts the sending rate based on the network state periodically estimated. Differently from IVS in which a sender produces one single data stream, in RLM a sender transmits several data streams generated by the layered coding scheme and each receiver selects some data streams based on its own network state. The more data streams a receiver receives, the better quality of image or voice the receiver can produce. The two techniques, however, can degrade the performance since IVS increases its sending rate statically and RLM accepts one more data stream at arbitrary time regardless of the network state respectively. We introduce two new techniques called TCP-like IVS and Adaptive RLM; TCP-like IVS can determine the sending rate dynamically and Adaptive RLM can select the right time to add one more data stream. Our simulation experiments show that two techniques can achieve better utilization and less packet loss by 10-20% over various network topologies.