• 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.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4331-4354
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• 2020

Nepal is a sensitive and disaster-prone country where safety and security risk are of high concern for travelers. Digital technologies can play a vital role in addressing safety and security issues in the country. This research work proposes a Digital tourism security system design for addressing the safety and security issues in tourism industry of Nepal. The study uses Design science research methodology to identify artifacts, interactions, information flow and dependencies between them which are then mapped with existing prevalent technology to provide design solutions. Data is obtained from interview of tourist and experts as a primary source and technical documents/draft, software documentations, surveys as secondary source. Generalized information model, Use cases model, Network architecture model, Layered taxonomy model and Digital tourism technology reference model are the outcomes of the study. The work is very important as it talks specifically about implementation and integration of digital technologies in tourism security governance at federal, provincial, municipal and rural level. The research supplements as a knowledge document for design and implementation of digital tourism security system in practice. As there is very less work on digital systems in tourism security of Nepal, this work is a pioneer and first of its kind.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4B
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• pp.368-376
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• 2009

Over recent years, the development of multimedia devices has meant that a wider multimedia streaming service can be supported, and there are now many ways in which TV channels can communicate with different terminals. Generally, scalable video streaming is known to provide more efficient channel capacity than simulcast video streaming. Simulcast video streaming requires a large network bandwidth for all resolutions, but scalable video streaming needs only one flow for all resolutions. On the contrary, to preserve the same video quality, SVC(Sealable Video Coding) needs a higher bit-rate than AVC(non-layered Video Coding) due to the coding penalty($10%{\sim}30%$). In previous research, scalable video streaming has been compared with simulcast video streaming for network channel capacity, in two-user simulation environments. The simulation results show that the channel capacity of SVC is $16{\sim}20%$ smaller than AVC, but scalable video streaming is not efficient because of the limit of the present network framework. In this paper, we propose a new network framework with a new router using EDE(Extraction Decision Engine) and SVC Extractor to improve network performance. In addition, we compare the SVC environment in the proposed framework with previous research on the same way subject. The proposed network framework shows a channel capacity 50%(maximum) lower than that found in previous research studies.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.