• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.483-489
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• 2016

This paper proposes a repeat-back jamming signal detection scheme that utilizes a combined pseudo random noise signal that is effective for processing a global positioning system (GPS) repeat-back jamming signal with the early minus late phase scheme to alleviate any existing multipath signal detection. The proposed scheme uses the combined pseudo random noise signal to treat repeat-back jamming signals like similar multipath signals and can effectively detect a repeat-back jamming signal by applying the early minus late phase scheme to a combined pseudo random noise signal. Through a Monte-Carlo simulation, the detection probability of the proposed scheme is better than the one of the conventional scheme under low jamming to signal power ratio.

• Journal of Communications and Networks
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• v.3 no.4
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• pp.334-341
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• 2001

The provision of QoS control is a key of the successful deployment of multimedia networks. Buffer management plays an important role in QoS control. Therefore, this paper proposes a novel QoS buffer management algorithm named GREEN (Global Random Early Estimation for Nipping), which extends the concepts of ERD (early random drop) and RED (random early detection). Specifically, GREEN enhances the concept of "Random" to "Global Random" by globally considering the random probability function. It also enhances the concept of "Early" to "Early Esti mation" by early estimating the network status. For performance evaluation, except compared with RED, extensive simulation cases are performed to probe the characteristics of GREEN.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.517-526
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• 2001

In order to reduce the increasing packet loss rates caused by an exponential increase in network traffic, the IETF(Internet Engineering Task Force) is considering the deployment of active queue management techniques such as RED(Random Early Detection). While active queue management in routers and gateways can potentially reduce total packet loss rates in the Internet, this paper has demonstrated the inherent weakness of current techniques and shows that they are ineffective in preventing high loss rates. The inherent problem with these queue management algorithms is that they all use queue lengths as the indicator of the severity of congestion. In this paper, in order to solve this problem, a new active queue management algorithm called MRED(Modified Random Early Detection) is proposed. MRED computes the packet drop probability based on our heuristic method rather than the simple method used in RED. Using simulation, MRED is shown to perform better than existing queue management schemes. To analyze the performance, we also measure throughput of traffics under the FIFO control, and compared the performance with that of this MRED system.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.695-700
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• 2005

To provide the fair rate and achieve the fair bandwidth allocation, many per-flow scheduling algorithms have been proposed such as fair queueing algorithm for congestion control. But these algorithms need to maintain the state, manage buffer and schedule packets on a per-flow basis; the complexity of these functions may prevent them from being cost-effectively implemented. In this paper, therefore, to acquire cost-effectively for implementation, we propose a CS-FNE(Core Stateless FNE) algorithm that is based on FM(Flow Number Estimation), and evaluated CS-FNE scheme together with CSFQ(Core Stateless Fair Queueing), FRED(Fair Random Early Detection), RED(Random Early Detection), and DRR(Dynamic Round Robin) in several different configurations and traffic sources. Through the simulation results, we showed that CS-FNE algorithm can allocate fair bandwidth approximately than other algorithms, and CS-FNE is simpler than many per-flow basis queueing mechanisms and it can be easily implemented.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.337-352
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• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.177-183
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• 2007

Active queue management (AQM) refers to a family of packet dropping mechanisms for router queues that has been proposed to support end-to-end congestion control mechanisms in the Internet. The proposed AQM algorithm by the IETF is Random Early Detection (RED). The RED algorithm allows network operators simultaneously to achieve high throughput and low average delay. However. the resulting average queue length is quite sensitive to the level of congestion. In this paper, we propose the Refined Adaptive RED(RARED), as a solution for reducing the sensitivity to parameters that affect RED performance. Based on simulations, we observe that the RARED scheme improves overall performance of the network. In particular, the RARED scheme reduces packet drop rate and improves goodput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.1085-1100
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• 2011

The Random Early Detection algorithm is widely used in the queue management mechanism of the router. We find that the parameters of the RED algorithm have a significant influence on the defense performance of the random early detection algorithm and discuss the robust of the algorithm in mitigating Low-rate Denial-of-Service attack in details. Simulation results show that the defense performance can be effectively improved by adjusting the parameters of $Q_{min}$ and $Q_{max}$. Some suggestions are given for mitigating the LDoS attack at the end of this paper.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.437-446
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• 2002

In order to reduce the increasing packet loss rates caused by an exponential increase in network traffic, the IETF (Internet Engineering Task Force) is considering the deployment of active queue management techniques such as RED (Random Early Detection). While active queue management in routers and gateways can potentially reduce packet loss rates in the Internet, this paper has demonstrated the inherent weakness of current techniques and shows that they are ineffective in preventing high loss rates. The inherent problem with these queue management algorithms is that they all use static parameter setting. So, in case where these parameters do not match the requirement of the network load, the performance of these algorithms can approach that of a traditional Drop-tail. In this paper, in order to solve this problem, a new active queue management algorithm called ARED (Active RED) is proposed. ARED computes the parameter based on our heuristic method. This algorithm can effectively reduce packet loss while maintaining high link utilizations.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.