• Journal of Communications and Networks
• /
• v.7 no.1
• /
• pp.54-64
• /
• 2005

This paper presents an advanced architecture for a traffic generator capable of producing ATM traffic streams according to fully general semi-Markovian stochastic models. The architecture employs a basic traffic generator platform and enhances it by adding facilities for 'driving' the cell generation process through high-level specifications. Several kinds of optimization are employed for enhancing the software's speed to match the hardware's potential and for ensuring that traffic streams corresponding to models with a wide range of parameters can be generated efficiently and reliably. The proposed traffic generation procedure is highly modular. Thus, although this paper deals with ATM traffic, the main elements of the architecture can be used equally well for generating traffic loads on other networking technologies, IP-based networks being a notable example.

• Journal of Communications and Networks
• /
• v.11 no.2
• /
• pp.134-147
• /
• 2009

Tracking and exploiting instantaneous spectrum opportunities are fundamental challenges in opportunistic spectrum access (OSA) in presence of the bursty traffic of primary users and the limited spectrum sensing capability of secondary users. In order to take advantage of the history of spectrum sensing and access decisions, a sequential decision framework is widely used to design optimal policies. However, many existing schemes, based on a partially observed Markov decision process (POMDP) framework, reveal that optimal policies are non-stationary in nature which renders them difficult to calculate and implement. Therefore, this work pursues stationary OSA policies, which are thereby efficient yet low-complexity, while still incorporating many practical factors, such as spectrum sensing errors and a priori unknown statistical spectrum knowledge. First, with an approximation on channel evolution, OSA is formulated in a multi-armed bandit (MAB) framework. As a result, the optimal policy is specified by the wellknown Gittins index rule, where the channel with the largest Gittins index is always selected. Then, closed-form formulas are derived for the Gittins indices with tunable approximation, and the design of a reinforcement learning algorithm is presented for calculating the Gittins indices, depending on whether the Markovian channel parameters are available a priori or not. Finally, the superiority of the scheme is presented via extensive experiments compared to other existing schemes in terms of the quality of policies and optimality.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.3
• /
• pp.264-269
• /
• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• Journal of applied mathematics & informatics
• /
• v.39 no.5_6
• /
• pp.601-616
• /
• 2021

Retrial queueing models have been studied extensively in the literature. These have many practical applications, especially in service sectors. However, retrial queueing models have their own limitations. Typically, analyzing such models involve level-dependent quasi-birth-and-death processes, and hence some form of a truncation or an approximate method or simulation approach is needed to study in steady-state. Secondly, in general, the customers are not served on a first-come-first-served basis. The latter is the case when a new arrival may find a free server while prior arrivals are waiting in the retrial orbit due to the servers being busy during their arrivals. In this paper, we take a different approach to the study of multi-server retrial queues in which the signals are generated in such a way to provide a reasonably fair treatment to all the customers seeking service. Further, this approach makes the study to be level-independent quasi-birth-and-death process. This approach is different from any considered in the literature. Using matrix-analytic methods we analyze MAP/M/c-type retrial queueing models along with Poisson signals in steady-state. Illustrative numerical examples including a comparison with previously published retrial queues are presented and they show marked improvements in providing a quality of service to the customers.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.5
• /
• pp.614-621
• /
• 1999

In this paper, the analytical appproaches are presented for jointly determining the profit-miximizing configuration of the fault-tolerance real time modular cell manufacturing system. The transient(time-dependent) analysis of Markovian models is firstly applied to modular cell manufacturing system from a performability viewpoint whose modeling advantage lies in its ability to express the performance that truly matters - the user's perception of it - as well as various performance measures compositely in the context of application. The modular cells are modeled with hybrid decomposition method and then availability measures such as instantaneous availability, interval availability, expected cumulative operational time are evaluated as special cases of performability. In addition to this evaluation, sensitivity analysis of the entire manufacturing system as well as each machining cell is performed, from which the time of a major repair policy and the optimal configuration among the alternative configurations of the system can be determined. Secondly, the recovery policies from the machine failures by computing the minimal number of redundant machines and also from the task failures by computing the minimum number of tasks equipped with detection schemes of task failure and reworked upon failure detection, to meet the timing requirements are optimized. Some numerical examples are presented to demonstrate the effectiveness of the work.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3095-3111
• /
• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.4
• /
• pp.464-469
• /
• 2009

This paper discusses a robust observer-based output-feedback stabilization of an uncertain Takagi-Sugeno (T-S) fuzzy system in a network. In the networked control system, the input delay occurs inevitably and it is expressed by the Markovian stochastic process. To design robust sampled-data observer-based output-feedback controller, we discretize the T-S fuzzy system and represent as a jump system. Stochastic robust stabilization condition is formulated in terms of linear matrix inequalities.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.5
• /
• pp.805-814
• /
• 1994

In this paper, we introduce a new method which is available for analyzing the throughput of the packet radio network by using the auxiliary Markov transient matrix with a failure state and a success state. And we consider the effect of symbol error for the network state(X, R) consisted of the number of transmitting PRU X and receiving PRU R. We examine the packet radio network of a continuous time Markov chain model, and the direct sequence binary phase shift keying CDMA radio channel with hard decision Viterbi decoding and bit-by-bit changing spreading code. For the unslotted distributed multi-hop packet radio network, we assume that the packet error due to a symbol error of radio channel has Poisson process, and the time period of an error occurrence is exponentially distributed. Through the throughputs which are found as a function of radio channel parameters, such as the received signal to noise ratio and chips of spreading code per symbol, and of network parameters, such as the number of PRU and offered traffic rate, it is shown that this composite analysis enables us to combine the Markovian packet radio network model with a coded DS/BPSK CDMA radio channel.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.4B
• /
• pp.162-169
• /
• 2005

Network traffic measurements show that the data traffic on packet switched networks has the self-similar features which is different from the traditional traffic models such as Poisson distribution or Markovian process model. Most of the call admission control researches have been done on the performance analysis of a single network switch. It is necessary to consider the performance analysis of the proposed admission control scheme under interconnected switch environment because the data traffic transmits through switches in networks. From the simulation results, it is shown that the call admission control scheme may not operate properly on the interconnected switch even though the scheme works well on a single switch. In this parer, we analyze the cell loss probability, utilization and self-similarity of output ports of the interconnected networks switch by using shared buffer memory management schemes and propose the new call admission control scheme considering the interconnected network switches under self-similar traffic environments.

• Smart Structures and Systems
• /
• v.21 no.3
• /
• pp.305-320
• /
• 2018

The second half of the 20th century was marked with a significant raise in amount of railway bridges in Austria made of reinforced concrete. Today, many of these bridges are slowly approaching the end of their envisaged service life. Current methodology of assessment and evaluation of structural condition is based on visual inspections, which, due to its subjectivity, can lead to delayed interventions, irreparable damages and additional costs. Thus, to support engineers in the process of structural evaluation and prediction of the remaining service life, the Austrian Federal Railways (${\ddot{O}}$ BB) commissioned the formation of a concept for an anticipatory life cycle management of engineering structures. The part concerning concrete bridges consisted of forming a bridge management system (BMS) in a form of a web-based analysis tool, known as the LeCIE_tool. Contrary to most BMSs, where prediction of a condition is based on Markovian models, in the LeCIE_tool, the time-dependent deterioration mechanisms of chloride- and carbonation-induced corrosion are used as the most common deterioration processes in transportation infrastructure. Hence, the main aim of this article is to describe the background of the introduced tool, with a discussion on exposure classes and crucial parameters of chloride ingress and carbonation models. Moreover, the article presents a verification of the generated analysis tool through service life prediction on a dozen of bridges of the Austrian railway network, as well as a case study with a more detailed description and implementation of the concept applied.