Application of fuzzy inference to CDMA soft handoff in mobile communication systems

Homnan, B.; Benjapolakul, Watit

doi:10.1016/s0165-0114(03)00206-9

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…Fuzzy logic plays an important role in biomedical signal analysis fields such as pattern recognition, computer vision, machine learning, image analysis, communication, knowledge discovery, and data mining [2,7,[10][11][12]28]. In the study reported in this paper, we formulated another related measure of time series complexity, i.e., fuzzy entropy (FuzzyEn), and applied it to characterize local muscle fatigue electromyography (EMG) signals.…”

Section: Introductionmentioning

confidence: 99%

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Xie

Chen

et al. 2011

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Xie

Chen

et al. 2011

Applied Soft Computing

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“…Fuzzy inference is used in [78,79] to adjust the soft handover threshold dynamically. The aims of this approach are to increase the thresholds at high traffic loads in order to release the traffic channel for supporting more carried traffic, and to decrease the thresholds at low traffic loads in order to give high traffic channel [78].…”

Section: Handover Threshold Determinationmentioning

confidence: 99%

Application of Mathematical Theories in Mobility Management

Chen

Shi

et al. 2016

Mobility Management

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“…Therefore, the application of FRA in risk assessment may have the following advantages [7,12,[15][16][17][18][19][20][21].…”

Section: Application Of Framentioning

confidence: 99%

“…The number of qualitative descriptors used to describe FO and CS is flexible and depends on particular cases. It is commonly understood that risk can be expressed by degrees to which it belongs with such qualitative descriptors, 'Low', 'Possible', 'Substantial' and 'High', that are referred to as risk expressions [7,12,[15][16][17][18][19][20][21]. Table 3 shows the qualitative descriptor categories of RL.…”

Section: Application Of Framentioning

confidence: 99%

“…FRA method provides a useful tool for modelling risks and other risk parameters for risk analysis involving the risks with incomplete or redundant safety information [7,[11][12][13][14][15][16][17][18][19][20][21]. Because the contribution of each hazardous event to the safety of a railway depot is different, the weight of the contribution of each hazardous event should be taken into consideration in order to represent its relative contribution to the risk level (RL) of the railway depot.…”

Section: Introductionmentioning

confidence: 99%

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Railway risk assessment - the fuzzy reasoning approach and fuzzy analytic hierarchy process approaches: A case study of shunting at Waterloo depot

Huang

Baker

2007

Proceedings of the Institution of Mechanical Engineers, Part F:

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Railway safety is a very complicated subject, which is determined by numerous aspects including human error. Many of the railway risk assessment techniques currently used are comparatively mature tools. However, in many circumstances, the application of these tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents a risk assessment methodology for conducting systematic risk assessment using fuzzy reasoning approach and fuzzy analytical hierarchy decision-making process. In this method, qualitative descriptors are employed to describe frequency of occurrence, consequence severity, weight factor, and risk level. The proposed risk assessment method can evaluate both qualitative and quantitative risk data, and information associated with railway operation efficiently and effectively. The outcomes of risk assessment are represented as the risk degrees and the defined risk categories of risk levels (RLs) with a belief of percentage, which provide very useful risk information to decision makers. This will provide railway risk analysts, managers, and engineers with a method and tool to improve their safety management and set safety standards. A case study of risk assessment of shunting at Waterloo depot is used to illustrate the application of the proposed methodology. by railway maintenance work in the past ten years, brought about through either a major injury or an injury that rendered them unable to work for over three days. This shows the dangerous nature of the railway industry and demonstrates the need for increased awareness and better safety management [2, 4, 7]. Many accidents and incidents occurred in the railway depots over the years, demanding improvement in safety management. To assess how this can be effectively achieved, knowledge on the nature and causes of these accidents is fundamental. Therefore, risk analysis plays a central role in the railway safety and health management framework. The most common hazards in the railway depots identified by the railway industry over the years [2-4, 6, 8-10] provide very useful information for risk analysis, for example, derailment hazards, collision hazards, fire hazards, electrocution hazards, falls hazards, train strike hazards, slip/trip hazards, platform train interface JRRT106

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Application of fuzzy inference to CDMA soft handoff in mobile communication systems

Cited by 6 publications

References 13 publications

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Application of Mathematical Theories in Mobility Management

Railway risk assessment - the fuzzy reasoning approach and fuzzy analytic hierarchy process approaches: A case study of shunting at Waterloo depot

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