BIO-03 DESIGN AND CONTROL OF A MAGNETICALLY DRIVEN CAPSULE-ROBOT FOR ENDOSCOPY(Bio-medical Equipments I,Technical Program of Oral Presentations)

Hosseini, Saman; Khamesee, Behrad

doi:10.1299/jsmemipe.2009.219

Cited by 6 publications

(4 citation statements)

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“…Numerous approaches based on AHC have been introduced thus far, with each differing in how the distance is determined. 10 It turns out that the definition of the distance is where these methods diverge. Assume that the details of the samples assigned to clusters 1 and 2 are c a = ⟨n 0 , n 1 , … , n s ⟩ and c b = ⟨m 0 , m 1 , … , m k ⟩, respectively.…”

Section: Connection Methods References Interval Function Explanationmentioning

confidence: 99%

“…We only eliminate the sample 𝜉 j from the cluster c h once for each iteration. In this case, 𝜉 j is defined using Equation (10).…”

Section: Sample N I Is Removed From the Cluster C H Truementioning

confidence: 99%

“…Numerous approaches based on AHC have been introduced thus far, with each differing in how the distance is determined 10 . It turns out that the definition of the distance is where these methods diverge.…”

Section: Introductionmentioning

confidence: 99%

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Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Wang,

2024

Concurrency and Computation

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SummaryA generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques.

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Section: Connection Methods References Interval Function Explanationmentioning

confidence: 99%

“…We only eliminate the sample 𝜉 j from the cluster c h once for each iteration. In this case, 𝜉 j is defined using Equation (10).…”

Section: Sample N I Is Removed From the Cluster C H Truementioning

confidence: 99%

See 1 more Smart Citation

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Wang,

2024

Concurrency and Computation

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“…Among the methods of fault localization, the most common of these methods is based on fundamental phase-based methods, whose approximation tends to converge with the stable state structure prior to opening the circuit breaker. Tus, conventional troubleshooters typically consider phase estimates obtained from shield relays or class P phase-measuring units, which show the time of flter delay less than the phasor measurement units of the class M. However, it is usually assumed that phasor measurement samples of M class are not suitable for fault location applications due to inherent fltering delays; studies on the possibility of using class M data-based fault locating programs are still scarcely found in literature [31]. Te paper [32] was introduced to investigate whether the phasor measurement units of class M could be used in real location fault localization schemes using previously used measurement systems.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Najafzadeh

Pouladi

Daghigh

et al. 2023

International Transactions on Electrical Energy Systems

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Electric power grids are always affected by numerous unexpected faults. Occurrence of these faults will have a negative impact on network availability and reliability indices of the network. But the indicators of reliability and quality of electrical energy in the network can be augmented by locating the fault in the shortest time. Special features of distribution networks such as load and network imbalance, existence of different types of load with different connections, existence of multiphase branches, effects related to different conductors, capacitive effects of distribution lines, and limited numbers of measuring devices complicate the process of fault localization in distribution networks. On the other hand, increasing penetration of distributed generation units has caused conventional methods of fault localization. Therefore, it is mandatory to introduce new methods of fault locating by considering the mentioned features. Hence, in the current study, nonlinear methods are presented for identifying the location of ground faults in the power distribution network with the help of voltage phasor measurement at different network buses by the D-PMU phased distribution unit. In the first technique, the genetic optimization algorithms and particle swarm optimization for nonlinear modeling of fault position along the distribution line have been utilized for different single-phase, two-phase, and three-phase faults, and in the second technique, neural fuzzy network training has been proposed by different phasor measurement devices. In this case, it is enough to access the phase information of the network bus voltage. In order to show the effectiveness of the proposed algorithms, a 9-bus system is defined by MATLAB software and also defining different line lengths and line characteristics in different buses. Moreover, after applying single-phase, two-phase, and three-phase faults, as well as presenting the results, fault localization is detected in the shortest time.

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Numerical investigation of the effect of changes in blood viscosity on parameters hemodynamic blood flow in the left coronary artery with consideration capturing fluid–solid interaction

Nogourani,

Alizadeh,

Salman

et al. 2023

Alexandria Engineering Journal

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BIO-03 DESIGN AND CONTROL OF A MAGNETICALLY DRIVEN CAPSULE-ROBOT FOR ENDOSCOPY(Bio-medical Equipments I,Technical Program of Oral Presentations)

Abstract: E-maibkhamusee/asijff['JJenv.t.it'ivatei'.ier).c'a E-mail:nt,!kasEstlkQgllgfi h tD "ait.yM'atettgsJ.c./nt,

Cited by 6 publications

References 0 publications

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Numerical investigation of the effect of changes in blood viscosity on parameters hemodynamic blood flow in the left coronary artery with consideration capturing fluid–solid interaction

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