Innovative Fuzzy Techniques for Characterizing Defects in Ultrasonic Nondestructive Evaluation

Versaci, Mario; Calcagno, Salvatore; Cacciola, Matteo; Morabito, Francesco Carlo; Palamara, Isabella; Pellicanó, Diego

doi:10.1007/978-3-319-10566-6_7

Cited by 13 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although each pedestrian has personal preferences, the dynamics of the movement can be shaped as a social force of the crowd. The corresponding forces can be controlled for each individual and represent a different variety of behaviors that can be associated with panic situations, such as avoiding danger, crowding, and pushing [ 38 , 39 ].…”

Section: Resultsmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

View full text Add to dashboard Cite

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

show abstract

Section: Resultsmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…By assigning the items in the dataset to k fuzzy classes, fuzzy classification can minimize the dimensionality of multivariate datasets [ 32 – 35 ]. As a result of this procedure, a new dataset is created in which the original spatial coordinates are defined just by membership in the k classes.…”

Section: Diabetes Mellitus Disease Prediction and Severity Level Estimation Using Deep Artificial Neural Networkmentioning

confidence: 99%

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

Annamalai¹,

Nedunchelian

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

show abstract

“…Regarding fuzzy modeling for industrial applications, there exist several studies which were applied to different industrial sectors. Among these research studies, it is worth mentioning the application of soft computing techniques for both detection and classification of defects [18,19], fault diagnosis of rolling bearing in industrial robots [20], airport classification [21], control of piezoelectric actuators [22], monitoring of fuel system of an industrial gas turbine [23], control of brushless direct current (DC) motors [24], and fault detection in wind turbines [25]. In addition, fuzzy systems are able to handle uncertainties in an efficient way, as shown in Reference [26], where a Takagi-Sugeno-Kang (TSK) type-2 fuzzy neural network was proposed for system modeling and noise cancellation, or in Reference [27], where a design methodology based on interval type-2 TSK fuzzy logic controllers for modular and reconfigurable robots manipulators with uncertain dynamic parameters was shown, among many others [28,29].…”

Section: State Of the Artmentioning

confidence: 99%

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Pérez

2020

Mathematics

View full text Add to dashboard Cite

Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

show abstract

Innovative Fuzzy Techniques for Characterizing Defects in Ultrasonic Nondestructive Evaluation

Cited by 13 publications

References 13 publications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Contact Info

Product

Resources

About