Introduction to Pattern Identification

This paper presents a study of flow pattern identification and gas void fraction (GVF) measurement of a pressurized gas-liquid two-phase flow using electrical capacitance tomography (ECT).

This is mainly because the reliable measurements of the two-phase flow parameters, such as void fraction, phase velocity, and flow pattern identification, are important for accurate modeling and/or in the operation of energy systems.

This paper presents a review on the basic sensing principle, different types of conductance sensors and their applications in flow monitoring, flow pattern identification, phase fraction determination and velocity measurement of multiphase flow.

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall.

The proposed variable pattern comparison tool (Spatial pattern identification (SPI) for ecological modelling) provides an opportunity of deep examination of spatial connections between environmental variables and occurrence data in GIS models.

Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs).

Hence, this study put forwards a novel GMPPT scheme for effective tracking based on shading pattern identification using artificial neural network (ANN).

This paper is put forward a novel photovoltaic (PV) inverter topology for maximum solar power utilization, which incorporates a new maximum power point tracking (MPPT) scheme based on shading pattern identification using the artificial neural network, single-input and multi-output (SIMO) converter, and multilevel inverter (MLI).

However, in terms of unknown attack pattern identification, big data analytics and speed computation, several improvements must be performed in these techniques.

The experiments use the IoT network testbed that mimic the internal attack scenario with three major goals: (i) to provide a topological description on how an insider attack occurs; (ii) to achieve attack pattern extraction from raw sniffed data; and (iii) to establish attack pattern identification as a parameter to visualize real-time attacks.

Finally, the obtained feature is fed into the XGBoost model to accomplish the damage pattern identification.

A novelty compared to existing models is thereby that the proposed model is free from restrictive assumptions on the microscale constitutive response and from any subscale damage pattern identification.

An animation of intervals supports pattern identification.

An animation of intervals supports pattern identification.

George's respiratory questionnaire, EuroQOL five dimensions questionnaire, and Korean pattern identification questionnaire.

The secondary outcomes include Visual Analog Fatigue Scale (VAFS), Functional Assessment of Cancer Therapy (FACIT) Fatigue scale, Hospital Anxiety Depression Scale (HADS), Montreal Cognitive Assessment Korean version (MoCA-K), and Korean pattern identification questionnaire.

The main contributions of the proposed method are as follows: 1) the HDE method can characterize the complexity and uncertainty of the signal in full frequency band; 2) the JADE method further eliminates redundant information while greatly retaining fault-relevant information; and 3) the proposed method combines the advantages of HDE’s hierarchical analysis and JADE’s information fusion capabilities, so the fusion features extracted by the proposed method can be more effective for the establishment of fault pattern identification model.

At the end, the selected features are fed into the least square support vector machine (LSSVM) for effective fault pattern identification.

George's respiratory questionnaire, EuroQOL five dimensions questionnaire, and Korean pattern identification questionnaire.

The aim of this study was to evaluate the effects of Hwanglyeonhaedok-tang-based intranasal herbal ointment Biyeom-go based on the cold and heat pattern identification questionnaire(CHPIQ).

In addition to creating a novel AI-based ASD classification, pattern identification may facilitate treatment optimization by educating surgeons on which treatment patterns yield optimal improvement with lowest risk.

In addition to creating a novel AI-based ASD classification, pattern identification may facilitate treatment optimization by educating surgeons on which treatment patterns yield optimal improvement with lowest risk.

Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

This paper describes and solves an unsupervised mentor pattern identification problem from product usage logs for softening both challenges.

The main contributions of the proposed method are as follows: 1) the HDE method can characterize the complexity and uncertainty of the signal in full frequency band; 2) the JADE method further eliminates redundant information while greatly retaining fault-relevant information; and 3) the proposed method combines the advantages of HDE’s hierarchical analysis and JADE’s information fusion capabilities, so the fusion features extracted by the proposed method can be more effective for the establishment of fault pattern identification model.

Here we propose a method to explore the multiway nature of urban spaces data in outliers detection which includes three stages: (i) dimensionality reduction, where we model data as a 3rd-order tensor; from this reduction, we extract a set of latent factors to obtain the best fit for the next classification step; (ii) classification of latent factors, where the latent factors from the stage (i) are used to generate instances of similar events in monitoring smart urban spaces which result in high-quality clusters from the factorization; and (iii) combining steps (i) and (ii) to generate a refined urban space pattern identification model.