Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection

Rod insulators are vital parts of the catenary of high speed railways(HSRs).There are many different catenary insulators,and the background of the insulator image is complicated.It is difficult to recognise insulators and detect defects automatically.In this paper,we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network(R-CNN)and an image processing model.Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator.Gradient,texture,and gray feature fusion(GTGFF)and a K-means clustering analysis model(KCAM)are proposed to detect broken insulators,dirt,foreign bodies,and flashover.Using this model,insulator recognition and defect detection can achieve a high recall rate and accuracy,and generalized defect detection.The algorithm is tested and verified on a dataset of realistic insulator images,and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Maternal and Child Health Care Quality Assessment: An Improved Approach Using K-Means Clustering

High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) services, the government and other stakeholders in MNCH emphasize the importance of quality assessment. However, effective quality assessment approaches are mostly lacking in most developing countries, particularly in Tanzania. This study, therefore, aimed at developing a quality assessment approach that can effectively assess and report on the quality of MNCH services. Due to the need for a good quality assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a clustering model that groups MNCH data and performs cluster summarization to discover the knowledge portrayed in each group on the quality of MNCH services. Results confirmed the clustering model’s ability to assign the data points into appropriate clusters;cluster analysis with the collaboration of MNCH experts successfully discovered insights on the quality of services portrayed by each group.

Macro and micro mechanics behavior of granite after heat treatment by cluster model in particle flow code

In this paper, a cluster model in particle flow code was used to simulate granite specimens after heat treatment under uniaxial compression. The results demonstrated that micro-cracks are randomly distributed in the specimen when the temperature is below 300?C, and have partial coalescence when the temperature is up to 450?C, then form macro-cracks when the temperature is above 600?C. There is more inter-granular cracking than intra-granular cracking, and their ratio increases with increasing temperature.The micro-cracks are almost constant when the temperature decreases from 900?C to room temperature, except for quartz α–β phase transition temperature(573?C). The fracture evolution process is obviously affected by these cracks, especially at 600–900?C. Elevated temperature leads to easily developed displacement between the grains, and the capacity to store strain energy becomes weaker, corresponding to the plasticity of granite after heat treatment.

Spatial correlation-based characterization of acoustic emission signal-cloud in a granite sample by a cube clustering approach

To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the cube-cluster model.First,the complete connection of the fracture network is regarded as a critical state.Then,according to the Hoshen-Kopelman(HK)algorithm,the real-time estimation of fracture con-nection is effectively made and a dichotomy between cube size and pore fraction is suggested to solve such a challenge of the one-to-one match between complete connection and cluster size.After,the 3D cube clusters are decomposed into orthogonal layer clusters,which are then transformed into the ellip-soid models.Correspondingly,the anisotropy evolution of fracture network could be visualized by three orthogonal ellipsoids and quantitatively described by aspect ratio.Besides,the other three quantities of centroid axis length,porosity,and fracture angle are analyzed to evaluate the evolution of cube cluster.The result shows the sample dilatancy is strongly correlated to four quantities of aspect ratio,centroid axis length,and porosity as well as fracture angle.Besides,the cube cluster model shows a potential pos-sibility to predict the evolution of fracture angle.So,the cube cluster model provides an in-depth view of spatial correlation to describe the AE signal-cloud.

Influence of Spin-Orbit Force on Nucleon-Nucleon Scattering in the Quark Delocalization Colour Screening Model

The symmetric spin-orbit interactions of one-gluon-exchange and confinement are included in the nucleon-nucleon phase shift calculation in the framework of quark delocalization eolour screening model. The spin-orbit interaction has little influence on D wave phase shift. For the triplet P waves, aPT is in good agreement with the experimental data and 3pLs is attractive but not strong enough, whereas 3 Pc is too strongly repulsive. Our results indicate that the symmetric spin-orbit interaction of one-gluon-exchange and confinement potential cannot give a good description of the triplet P wave phase shifts. More sophisticated considerations, the delocalization depending on the relative orientation between two cluster, might be needed to improve the description of P-wave NN interaction.

Cluster modeling of the short-range correlation of acoustically emitted scattering signals

As a widely used measurement technique in rock mechanics,spatial correlation modeling of acoustic emission(AE)scattering signals is attracting increasing focus for describing mechanical behavior quantitatively.Unlike the statistical description of the spatial distribution of randomly generated AE signals,spatial correlation modeling is based mainly on short-range correlation considering the interrelationship of adjacent signals.As a new idea from percolation models,the covering strategy is used to build the most representative cube cluster,which corresponds to the critical scale at peak stress.Its modeling process of critical cube cluster depends strongly on the full connection of the main fracture network,and the corresponding cube for coverage is termed the critical cube.The criticality pertains to not only the transition of local-to-whole connection of the fracture network but also the increasing-to-decreasing transition of the deviatoric stress with an obvious stress drop in the brittle failure of granite.Determining a reasonable critical cube guarantees the best observation scale for investigating the failure process.Besides,the topological connection induces the geometric criticality of three descriptors,namely anisotropy,pore fraction,and specific surface area,which are evaluated separately and effectively.The results show that cluster modeling based on the critical cube is effective and has criticality in both topology and geometry,as well as the triaxial behavior.Furthermore,the critical cube length presents a high confidence probability of being correlated to the mineral particle size.Besides,its pore fraction of cube cluster is influenced strongly by the critical cube length and confining pressure.

Catalytic activity of phosphorus and steam modified HZSM-5 and the theoretical selection of phosphorus grafting model

The modification of HZSM-5 zeolite with phosphorus and steam has been studied. Results show that 1% phosphorus and steam modified HZSM-5 has the highest catalytic activity for n-heptane. Physicochemical and catalytic properties of 1% phosphorus and steam modified HZSM-5 zeolites have been investigated. The X-ray diffraction （XRD） results exhibit that there is considerable variation in the relative intensity of the individual diffraction peaks. The acidity of the samples decreases with an increase in the steaming temperature, which is determined by the IR of adsorbed pyridine and temperature programmed desorption （TPD） of ammonia. The oxidation state of phosphorus shown by XPS is ＋5, and a model for surface structure modification is proposed. The nitrogen adsorption isotherm for all samples is a combination of type I and type IV, all hysteresis loops resemble the H4-type. The density functional and cluster model methods have been invoked to select the phosphorus grafting model, and it was found that the phosphorus grafting model were more probable in the form of the terminal oxygen coordinating with aluminum.

The Accumulation of He on a W Surface During keV-He Irradiation:Cluster Dynamics Modeling

The accumulation of He on a W surface during keV-He ion irradiation has been simulated using cluster dynamics modeling. This is based mainly on rate theory and improved by involving different types of objects, adopting up-to-date parameters and complex reaction processes, as well as considering the diffusion process along with depth. These new features make the simulated results compare very well with the experimental ones. The accumulation and diffusion processes are analyzed, and the depth and size dependence of the He concentrations contributed by different types of He clusters is also discussed. The exploration of the trapping and diffusion effects of the He atoms is helpful in understanding the evolution of the damages in the near-surface of plasma-facing materials under He ion irradiation.

Parallel Computation of Shallow-water Model on Workstations Cluster

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A numerical model for cloud cavitation based on bubble cluster

The cavitation cloud of different internal structures results in different collapse pressures owing to the interaction among bubbles. The internal structure of cloud cavitation is required to accurately predict collapse pressure. A cavitation model was developed through dimensional analysis and direct numerical simulation of collapse of bubble cluster. Bubble number density was included in proposed model to characterize the internal structure of bubble cloud. Implemented on flows over a projectile, the proposed model predicts a higher collapse pressure compared with Singhal model. Results indicate that the collapse pressure of detached cavitation cloud is affected by bubble number density.

Study on electronic density topology of various cluster models of Mg/Al hydrotalcite by density functional theory

The geometry and electronic topology properties of Mg/Al hydrotalcite cluster models were comparatively investigated by means of density functional theory at GGA/DND levels.The results suggested that cluster model containing seven octahedral cations was the smallest size to be employed to simulate other properties.The fact that the n＋ charge of cluster models containing n aluminum atoms can reflect electronic properties of anionic clay layer sheet.The bond lengths of clusters can be modified by terminating with or without OH-/H2O groups in terms of principle of bond order conservation.

SELECTING CLUSTER MODEL IN Sn - BASED SOLDER ALLOY DESIGN WITH DV - X_α CALCULATION METHOD

Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have been investigated by employing DV - Xa method, in which different cluster models were adopted to calculate electron structure.It is proved that some regulations must be taken into ac- count in order to carry out alloy design calculation successfully,which are described in this paper in detail.

An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd

Assemblage at public places for religious or sports events has become an integral part of our lives.These gatherings pose a challenge at places where fast crowd verification with social distancing(SD)is required,especially during a pandemic.Presently,verification of crowds is carried out in the form of a queue that increases waiting time resulting in congestion,stampede,and the spread of diseases.This article proposes a cluster verification model(CVM)using a wireless sensor network(WSN),single cluster approach(SCA),and split cluster approach(SpCA)to solve the aforementioned problem for pandemic cases.We show that SD,cluster approaches,and verification by WSN can overcome the management issues by optimizing the cluster size and verification time.Hence,our proposed method minimizes the chances of spreading diseases and stampedes in large events such as a pilgrimage.We consider the assembly points in the annual pilgrimage to Makkah Al-Mukarmah and Umrah for verification using Contiki/Cooja tool.We compute results such as verified cluster members(CMs)to define cluster size,success rate to determine the best success rate,and verification time to determine the optimal verification time for various scenarios.We validate ourmodel by comparing the results of each approach with the existing model.Our results showthat the SpCAwith SD is the best approach with a 96% success rate and optimization of verification time as compared to SCA with SD and the existing model.

Electronic Structure Effect on Model Cluster for L1_2 Structure of Al_3Ti Intermetallic Compound with an Addition of Alloying Elements Fe, Ni and Cu

By use of self-consistent field Xα scattered-wave (SCF-Xα-SW) method, the electronic structure was calculated for four models of Ti4Al14X (X=Al, Fe, Ni and Cu) clusters. The Ti4Al14X cluster was developed based on L12 Al3Ti-base intermetallic compound. The results are presented using the density of states (DOS) and one-electron properties, such as relative binding tendency between the atom and the model cluster, and hybrid bonding tendency between the alloying element and the host atoms. By comparing the four models of Ti4Al14X cluster, the effect of the Fe, Ni or Cu atom on the physical properties of Al3Ti-based L12 intermetallic compounds is analyzed. The results indicate that the addition of the Fe, Ni or Cu atom intensifies the relative binding tendency between Ti atom and Ti4Al14X cluster. It was found that the Fermi level (EF) lies in a maximum in the DOS for Ti4Al14Al cluster; on the contrary, the EF comes near a minimum tn the DOS for Ti4Al14X (X=Fe, Ni and Cu) cluster. Thus the L12 crystal structure for binary Al3Ti alloy is unstable, and the addition of the Fe, Ni or Cu atom to Al3Ti is benefical to stabilize L12 crystal structure. The calculation also shows that the Fe, Ni or Cu atom strengthens the hybrid bonding tendency between the central atom and the host atoms for Ti4Al14X cluster and thereby may lead to the constriction of the lattice of Al3Ti-base intermetallic compounds.

Impairment of Continuous Insulin Delivery Therapy and Analysis from Graeco-Latin Square Design Model

The desire to deliver measured amount of insulin continuously to patients with type I diabetes, for glycemic control, has attracted a lot of attention. Continuous subcutaneous insulin infusion has seen some success in recent years. However, occlusion of insulin delivery may prevent the patient from receiving the prescribed dosage, with adverse consequence. An in vitro study of insulin delivery is performed, using different insulin pumps, insulin analogs and operating conditions. The aim is to identify incidences of occlusion due to bubble formation in the infusion line. A detailed statistical analysis was performed on the data collected to determine any significant differences and deviations in insulin delivery rates that might be due to factors such as: pump type, the set basal flow rate, insulin type, vibration, and possible insulin occlusion due to air bubble formation within the infusion line. Our findings from the Graeco-Latin Square design model show that there are statistical differences due to the devices, and statistical identifiable clusters are used to distinguish the devices. Such hierarchical models used to describe the analyses, include the flow rate, the pump types, and the activity level.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

Selecting the Quantity of Models in Mixture Regression

Mixture regression is a regression problem with mixed data. Specifically, in the observations, some data are from one model, while others from other models. Only after assuming the quantity of the model is given, EM or other algorithms can be used to solve this problem. We propose an information criterion for mixture regression model in this paper. Compared to ordinary information citizen by data simulations, results show our citizen has better performance on choosing the correct quantity of models.

A bibliometric analysis of research on nursing models in countries other than China

Objectives＂ To deepen our understanding of the status quo and to identify the hot topics and develop- mental trends of research on nursing models in countries other than China in the most recent decade. Methods： The study subjects were the publications retrieved from the PubMed database using the MeSH terms of ＂Models, Nursing＂. Bibliographic item co-occurrence mining system （BICOMS） software was used for conventional bibliometric analysis of publications during two time periods, 2005-2009 and 2010-2014. The number of published journal articles, journal distribution, authors of publications, country of origin of journals, and language of publications were analyzed to establish a high-frequency keyword profile and co-occurrence matrix. Graphical clustering toolkit （gCLUTO） software was applied for two-way clustering analysis and visualized analysis. Results： A total of 1472 journal articles with a key theme of nursing models were retrieved for final analysis, including 771 published during 2005-2009 and 701 during 2010-2014. The bibliometric analysis revealed that publications other than China concerning nursing models were mostly concentrated in the United States and the United Kingdom and that the number of relevant publications has been continuously decreasing. The two-way clustering analysis showed that there were mainly four types of research themes in the relevant publications in countries other than China during 2005-2009, i.e., nursing education and theoretical research, clinical nursing and psychological care, nursing administration, and models of nursing education, whereas there were five types during 2010-2014, i.e., nursing theories and clinical nursing practice, nursing administration models and assessments of nurses＇ knowledge and skills, community nursing administration models, nursing human resource management, and nursing education models and approaches. Conclusions： Research on nursing models in countries other than China is relatively mature and stable with a broader view, but it has shown a declining trend in recent years. It emphasizes both theory and practice, with research content tending to be structured into four modules, i.e., nursing education, administration, clinical practice, and theoretical research. Community nursing models may become a key research direction in the international research on nursing models in the future.

Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition

As a kind of statistical method, the technique of Hidden Markov Model （HMM） is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model （SDCHMM）, derived from the Continuous Density Hidden Markov Model （CDHMM）, is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.