Prioritizing Maintenance Spare Parts Based on Supportability Analysis and Neural Network

In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.

“To measure is to know”: how advances in image analysis are supporting neural repair strategies

Neuronal regeneration in the peripheral nervous system arises via a synergistic interplay of neurotrophic factors,integrins,cytoskeletal proteins,mechanical cues,cytokines,stem cells,glial cells and astrocytes.

Electromagnetic–thermal–structural coupling analysis of the ITER edge localized mode coil with flexible supports

In a fusion reactor, the edge localized mode（ELM） coil has a mitigating effect on the ELMs of the plasma. The coil is placed close to the plasma between the vacuum vessel and the blanket to reduce its design power and improve its mitigating ability. The coil works in a high-temperature,high-nuclear-heat and high-magnetic-field environment. Due to the existence of outer superconducting coils, the coil is subjected to an alternating electromagnetic force induced by its own alternating current and the outer magnetic field. The design goal for the ELM coil is to maintain its structural integrity in the multi-physical field. Taking as an example the middle ELM coil（with flexible supports） of ITER（the International Thermonuclear Fusion Reactor）, an electromagnetic–thermal–structural coupling analysis is carried out using ANSYS. The results show that the flexible supports help the three-layer casing meet the static and fatigue design requirements. The structural design of the middle ELM coil is reasonable and feasible. The work described in this paper provides the theoretical basis and method for ELM coil design.

Numerical Simulation Study of Support Pressure around the Fully Mechanized Caving Face

During the fully mechanized caving face re-recovery process, due to the influence of mining and the redistribution of surrounding rock stress, a higher advanced support pressure and lateral support pressure will be formed around the working surface. The superimposed advanced and lateral support pressure will have a greater impact on the advanced support of the working surface roadway. In order to improve the stability of the surrounding rock, the three Hebi mines were used as the subject of the study. At the same time, Universal Distinct Element Code software was used to study the pressure distribution pattern of over-support at the working face. Finally, the results of the study are used as theoretical support and reference for the support scheme.

LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.

An Intelligent Tool to Support Requirements Analysis and Conceptual Design of Database Design

As an application of artificial intelligence and expert system technology to database design,this paper presents an intelligent design tool NITDT,which comprises a requirements specification lan- guage NITSL,a knowledge representation language NITKL,and an inference engine with uncertainty reasoning capability.NITDT now covers the requirements analysis and conceptual design of database design.However,it is possible to be integrated with another database design tool, NITDBA,developed also at NIT to become an integrated design tool supporting the whole process of database design.

Recent developments in the structural design and optimization of ITER neutral beam manifold

This paper describes a new design of the neutral beam manifold based on a more optimized support system.A proposed alternative scheme has presented to replace the former complex manifold supports and internal pipe supports in the final design phase.Both the structural reliability and feasibility were confirmed with detailed analyses.Comparative analyses between two typical types of manifold support scheme were performed.All relevant results of mechanical analyses for typical operation scenarios and fault conditions are presented.Future optimization activities are described,which will give useful information for a refined setting of components in the next phase.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.

SIGNAL DETECTION OF GLOBAL CLIMATE CHANGE AND EXTERNAL FORCING FACTORS

In this paper,we displayed one-dimensional climate signals,such as global temperature variation,Southern Oscillation Index and variation of external forcing factors,on a two- dimensional time-scale plane using compactly supported wavelet decomposition.Using the lag- correlation analysis method,and interpretative variance analysis method,and phase comparison method to the wavelet analysis result,we not only gained the variation on different scales to the global temperature and El Nino signals,the location of the jump point and intrinsic scale of these series,but also indicated the magnitude,extent and time of the effect of external forcing factors on them.We also put forward reasonable explanation to the main variation of recent 140 years.