Intelligent methods for the process parameter determination of plastic injection molding

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories： Case-based reasoning methods, expert sys- tem-based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

Physics-data-driven intelligent optimization for large-aperture metalenses

Metalenses have gained significant attention and have been widely utilized in optical systems for focusing and imaging,owing to their lightweight,high-integration,and exceptional-flexibility capabilities.Traditional design methods neglect the coupling effect between adjacent meta-atoms,thus harming the practical performance of meta-devices.The existing physical/data-driven optimization algorithms can solve the above problems,but bring significant time costs or require a large number of data-sets.Here,we propose a physics-data-driven method employing an“intelligent optimizer”that enables us to adaptively modify the sizes of the meta-atom according to the sizes of its surrounding ones.The implementation of such a scheme effectively mitigates the undesired impact of local lattice coupling,and the proposed network model works well on thousands of data-sets with a validation loss of 3×10^(−3).Based on the“intelligent optimizer”,a 1-cm-diameter metalens is designed within 3 hours,and the experimental results show that the 1-mm-diameter metalens has a relative focusing efficiency of 93.4%(compared to the ideal focusing efficiency)and a Strehl ratio of 0.94.Compared to previous inverse design method,our method significantly boosts designing efficiency with five orders of magnitude reduction in time.More generally,it may set a new paradigm for devising large-aperture meta-devices.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

Intelligent fault diagnosis methods toward gas turbine: A review

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

Real- Time Color Enhancement Method Used for Intelligent Mobile Terminals

In certain environments and under some conditions, the video images taken by the intelligent mobile video phones seem dark, and the colors are not bright or saturated enough.This paper presents an adaptive method to enhance the video image brightness visualization and the color performance depending on the certain hardware property and function parameters. The experimental results prove that this method can enhance the colors and the contrast of the video images, based on the estimated quality feature values of each frame, without using the extra Digital Signal Processor (DSP).

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Effects of intelligent feedingmethod on the growth,immunity and stress of juvenile Micropterus salmoides

The feeding method is of great significance for aquaculture production and cost.In recent years,research on intelligent feeding,a method based on fish appetite,has been trending.Fewstudies,however,have focused on fish welfare issues that can result from intelligent feeding.In this study,an adaptive feeder based on a practical intelligent feeding method was designed to evaluate whether this intelligent feeding method would impair fish welfare compared to traditional automatic feeding.The results indicated that the amount of residual feed and size inhomogeneity in the traditional group was significantly higher than that in the intelligent group.The results of the growth indicator showed that theweight gain rate(WGR)in the intelligent feeding groupwas significantly higher(30.17%)than in the traditional feeding group.Although no significant differences were observed in the survival rate(SR),the condition factor(CF)and the hepatosomatic index(HSI),the specific growth rate(SGR)in the intelligent feeding group was significantly increased by 8.33%while the feed conversion rate(FCR)was reduced by a remarkable 17.07%compared to the traditional feeding group.Moreover,intelligent feeding significantly improved the pepsin activity of the bass.In terms of immunity and antioxidant capacity,however,the fish in the intelligent feeding group had a significantly lower lysozyme(LZM)level than those fed with the traditional method.For superoxide dismutase(SOD),the intelligent group also displayed a lower activity value,although it was not significant.The intelligent feeding group had 0.14 nmol/mg protein higher malondialdehyde(MDA)activity than the traditional group.Regarding stress,although no statistical significance was observed,the cortisol level in the intelligent group was 1.9 ng/ml higher than in the traditional group.Together,these data suggested that the intelligent feeding method can significantly improve fish growth but may also result in stress and suppress innate immunity.

Prediction of reservoir brine properties using radial basis function (RBF) neural network

Aquifers,which play a prominent role as an effective tool to recover hydrocarbon from reservoirs,assist the production of hydrocarbon in various ways.In so-called water flooding methods,the pressure of the reservoir is intensified by the injection of water into the formation,increasing the capacity of the reservoir to allow for more hydrocarbon extraction.Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods.Furthermore,various characteristics of brines are required for different calculations used within the petroleum industry.Consequently,it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs.The properties of brine that are of great importance are density,enthalpy,and vapor pressure.In this study,radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties.The root mean squared error of 0.270810,0.455726,and 1.264687 were obtained for reservoir brine density,enthalpy,and vapor pressure,respectively.The predicted values obtained by the proposed models were in great agreement with experimental values.In addition,a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.