恒定低温法在低湿区露点检测中的应用

在低湿区环境下的露点检测中,电容式露点传感器测量的频率与露点值呈现非线性,且受环境温度影响很大,此外,湿度越低,测量的分辨力也越低。采用了一种恒定低温方法来检测低湿区露点,即传感器的环境温度恒定保持在0℃以下,可大大提高低湿区的测量分辨力,再根据露点值与频率值间具有的确定的非线性关系,将采集的频率经牛顿插值计算后就可得到露点值。采用该方法大大提高了电容式露点仪在低湿区露点检测的分辨力,扩展了其测量范围。     

The hybrid grey-based models for temperature prediction

In this paper several grey-based models are applied to temperature prediction problems. Standard normal distribution, linear regression, and fuzzy techniques are respectively integrated into the grey model to enhance the embedded GM(1, 1), a single variable first order grey model, prediction capability. The original data are preprocessed by the statistical method of standard normal distribution such that they will become normally distributed with a mean of zero and a standard deviation of one. The normalized data are then used to construct the grey model. Due to the inherent error between the predicted and actual outputs, the grey model is further supplemented by either the linear regression or fuzzy method or both to improve the prediction accuracy. Results from predicting the monthly temperatures for two different cities demonstrate that each proposed hybrid methodology can somewhat reduce the prediction errors. When both the statistics and fuzzy methods are incorporated with the grey model, the prediction capability of the hybrid model is quite satisfactory. We repeat the prediction problems in neural networks and the results are also presented for comparison.     

Oxygen consumption prediction models for individual and combination materials handling tasks

An experiment was conducted to develop models to predict oxygen consumption of males and females engaged in common materials handling tasks including lifting, lowering, pushing, pulling, (de)palletising and combination tasks involving lifting or lowering a box and carrying it a set distance and lifting or lowering it to the destination. Nineteen male and 19 female subjects participated in the study. A psychophysical approach was used to set load limits for individual subjects for the oxygen consumption protocol. The 8398 oxygen consumption values collected were entered into the initial regression analyses and 168 potential outliers were removed before the final models were run. In addition to relevant task variables, body weight was a significant predictor variable in all models. The r² values for the final models ranged from 0.54 to 0.82 and the root mean square errors ranged from 90.2 ml to 294.8 ml.     

Oxygen consumption prediction models for individual and combination materials handling tasks

An experiment was conducted to develop models to predict oxygen consumption of males and females engaged in common materials handling tasks including lifting, lowering, pushing, pulling, (de)palletising and combination tasks involving lifting or lowering a box and carrying it a set distance and lifting or lowering it to the destination. Nineteen male and 19 female subjects participated in the study. A psychophysical approach was used to set load limits for individual subjects for the oxygen consumption protocol. The 8398 oxygen consumption values collected were entered into the initial regression analyses and 168 potential outliers were removed before the final models were run. In addition to relevant task variables, body weight was a significant predictor variable in all models. The r(2) values for the final models ranged from 0.54 to 0.82 and the root mean square errors ranged from 90.2 ml to 294.8 ml.     

Radial forging force prediction through MR,ANN, and ANFIS models

The application of finite element method and intelligent systems techniques to predict the applied force during the radial forging process is studied. Radial forging is a unique process used for the precision forging of round and tubular components, with or without an internal profile. More than 800 radial forging machines are currently operating worldwide. Since the maximum forging force per die is constant, determining the die force before the process can prevent die damage and material wastage. Then, the results of the FE simulation are applied for two intelligent forecasting systems in artificial neural network and adaptive neuro-fuzzy inference system. Initial billet temperature, die inlet angle, feed rate, and reduction in cross-section are applied as input parameters, and radial forging force is applied as the output parameter. Finally, the results of these two intelligent systems are compared with the multiple regressions method. A sensitivity analysis is carried out to determine how the radial forging force is influenced by the input parameters.     

Explaining prediction models and individual predictions with feature contributions

We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model.     

Comparison of sparse point distribution models

This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior model with respect to sparsity, reconstruction error and interpretability is found to be a varimax rotated model with a threshold applied to small loadings. The models describe the biological variation in the database and are used for developing robotic tools when automating labor-intensive procedures in abattoirs.     

几种神经网络在混合气体预测中的比较

气体传感器阵列的交叉敏感性严重影响气体传感器对混合气体的测量。用M atlab平台的神经网络工具箱,分别构建了BP,径向基(RBF)和模糊(FNN)神经网络,利用掺杂不同材料的4种SnO2气体传感器组成阵列,实现对甲醛、甲苯、丙酮和乙醇混合气体的体积分数预测。结果表明:FNN神经网络对混合气体体积分数预测的精度要高于其他2种网络。而且,结合PCA和ICA对数据样本进行预处理,有利于提高神经网络对体积分数预测的精度。     

Multiple regression,ANN and ANFIS models for prediction of backbreak in the open pit blasting

Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and explosive properties. In this study, the application of the artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) for prediction of backbreak, was described and compared with the traditional statistical model of multiple regression. The performance of these models was assessed through the root mean square error, correlation coefficient (R ²) and mean absolute percentage error. As a result, it was found that the constructed ANFIS exhibited a higher performance than the ANN and multiple regression for backbreak prediction.     

Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs

One of the most critical quantities for characterizing a gas condensate reservoir is dew point pressure. But, accurate determination of dew point pressure is a very challengeable task in reservoir development. Experimental measurement of dew point pressure in PVT (Pressure, Volume, Temperature) cell is often difficult, especially in the case of lean retrograde gas condensate. So, different empirical correlations and equations of state are developed by researchers to calculate this property. Empirical correlations do not have ability to reliably duplicate the temperature behavior of constant composition fluids, and equations of state have convergence problem and need to be tuned against some experimental data. In addition, these approaches are not generalizable to unseen data, and they usually memorize the data used to develop them. In this paper, we develop an intelligent model to predict dew point pressure of gas condensate reservoirs using Gaussian processes optimized by particle swarm optimization. The developed model is generalizable and can estimate unseen data with the same distribution of training data accurately. Results show that the proposed method in this paper outperforms the previous published models and correlations.     

Statistical prediction of air pollution levels using non-physical models

This paper treats the prediction problem of air pollution levels at a short range by non-physical models. Main results are given as follows: (i) The prediction accuracy of the pollution levels by time series models is compared by evaluating three performance indices, and it is shown that the multiple linear regression model already proposed is better than the auto-regressive model, the Box-Jenkins' model and the persistence model. (ii) The multiple linear regression model is more improved if the model is classified by weather. (iii) The modeling accuracy is discussed for various sample sizes, and an appropriate sample size is determined from the experiment. (iv) The confidence intervals of the predicted means at a fixed time are calculated, and the combinations of the measurement times and the measured factors that improve the prediction accuracy are chosen. (v) A revised GMDH is proposed and the accuracy by this method is more improved than those by the time series models already presented. (vi) The Kalman filtering method is applied to the prediction of pollution levels, and the measured factors that improve the prediction accuracy are chosen.     

Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage

Accurately predicting tidal levels, including tidal and freshwater discharge effects, is important for human activities in estuaries. The traditional harmonic analysis method and numerical modeling are usually adopted to simulate and predict estuary water stages. This study applied artificial neural networks (ANNs) as an alternative modeling approach to simulate the water stage time-series of the Danshui River estuary in northern Taiwan. We compared this approach with vertical (laterally averaged) 2D and 3D hydrodynamic models. Five ANN models were constructed to simulate the water stage time-series at the Shizi Tou, Taipei Bridge, Rukuoyan, Xinhai Bridge, and Zhongzheng Bridge locations along the Danshui River estuary. ANN models can preserve nonlinear characteristics between input and output variables and are superior to physical-based hydrodynamic models during the training phase. The simulated results reveal that the vertical 2D and 3D hydrodynamic models could not capture the observed water stages during an input of high freshwater discharge from upstream boundaries, while the ANN could match the observed water stage. However, during the testing phase, the ANN approach was slightly inferior to the 2D and 3D models at the Xinhai Bridge, Zhongzheng Bridge, and Rukouyan locations. Our results show that the ANN was able to predict the water stage time-series with reasonable accuracy, suggesting that ANNs can be a valuable tool for estuarine management.     

Comparison of prediction models for the compression force on the lumbosacral disc

The main objective of this research was to compare three representative methods of predicting the compressive forces on the lumbosacral disc: LP-based model, double LP-based model, and EMG-assisted model. Two subjects simulated lifting tasks that are frequently performed in the refractories industry of Korea, in which vertical and lateral distances, and weight of load were varied. To calculate the L5/ SI compressive forces, EMG signals from six trunk muscles were measured, and postural data and locations of load were recorded using the Motion Analysis System. The EMG-assisted model was shown to reflect well all three factors considered here, whereas the compressive forces from the two LP-based models were only significantly affected by weight of load. In addition, low lifting index (LI) values were observed for relatively high L5/S1 compressive forces from the EMG-assisted model, suggesting that the 1991 NIOSH lifting equations may not fully evaluate the risk of dynamic asymmetric lifting tasks.     

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively.     

Potential ANN prediction model for multiperformances WEDM on Inconel 718

Neural Computing and Applications - This paper proposes a machining performance prediction approach on multiple performances of wire electrical discharge machining (WEDM) on Inconel 718. Artificial...     

Artificial neural network models for indoor temperature prediction: investigations in two buildings

The problem how to identify prediction models of the indoor climate in buildings is discussed. Identification experiments have been carried out in two buildings and different models, such as linear ARX-, ARMAX- and BJ-models as well as non-linear artificial neural network models (ANN-models) of different orders, have been identified based on these experiments. In the models, many different input signals have been used, such as the outdoor and indoor temperature, heating power, wall temperatures, ventilation flow rate, time of day and sun radiation. For both buildings, it is shown that ANN-models give more accurate temperature predictions than linear models. For the first building, it is shown that a non-linear combination of sun radiation and time of day is important when predicting the indoor temperature. For the second building, it is shown that the indoor temperature is non-linearly dependent on the ventilation flow rate.     

人工神经网络和可视化方法分析和比较

人工神经网络和可视化方法都是对原始数据进行重新挖掘重新整理而得到新数据的两种算法。该文对人工神经网络和可视化方法的有关算法特性进行了分析和比较,总结出了两种算法的相似性和共同点。     

Probabilistic models for melodic prediction

Chord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments.     

Comparison of models created for the prediction of the mechanical properties of galvanized steel coils

The mechanical properties of steel coils can be predicted prior to the galvanizing process, thereby preventing the production of galvanized steel coils that are not suitable for the customer’s purpose. At the same time, this will reduce the waste of zinc and unfriendly effects on the environment. The purpose of this paper is to present a comparative assessment of the different techniques currently being used to estimate the mechanical properties of steel coils before being processed on the hot dip galvanizing line (HDGL). The authors have evaluated the models established by linear and non-linear, bagging and other more aggregated construction techniques. Using a total of 30 models, predictions were made for each parameter in order to obtain valuable information about the capabilities of these models and techniques. Each model was tested against each data set ten times. Also, models were constructed to determine the relevance of steel grade to the accuracy of the models’ predictions by excluding from the models any consideration of steel grade. As a result of our tests of these models, it is possible to recommend the model to choose for each parameter.