Research on a feature selection method based on median impact value for modeling in thermal power plants

Qi, Minfang; Fu, Zhongguang; Chen, Fei

doi:10.1016/j.applthermaleng.2015.10.104

Cited by 18 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details on TreeExplainer can be found in Lundberg et al (2019a).TreeExplainer is employed in this study to calculate the SHAP values for the XGBoost, RandomForest, and CART models to assess the contribution of hydro-environmental features to the prediction. "Connection weight" (Garson, 1991), Partial derivative (Dimopoulos et al, 1995), or input variable disturbance (Qi et al, 2016) are some more approaches for analyzing the feature sensitivity of neural network models. These approaches are, nevertheless, "local sensitivity analysis" methods, which only assess the impact of one input variable on the outcome.…”

Section: Shap Analysismentioning

confidence: 99%

Hydrologic Interpretation of Machine Learning Models for 10-daily streamflow simulation in Climate sensitive Upper Indus Catchments

Mushtaq¹,

Akhtar

Hashmi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Machine learning for hydrologic modeling has seen significant development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus and Kabul rivers. These are high elevation data scarce catchments and generated streamflows are highly seasonal and prone to climate change. Given the catchment characteristics, there is utmost need to develop machine learning models that are hydrologically robust. Thus, the current study besides evaluating the potential of three machine learning models for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations(SHAP).XGBOOST, RandomForest and Classification and Regression Trees(CART) were evaluated. All of these models performed well and range of R 2 and Nash Efficiency for all three models lies between 0.65 to 0.90. Our study’s most crucial contribution is SHapley Additive exPlananations (SHAP) method which gives extensive insights into the influence of each variable on simulated streamflow. SHAP analysis highlighted the significance of minimum temperature in high elevation zones for both Indus and Chenab catchment where streamflows are dominated by snow and glacier melt. We strongly believe that the findings of this study will promote the use of SHAP analysis for streamflow forecasting in data scarce and high elevation catchments in Pakistan.

show abstract

Section: Shap Analysismentioning

confidence: 99%

Hydrologic Interpretation of Machine Learning Models for 10-daily streamflow simulation in Climate sensitive Upper Indus Catchments

Mushtaq¹,

Akhtar

Hashmi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…., P m ] T were input to the well-trained DBN model to obtain two results R 1 and R 1 . Then, the mean difference of R 1 and R 1 , which is defined as the MIV of P 1 , was calculated using Formula (3) (Dombi et al, 1995;Liu et al, 2012;Qi et al, 2016):…”

Section: Evaluation Index Of Significance Of Individual Muscle Group mentioning

confidence: 99%

“…All the principal components were sorted according to the absolute values of the MIVs to obtain the relative impact of each model input on the output. The one corresponding to the largest MIV exerted the most important influence on the output (Dombi et al, 1995;Liu et al, 2012;Qi et al, 2016). If over one input corresponds to a muscle group, the maximum MIV of these input units was considered as the final index for this muscle group.…”

Section: Evaluation Index Of Significance Of Individual Muscle Group mentioning

confidence: 99%

Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network

Chen

Cao

et al. 2020

Front. Neurosci.

View full text Add to dashboard Cite

In this study, research was carried out on the end-effector force estimation of two representative multi-muscle contraction tasks: elbow flexion and palm-pressing. The aim was to ascertain whether an individual muscle or a combination of muscles is more suitable for the end-effector force estimation. High-density surface electromyography (HD-sEMG) signals were collected from four primary muscle areas of the upper arm and forearm: the biceps brachii (BB), brachialis (BR), triceps brachii (TB), brachioradialis (BRD), and extensor digitorum communis (EDC). The wrist pulling and palm-pressing forces were measured in elbow flexion and palm-pressing tasks, respectively. The deep belief network (DBN) was adopted to establish the relation between HD-sEMG and the measured force. The representative signals of the four primary areas, which were considered as the input signal of the force estimation model, were extracted by HD-sEMG using the principle component analysis (PCA) algorithm, and fed separately or together into the DBN. An index termed mean impact value (MIV) was proposed to describe the priority of different muscle groups for estimating the end-effector force. The experimental results demonstrated that, in multi-muscle isometric contraction tasks, the dominant muscles with the highest activation degree could track variations in the end-effector force more effectively, and are more suitable than a combination of muscles. The main contributions of this research are as follows: (1) To fuse the activation information from different muscles effectively, DBN was adopted to establish the relationship between HD-sEMG and the generated force, and achieved highly accurate force estimation. (2) Based on the well-trained DBN force estimation model, an index termed MIV was presented to evaluate the priority of muscles for estimating the generated force.

show abstract

“…Finally, the PNN algorithm is improved from the two aspects of accuracy and complexity. On the one hand, the mean impact value (MIV) algorithm [26,27] is used for variable selection, which simplifies the complexity of the algorithm and eliminates the characteristic values with large errors in the ECG detection and extraction process. On the other hand, therobabilistic neural network by whale optimization algorithm (WOA-PNN) is proposed, which uses WOA [28][29][30] to optimize the smoothing factor in PNN, improve the accuracy of the model classification and solve the problem that the smoothing factor of the PNN algorithm needs to be artificially given.…”

Section: Introductionmentioning

confidence: 99%

The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

et al. 2022

Sensors

View full text Add to dashboard Cite

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.

show abstract

Research on a feature selection method based on median impact value for modeling in thermal power plants

Cited by 18 publications

References 14 publications

Hydrologic Interpretation of Machine Learning Models for 10-daily streamflow simulation in Climate sensitive Upper Indus Catchments

Hydrologic Interpretation of Machine Learning Models for 10-daily streamflow simulation in Climate sensitive Upper Indus Catchments

Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network

The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

Contact Info

Product

Resources

About