A multivariate statistical combination forecasting method for product quality evaluation

Data-driven interpretable analysis for polysaccharide yield prediction

Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.

Blockchain-Based Wine Supply Chain for the Industry Advancement

The wine sector is one of the most ‘amazing’ and significant agri-food sectors worldwide since ancient times, considering revenue or employment as well as health aspects. This article aims to describe the impact of the implementation of blockchain technology (BCT) in the wine supply chain. After the literature review, the study is based on Agent Based Models (ABMs) and carried out by the GAMA program. Then, the model and simulation of BCT wine supply chain is designed. Finally, the paper compares traditional and BCT-based supply chains, and the advantages of the last one are evident. Blockchain is a useful tool to ensure a traceability system and to protect the production from any type of fraud and contamination.

Mining technology hot spots in the 3D printing industry for technology strategic planning based on MRCAI

3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry.

A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis

Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.

An Intelligent Actuator Fault Reconstruction Scheme for Robotic Manipulators

This paper investigates a difficult problem of reconstructing actuator faults for robotic manipulators. An intelligent approach with fast reconstruction property is developed. This is achieved by using observer technique. This scheme is capable of precisely reconstructing the actual actuator fault. It is shown by Lyapunov stability analysis that the reconstruction error can converge to zero after finite time. A perfect reconstruction performance including precise and fast properties can be provided for actuator fault. The most important feature of the scheme is that, it does not depend on control law, dynamic model of actuator, faults' type, and also their time-profile. This super reconstruction performance and capability of the proposed approach are further validated by simulation and experimental results.