Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals

Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.

Simulation logging experiment and interpretation model of array production logging measurements in a horizontal well

The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.

Optimized Non-hyperbolic Stack Imaging Based on Interpretation Model

In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.

Research on Well Testing Interpretation of Low Permeability Deformed Dual Medium Reservoir

Considering the influence of quadratic gradient term and medium deformation on the seepage equation, a well testing interpretation model for low permeability and deformation dual medium reservoirs was derived and established. The difference method was used to solve the problem, and pressure and pressure derivative double logarithmic curves were drawn to analyze the seepage law. The research results indicate that the influence of starting pressure gradient and medium deformation on the pressure characteristic curve is mainly manifested in the middle and late stages. The larger the value, the more obvious the upward warping of the pressure and pressure derivative curve;the parameter characterizing the dual medium is the crossflow coefficient. The channeling coefficient determines the time and location of the appearance of the “concave”. The smaller the value, the later the appearance of the “concave”, and the more to the right of the “concave”.

Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.

Analysis of Feature Importance and Interpretation for Malware Classification

This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified.Initially,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted information.The 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature elimination.Next,these important features were analyzed.The level of contribution from each one was assessed by the Random Forest classifier method.The results showed that System call features were mostly allocated.At the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples available.These were the Trojan,Adware,Downloader,and Backdoor malware.

Analysis of Ecosystem Degradation Factors in Yuanmou Arid-Hot Valleys Based on Interpretative Structural Model

For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

With the soaring generation of hazardous waste(HW)during industrialization and urbanization,HW illegal dumping continues to be an intractable global issue.Particularly in developing regions with lax regulations,it has become a major source of soil and groundwater contamination.One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites,which makes HW illegal dumping difficult to be found,thereby causing a long-term adverse impact on the environment.How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed.In this study,a novel machine learning model based on the positive-unlabeled(PU)learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases.Validation of the random forest-based PU model showed that the predicted top 30%of high-risk areas could cover 68.1%of newly reported cases in the studied region,indicating the reliability of the model prediction.This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.

Experimental study of gas-water elongated bubble flow during production logging

In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.

Organic matter occurrence and pore-forming mechanisms in lacustrine shales in China

The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.