In the last two decades, several highly sophisticated cyberattacks have targeted processcontrol systems (PCSs) that operate chemical processes. To enhance PCS cybersecurity, cyberattack detection schemes utilizing op...
ISBN:
(纸本)9798350382662;9798350382655
In the last two decades, several highly sophisticated cyberattacks have targeted processcontrol systems (PCSs) that operate chemical processes. To enhance PCS cybersecurity, cyberattack detection schemes utilizing operational data to reveal the presence of attacks on PCSs have received extensive attention. Stealthy attacks are designed to evade detection by an operational technology-based detection scheme. Their detection may require an active detection method, which perturbs the process by utilizing an external intervention for attack detection. In this work, two control modes that may be used to induce perturbations for active attack detection of steathly false-data injection cyberattacks are presented. A reachability analysis is used to develop a set-based condition indicating that if met by a specific stealthy attack, the attack will be detected and therefore, the control mode is considered to be "attack-revealing". Leveraging the condition, a screening algorithm that may be used to select an attack-revealing control mode is presented. Using an illustrative process, the application of the screening algorithm is demonstrated.
Methanol reforming fuel cells (MRFCs) remains a promising innovation towards the establishment of sustainable energy production. This is evident in the fact that MRFC plays a critical role in the offering of low envir...
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In recent years, machine learning (ML) techniques have been extensively investigated to strengthen the understanding of the complex process dynamics underlying metal additive manufacturing (AM) processes. This paper p...
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In recent years, machine learning (ML) techniques have been extensively investigated to strengthen the understanding of the complex process dynamics underlying metal additive manufacturing (AM) processes. This paper presents a comprehensive review and discussion on the latest successful applications of ML to one category of metal AM processes, i.e., laser powder bed fusion or LPBF. This paper will focus on three aspects of LPBF including processmodeling, in-situ process monitoring, defect detection, off-line process optimization, and online processcontrol. Due to the multi-physics mechanisms of LPBF and associated heterogeneous process sensing, different ML techniques naturally play a significant role in discovering the patterns underlying sensing data. The unsupervised component analysis helps to fuse features extracted from sensing data to facilitate the efficiency of dataprocessing and modeling. Supervised regression techniques are applicable to advancing the causal reasoning of relationship among process parameters, thermal dynamics, structural formation and evolution, and achieved property of printed parts, which is also termed as the process-thermal dynamics-structureproperty (PTSP) relationship. Supervised classification and unsupervised clustering techniques can be applied to classify in-situ sensing data to detect defect occurrence and identify defect type (e.g., balling) and severity (e.g., porosity level, crack density). The obtained PTSP relationship can then be used as a basis for off-line optimization of process parameters to achieve better printing quality, while real-time processing of in-situ sensing data through advanced ML techniques (e.g., reinforcement learning) allows online feedback control. Knowledge gaps and future research directions in the three aspects are also identified in this paper.
Higher education has experienced an unparalleled digital transformation, driven by the widespread adoption of online learning with massive users, which has risen to an explosive growth in the generation and analysis o...
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ISBN:
(纸本)9783031643149;9783031643156
Higher education has experienced an unparalleled digital transformation, driven by the widespread adoption of online learning with massive users, which has risen to an explosive growth in the generation and analysis of student-related data. Within this transformation, predictive modeling has emerged as a useful tool for predicting critical indicators in the learning process, encompassing students' academic performance, class retention, and dropout rates. With this backdrop, this study aims to conduct a systematic review of recent publications focused on predictive modeling, with a specific emphasis on the Open University Learning Analytics datasets (OULAD). Following the PRISMA process, we identified 17 research articles published from 2017 to 2024, concentrating on OULAD in higher education. For our analysis, we categorized the purpose of predictive modeling into three types: (a) predicting students' performance, (b) identifying at-risk students, and (c) predicting student engagement. The central focus lies on the identification of algorithms predominantly employed in these studies, including machine learning, deep learning, and statistical models. By investigating the methodologies and algorithms employed, this review informs researchers in learning analytics and educational data mining of the potential opportunities and challenges associated with predictive modeling using OULAD in higher education.
In this position paper, we make a case for the appropriateness, utility, and effectiveness of graph models for big dataanalysis focusing on Multilayer Networks (or MLNs) – a specific type of graph. MLNs have been sh...
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This study presents a comprehensive approach for assessing Advanced Display and control Systems (ADCS) in Electric Vertical Takeoff and Landing (eVTOL) aircraft. Integrating Analytic Hierarchy process (AHP) modeling w...
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ISBN:
(纸本)9798350351088;9798350351095
This study presents a comprehensive approach for assessing Advanced Display and control Systems (ADCS) in Electric Vertical Takeoff and Landing (eVTOL) aircraft. Integrating Analytic Hierarchy process (AHP) modeling with a review of patented technologies using the European Patent Office (EPO) database, Espacenet, the research systematically evaluates ADCS effectiveness, safety, and usability. The AHP model facilitates multi-criteria decision- making from the operator's point of view, while patent analysis identifies innovative solutions for ADCS design optimization. Findings demonstrate the significance of trajectory planning algorithms, collision avoidance systems, and intuitive user interfaces in enhancing eVTOL operational efficiency and safety. The synergistic integration of AHP analysis and patent review offers engineers and regulatory bodies a robust framework to optimize ADCS design, ensuring safe and efficient eVTOL operations.
BPMN process models have been widely used in software designs. The BPMN process models are characterized by a static graph-oriented modeling language and a lack of analytical capabilities as well as dynamic behavior v...
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BPMN process models have been widely used in software designs. The BPMN process models are characterized by a static graph-oriented modeling language and a lack of analytical capabilities as well as dynamic behavior verification capabilities, which not only leads to inconsistencies in the semantics of the BPMN process models, but also leads to a lack of model error detection capabilities for the BPMN process models, which also hinders the correctness verification and error correction efforts of the models. In this study, we propose an executable modeling approach for CPN-based data flow well-structured BPMN (dw-BPMN) process models, and consider both control-flow and data-flow perspectives. First, we present a formal definition of the dw-BPMN process model, which is formally mapped into a CPN executable model in three steps: splitting, mapping and combining. Then, we discuss four types of data flow errors that can occur in the model: missing, lost, redundant, and inconsistent data error. To detect these four data flow errors, we propose a detection method based on the execution results of the CPN model. Subsequently, we propose correction strategies for these four data flow errors. Finally, a dw-BPMN process model of a robot's temperature detection system for COVID-19 prevention and control in a kindergarten was used as an example to verify the validity of the method.
Co-incineration is one of the best ways to treat sewage sludge (SS) and municipal solid waste (MSW), but it is very important to understand its combustion and gas reaction in the grate to control the emission of pollu...
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ISBN:
(纸本)9798350387780;9798350387797
Co-incineration is one of the best ways to treat sewage sludge (SS) and municipal solid waste (MSW), but it is very important to understand its combustion and gas reaction in the grate to control the emission of pollutants. In this paper, the discussion of SS co-incineration based on the dioxin emission numerical simulation model for the MSW incineration (MSWI) process is proposed. Firstly, the numerical simulation model of DXN emission for the MSWI process is established in ASPEN Plus software by using collected sample data in an MSWI plant in Beijing. Then, the SS and MSW co-incineration numerical simulation model is constructed. Finally, based on SS data in reference, the discussion and analysis arc made. These research results provide support for the future research on co-incineration of SS and MSW in Beijing.
Autism Spectrum Disorder (ASD), as a complex neurodevelopmental disorder, is closely associated with attention deficits that manifest through eye and head movements. Previous studies on the eye gaze and head posture o...
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Autism Spectrum Disorder (ASD), as a complex neurodevelopmental disorder, is closely associated with attention deficits that manifest through eye and head movements. Previous studies on the eye gaze and head posture of children with ASD have been somewhat limited by the contexts in which the children were observed, Exploring head and eye coordination in natural environments is crucial for developing effective intervention strategies applicable to everyday life. This paper aims to examine the perceptual and behavioral responses of children with ASD in simulated real-life social environments. Using asocial interaction paradigm based on real-life scenarios, we propose a joint probabilistic modeling method for head and eye behaviors. This method includes the influence of head posture on gaze direction in eye-tracking studies within social interaction contexts. For each participant, we establish a data-driven Markov chain model based on individual data, preserving the temporal nature of eye movement behavior and the highly individualized nature of visual behavior. We conducted experiments on a video dataset of children with ASD that we collected, achieving a classification accuracy of 79.66%, demonstrating the feasibility and effectiveness of our proposed method. Additionally, we found that one manifestation of attention deficits in children with ASD is an increased occurrence of head-eye counter movement. This finding provides new reference indicators for the early diagnosis and screening of ASD.
The coupling between anaerobic digestion and hydrothermal carbonization (HTC) is a promising alternative for sustainable energy production. This study presents a dynamic model tailored for a lab-scale anaerobic digest...
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The coupling between anaerobic digestion and hydrothermal carbonization (HTC) is a promising alternative for sustainable energy production. This study presents a dynamic model tailored for a lab-scale anaerobic digester operating on HTC products, specifically hydrochar and HTC liquor derived from sewage and agroindustrial digestate. Leveraging a modified version of the Anaerobic Model 2 (AM2), our simplified model of four states integrates pH and biomass decay rates into biomass kinetics. Simulation results of the mode were compared with experimental data collected over 164 days from the digester. The obtained results have proven the ability of the proposed model to predict the trend of the biogas production as well as important measured outputs of the bioreactor. The developed model could be used to control and optimize the performance of the digester, which provides potential for bioenergy production from waste streams such as digestate and digestate treated through the HTC process.
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