Power Capability Research Articles

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Roll-to-roll fabrication and characterization of ultra-thin ceramic-coated separator for high-energy-density lithium-ion batteries

Ceramic-coated separators (CCSs) used in lithium-ion batteries (LIBs) play an important role in ensuring the safety of LIBs. However, conventional CCSs fabricated by coating the slurry consisting of ceramic particles and binder have limitations due to the micrometer-thick ceramic coating layer (CCL), compromising the energy density and power capabilities of LIBs. To address this relationship, ultra-thin CCSs fabricated by the sputtering process have attracted attention due to their high thermal stability even with nanometer-thick CCL. Nevertheless, batch-type sputtering machines cannot provide roll-based CCSs for commercial LIBs. Herein, we demonstrate the continuous fabrication of ultra-thin CCS rolls with a pilot-scale direct current (DC) roll-to-roll sputtering process. The ionic conductance and thermal stability are compared with the slurry-based CCS by controlling the coating thickness through the line speed. In addition, the electrochemical performance of the pouch cell (LiNi0.6Co0.2Mn0.2O2/graphite, 550 mAh) is evaluated to confirm the applicability of the roll-to-roll sputtered ultra-thin, binder-free CCS (R2R-UB-CCS) to LIBs. Owing to the nanometer-thick CCL, the pouch cell with the R2R-UB-CCS shows higher power capabilities than the bare polyethylene (PE) separator and slurry-based CCS. Thus, the roll-to-roll sputtering process for large-scale, high-speed mass production of ultra-thin CCS has the potential for high safety and high-energy-density LIBs.

Artificial Intelligence in Cybersecurity Threat Detection

With the increasing frequency and complexity of cyberattacks, traditional cybersecurity threat detection methods have been difficult to cope with new types of threats. Artificial Intelligence (AI) technology, with its powerful data processing and pattern recognition capabilities, has gradually become an important tool for enhancing cyber security. This paper aims to explore the application of AI in cybersecurity threat detection, firstly outlining the current status of the development of AI technology in cybersecurity, and then focusing on analyzing the application of core methods such as machine learning and deep learning in threat detection, and discussing the advantages of integrated learning and multimodal methods. Finally, this paper summarizes the current challenges faced by AI technology in the field of cyber security and looks forward to the future development direction. Through the research in this paper, it is expected to provide reference for improving the accuracy and efficiency of cybersecurity threat detection.

A phase field model with modified volumetric-deviatoric decomposition for the mixed-mode fracture of rock

Recent attempts of phase field models to simulate rock fracture problems have been regarded as fruitful, owing to their powerful crack characterization capability. However, existing phase field models still exhibit limitations in simulating quasi-coplanar shear cracks and related coalescence patterns that often occur in rock failure. In this study, we propose a novel phase field model to simulate the mixed-mode fracture, in which a modified volumetric-deviatoric decomposition that combines the advantages of the volumetric-deviatoric decomposition and the spectral decomposition is conducted to distinguish between tensile, tensile-shear, and compressive-shear fractures; to reduce unrealistic damage in the area outside the crack trajectory, a threshold parameter is introduced into the degradation function to control whether the related energy participates in damage evolution; and the hybrid formulation is employed to efficiently and robustly solve the displacement and phase fields alternately. The feasibility of the proposed phase field model is first validated by a benchmark example. Next, the application of this model to the simulations of different rock specimens under uniaxial compression reveals a better agreement with experimental observations than previous simulation results, demonstrating an advancement over existing phase field models.

Research on a New Model of Cultivating Creative Thinking in the Language Discipline Based on ChatGPT--Taking Project-based Learning of Senior Secondary Language as an Example

This paper This paper discusses a new model of cultivating creative thinking in the language discipline based on ChatGPT, aiming to provide new ideas and methods for language teaching reform. ChatGPT, an advanced natural language processing technology with powerful language comprehension and generation capabilities, provides new means and methods for language teaching. The study presents a case study of the teaching practice of the new model of creative thinking cultivation in language discipline based on ChatGPT through the casestudy method, which provides guidance for the further improvement and optimization of the new model. The model takes ChatGPT as the core, combines the characteristics of language teaching and students' needs, and designs teaching processes and activities suitable for cultivating students' creative thinking. The study emphasizes the importance of changing teaching concepts when using the Humanistic Version of the senior secondary language textbook, as well as incorporating practical language activities to improve students' core literacy through personalized participation and practice.

Towards an efficient variational quantum algorithm for solving linear equations

Variational quantum algorithms are promising methods with the greatest potential to achieve quantum advantage, widely employed in the era of noisy intermediate-scale quantum computing. This study presents an advanced variational hybrid algorithm (EVQLSE) that leverages both quantum and classical computing paradigms to address the solution of linear equation systems. Initially, an innovative loss function is proposed, drawing inspiration from the similarity measure between two quantum states. This function exhibits a substantial improvement in computational complexity when benchmarked against the variational quantum linear solver. Subsequently, a specialized parameterized quantum circuit structure is presented for small-scale linear systems, which exhibits powerful expressive capabilities. Through rigorous numerical analysis, the expressiveness of this circuit structure is quantitatively assessed using a variational quantum regression algorithm, and it obtained the best score compared to the others. Moreover, the expansion in system size is accompanied by an increase in the number of parameters, placing considerable strain on the training process for the algorithm. To address this challenge, an optimization strategy known as quantum parameter sharing is introduced, which proficiently minimizes parameter volume while adhering to exacting precision standards. Finally, EVQLSE is successfully implemented on a quantum computing platform provided by IBM for the resolution of large-scale problems characterized by a dimensionality of 220.

A GPT-enhanced framework on knowledge extraction and reuse for geographic analysis models in Google Earth Engine

ABSTRACT Geographic analysis models are becoming increasingly important in the era of geospatial big data. As a cloud platform that combines massive geospatial data resources with powerful computational capabilities, the Google Earth Engine (GEE) gathers a large number of workflow scripts for different geographic analysis tasks and gradually evolves into a comprehensive knowledge repository. However, due to the complexity nature of GEE workflow scripts, extracting modeling knowledge from these GEE workflow scripts remains a challenge. Besides, these GEE workflow scripts are often tightly coupled with the GEE environment, which limits their reuse in the broader geospatial community. To address these issues, this paper proposes a GEE knowledge extraction and reuse framework, which uncovers factual meta-information and modeling processes embedded within the GEE workflow script by leveraging ChatGPT and the abstract syntax tree (AST). A GEE knowledge encapsulation template is developed to systematically describe this modeling knowledge. Through case studies, we demonstrate that this framework can be integrated with existing geospatial service technologies, simplify the extraction of modeling knowledge from GEE workflow scripts, and enhance their application in different modeling environments. Moreover, by incorporating ChatGPT, this framework exhibits the potential for intelligent generation of geographic analysis models.

RDSC: Range-Based Device Spatial Clustering for IoT Networks.

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms.

Research on Space Operation Control of Air Float Satellite Simulator Based on Constraints Aware Particle Filtering-Nonlinear Model Predictive Control

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications.

Elastic MSM: A Fast, Elastic and Modular Preprocessing Technique for Multi-Scalar Multiplication Algorithm on GPUs

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed.In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively.From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.

Mechanically robust and anisotropic hydrogel composites for high efficiency steam generation

Solar-driven interfacial evaporation (SDIE) has now become a promising desalination technique to harvest fresh water, and challenges remain to develop high performance solar evaporators that can achieve high performance SDIE while at the same time maintain good mechanical properties. In this work, anisotropic Polydopamine (PDA)/polyvinyl alcohol (PVA)@acidified carbon nanotubes (ACNTs) hydrogels (PPCHs) are prepared for SDIE and dye adsorption. The introduction of PDA and ANCTs improves the overall mechanical properties of the hydrogels and also imparts the hydrogel abundant active adsorption sites. PPCHs possess hierarchically vertical channels and hence powerful water transport capability. The PPCH evaporator with reduced water enthalpy exhibits high evaporation rate and efficiency, up to 3.43 kg m−2 h−1 and 91.6 % even during the long-term evaporation, respectively. In addition, PPCH can also work stably in harsh conditions such as high concentration brine, emulsions, acid and alkali solutions, etc. Due to the electrostatic interaction, PPCHs can effectively adsorb methylene blue (MB) in wastewater. PPCHs show broad application prospects in the field of solar desalination and dye adsorption. This study provides a rational structure design of the hydrogel composites for simultaneous SDIE and organic pollutant removal.

Design concept of intelligent integrated control system for neutral beam injection

Abstract Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

Mesoscale Explorer - Visual Exploration of Large-Scale Molecular Models.

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.

Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery.

Techno-Economic Suitability of Batteries for Different Mobile Applications—A Cell Selection Methodology Based on Cost Parity Pricing

Rapid advancements in lithium-ion battery (LIB) technology have paved the way for the electrification of diverse applications, with continuous improvements in performance, substantial cost reductions, and the emergence of new manufacturers, formats, and cell chemistries. However, this diversity poses challenges in identifying the most suitable battery cells for specific applications. Here, we present a high-level techno-economic framework for cell selection, leveraging an extensive database of over 500 real-world cells, techno-economic analyses of emerging applications, and a Python-based modeling approach. We apply this method to three electrifiable mobile applications with distinct characteristics: battery electric cars, industrial forklifts, and regional passenger trains. Our results emphasize substantial variations in technical requirements, from power capability to energy density or longevity. We observe no particular differentiation according to cell formats, but tendencies for most suitable chemistries per application. No cell is suitable for all applications, particularly regarding the required maximum cell costs to ensure profitability, ranging from a few to several hundred Euros per kWh to achieve cost parity with a state-of-the-art reference technology. These findings highlight the importance of tailored cell selection strategies for decision makers to optimize performance and cost-effectiveness across different applications.

Makeup-Invariant Faces Recognition Using a Pre-Trained Neural Network, Grasshopper Optimization Algorithm, and Random Forest

Makeup-Invariant Face Recognition is a critical area of research that aims to identify and classify manipulated or forged face images in the field of face recognition systems. Advances in deep learning techniques, such as pre-trained neural networks, have shown promising results in this domain. In this paper, a novel approach for detecting manipulated faces is proposed, combining the strengths of pre-trained neural networks, the Grasshopper Optimization (GOA) Algorithm, and Random Forest classifier. The proposed method utilizes the powerful feature extraction capabilities of pre-trained deep neural networks to capture intricate details and patterns in face images. Subsequently, the GOA algorithm is employed to select optimal features. Additionally, the Random Forest classifier is utilized for effective classification of face images and identifying different individuals based on the selected features. By integrating these three algorithms, the proposed approach demonstrates significant improvements in detecting manipulated faces, achieving higher detection rates and lower false positive rates compared to existing state of the art methods. Simulation results on data from 25 different individuals with manipulated face images show an average accuracy of 97.23%, which represents an enhancement over the compared methods.

FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm

Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

A Broad Generative Network for Two-Stage Image Outpainting.

Image outpainting is a challenge for image processing since it needs to produce a big scenery image from a few patches. In general, two-stage frameworks are utilized to unpack complex tasks and complete them step-by-step. However, the time consumption caused by training two networks will hinder the method from adequately optimizing the parameters of networks with limited iterations. In this article, a broad generative network (BG-Net) for two-stage image outpainting is proposed. As a reconstruction network in the first stage, it can be quickly trained by utilizing ridge regression optimization. In the second stage, a seam line discriminator (SLD) is designed for transition smoothing, which greatly improves the quality of images. Compared with state-of-the-art image outpainting methods, the experimental results on the Wiki-Art and Place365 datasets show that the proposed method achieves the best results under evaluation metrics: the Fréchet inception distance (FID) and the kernel inception distance (KID). The proposed BG-Net has good reconstructive ability with faster training speed than those of deep learning-based networks. It reduces the overall training duration of the two-stage framework to the same level as the one-stage framework. Furthermore, the proposed method is adapted to image recurrent outpainting, demonstrating the powerful associative drawing capability of the model.

An ALCHEMI inspection of sulphur-bearing species towards the central molecular zone of NGC 253

Context. Sulphur-bearing species are detected in various environments within Galactic star-forming regions and are particularly abundant in the gas phase of outflow and shocked regions in addition to photo-dissociation regions. Thanks to the powerful capabilities of millimetre interferometers, studying sulphur-bearing species and their region of emission in various extreme extra-galactic environments (e.g. starburst and active galactic nuclei) and at a high-angular resolution and sensitivity is now possible. Aims. In this work, we aim to investigate the nature of the emission from the most common sulphur-bearing species observable at millimetre wavelengths towards the nuclear starburst of the nearby galaxy NGC 253. We intend to understand which type of regions are probed by sulphur-bearing species and which process(es) dominate(s) the release of sulphur into the gas phase. Methods. We used the high-angular resolution (1.6″ or ∼27 pc) observations from the ALCHEMI ALMA Large Program to image several sulphur-bearing species towards the central molecular zone (CMZ) of NGC 253. We performed local thermodynamic equilibrium (LTE) and non-LTE large velocity gradient (LVG) analyses to derive the physical conditions of the gas where the sulphur-bearing species are emitted, and their abundance ratios across the CMZ. Finally, we compared our results with previous ALCHEMI studies and a few selected Galactic environments. Results. To reproduce the observations, we modelled two gas components for most of the sulphur-bearing species investigated in this work. We found that not all sulphur-bearing species trace the same type of gas: strong evidence indicates that H2S and part of the emission of OCS, H2CS, and SO are tracing shocks, whilst part of SO and CS emission rather traces the dense molecular gas. For some species, such as CCS and SO2, we could not firmly conclude on their origin of emission. Conclusions. The present analysis indicates that the emission from most sulphur-bearing species throughout the CMZ is likely dominated by shocks associated with ongoing star formation. In the inner part of the CMZ where the presence of super star clusters was previously indicated, we could not distinguish between shocks or thermal evaporation as the main process releasing the S-bearing species.

Transformer neural network based real-time process monitoring and direct visualization of top-down vat photopolymerization

Top-down vat photopolymerization (TVPP) technology is rapidly developing to all of the industries for new products development and manufacturing due to its low cost, fast speed and high precision. The powerful capability of TVPP for large size, highly customized and medium batch production renders it one of the most popular additive manufacturing techniques today. An effective real-time process monitoring method providing timely feedback for part defects, especially in case of print failure, is highly desirable but still rarely reported for TVPP 3D printing. Large 3D objects are normally segmented into smaller parts to reduce the risk of failure and materials waste, resulting in the complexity of building while sacrificing component integrity. Herein, a transformer neural network based real-time and visualized process monitoring (TransRV) was constructed as an effective method to enhance the manufacturing performance and quality. Upon the challenge of visualizing and capturing the real-time fabricated layer from the around liquid photoresin, a real-time dataset including in-situ standard reference images and real-time mask fabricated layers was initially constructed. Based on the dataset foundation, we then developed a novel neural network model for effective segmentation of captured images by introducing multiple attention mechanisms and adopting the architecture of Swin Transformer. The experimental results showed that the real-time taken images during the printing process could be accurately segmented through our designed neural network model. The mIoU, which is the ratio of mean intersection over union, was considered as the main evaluation index in the test set. And the value of mIoU could achieve as high as 96.14 %. On the basis of this result, we further constructed a multiple quality monitoring indicator for quality assessment and defect detection of TVPP process. It was proved that this indicator enabled real-time accurate recognition and in time feedback. The typical defects such as overall collapse and partial missing of the printed parts that usually occur during TVPP process would be timely detected and subsequently stopped printing. Apparently, the methods developed in this work provide a promising strategy to effectively eliminate the material waste and highly improve the productivity. Most importantly, the presented real-time process monitor holds the great potential for quality control and defect detection of widespread TVPP manufacturing.