Generation Method Research Articles

Efficient reliability analysis of generalized [formula omitted]-out-of-[formula omitted] phased-mission systems

A k-out-of-n phased-mission system (PMS) is a PMS where the system structure is k-out-of-n: G in each phase. This paper investigates k-out-of-n PMSs with phase-K-out-of-N requirement, where the entire mission is successful if at least K out of the N phases achieve success. Such system is referred to as a generalized k-out-of-n PMS (k/n-GPMS). The k/n-GPMSs are prevalent in applications such as satellites, unmanned aerial vehicles (UAVs), wireless sensor networks and so on. In this paper, a novel method based on multi-valued decision diagram (MDD) is proposed to analyze the reliability of k/n-GPMSs, where the number of available components n, the required number of components k, and the components failure behaviors in different phases may vary. Distinguishing from the traditional phase-by-phase MDD generation method, the proposed method considers the behavior of all phases simultaneously and generates only one MDD model in a top-down manner. To illustrate the application of the proposed method, the reliability and the sensitivity of a four UAVs system which conducts supplies delivery mission is analyzed. The complexity analysis is performed. The correctness and efficiency are verified and demonstrated by several case studies. The proposed method is also compared with Monte Carlo simulation method.

Adaptive assessment based on fractional CBCT images for cervical cancer.

Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images. Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA. The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan. This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.

A Fast False Large-Scene Images Generation Method Against SAR Based on Two-Dimensional CZT and Multitransmitter Cooperation

A Fast False Large-Scene Images Generation Method Against SAR Based on Two-Dimensional CZT and Multitransmitter Cooperation

An Automatic Generation Method of Maintenance Worker Schedules at a Railroad Terminal Station by Tabu Search

An Automatic Generation Method of Maintenance Worker Schedules at a Railroad Terminal Station by Tabu Search

Slot coating of time-dependent structured materials: Effects of thixotropy on the flow dynamics and operating limits

Some of the most common liquid-like formulations rooted in the coating industry consist of rheologically complex structured materials such as inks, paints, and slurries. Yet, the impact of time-dependent structuring effects due to thixotropy on thin film coating applications remains elusive and still unclear. Here, we present a computational study of the effects of thixotropy on slot coating of time-dependent structured materials. By coupling a recent fluidity-based constitutive model for thixotropic materials with a well-established finite element/elliptic mesh generation method for free surface flows, we assess the role of thixotropy by comparing the predictions from the thixotropic model with those from a simple Generalized Newtonian Fluid model that uses the same flow curve for the material steady-state equilibrium viscosity. We find that thixotropy can indeed have a major impact on slot coating applications, potentially bringing strong implications not only to the flow dynamics but also to the operating limits of the process. In conclusion, the results and discussions we present in this study underscore the importance of accounting for thixotropy to genuinely model and fundamentally understand the behavior of time-dependent structured materials with complex rheology in processing flows like those ubiquitous across the broad fields of coating science and engineering.

Atomic-scale investigation on the structures and mechanical properties of metastable grain boundaries in aluminum

Grain boundary (GB) structural transition in pure metals open up new possibilities for material microstructure design. By managing the distribution of ground-state and metastable GB structures, the beneficial effects of GBs on materials can be maximised. However, the application of GB transitions to develop high-performance aluminum is limited by the inadequate understanding of the metastable GB structure in aluminium and its associated plastic deformation mechanisms. In this work, we firstly used a high-throughput GB generation method to construct multiple GB structure for a given misorientation angle, and screen out the ground-state and metastable GB structures according to the energy principle. Six typical symmetrically tilted GBs were investigated, including Σ5(210), Σ17(410) and Σ17(530) GBs around <001> tilt axis, and Σ9(221), Σ11(332), Σ17(223) GBs around <110> axis. Then, molecular dynamics simulations were carried out to perform uniaxial tensile tests on the six GBs and their corresponding metastable GBs at different temperatures. The results show that the tensile strength of <001> metastable GBs differs slightly from the ground-state GB at various temperatures, but for <110> GBs, the strength of most metastable GBs is significantly stronger or weaker compared to that of ground-state GB at 10 K. An increase in temperature reduced this difference in tensile strength of <110> GBs. According to atomic analysis of the GB structural characteristics, it was found that stress and temperature-induced GB structural transition, GB strain localization, and the prevention of dislocation nucleation or propagation from the dissociated structure are the main causes of the differences in tensile responses between the ground-state and metastable GBs.

High-efficiency mid-infrared CW-seeded optical parametric generation in PPLN waveguide

Mid-infrared optical sources have been extensively used in a variety of applications, including gas sensing and metrology, by exploiting the fingerprint fundamental vibrational absorption bands of molecules in this spectral region. Parametric frequency conversion techniques, such as optical parametric generation (OPG) and optical parametric oscillator (OPO), represent common methodologies for mid-infrared generation. Notably, continuous-wave (CW)-seeded OPG exhibits superior stability and performance compared to conventional OPG. Here, we present a simple method for mid-infrared source generation based on femtosecond OPG in periodically poled lithium niobate (PPLN) waveguides. In addition to its robustness and simplicity, mid-infrared CW-seeded OPG features a high quantum conversion efficiency of 46.5% and a low threshold. The phase of CW-seeded OPG was passively referenced to the CW laser, making it suitable for dual-comb applications. This system shows great promise in the miniaturization of mid-infrared combs and will be applied to non-laboratory applications, including open-path atmospheric gas sensing and portable environmental monitoring.

A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment.

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance.

Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation.

Niobium Interlayer Coating: Is it a Practical Approach to Tune the Protective Pt Loading in PEM Water Electrolyzers?

Proton exchange membrane (PEM) water electrolysis is a pioneering and promising method for sustainable green hydrogen generation. However, the high cost of PEM water electrolyzer components poses a significant barrier to the commercialization and widespread application of this technology. In this research, a novel Niobium-Platinum coating is developed for the porous transport layer (PTL) to reduce the precious metal loading. Niobium (Nb)-based coating was applied as an interlayer between Pt and titanium (Ti) felt substrates to increase the adhesion of Pt. For this study, pulsed laser deposition (PLD) technique was applied for the first time to coat Nb and Pt multilayers onto Ti felt substrates. The results demonstrate that incorporating a Nb coating as an interlayer lead to reducing the Pt loading, which holds promise for the practical application of PTLs in PEM water electrolyzers.

Probing the alkylidene carbene-strained alkyne equilibrium in polycyclic systems via the Fritsch-Buttenberg-Wiechell rearrangement.

Strained cycloalkynes are valuable building blocks in synthetic chemistry due to their high degree of reactivity and ability to form structurally complex scaffolds, common features of many pharmaceuticals and natural products. Alkylidene carbenes provide a pathway to the formation of strained cycloalkynes through Fritsch-Buttenberg-Wiechell rearrangements, but this strategy, like other methods of alkyne generation, is believed to depend upon a thermodynamic equilibrium that favors the alkyne over the carbene. Herein three highly strained, polycyclic alkynes, previously thought to be thermodynamically inaccessible, are generated under mild conditions and intercepted through Diels-Alder cycloaddition with a diene trapping agent. The use of a different trapping agent also allows for the interception of the alkylidene carbene, providing the first instance in which both an exocyclic alkylidene carbene and its cycloalkyne Fritsch-Buttenberg-Wiechell rearrangement product have been trapped.

The regional development and implementation of home-based stroke rehabilitation using participatory action research

Purpose This study aims to overcome the challenges experienced in the regional development and implementation of home-based stroke rehabilitation (HBSR) and to understand the change process needed. Materials and methods Using participatory action research (PAR), participants and researchers collaboratively produced knowledge and took action to improve the offered HBSR. Different methods for data generation and analysis were used, depending on the aim of the PAR phase and the participants’ stages of change. The Consolidated Framework for Implementation Research (CFIR) was used to select implementation strategies and to evaluate the implementation process. Results Developing and implementing HBSR resulted in multiple products that promoted the implementation of a regional stroke network and affiliated work arrangements. Work arrangements were embodied in a stroke care pathway, follow-up tool, and expertise requirements. Evaluating the PAR process identified participants being able to take the lead, being facilitated by others, and making progress visible, as implementation facilitators. Collaborating within a primary care project can be challenging but is considered essential and has a positive impact on multiple levels. Also, the implementation of HBSR calls for multiple implementation strategies reflecting multiple CFIR constructs. Conclusion This study highlights the complexity and achievements of developing and implementing HBSR using PAR.

Research on carotid artery plaque anomaly detection algorithm based on ultrasound images

Carotid artery plaque is a key factor in stroke and other cardiovascular diseases. Accurate detection and localization of carotid artery plaque are essential for early prevention and treatment of diseases. However, current carotid artery ultrasound image anomaly detection algorithms face several challenges, such as scarcity of anomaly data in carotid arteries and traditional convolutional neural networks (CNNs) overlooking long-distance dependencies in image processing. To address these issues, we propose an anomaly detection algorithm for carotid artery plaques based on ultrasound images. The algorithm innovatively introduces an anomaly sample pair generation method to increase dataset diversity. Moreover, it employs an improved adaptive recursive gating pyramid pooling module to extract image features. This module significantly enhances the model’s capacity for high-order spatial interactions and adaptive feature fusion, thereby greatly improving the neural network’s feature extraction ability. The algorithm uses a Sigmoid layer to map each pixel’s feature vector to a probability distribution between 0 and 1, and anomalies are detected through probability threshold binarization. Experimental results show that our algorithm’s AUROC index reached 90.7% on a carotid artery dataset, improving by 2.1% compared to the FPI method. This research is expected to provide robust support for the early prevention and treatment of cardiovascular diseases.

VQGNet: An Unsupervised Defect Detection Approach for Complex Textured Steel Surfaces

Defect detection on steel surfaces with complex textures is a critical and challenging task in the industry. The limited number of defect samples and the complexity of the annotation process pose significant challenges. Moreover, performing defect segmentation based on accurate identification further increases the task’s difficulty. To address this issue, we propose VQGNet, an unsupervised algorithm that can precisely recognize and segment defects simultaneously. A feature fusion method based on aggregated attention and a classification-aided module is proposed to segment defects by integrating different features in the original images and the anomaly maps, which direct the attention to the anomalous information instead of the irregular complex texture. The anomaly maps are generated more confidently using strategies for multi-scale feature fusion and neighbor feature aggregation. Moreover, an anomaly generation method suitable for grayscale images is introduced to facilitate the model’s learning on the anomalous samples. The refined anomaly maps and fused features are both input into the classification-aided module for the final classification and segmentation. VQGNet achieves state-of-the-art (SOTA) performance on the industrial steel dataset, with an I-AUROC of 99.6%, I-F1 of 98.8%, P-AUROC of 97.0%, and P-F1 of 80.3%. Additionally, ViT-Query demonstrates robust generalization capabilities in generating anomaly maps based on the Kolektor Surface-Defect Dataset 2.

A customised ConvNeXt-SCC network: integrating improved principal component analysis with ConvNeXt to enhance tire crown defect detection

ABSTRACT Defects such as bubbles and rubber shortages occasionally occur during vehicle tire production. Tiny defects in the crown gradually expand under load, ultimately causing tire failure. However, machine-vision-based crown defect detection faces challenges like limited datasets, varying groove patterns, low colour contrast, and small defect sizes. Therefore, this article integrates principal component analysis (PCA) and deep learning techniques for the visual detection of tire crown defects. A dataset of tire crown defects suitable for deep learning was constructed using an improved-PCA method and composite sampling. We propose the ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model, which includes an improved convolutional block attention module (CBAM) in a shallow stacked structure. And we propose an efficient and accurate method called Improved Selective Search (ISS) to detect and locate tire crown defective regions. The ISS pre-selected box generation method, enhanced with a priori knowledge filtering module, was used alongside a classification model to achieve online inference detection of large-size tire crown images. The experimental results show that the performance indices of the proposed ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model significantly improve compared to the original ConvNeXt-T. The defect detection and localisation method based on ISS meets the online inspection requirements of tire production.

DICTIONARY-BASED DETERMINISTIC METHOD OF GENERATION OF TEXT CORPORA

This paper examines the problem of solving software engineering tasks in developing information systems for natural language processing. Generating corpora of text data is highlighted as a specific task of this problem. An analysis of the basic CorDeGen method was carried out, which is one of the corpus generation methods specially developed for this specific problem. This study shows that this method has a limited scope due to the use of “artificial” terms to fill the texts.The paper proposes a new modified DBCorDeGen method that solves this shortcoming thanks to the use of an additional dictionary of terms that is supplied to the input of the method. The DBCorDeGen method preserves most of the characteristic features of the basic method, which are important for its use in solving software engineering tasks: determinism, speed of operation (including the possibility of combining with parallel modification), the possibility of a priori description of the structure and properties of the generated corpus. The only disadvantage compared to the basic method is the increase in the number of input parameters, however, compared to other methods of generating corpora presented in the literature, it is relatively small, and due to it, the scope of application of corpora generated by this method significantly increases.As an experimental test of the proposed modified DBCorDeGen method, the task of sentiment analysis of the texts of the generated corpus is considered. The study shows that when using the basic CorDeGen method, it is impossible to obtain sentiment analysis results different from neutral polarity for all texts. When using the proposed method, it is possible to obtain different results using different dictionaries. Thus, it is confirmed that the proposed DBCorDeGen method has a larger scope than the basic method.

High‐Throughput Production of Gelatin‐Based Touch‐Spun Nanofiber for Biomedical Applications

Nanofiber production techniques have become increasingly important due to their wide range of applications. However, the complex design of the setup and difficulty in scaling up to high production rate have limited the industrial applicability of some of the conventional fiber generation techniques such as electrospinning. Herein, the touch spinning method is scaled up for nanofiber production using a simple rotating drawing setup with polymers that are relevant for biomedical applications such as polyethylene oxide and gelatin. The process is amenable to use of benign solvent such as water and production of a wide variety of submicron‐scale gelatin‐based nanofibers at a high throughput (≈2.45 g hr−1 with the single channel flow), which is an order of magnitude higher than those produced by other fiber generation methods is shown. The parametric study indicates that the fiber production process can be tuned at a desired rate without sacrificing the fiber quality by simply altering the number of drawing rods, the size of the rotating disk, and the number of solution flow supply channels. The utility of this technique for different biomedical applications such as cell culture and air filtration applications is also demonstrated.

Accelerating large-scale multi-scalar multiplication in Zk-SNARK through exploiting its multilevel parallelism

In the context of zk-SNARK, MSM emerges as a major computational bottleneck, particularly due to its high computational and memory overhead. In this work, we exploit multiple levels of parallelism to effectively accelerate largescale MSM in zk-SNARKs. Firstly, a distributed parameter generation method is proposed in this paper to replace that of centralized method. Based on this methodology, the paper realizes a system with extraordinary scalability, capable of computing large-scale MSMs. Subsequently, the approach presented in this paper elevates the computation of Bellperson, the most prominent zero-knowledge proof system in real-world applications, from a single-node computation to a clustering mode, significantly enhancing its computational performance – a crucial advancement for practical applications. Finally, we implement a multi-level, fully parallelised MSM computing system by leveraging hierarchical sub-task partitioning and cross-node communication optimization, thereby thoroughly exploiting parallelism at diverse granularities. Experimental results show that in the cluster scenario, the proposed approach achieves acceleration ratios of approximately 3.60 and 6.50 times compared to the cutting-edge heterogeneous version Bellperson in dual-node and quad-node settings, respectively. On a single node, the proposed optimization approach achieves an acceleration ratio of 1.38 times compared to the current State-of-the-Art MSM calculation module of cuZK, outperforms the industry-popular Bellman by 186 times and the Bellperson by 1.96 times.

SPMGAE: Self-purified masked graph autoencoders release robust expression power

To tackle the scarcity of labeled graph data, graph self-supervised learning (SSL) has branched into two paradigms: Generative methods and Contrastive methods. Inspired by MAE and BERT in computer vision (CV) and natural language processing (NLP), masked graph autoencoders (MGAEs) are gaining popularity in the generative genre. However, prevailing MGAEs are mostly designed under the assumption that the data has high homophilic score and is out of adversarial distortion. When people deliberately improve the performance on homophilic graph datasets, they ignore a critical issue that both internal heterophily and artificial attack noise are quite common in the real world. Therefore, when data itself is highly heterophilic or confronted with attacks, they merely have no defensive capability. Especially under self-supervised conditions, it is much more difficult to detect internal heterophily and resist artificial attacks. In this paper, we propose a Self-Purified Masked Graph Autoencoder (SPMGAE) to make up for the shortcomings of prevailing MGAEs in terms of robustness. SPMGAE first utilizes a self-purified module to prune raw graph data and separate perturbation information. The purified graph provides a robust graph structure for the entire pre-training process. Next, the encoding module reuses perturbation information for auxiliary training to enhance robustness, while the decoding module reconstructs the effective graph data at a finer granularity. Extensive experiments on homophilic and heterophilic datasets attacked by various attack methods demonstrate SPMGAE has a considerable robust expressive ability. Especially on small datasets with large perturbations, the improvement of defensive performance could reaches 10%–25%.

VoiceStyle: Voice-Based Face Generation via Cross-Modal Prototype Contrastive Learning

Can we predict a person’s appearance solely based on their voice? This article explores this question by focusing on generating a face from an unheard voice segment. Our proposed method, VoiceStyle, combines cross-modal representation learning with generation modeling, enabling us to incorporate voice semantic cues into the generated face. In the first stage, we introduce cross-modal prototype contrastive (CMPC) learning to establish the association between voice and face. Recognizing the presence of false negative and deviate positive instances in real-world unlabeled data, we not only use voice–face pairs in the same video but also construct additional semantic positive pairs through unsupervised clustering, enhancing the learning process. Moreover, we recalibrate instances based on their similarity to cluster centers in the other modality. In the second stage, we harness the powerful generative capabilities of StyleGAN to produce faces. We optimize the latent code in StyleGAN’s latent space, guided by the learned voice–face alignment. To address the importance of selecting an appropriate starting point for optimization, we aim to automatically find an optimal starting point by utilizing the face prototype derived from the voice input. The entire pipeline can be implemented in a self-supervised manner, eliminating the need for manually labeled annotations. Through extensive experiments, we demonstrate the effectiveness and performance of our VoiceStyle method in both cross-modal representation learning and voice-based face generation.