Original Form Research Articles

Stacked dynamic target regularization enhanced autoencoder for soft sensor in industrial processes

AbstractStacked autoencoders (SAEs) have great potential in developing soft sensors due to their excellent feature extraction capabilities. However, the pre‐training stage of SAE is unsupervised and some important information related to target variables may be discarded. Meanwhile, as the depth of the network increases, reconstruction errors continue to accumulate, resulting in incomplete feature representations of the original input. In addition, the dynamic nature of the data affects the predictive results of the model. To address these issues, the stacked dynamic target regularization enhanced autoencoder (SDTR‐EAE) method is proposed, which adds the DTR and the original input information layer by layer to enhance the feature extraction. To adapt to the dynamic changes in data and extract target‐related features, entropy weight grey relational analysis (EW‐GRA) is used as the DTR term to constrain the weight matrix and suppress irrelevant features. To reduce the accumulation of information loss during the reconstruction, an information enhancement layer is introduced, where the original inputs and the information of the hidden layers of previous DTR‐EAE units are added to the follow‐up DTR‐EAE unit. Finally, in the regression process, the DTR term is used again to fully utilize depth features for quality prediction and prevent overfitting. Experimental verifications using the debutanizer column and thermal power plant are conducted to validate the effectiveness of the proposed modelling method.

Multimodal Recipe Recommendation with Heterogeneous Graph Neural Networks

Recipe recommendation is the process of recommending suitable recipes to users based on factors such as user preferences and dietary needs. Recipes typically involve multiple modalities, with text and images being common, while most typical recipe recommendation methods recommend recipes to users based on text. Obviously, the expressiveness of a single modal is often not enough, and the semantic information of images is more abundant. Moreover, it is difficult to grasp the feature fusion granularity of different kinds of modal information and the relationship between recipes and users. To solve the above problem, this paper proposes a Multimodal Heterogeneous Graph Neural Network Recipe Recommendation (MHGRR) architecture, which aims to fully fuse the various kinds of modal information of recipes and handle the relationship between users and recipes. We use embedding and shallow Convolutional Neural Networks (CNNs) to extract original text and image information for unifying feature fusion granularity, and use Heterogeneous Graph Neural Networks based on GraphSAGE to capture the complex relationship between users and recipes. To verify the effectiveness of our proposed model, we perform some comparative experiments on a real dataset; the experiments show that our method outperforms most popular recipe recommendation methods. Through an ablation experiment, we found that adding image information to recipe recommendation is more effective, and we additionally found that as the output dimensions of GraphSAGE increased, the performance of the model varied little.

Identifying fine-scale archaeological features using KH-9 HEXAGON mapping and panoramic camera images: evidence from Liangzhu Ancient City

ABSTRACT Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications.

Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.

Subgraph autoencoder with bridge nodes

Subgraph representation learning is a burgeoning field within graph representation learning. However, current methods in this field face several issues, including the inability to facilitate information interaction between subgraphs, the destruction of original node feature information, and the detrimental effect of the structural information of the entire graph on the extracted subgraph structural information. To address these issues, this paper proposes a method named the subgraph autoencoder with bridge nodes (SAWBN). Specifically, first, SAWBN enhances the data interaction capability by adding a bridge node and connecting it to all nodes within a subgraph. The addition of the bridge node creates indirect connections between subgraphs, enabling them to interact and exchange information during the message-passing phase. To the best of our knowledge, this is the first paper to propose the concept of bridge nodes and to introduce bridge nodes into subgraph representation learning. Second, SAWBN utilizes a new message-passing paradigm called graph convolution with different weights (GDW). During the convolution process, GDW can assign different weights to nodes inside and outside a subgraph, effectively distinguishing the importance of nodes without compromising the original node feature information. Third, SAWBN uses an autoencoder to reconstruct subgraph node features. This reconstruction can eliminate the interference of the structural information of the entire graph and regenerate subgraph node features that are rich in subgraph structural information. Finally, these subgraph node features are aggregated to achieve a good representation of the subgraph structural information. Extensive results on four real-world subgraph datasets demonstrate that SAWBN outperforms state-of-the-art baselines. The source code of SAWBN is publicly available at https://github.com/denggaoqin/SAWBN.

Tracking control of T-S fuzzy systems under probabilistic constraints and fading channels

This work investigates the probabilistic-constrained tracking control problem for T-S fuzzy systems under fading channels described by the memoryless multiplicative noise model. The main control objective is to confine the tracking error into its pre-given bounds no lower than a certain probability. To this end, a tracking controller is firstly synthesised by introducing the fading parameter as an amplification factor towards ‘retrieving’ the original information to some extent. An auxiliary function and the multi-dimensional Chebyshev inequality make it possible to derive sufficient conditions such that the tracking error falls into its specified domain with a frequency larger than a pre-given value. By employing an inequality involving unmatched membership functions of the controller and fuzzy system, the favourite property of matched membership functions can be employed. A numerical example illustrates the effectiveness of the proposed method.

Intracranial Empyemas: Experience of the Neurosurgery Department of Conakry University Hospital

Introduction: Intracranial empyemas are rare intracranial suppurations but carry serious and potentially fatal prognosis In the absence of adequate treatment. The objective of our study was to determine the epidemiological, clinical features, therapeutic and evolutionary characteristics of these suppurations in our department. Materials and methods: It was a descriptive retrospective study of 88 intracranial empyemas files collected over a period of 5 years (January 2014-December 2018) in the archives of the neurosurgery department of Conakry University Hospital. Results: The frequency was 43.1% for all intracranial suppurations. The mean age was 14.6 years (2-42 years) with a sex ratio of 1.8 (57 males/31 females). The port of infection was dominated by otorhinolaryngological infections, 76.5% and unknown in 19%. The clinical manifestations were dominated by fever (90%), intracranial hypertension (66%) and focal signs (45%). The site was subdural in 59 cases and extradural in 29 cases. Surgical treatment involved 91% of patients and 9%were treated with antibiotic therapy alone. The evolution was favorable in 68.1% of cases with a mortality rate of 13.6%. Conclusion: Intracranial empyemas are medical and surgical emergency. The forms of otorhinolaryngology origin are more frequent. Despite the therapeutic difficulties, the prognosis remains acceptable in our study with 13.6% of deaths.

Multi-perspective feedback-attention coupling model for continuous-time dynamic graphs

Abstract Representation learning over graph networks has recently gained popularity, with many models showing promising results. However, several challenges remain: (1) most methods are designed for static or discrete-time dynamic graphs; (2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and (3) many continuous-time dynamic graph approaches necessitate numerous temporal neighbors to capture long-term dependencies. In response, this paper introduces a Multi-Perspective Feedback-Attention Coupling (MPFA) model. MPFA incorporates information from both evolving and original perspectives to effectively learn the complex dynamics of dynamic graph evolution processes. The evolving perspective considers the current state of historical interaction events of nodes and uses a temporal attention module to aggregate current state information. This perspective also makes it possible to capture long-term dependencies of nodes using a small number of temporal neighbors. Meanwhile, the original perspective utilizes a feedback attention module with growth characteristic coefficients to aggregate the original state information of node interactions. Experimental results on one dataset organized by ourselves and seven public datasets validate the effectiveness and competitiveness of our proposed model.

Robust multi-view clustering via structure regularization concept factorization

Recently, many concept factorization-based multi-view clustering methods have been proposed and achieved promising results on text multi-view data. However, existing methods are limited in the following two aspects. (1) The Frobenius norm used in these methods is sensitive to noise and outliers; (2) These methods ignore the global structural information of the data. To address the above problems, we propose a robust concept factorization framework for multi-view clustering, which not only improves the robustness but also fully exploits the available information of multi-view data. Specifically, L2,1-norm is used to evaluate the error of the factorization, thus eliminating the effect of outliers and improving robustness. In addition, to retain more structure information of the original data, the global and local structure information are taken into consideration simultaneously, which makes the learned low-dimensional matrix more discriminative. Further, to make use of the complementary information of the different views, we introduce an adaptive weight learning strategy to assign weights for different views. An iterative updating algorithm is proposed to solve the proposed optimization problem. We compare the proposed method with state-of-the-art alternative methods on benchmark multi-view data sets. The extensive experimental results show the effectiveness and superiority of the proposed method.

An adversarial network reinforcement method for power data time series generation based on knowledge transfer and Rayleigh differential privacy

Abstract In order to solve the problem of sensitive information leakage of power data in machine learning and artificial intelligence processing, this paper proposes a method of civic-text data reinforcement based on time series generation adversarial network, which combines knowledge transfer and Rényi differential privacy protection framework to balance privacy and availability, while improving the accuracy of the model. First, the generative adversarial network generates training data that is similar to the real data but secure through adversarial learning, ensuring that even if the data is stolen, the original information cannot be inferred. Secondly, through the integration of recurrent neural network training and autoencoder, the dynamic and static characteristics of the power load data can be effectively captured, and the high dimension of the learning space can be reduced. In addition, knowledge distillation technology is used to guide the generator of training student models through a powerful teacher network of knowledge transfer, improving the diversity and quality of the generated data. Finally, from the four aspects of privacy, data similarity and diversity, and practicability, the practical application value and potential of the framework for electricity consumption data are comprehensively evaluated.

Taming Reversible Halftoning Via Predictive Luminance.

Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color image into a binary halftone with full restorability to its original version. Our novel base halftoning technique consists of two convolutional neural networks (CNNs) to produce the reversible halftone patterns, and a noise incentive block (NIB) to mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the conflicts between the blue-noise quality and restoration accuracy in our novel base method, we proposed a predictor-embedded approach to offload predictable information from the network, which in our case is the luminance information resembling from the halftone pattern. Such an approach allows the network to gain more flexibility to produce halftones with better blue-noise quality without compromising the restoration quality. Detailed studies on the multiple-stage training method and loss weightings have been conducted. We have compared our predictor-embedded method and our novel method regarding spectrum analysis on halftone, halftone accuracy, restoration accuracy, and the data embedding studies. Our entropy evaluation evidences our halftone contains less encoding information than our novel base method. The experiments show our predictor-embedded method gains more flexibility to improve the blue-noise quality of halftones and maintains a comparable restoration quality with a higher tolerance for disturbances.

Secure consensus for PDEs-based multiagent systems under DoS attacks via boundary control approach

This paper focuses on secure consensus for leader-following multiagent systems (MASs) modeled by partial differential equations (PDEs) under denial of service (DoS) attacks. To mitigate the negative effects of DoS attacks, which can paralyze communication and cause agents to fail to receive valid control inputs, a buffer region is established in the communication channels among agents to temporarily store messages from neighbors. Additionally, since the states of the leader and followers are not always measurable, observers are used to estimate these states. To address these challenges, this paper proposes two boundary controllers to ensure leader-following consensus in both measurable and unmeasurable states. One controller is based on original boundary information, while the other utilizes observation information from both the leader and followers. To the best of our knowledge, this is the first attempt to use buffers to solve a class of PDEs-based MASs under DoS attacks. Furthermore, the boundary control approach has the potential to significantly reduce the number of actuators required, thereby lowering control costs. Finally, we present two numerical examples to validate the feasibility of the proposed methods.

Credit risk: A new privacy-preserving decentralized credit assessment model

To facilitate the collaborative modelling of privacy-preserving credit assessments under multi-party data sharing, this study suggests a privacy-preserving method for the collaborative modelling of linear regression credit assessments that is based on random invertible matrix transformation. The results indicate that the proposed method is capable of effectively protecting the original data information from being compromised during the collaborative modelling process. Besides, our model can integrate multidimensional data to alter the data distribution by employing random invertible matrix transformation. Finally, our collaborative modelling method can incorporate multi-party data to train a linear regression credit assessment model while maintaining data privacy.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Sparse Flow Sensor Placement Optimization for Flight-by-Feel Control of 2D Airfoils

This research introduces the Sparse Sensor Placement Optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement Optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10° and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond.

Infrared and visible image fusion method based on target enhancement and rat swarm optimization

In order to solve the target ambiguity and information loss in the fusion results of traditional infrared and visible images, a fusion method of infrared and visible images based on the target enhancement and mouse swarm optimization, which is abbreviated as TERSFuse. Firstly, in order to reduce the loss of the original image details in the fusion results, the infrared contrast enhancement module and the visible image enhancement module based on the brightness perception are constructed respectively. Secondly, the infrared and visible enhanced images were decomposed by using the Laplace pyramid transform to obtain the corresponding high and low frequency images. In order to make the fusion result fully retain the original information, the "maximum absolute value" rule is used to fuse the infrared and visible high frequency images, and the low frequency images are fused by calculating the weight coefficient. Finally, the image reconstruction module based on the rat swarm optimization is designed to achieve the adaptive allocation of weight parameters of high frequency and low frequency image reconstruction, and then improve the visual effect of the fused image. In order to verify the advantages of the present algorithm, the experimental results show that the present algorithm not only obtains the good visual effects, but also can retains the rich edge texture and contrast information of the original image.

A global comparative analysis of guidelines for herbal medicines Pharmacopoeia and Good Manufacturing Practice, with a case study of Panax ginseng C.A Meyer

IntroductionThe use of herbal medicines is widespread, and ensuring their quality control, as guaranteed by pharmacopoeia and Good Manufacturing Practice (GMP) guidelines, has garnered significant attention. Discrepancies in quality standards across different regulatory agencies challenge the global marketing of herbal medicines and hinder public access to these products. Therefore, this research aims to compare pharmacopeia on Panax ginseng C.A. Meyer (P. ginseng) and GMP guidelines for herbal medicines to provide an evidence-based understanding of quality control standards. MethodsDefinitions of herbal medicines across regulatory authorities, including the World Health Organization (WHO), Pharmaceutical Inspection Co-operation Scheme (PIC/S), European Union (EU), China, Japan, South Korea, and Vietnam, were collected. Additionally, pharmacopeial standards on Panax ginseng case study were analysed, focusing on aspects such as origin, identification, testing methods, extractives, assay, purity, and drug information. The study also compared GMP requirements for herbal medicines, which included personnel, premises and equipment, documentation, production, quality control, contract manufacturing and analysis, complaints and product recall, and self-inspection. ResultsDefinitions of herbal medicines are similar among the WHO, PIC/S, and the EU, while distinct definitions based on scientific evidence and traditional practices were presented in China, Japan, South Korea, and Vietnam. Pharmacopeia standards varied across regulatory agencies, as evidenced by the P. ginseng case study. However, testing methods closely resembled each other between China and Vietnam, as well as between Japan and South Korea. Among pharmacopoeias, the WHO monographs offered the most comprehensive drug information on P. ginseng. In contrast, the European Pharmacopoeia lists such details separately in a P. ginseng assessment report. Regarding GMP requirements, although examined components varied among regulatory agencies, significant resemblances exist between the GMP guidelines of PIC/S, the EU, and South Korea, and between those of the WHO and Vietnam. ConclusionsDisparities in quality standards across jurisdictions highlight the necessity for international collaboration to achieve harmonization. Harmonizing these standards aims to promote the global availability of standardized herbal medicinal products, benefiting public health.

Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design

Recent advancements in mobile neural networks, such as the squeeze-and-excitation (SE) attention mechanism, have significantly improved model performance. However, they often overlook the crucial interaction between location information and channels. The interaction of multiple dimensions in feature engineering is of paramount importance for achieving high-quality results. The Transformer model and its successors, such as Mamba and Vision Mamba, have effectively combined features and linked location information. This approach has transitioned from NLP (natural language processing) to CV (computer vision). This paper introduces a novel attention mechanism for mobile neural networks inspired by the structure of Vim (Vision Mamba). It adopts a “1 + 3” architecture to embed multi-dimensional information into channel attention, termed ”Multi-Dimensional Vim-like Attention Mechanism”. The proposed method splits the input into two major branches: the left branch retains the original information for subsequent feature screening, while the right branch divides the channel attention into three one-dimensional feature encoding processes. These processes aggregate features along one channel direction and two spatial directions, simultaneously capturing remote dependencies and preserving precise location information. The resulting feature maps are then combined with the left branch to produce direction-aware, location-sensitive, and channel-aware attention maps. The multi-dimensional Vim-like attention module is simple and can be seamlessly integrated into classical mobile neural networks such as MobileNetV2 and ShuffleNetV2 with minimal computational overhead. Experimental results demonstrate that this attention module adapts well to mobile neural networks with a low parameter count, delivering excellent performance on the CIFAR-100 and MS COCO datasets.

How does information competition affect new product diffusion? Insights from computational experiments

This study conducts extensive computational experiments to analyze the effects of two factors governing information competition on diffusion outcomes and compares the performance of a decision rule (DR)-inspired seeding strategy with several traditional seeding strategies. The results show that as the probability of posting original information increases, the net present value (NPV) of diffusion shows an inverted U-shaped trend, as does the ratio of the NPV of the seeding strategy to that of no seeding strategy (NPVR). Second, as the average number of reposts per consumer increases, the NPV and NPVR for seeding strategies increase, but the growth rate decreases. Finally, DR strategies outperform traditional strategies. Based on these insights, we offer suggestions for social marketing campaigns that focus on selecting an appropriate information environment.

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.