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In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Municipal papers and polyethylene terephthalate (PET) wastes are primary constituents of municipal solid waste (MSW) which necessitates effective disposal. Co-pyrolysis is an efficient transformation technology for the production of valuable gas and liquid fuels. Therefore, co-pyrolysis characteristics, kinetics, gas and liquid products of cellulose and PET mixtures were investigated in a two-stage reactor by multiple characterizations coupling with deep learning approach. The results indicated that there exhibited positive interaction for cellulose and PET co-pyrolysis in terms of mass difference (δ m) and mass loss rate difference (δ r) between experimental and calculated values. The thermal decomposition process and synergistic effects (δ m and δ r) were accurately predicted by deep learning approaches (R 2 =0.9823–0.9993) using temperature, catalyst, mixing ratio and heating rate variables. Feature importance revealed that the temperature, catalyst, heating rate and mixing ratio were in the descending order to affect the remaining mass from TG curves. And the temperature played a predominant role in synergistic effects. The Flynn-Wall-Ozawa (FWO) method was more suitable for fitting the pyrolysis process to obtain activation energy (E a) with higher R 2 (0.94714–0.99640) compared with Kissinger-Akahira-Sunose (KAS) method. When the mixing ratio of cellulose and PET was 1:1, the E a was significantly reduced to 80.73 KJ/mol, and further reduced to 73.04 KJ/mol by introducing the catalyst, as compared to E a of individual cellulose (178.95 KJ/mol) and PET (198.34 KJ/mol). Co-pyrolysis of cellulose and PET led to a reduction of carbohydrates, furans, alcohols, ketones, and phenols, while the content of acids was remarkably increased which was consistent with the observation in FTIR that the absorption peak of C＝O became sharp and strong. The introduction of ZSM-11 resulted in an increase in aromatics and a decrease in acids content owing to aromatization and deoxygenation. This work is expected to offer beneficial guidance for the efficient disposal of MSW and the application of deep learning methods. [Display omitted] • The interactive pathway for catalytic co-pyrolysis of cellulose and PET was proposed. • The activation energy reached the lowest 73.04 KJ/mol when the mixing ratio was 1:1. • Optimized convolutional neural network predicted synergistic effects with R 2 > 0.99. • Feature importance for TG was temperature > catalyst > heating rate > mixing ratio. [ABSTRACT FROM AUTHOR]

3D printed ceramic parts often have defects due to their inherent brittleness. These defects are of various scale sizes. Detecting these defects, especially with multiple sizes, is a challenging task in the field of detection. Furthermore, there is a lack of surface defect real-time detection systems suitable for industrial applications in this field. Based on this, this paper makes two contributions: the design and development of a real-time defect detection system, and the proposal of a multiscale defect detection method for 3D printed ceramic surfaces within this system. Firstly, the paper establishes the overall structure of the real-time detection system for surface defects on 3D printed ceramic components, and describes the hardware and software components of the system. On this basis, a dataset of ceramic surface defect images is collected and constructed. Then, experimental analyses point out the shortcomings of the You Only Look Once version-5 (YOLOv5) model for multiscale defect detection. To address the shortcomings, the YOLOv5 model is optimized from three aspects, resulting in the Deep separable convolution + residual network-SKNetwork-Efficient Channel Attention Network-YOLOv5 (DepRes-SK-ECA-YOLOv5) model for multiscale defect detection. This model improves the ability to extract and fuse features of defects at different scales. The experimental results show that the DepRes-SK-ECA-YOLOv5 model can achieve 93.5 %, 91.6 %, 94.3 %, and 0.198 s for Precision, Recall, mAP, and Speed for the test set, respectively. Finally, the paper designs the workflow for the system software. The system hardware and system software are integrated to form a real-time detection system. The performance of the detection system is verified through experiments. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1 % range-normalised and 1 deg, respectively, an established rule of thumb for the navigation measurement accuracy during final approach. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure. • A novel AI-based solution for spacecraft docking and refuelling is presented. • First demonstration of deep learning for vision-based pose estimation in docking. • Various CNN backbones are benchmarked on photorealistic ISS refuelling datasets. • ResNet-101 reports average 1.19% error in position, 0.29 ∘ in attitude per trajectory. • Prototypes of the developed mechanism are validated in lab with 7-DOF manipulator. [ABSTRACT FROM AUTHOR]

Feature extraction and matching play integral roles in autonomous vision navigation. As a typical morphological feature, craters can be used as navigation landmarks. However, the spin of small bodies introduces substantial variations in illumination and viewing angles within acquired images, thereby posing challenges to feature extraction and matching algorithms. Addressing these challenges, a deep learning-based approach emerges as pivotal in mitigating the limitations of existing navigation algorithms. This paper presents an algorithm designed to segment noteworthy craters and irregular regions on such small bodies, thereby furnishing crucial autonomous navigation data for missions involving uncharted terrains. By seamlessly integrating deep learning techniques with conventional computer vision-based feature description methods, the algorithm achieves matching segmentation results of noteworthy precision. The experimental results show that the algorithm has better results compared to the existing mainstream segmentation networks. And it can achieve the accurate matching of segmentation results. • Accurate segmentation of small-body craters via deep-learning Algorithms. • Gradient descriptor construction method based on segmentation results. • The proposed method has viewpoint, illumination, scale, and rotation invariance. [ABSTRACT FROM AUTHOR]

The large number of near-Earth asteroids (NEAs) has greatly impacted human space activities and Earth security. However, detecting NEAs in astronomical images with complex, varying backgrounds is still extremely challenging. In this paper, we propose a deep segmentation assisted asteroid tracking algorithm, termed DSAT, to construct a possible pipeline for faint NEA tracking in astronomical images. First, the single-frame object detection problem is converted to a segmentation problem, enabling robust extraction of faint potential moving objects. Then, a multiframe motion prior-based moving object tracking algorithm is proposed to find real NEAs. We further propose a distance tolerance criterion to help DSAT achieve effective tracking in practical situations when detection has partially failed. Finally, the pipeline is tested with both simulated and real astronomical images at different SNRs and in crowded fields. The results showed that our pipeline has the potential to detect and track faint NEAs in complex backgrounds. Our code is publicly available at https://github.com/zhenhongdu/DeepSegAsteroidTracker. [ABSTRACT FROM AUTHOR]

The paper explores the tensile behaviour of plain weave ceramic matrix composites (CMCs) by employing a nonlinear constitutive model for fibre tow composites, driven by in-situ X-ray computed tomography data. Through the application of a deep-learning-based image segmentation method, the study successfully identified material damage modes, including matrix cracks, fibre pull-out, and interface debonding. Three-dimensional characterisation and quantitative analyses of damage have been conducted. At the mesoscopic level, a theoretically derived nonlinear constitutive model for fibre tow composites, accounting for continuous damage to both fibre and matrix, is established by using damage data. At the macroscopic level, a unit cell model is constructed for the numerical simulation of plain weave CMCs. Material's initial defects are prone to induce crack initiation. External matrix cracks can propagate to tows, and crack deflection may occur during this process. Furthermore, adjacent microcracks have the potential to connect, forming a larger one under high stress. Theoretical results predicted by the nonlinear constitutive model for fibre tow composites under tension exhibit good agreement with test data. The built unit cell model accurately predicts the tensile mechanical behaviour of plaine weave CMCs. [ABSTRACT FROM AUTHOR]

• Proposing physics-informed DCNN for HFI-SCB temperature field reconstruction. • Proposing Deep MC-QR method to quantify data uncertainty caused by noise. • Proposing HFI-SCB reliability analysis method based on interval multilevel BN. Temperature field reconstruction is essential for analyzing the reliability of a high-density functionally integrated satellite circuit board (HFI-SCB). As a representative deep learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the HFI-SCB temperature field. However, DCNN needs a lot of labeled data to learn its parameters, which is contrary to the fact that actual satellite engineering can only acquire noisy unlabeled data. Thus, this paper proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing the HFI-SCB temperature field and quantifying the data uncertainty caused by sensor noise. The proposed method combines a DCNN with known physics knowledge to reconstruct an accurate HFI-SCB temperature field using only monitoring point temperatures. Besides, the proposed method can quantify the data uncertainty by the Monte Carlo quantile regression. Based on the reconstructed temperature field and the quantified data uncertainty, this paper builds an interval multilevel Bayesian Network to analyze the HFI-SCB reliability. Two case studies are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

• Transformer-based multitask network is designed for spacecraft pose estimation. • Matrix Fisher & Gaussian distributions are utilized for the pose uncertainty. • The robust neural network-based network vision navigation pipeline is created. Spacecraft pose estimation (SPE) plays a vital role in the relative navigation system for on-orbit servicing and active debris removal. Current deep learning-based methods have made great achievements on object pose estimation. However, towards the challenging onboard SPE missions, most existing Convolutional Neural Network (CNN) methods failed to capture remote vision attention, leading to the reduction of accuracy and robustness. In this paper, we presented an end-to-end multi-task Pyramid Transformer SPE network (PVSPE) consisting of two novel feature extraction modules: EnhancedPVT (EnPVT) and SlimGFPN. The EnPVT module is designed to combine global spatial and channel attention, while the Slim GFPN module can fuse features more effectively. Matrix Fisher and multivariate Gaussian distributions are further employed to model the uncertainty of pose regression to increase its accuracy. Extensive experiments are carried out on challenging SPEED + and SHIRT datasets, to validate the performances on pose estimation and vision-based navigation, respectively. The results show that the proposed PVSPE model achieved high accuracy for SPE on the SPEED + dataset even under different scales and severe illumination, demonstrating its robustness and high generalization. Leveraging the insightful uncertainty model of PVSPE, the vision-based navigation pipeline, combined with Kalman filters, accurately estimated the satellite pose under challenging rendezvous scenarios on the SHIRT dataset, with degree-level attitude errors and centimeter-level translation accuracy at steady-state. [ABSTRACT FROM AUTHOR]

The utilization of deep learning methodologies for addressing partial differential equations (PDEs) has garnered significant attention in recent years. This paper introduces an improved network structure tailored for the discontinuity-capturing, enabling the resolution of interface problem through a unified neural network framework. Employing the probability space filling argument, we show that our model can generate convergent sequences, where the convergence rate depends on the number of sampling points. Several numerical experiments with regular and irregular interfaces are conducted to elucidate the convergence characteristics, thereby validating the theoretical assertions. Furthermore, we apply our approach to effectively solve the size-modified Poisson-Boltzmann test model, utilizing it for predicting electrostatics and the solvation free energies for proteins immersed in ionic solvents, thus showcasing practical applications of our method. • Improved multi-scale fusion network uses only a single neural network. • Our network model is proposed to solve the elliptical interface problem. • Theoretically give a least convergence rate of our numerical method. • Numerical experiments agree with our theoretical convergence rate. • We calculate the solvation free energies of immersed biomolecules by our method. [ABSTRACT FROM AUTHOR]

Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements. • CNN-LSTM and CNN models employed for accurate gust identification. • Methods rigorously applied to both numerical and experimental data. • Utilizing aeroelastic aircraft models with varied fidelity for identification • Identified gusts compared with those from gust generator characterization. • Results showcase significant capability in accurate gust prediction. [ABSTRACT FROM AUTHOR]

• A solar image captioning method that applies a transformer-based deep learning natural language processing is proposed. • A DeepSDO description dataset for training solar image captioning models including nine solar events is provided. • The experimental results show that the proposed method outperforms other benchmark methods in terms of four evaluation metrics. Solar images are essential for identifying and predicting solar phenomena, and have been used as key information for analyzing space weather. In this paper, we propose a solar image captioning method that applies a transformer-based deep learning (DL) natural language processing method. In addition, we provide a new DeepSDO description dataset for training solar image captioning models. First, we develop the DeepSDO description dataset using solar image data from Korean Data Center for solar dynamics observatory (SDO) and scripts from the National Aeronautics and Space Administration (NASA) SDO gallery website. The DeepSDO description dataset includes nine solar events: sunspots, flares, prominences, prominent eruptions, coronal holes, coronal loops, filaments, active regions, and eclipses. Second, we train the DL-based image captioning model, the meshed-memory transformer, using the DeepSDO description dataset. The experimental results show that the proposed method outperforms other benchmark methods in terms of four evaluation metrics. This study demonstrates that DL-based image captioning can successfully generate solar image captions for multiple solar features, and could potentially be used in other themes of solar physics and space weather. [ABSTRACT FROM AUTHOR]

The impact crater is the most dominant geomorphic structural units over the planetary surface and its accurate extraction is of great significance to investigate the evolution of the Moon, the impact history of the solar system and the space environment. Through the impact crater's identification, especially small-scale, and their spatial distribution characteristics, the impact flux, morphological characteristics, age of impact craters and surface degradation can be revealed. Therefore, this paper presents an automatic method for the impact crater detection, especially small-scale ones, using anchor-free deep learning. Due to the limitation of traditional object proposals for detecting different types of impact craters, we retrain the anchor-free CenterNet model using a transfer learning strategy, where the detection task of impact crater is formulated into the detection of its center point and regression of its property (i.e., size) without non-maximal suppression. We select the stacked Hourglass network as backbone to aggregate different levels of feature for enhancing the capability of estimating the impact crater centers. Moreover, we find the center points of impact craters on the image features' heatmap only based on their locations, instead of box overlap, which allows us to detect different types of impact craters, even the impact craters that contain other impact craters. The model is trained in an end-to-end manner and applied to detect the impact craters on the lunar images between ±50° of latitude, with the spatial resolution of 100 m/pixel, from Wide Angle Camera (WAC) onboard Lunar Reconnaissance Orbiter (LRO) mission. The obtained results are compared relative to an existing crater database both qualitatively and quantitatively that suggests the reliability and robustness of the developed method in the automatic detection of small-scale impact craters, where the smallest one is 500 m. Moreover, the developed method is capable to detect different types of impact craters, including dispersal and connective ones, with the recall of 73.66% and precision of 78.27% compared with an existing crater database. The code is publicly available at https://github.com/ShuoweiZhang/crater_detection. [ABSTRACT FROM AUTHOR]

In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images. [Display omitted] • Structural reparameterization dehazenet: outstanding performance, faster speed. • Replacing Tanh with ReLU leads to better results. • Transfer learning addresses the problem of insufficient samples. • Dual subnets method proves highly effective in datasets of different scales. [ABSTRACT FROM AUTHOR]

The paper presents a novel solution to the issue of incomplete regions in 3D meshes obtained through digitization. Traditional methods for estimating the surface of missing geometry and topology often yield unrealistic outcomes for intricate surfaces. To overcome this limitation, the paper proposes a neural network-based approach that generates points in areas where geometric information is lacking. The method employs 2D inpainting techniques on color images obtained from the original mesh parameterization and curvature values. The network used in this approach can reconstruct the curvature image, which then serves as a reference for generating a polygonal surface that closely resembles the predicted one. The paper's experiments show that the proposed method effectively fills complex holes in 3D surfaces with a high degree of naturalness and detail. This paper improves the previous work in terms of a more in-depth explanation of the different stages of the approach as well as an extended results section with exhaustive experiments. [Display omitted] • We perform 3D surface reconstructions using generative inpainting techniques. • 2D representation of a 3D surface geometry based on its curvature. • Application of a general purpose neural network for inpainting. • Our approach does not require dataset nor training time. • Results outperform state-of-the-art quality and naturalness of the reconstructions. [ABSTRACT FROM AUTHOR]

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S 3 FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer's input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net. • A subspace similar recombination module is proposed. • The selectable self-attention spectral feature extraction module is designed. • The multi-stream Gist spatial feature extraction module is introduced. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

• Review of papers on the diagnosis of brain diseases using the fusion of neuroimaging modalities is presented. • For each brain disease, the most appropriate neuroimaging modalities and best combinations are introduced. • Advantages and disadvantages of fusion levels and different modalities are introduced. • Details of various types of deep neural networks used for the task at hand are also examined. • Finally, future directions and challenges in the field are discussed. Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer complementary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep Learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. Moreover, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging modalities based on MRI and DL models are reported. In the following, the most important future directions include Datasets, DA, imbalanced data, DL models, explainable AI, and hardware resources are presented. Finally, the main findings of this study are presented in the conclusion section. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]

In recent years, many image denoising methods have been proposed based on convolutional neural networks (CNNs). While these methods have shown continuous performance improvement by introducing various mechanisms and structures, their computational cost tends to become increasingly expensive, owing to the resulting complex network architectures. This paper aims at winning the trade-off between computational efficiency and denoising performance for CNN-based image denoisers. Towards this end, we draw inspirations from traditional variational models with wavelet analysis operators for CNN architecture design. A model-inspired CNN is proposed with four key modules: iterative encoding-decoding units inspired by the iterative denoising process, directional convolutions inspired by the separable wavelet filters, inception modules inspired by the multi-scale analysis of wavelets, and stage-wise connections inspired by the adding noise back operation. In experiments, our CNN shows high computational efficiency in both training and test, with competitive results to state-of-the-art approaches. • A neural network inspired by the key ingredients of wavelet analysis model. • A sequential encoding-decoding network for image denoising. • A deep image denoiser well balancing effectiveness and computational efficiency. [ABSTRACT FROM AUTHOR]

• The deep learning model can better extract the deeper features of the multi-point deformation sequence. • MVMD algorithm reduces the complexity of multi-point displacement sequence. • The model considers the spatiotemporal characteristics of dam deformation, the prediction accuracy is obviously improved. It is difficult for single measuring point model to synchronously characterize the spatial correlation of deformation sequence of concrete dam and the similarity of displacements of different measuring points. Considering the limitation of conventional model in dealing with spatial-temporal data structure, In this study, a deep learning technique for multi-point and multi-output deformation prediction of concrete dams is proposed, which is based on Multivariate Variational Mode Decomposition (MVMD) and three-dimensional convolutional neural network (3D-CNN) combined model. Firstly, the multi-point sequence is decomposed into several relatively stable sub-sequences with different frequency scales by MVMD, which reduces the complexity of the multi-point displacement sequence. Then, In order to enhance the model 's ability to fit the time and spatial features of the sequence, the 3D-CNN model is used to predict the decomposed components and reconstruct the displacement data of the measuring points. This study focuses on the principle, process and calculation steps of the proposed model, and verifies the proposed model through the deformation data of three typical measuring points. The test results show that the prediction model proposed in this paper is an effective method for dam deformation prediction. [ABSTRACT FROM AUTHOR]

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions. • Saliency maps identified histomorphological features characterizing cancer. • VGG16 model utilized all the structures that are observable by the pathologist. • The method can identify standard patterns not used by the model. • The method can also identify new patterns not yet used by human pathologists. [ABSTRACT FROM AUTHOR]

The ionospheric foF2 prediction model GWO-ALSTM is proposed in this paper, which combines the Gray Wolf Optimization (GWO) algorithm and the attention mechanism based on the long short-term memory (LSTM) model. The datasets used in this model were obtained from the oblique ionosondes of the China Ground-based Seismo-ionospheric Monitoring Network. The data from 2010 to 2012 was used for training, and the data of 2013 was used for verification. The input parameters of the model include local time, day number, sunspot number, F10.7 solar flux, Ap index, Dst index, and foF2 at the previous moment, and the output parameter is foF2 at the current moment. The IRI2016 model, GABP model, LSTM model and GWO-ALSTM model were compared with the observed results for analysis. The results show that the GWO-ALSTM model is superior to the LSTM model, the GABP model and the IRI2016 model. Meanwhile, the comparative analyses of the diurnal variations of foF2 show that the curve of the GWO-ALSTM model fits the observed values more closely than the curve of any other model under the geomagnetic quiet and storm conditions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the forward problems on the data-driven rational solitons for the (2+1)-dimensional Kadomtsev–Petviashvili-I (KP-I) equation and spin-nonlinear Schrödinger (spin-NLS) equation via the deep neural networks learning. Moreover, the inverse problems of the (2+1)-dimensional KP-I equation and spin-NLS equation are studied via deep learning. The main idea of the data-driven forward and inverse problems is to use the deep neural networks with the activation function to approximate the solutions of the considered (2+1)-dimensional nonlinear wave equations by optimizing the chosen loss functions related to the considered nonlinear wave equations. • Forward problems of 2D KP-I and spin-NLS equations with rational solitons are studied via deep learning. • Inverse problems of 2D KP-I and spin-NLS equations are discussed via deep learning. • Inverse problems of the 2D combination of NLS and cmKdV equations are explored via deep learning. [ABSTRACT FROM AUTHOR]

The research topic of estimating hand pose from the images of hand-object interaction has the potential for replicating natural hand behavior in many practical applications of virtual reality and robotics. However, the intricacy of hand-object interaction combined with mutual occlusion, and the need for physical plausibility, brings many challenges to the problem. This paper provides a comprehensive survey of the state-of-the-art deep learning-based approaches for estimating hand pose (joint and shape) in the context of hand-object interaction. We discuss various deep learning-based approaches to image-based hand tracking, including hand joint and shape estimation. In addition, we review the hand-object interaction dataset benchmarks that are well-utilized in hand joint and shape estimation methods. Deep learning has emerged as a powerful technique for solving many problems including hand pose estimation. While we cover extensive research in the field, we discuss the remaining challenges leading to future research directions. [Display omitted] • Deep learning is effectively used for estimating hand pose from images. • The correlation between a hand and an object helps in estimating hand-object pose. • Hand model helps estimate hand shape, but it restricts within the model's prior. • Implicit function methods have emerged in hand-object pose estimation. [ABSTRACT FROM AUTHOR]

Increasingly complex 3D CAD models are essential during different life-cycle stages of modern engineering projects. Even though these models contain several repeated geometries, instancing information is often not available, resulting in increased requirements for storage, transmission, and rendering. Previous research have successfully applied shape matching techniques to identify repeated geometries and thus reduce memory requirements and improve rendering performance. However, these approaches require consistent vertex topology, prior knowledge about the scene, and/or the laborious creation of labeled datasets. In this paper, we present an unsupervised deep-learning method that overcomes these limitations and is capable of identifying repeated geometries and computing their instancing transformations. The method also guarantees a maximum visual error and preserves intrinsic characteristics of surfaces. Results on real-world 3D CAD models demonstrate the effectiveness of our approach: the datasets are reduced by up to 83.93% in size. Our approach achieves better results than previous work that does not rely on supervised learning. The proposed method is applicable to any kind of 3D scene and geometry. [Display omitted] • Unsupervised deep learning method for 3D shape registration. • Does not require any previous knowledge of the 3D geometries. • Does not require a labeled dataset for any supervised training. • Guarantees an upper bound on any visual errors. • Generalizes for any 3D scene and geometry. [ABSTRACT FROM AUTHOR]

Multi-View Stereo (MVS) is a crucial technique for reconstructing the geometric structure of a scene, given the known camera parameters. Previous deep learning-based MVS methods have mainly focused on improving the reconstruction quality but overlooked the running efficiency during the actual algorithm deployment. For example, deformable convolutions have been introduced to improve the accuracy of the reconstruction results further, however, its inability for parallel optimization caused low inference speed. In this paper, we propose AdaptMVSNet which is device-friendly and reconstruction-efficient, while preserving the original results. To this end, adaptive convolution is introduced to significantly improve the efficiency in speed and metrics compared to current methods. In addition, an attention fusion module is proposed to blend features from adaptive convolution and the feature pyramid network. Our experiments demonstrate that our proposed approach achieves state-of-the-art performance and is almost 2 × faster than the recent fastest MVS method. We will release our source code. [Display omitted] [ABSTRACT FROM AUTHOR]

Raw point clouds captured by sensing devices are often contaminated with noise, which perturbs the fidelity of the original geometric information. Point cloud denoising is therefore an inseparable post-processing step, aiming to remove the noise in the point clouds. Existing point cloud denoising approaches are typically trained on datasets that have uniform point distributions and densities, making them unsuitable for effectively denoising point clouds with severe noise or irregular point distributions. In this paper, we introduce a novel random screening-based feature aggregation method for point cloud denoising. Our key insight is that merging features of dense and sparse points assists with enhancing the quality of point cloud denoising results. In specific, our approach involves randomly screening the features of local point patches and fusing richer geometric information of denser points into sparser point representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance in the point cloud denoising task on both synthetic and real-world datasets. [Display omitted] • Random screening-based point feature aggregation is useful for point cloud denoising. • Merging features of dense points into sparser points boosts point cloud denoising. • Our pipeline demonstrates robustness to noise and saves processing time. • Our technique achieves impressive results on severe noise and irregular points. [ABSTRACT FROM AUTHOR]

Images taken under poor lighting conditions generally present problems such as low brightness, noise, and color distortion, so dealing with low-light images is challenging. To solve this problem, current mainstream methods focus on designing special deep networks. For example, many methods are based on Retinex-based models. However, the solution of the Retinex model is an ill-conditioned problem in mathematics, so these methods are difficult to obtain the expected result. In this paper, following the new paradigm of embedding prior knowledge in deep learning, we design a simple yet surprisingly effective neural network, called Multi-Resolution Edge-aware Lighting Enhancement Network (MELENet). Since resolution and acutance are two of the key factors related to the visual quality of natural images, MELENet with a dual-branch structure aims to incorporate multi-resolution edge information into deep learning for guiding lighting enhancement. Additionally, by adopting special loss functions, the model suppresses noise and prevents color distortion while obtaining sound lighting enhancement effects. Experiments have verified that such a simple solution exhibits outstanding performance compared to existing mainstream lighting enhancement methods. We also directly apply the proposed model to image de-hazing and obtain state-of-the-art results. [Display omitted] • The proposed algorithm focuses on embedding prior knowledge into the network to assist the low-light image enhancement. • An effective loss function is designed to balance the color distortion and noise suppression. • Our model can be directly applied to other image processing tasks such as image de-hazing and gain competitive results. [ABSTRACT FROM AUTHOR]

Shadow detection is a common computer vision task that identifies the location of shadows. It has been used as a key primitive for many high-level tasks such as object segmentation and video tracking. Due to the complexity and variety of shadows, it is challenging to design a one-size-fits-all shadow detection algorithm in real-world scenarios. Instead, it is often necessary in practice to pick the best shadow detection algorithm for a particular vision application. While shadow detection algorithms can be assessed by comparing the detected shadow with the ground truth, such ground truth is often unavailable in real-world vision systems consistently generating new data. In this paper, we propose a no-reference shadow detection quality assessment network (NSDQA-Net) to rank shadow detection algorithms without needing the ground truth. For a given image, the proposed assessment network first learns a reference map that compensates the false positives and false negatives of multiple input shadow detection algorithms. The reference map, as well as the original image and the input shadow detection results, are then used to regress the quality scores of the shadow detection methods. Our framework supports the assessment of one, two, or multiple algorithms in a single run, making it easier for deployment in practical scenarios. Experiments results show that the proposed network achieves optimal accuracy in no-reference shadow detection assessment. Relying on it for adaptive algorithm selection also improves the performance of shadow detection using a fixed algorithm on benchmark datasets. [Display omitted] • We identify a task of no-reference quality assessment in shadow detection. • We design a novel no-reference shadow detection quality assessment network (NSDQA-Net). • We experimentally demonstrate the state-of-the-art performance of the proposed network. [ABSTRACT FROM AUTHOR]

Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications. • Deep Learning is used extensively for monocular spacecraft pose estimation. • Deployability is an important limitation of the current DL-based solutions. • Lack of space-borne images during training leads to the domain gap problem. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness. • The first combination of twin support vector machines (TSVM) and deep neural networks. • Nesting of more advanced auto encoder networks. • Incorporating multi-view learning capabilities. • Mutual negotiation and simultaneous learning between deep neural networks and TSVM. • The experiments prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

• Semantic-based segmentation (Semseg) methods are presented in deep learning architecture. • A comparative study is presented for reviewed paper based on performance. • Existing challenges, problems in DL-based semantic segmentation approaches are discussed. • The challenges of Semseg applications are mentioned for other researchers. • Prospect for future work in this area for stable medical image segmentation. Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors present perspectives on the development of an architectural, and operational mechanism of each machine learning-based semantic segmentation approach with merits and demerits. In this regard, researchers have proposed different Semseg methods and examined their performance in a variety of applications such as medical image analysis (e.g., medical image classification and segmentation). A review of recent advances in Semseg techniques are presented in this paper by applying computational image processing and machine learning methods. This article is further presented a comprehensive investigation on how different architectures are helpful for medical image segmentation. Finally, advantages, open challenges, and possible future directions are elaborated in the discussion part, beneficial to the research community to understand the significance of the available medical imaging segmentation technology based on Semseg and thus deliver robust segmentation solutions. Fig. 3: An overview of a latest trend to show the methodological evolution in the development of semantic-based segmentation models for medical images [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of capturing both the shape and the pose of a human character using a single depth sensor. Some previous works proposed to fit a parametric generic human template into the depth image, while others developed deep learning (DL) approaches to find the correspondence between depth pixels and vertices of the template. We designed a hybrid approach, combining the advantages of both methods, and conducted extensive experiments on the SURREAL Varol et al. (2017), DFAUST datasets Bogo etal (2017) and a subset of AMASS Mahmood et al. (2019). Results show that this hybrid approach enables us to enhance pose and shape estimation compared to using DL or model fitting separately. We also evaluated the ability of the DL-based dense correspondence method to segment also the background — not only the body parts. We also evaluated 4 different methods to perform the model fitting based on a dense correspondence, where the number of available 3D points differs from the number of corresponding template vertices. These two results enabled us to better understand how to combine DL and model fitting, and the potential limits of this approach to deal with real-depth images. Future works could explore the potential of taking temporal information into account, which has proven to increase the accuracy of pose and shape reconstruction based on a unique depth or RGB image. [Display omitted] • Dense correspondence and model fitting enhance 3D shape and pose reconstruction. • Bodypart and background color embedding enables character segmentation in depth map. • Averaging 3D candidate points before model fitting offers the best reconstruction. [ABSTRACT FROM AUTHOR]

This paper explores the potential of self-supervised learning as an alternative to supervised learning in the context of geometry-based 3D object retrieval. With the ongoing digitalization of many industries, an exponentially increasing number of 3D objects are processed by retrieval systems. In order to support new shapes, modern deep learning-based retrieval systems require retraining. The dominant paradigm for optimizing neural networks in this field is supervised classification training. Supervised learning requires time-consuming and expensive data annotation. Moreover, training neural networks for classification introduces a bias towards the classes in the training data, which is undesirable for retrieval systems encountering unseen object types in the wild. Through extensive experiments, we make a direct comparison between supervised and self-supervised learning on four datasets from three different domains (household, manufacturing and medical). For object classes seen during training, self-supervised and supervised learning are competitive. For unseen classes, self-supervised learning outperforms supervised learning in many cases. We conclude that self-supervised learning provides a powerful tool for circumventing labeling costs and providing more robust retrieval systems. [Display omitted] • Self-supervised learning is on par with supervised learning for 3D object retrieval. • Self-supervised object retrieval outperforms supervised learning out-of-distribution. • Object alignment is highly impactful on the performance of retrieval systems. • Non-contrastive siamese networks are a practical option in 3D deep learning. [ABSTRACT FROM AUTHOR]

• The 6.polynomial chaos expansion is extended to high dimensional scenarios by a novel modeling method. • A data driven method is implemented for computing the orthogonal polynomial bases within PCE layer. • High dimensional uncertainty propagation problems involving correlated input variables can be solved. • Several numerical examples are analyzed for validating the effectiveness of the proposed method. In this paper, deep kernel polynomial chaos expansion (DKPCE) is proposed as a surrogate model for high dimensional uncertainty propagation. Firstly, deep neural network (DNN) and polynomial chaos expansion (PCE) are connected to create a novel network model, the input dimensionality of PCE layer can thus be controlled by restricting the number of neurons in the feature layer. Then, the back-propagation algorithm is employed for computing all the parameters of DKPCE, the dimension reduction and modeling process of DKPCE are thus executed simultaneously. During the modeling process, a data driven method is first implemented for computing the orthogonal polynomial bases within the PCE layer in the forward propagation step, and the partial derivatives for the coefficients of orthogonal polynomial bases are computed first in the back-propagation step. After constructing DKPCE, the coefficients of PCE layer can be utilized to compute the statistical characteristics of system response. Finally, several numerical examples are utilized for validating the effectiveness of DKPCE. [ABSTRACT FROM AUTHOR]

Production of crops is increasing day by day in agriculture sectors. The insecurity of food is a main reason of plant disease and is a main global issue that humans face these days. With the design of contemporary environmental agriculture, more focus is devised for yielding the crop and elevating its quality. The occurrence of crops has elevated in years and the kind of disease has become more and more complex. The disease in plants and the pernicious insects are the major risks in agriculture field. Thus, earlier discovery and treatment of this disease are imperative. The major design of Deep Learning (DL) model helped in detecting the plant disease and grants a dynamic tool with accurate results. This paper presents DL-assisted technique for detecting and classifying the tomato disease and used deep batch-normalized eLu Alex Net (DbneAlexnet) for classifying the tomato plant leaves. Initially, tomato plant leaf images are taken as an input from specific dataset represented and it is subjected to preprocessing phase to eliminate unwanted distortions using anisotropic filtering. Then, the segmentation is carried out using U-net, which is trained by Gradient-Golden search optimization (Gradient-GSO) Algorithm and it is incorporation of both Golden search optimization (GSO) and Gradient concept. Thereafter the segmented image is given to image augmentation process, where position augmentation and color augmentation are considered. Finally, the multiclass plant leaf disease is classified using DbneAlexnet and is trained using proposed Gradient Jaya- Golden search optimization (GJ-GSO). Here, the GJ-GSO is devised with the integration of Gradient concept, Jaya algorithm, and GSO algorithm. The proposed GJ-GSO-based DbneAlexnet outperformed highest accuracy of 92.4%, True positive rate (TPR) of 91.9%, True negative rate (TNR) of 92.2% and smallest False Positive Rate (FPR) of 0.078. Hence, the technique with unified segmentation and classification is effectual for identifying the plant disease and the empirical research verifies the benefits of the developed model. • DL-assisted technique is provided for classifying tomato plant leaf disease. • Pre-processing is done with anisotropic filter. • U-net with Gradient-Golden search optimization is used for segmentation. • Accuracy, TPR, and TNR of 92.4%, 91.9%, 92.2% and FPR of 0.078 by proposed model. • Provided effective performance with certain metrics. [ABSTRACT FROM AUTHOR]

• Intention inference problem is formulated as a time-series learning problem. • A deep learning-based model is proposed for intention inference. • The intention space set is established based on typical space relative motions. • Self-attention mechanism captures correlation in time-series to improve efficiency. • Our model has better performance and robustness than existing models. Intention inference for space non-cooperative targets is the key to space situational awareness and assistant decision for collision avoidance. Given that the problem of target intention inference is essential to learn the dynamically changing time-series characteristics of space non-cooperative target intentions and infer their relative motion patterns for threat warning, this paper adopts a deep learning-based approach, introduces a bidirectional propagation mechanism and self-attention mechanism based on Gated Recurrent Unit (GRU) and proposes a bidirectional Gated Recurrent Unit (BiGRU)-Self Attention-based space non-cooperative target intention inference model. BiGRU is used to learn deep information in time-series characteristics of the space non-cooperative target, and self-attention mechanism is used to adaptively extract and assign weights to key characteristics to capture the internal correlations in time-series information, thus improving model performance. The line-of-sight measurements are used as the characteristics of target intention inference, and the typical target motion intentions are defined. Subsequently, the proposed model is trained and tested on the test set, with the accuracy reaching 97.1%. Besides, the effectiveness and advantages of the proposed model are verified by the simulation of a case study and comparison evaluations. The results demonstrate that our proposed model could significantly improve the accuracy, computational efficiency, and noise resistance for the space non-cooperative target intention inference compared with the existing intention inference models. [ABSTRACT FROM AUTHOR]

During the last few years, many deep learning (DL) methods have been proposed for the classification of hyperspectral images (HSI), and the final results show that these models have great potential. However, most existing deep models only consider the original spectral HSI datacube, excluding other sources of information such as textural and geometrical features. In addition to their computational complexity, most DL approaches cannot effectively consider local nonlinear relationships between features. In order to address these issues, this paper develops a new DL approach called MLKD-CNN, which uses multifeature local kernel descriptors (MLKDs) and convolutional neural networks (CNN) to classify HSIs. This method produces local kernel descriptors (LKDs) of spectral, extended morphological profiles, and Gabor features in the feature generation step. A simple CNN is then used to classify MLKD generated from spatial and spectral features. The proposed MLKD considers local nonlinear relationships of both spectral and spectral features, making them capable of achieving high accuracy levels while reducing the computational burden of deep methods. The experiments on four benchmark HSIs show that the proposed MLKD-CNN outperforms some recent HSI classification methods in terms of accuracy and computation time. [ABSTRACT FROM AUTHOR]

Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While deep learning has led to great advancements in feature detection and description, training and validating data-driven models for space applications is challenging due to the limited availability of large-scale, annotated datasets. This paper introduces AstroVision, a large-scale dataset comprised of 115,970 densely annotated, real images of 16 different small bodies captured during past and ongoing missions. We leverage AstroVision to develop a set of standardized benchmarks and conduct an exhaustive evaluation of both handcrafted and data-driven feature detection and description methods. Next, we employ AstroVision for end-to-end training of a state-of-the-art, deep feature detection and description network and demonstrate improved performance on multiple benchmarks. The full benchmarking pipeline and the dataset will be made publicly available to facilitate the advancement of computer vision algorithms for space applications. • Missions to small bodies rely heavily on humans for optical feature tracking. • Deep Learning has led to great advancements in feature detection and description. • We present AstroVision, a first-of-a-kind dataset for vision-based tasks near small bodies. • We conduct an extensive evaluation of feature detection and description methods. • We demonstrate increased performance by training a deep feature network on our data. [ABSTRACT FROM AUTHOR]

Scene understanding has made tremendous progress over the past few years, as data acquisition systems are now providing an increasing amount of data of various modalities (point cloud, depth, RGB...). However, this improvement comes at a large cost on computation resources and data annotation requirements. To analyze geometric information and images jointly, many approaches rely on both a 2D loss and 3D loss, requiring not only 2D per pixel-labels but also 3D per-point labels. However, obtaining a 3D groundtruth is challenging, time-consuming and error-prone. In this paper, we show that image segmentation can benefit from 3D geometric information without requiring a 3D groundtruth, by training the geometric feature extraction and the 2D segmentation network jointly, in an end-to-end fashion, using only the 2D segmentation loss. Our method starts by extracting a map of 3D features directly from a provided point cloud by using a lightweight 3D neural network. The 3D feature map, merged with the RGB image, is then used as an input to a classical image segmentation network. Our method can be applied to many 2D segmentation networks, improving significantly their performance with only a marginal network weight increase and light input dataset requirements, since no 3D groundtruth is required. • Geometry. • Computer vision. • 2D segmentation. • Neural network. • multi-modalities. [ABSTRACT FROM AUTHOR]