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To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals' output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring. [ABSTRACT FROM AUTHOR]

In the quality inspection process of high-voltage cables, several commonly used indicators include cable length, insulation thickness, and the number of conductors within the core. Among these factors, the count of conductors holds particular significance as a key determinant of cable quality. Machine vision technology has found extensive application in automatically detecting the number of conductors in cross-sectional images of high-voltage cables. However, the presence of scratch-type defects in cut high-voltage cable cross-sections can significantly compromise the precision of conductor count detection. To address this problem, this paper introduces a novel improved total variation (TV) algorithm, marking the first-ever application of the TV algorithm in this domain. Considering the staircase effect, the direct use of the TV algorithm is prone to cause serious loss of image edge information. The proposed algorithm firstly introduces multimodal features to effectively mitigate the staircase effect. While eliminating scratch-type defects, the algorithm endeavors to preserve the original image's edge information, consequently yielding a noteworthy enhancement in detection accuracy. Furthermore, a dataset was curated, comprising images of cross-sections of high-voltage cables of varying sizes, each displaying an assortment of scratch-type defects. Experimental findings conclusively demonstrate the algorithm's exceptional efficiency in eradicating diverse scratch-type defects within high-voltage cable cross-sections. The average scratch elimination rate surpasses 90%, with an impressive 96.15% achieved on cable sample 4. A series of conducted ablation experiments in this paper substantiate a significant enhancement in cable image quality. Notably, the Edge Preservation Index (EPI) exhibits an improvement of approximately 20%, resulting in a substantial boost to conductor count detection accuracy, thus effectively enhancing the quality of high-voltage cable production. [ABSTRACT FROM AUTHOR]

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices. [ABSTRACT FROM AUTHOR]

Positioning technology is one of the key steps of automatic docking. At present, vehicle docking technology is widely used in airport baggage cars, towing trailers and other fields, and the docking process is mostly done manually. In order to realize the automatic docking of vehicles in mixed scenes, this paper proposes a fast positioning method for docking vehicles in mixed traffic scenes. First, using the regression relationship between the template size in the camera's field of view and the actual distance, FFT template matching and threshold judgment methods are employed to locate, track, and match the template, as well as verify the threshold results, in order to achieve template long-distance positioning and ROI extraction. Then, based on the regression relationship between AprilTag pixels in the camera's field of view and actual distance, the experiment of AprilTag minimum recognition pixel is designed. Finally, the real-time pixels of AprilTag are calculated from the real-time ranging results, and compared with the minimum recognition pixel of AprilTag, it is judged whether the recognition conditions are met, so as to carry out the subsequent AprilTag positioning. The experimental results demonstrate that the method proposed in this paper can achieve rapid and accurate positioning of a target docking vehicle at a long distance, while reducing the mismatch rate. On this basis, by designing the identification conditions of AprilTag, the rapid identification and positioning of AprilTag is realized, which reduces the traversal time of the algorithm. The feasibility of this positioning method is verified by the attitude angle error experiment, which provides supporting conditions for the subsequent docking path planning and vehicle control. [ABSTRACT FROM AUTHOR]

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi–English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) Snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR; and (ii) analysis of QE using three datasets, i.e. FIRE, NN and Snippets. In the first case, result shows that the relevancy of Hindi–English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (MAP) values of retrieved documents after QE are 0.5379 (NN), 0.6018 (FIRE) and 0.6406 (Snippets) for the three test collections, whereas the MAP before QE is 0.37102. This clearly shows the significant improvement of retrieved results for all three test collections. Among the three test collections, QE has been found most effective along with Snippets as indicated by the results with the improvements of 6.48% and 19.12% over FIRE and NN test collections, respectively. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

This paper introduces a straightforward control strategy aimed at the reduction of current fluctuations within the low-frequency domain of open-loop V/f control in induction motor drives. Traditional control techniques necessitate the addition of a current compensator based on motor parameters and the use of digital filters such as band-pass or high-pass filters. These methods, however, rely on precise motor parameters and involve complex filter design and implementation. The proposed control is capable of suppressing current fluctuations without controlling the slip of the induction motor. The proposed control strategy generates the forced rotation angle and command input voltage using the V/f block and outputs the d-axis voltage using a proportional integral controller to keep the d-axis current constant at zero. The difference between the command input voltage and the d-axis voltage is applied as the q-axis voltage and then applied through SVPWM. In order to verify the effectiveness of the proposed control, the proposed control is implemented and analyzed using power simulation based on the results of the analysis of the causes of current fluctuations in the induction motor. Finally, the effect of suppressing current fluctuations of the induction motor is verified through experimental results. In the 10~19 Hz range, where the conventional V/f control method resulted in current fluctuation rates exceeding 10% and peaking at 113.3% at 13 Hz, the proposed method suppressed the fluctuation rate to below 8.6% across all frequencies. This paper validates the effectiveness of the proposed control strategy through these results. [ABSTRACT FROM AUTHOR]

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

With the surge in astronomical data volume, modern astronomical research faces significant challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical data processing is particularly prominent, limiting the efficiency of data processing. To address this issue, this paper proposes a tiered storage algorithm based on the access characteristics of astronomical data. The C4.5 decision tree algorithm is employed as the foundation to implement an astronomical data access correlation algorithm. Additionally, a data copy migration strategy is designed based on tiered storage technology to achieve efficient data access. Preprocessing tests were conducted on 418GB NSRT (Nanshan Radio Telescope) formaldehyde spectral line data, showcasing that tiered storage can potentially reduce data processing time by up to 38.15%. Similarly, utilizing 802.2 GB data from FAST (Five- hundred-meter Aperture Spherical radio Telescope) observations for pulsar search data processing tests, the tiered storage approach demonstrated a maximum reduction of 29.00% in data processing time. In concurrent testing of data processing workflows, the proposed astronomical data heat correlation algorithm in this paper achieved an average reduction of 17.78% in data processing time compared to centralized storage. Furthermore, in comparison to traditional heat algorithms, it reduced data processing time by 5.15%. The effectiveness of the proposed algorithm is positively correlated with the associativity between the algorithm and the processed data. The tiered storage algorithm based on the characteristics of astronomical data proposed in this paper is poised to provide algorithmic references for large-scale data processing in the field of astronomy in the future. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller. [ABSTRACT FROM AUTHOR]

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios, and it is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper investigates the Constrained Multi-Objective Travelling Salesman Problem (CMOTSP) and the Constrained Multi-Objective Solid Travelling Salesman Problem (CMOSTSP) under an uncertain environment with zigzag uncertain variables. To solve CMOTSP and CMOSTSP models under uncertain environment, the expected value and optimistic value models are developed using two different ranking criteria of uncertainty theory. The models are transformed to their deterministic forms using the fundamentals of uncertainty. The Models are solved using two solution methodologies Aspiration level-based Multi-Objective Quasi Oppositional Jaya Algorithm (AL-based MOQO Jaya) and Fuzzy Programming Technique (FPT) with linear membership function. Further, the numerical illustration is solved using both methodologies to demonstrate its application. The sensitivity of the OVM model's objective functions regarding confidence levels is also investigated to look at the variation in the objective function. The paper concludes that the developed approach has solved CMOTSP and CMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM. [ABSTRACT FROM AUTHOR]

This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities. [ABSTRACT FROM AUTHOR]

For a set E ⊂ Rn that contains the origin, we consider Im(E) - the set of all mth degree Taylor approximations (at the origin) of Cm functions on Rn that vanish on E. This set is a proper ideal in Pm(Rn) - the ring of all mth degree Taylor approximations of Cm functions on Rn. Which ideals in Pm(Rn) arise as Im(E) for some E? In this paper we introduce the notion of a closed ideal in Pm(Rn), and prove that any ideal of the form Im(E) is closed. We do not know whether in general any closed proper ideal is of the form Im(E) for some E, however we prove in a subsequent paper that all closed proper ideals in Pm(Rn) arise as Im(E) when m + n ≤ 5. [ABSTRACT FROM AUTHOR]

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. [ABSTRACT FROM AUTHOR]

In this study, we extended the applicability of a derivative-free algorithm to encompass the solution of operators that may be either differentiable or non-differentiable. Conditions weaker than the ones in earlier studies are employed for the convergence analysis. The earlier results considered assumptions up to the existence of the ninth order derivative of the main operator, even though there are no derivatives in the algorithm, and the Taylor series on the finite Euclidian space restricts the applicability of the algorithm. Moreover, the previous results could not be used for non-differentiable equations, although the algorithm could converge. The new local result used only conditions on the divided difference in the algorithm to show the convergence. Moreover, the more challenging semi-local convergence that had not previously been studied was considered using majorizing sequences. The paper included results on the upper bounds of the error estimates and domains where there was only one solution for the equation. The methodology of this paper is applicable to other algorithms using inverses and in the setting of a Banach space. Numerical examples further validate our approach. [ABSTRACT FROM AUTHOR]

Product disassembly is an effective means of waste recycling and reutilization that has received much attention recently. In terms of disassembly efficiency, the number of disassembly skills possessed by workers plays a crucial role in improving disassembly efficiency. Therefore, in order to effectively and reasonably disassemble discarded products, this paper proposes a partial parallel disassembly line balancing problem (PP-DLBP) that takes into account the number of worker skills. In this paper, the disassembly tasks and the disassembly relationships between components are described using AND–OR graphs. In this paper, a multi-objective optimization model is established aiming to maximize the net profit of disassembly and minimize the number of skills for the workers. Based on the bat algorithm (BA), we propose an improved discrete bat algorithm (IDBA), which involves designing adaptive composite optimization operators to replace the original continuous formula expressions and applying them to solve the PP-DLBP. To demonstrate the advantages of IDBA, we compares it with NSGA-II, NSGA-III, SPEA-II, ESPEA, and MOEA/D. Experimental results show that IDBA outperforms the other five algorithms in real disassembly cases and exhibits high efficiency. [ABSTRACT FROM AUTHOR]

In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm. [ABSTRACT FROM AUTHOR]

The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution. [ABSTRACT FROM AUTHOR]

This paper aims to provide a comprehensive analysis of the advancements made in understanding Iterative Fixed-Point Schemes, which builds upon the concept of digital contraction mappings. Additionally, we introduce the notion of an Iterative Fixed-Point Schemes in digital metric spaces. In this study, we extend the idea of Iteration process Mann, Ishikawa, Agarwal, and Thakur based on the ϝ-Stable Iterative Scheme in digital metric space. We also design some fractal images, which frame the compression of Fixed-Point Iterative Schemes and contractive mappings. Furthermore, we present a concrete example that exemplifies the motivation behind our investigations. Moreover, we provide an application of the proposed Fractal image and Sierpinski triangle that compress the works by storing images as a collection of digital contractions, which addresses the issue of storing images with less storage memory in this paper. [ABSTRACT FROM AUTHOR]

Simple Summary: The increased detection of pancreatic cysts in recent years has triggered extensive diagnostic investigations to clarify their potential risk of malignancy, resulting in a large number of patients undergoing numerous imaging follow-up studies for many years. Therefore, there is a growing need for optimization of the current surveillance protocol to provide a practical and reasonable solution in the face of an ever-growing demand. The aim of this paper is to discuss the current available evidence on whether the implementation of unenhanced abbreviated-MRI (A-MRI) protocols for cystic pancreatic lesion surveillance could improve healthcare economics and reduce waiting lists in clinical practice without significantly reducing diagnostic accuracy. The increased detection of pancreatic cysts in recent years has triggered extensive diagnostic investigations to clarify their potential risk of malignancy, resulting in a large number of patients undergoing numerous imaging follow-up studies for many years. Therefore, there is a growing need for optimization of the current surveillance protocol to reduce both healthcare costs and waiting lists, while still maintaining appropriate sensibility and specificity. Imaging is an essential tool for evaluating patients with intraductal papillary mucinous neoplasms (IPMNs) since it can assess several predictors for malignancy and thus guide further management recommendations. Although contrast-enhanced magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP) has been widely recommended by most international guidelines, recent results support the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool in patients with IPMN. In fact, A-MRI has shown high diagnostic performance in malignant detection, with high sensitivity and specificity as well as excellent interobserver agreement. The aim of this paper is, therefore, to discuss the current available evidence on whether the implementation of an abbreviated-MRI (A-MRI) protocol for cystic pancreatic lesion surveillance could improve healthcare economics and reduce waiting lists in clinical practice without significantly reducing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs' resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

Amid the conflict between the current huge data volumes and the requirement for large-compression-ratio compression in space-borne multichannel SAR, this paper proposes a data compression method that uses the adaptive bit allocation BAQ method in the Doppler domain of space-borne multichannel SAR. Specifically, 3MBAQ, which denotes multichannel, multipulse, multiweight block-adaptive quantization, performs signal reconstruction through a Krieger filter to obtain the Doppler spectrum under azimuthal multichannel and multipulse conditions and then achieves nonuniform bit allocation by using the multiple weight contributions between subband spectra. Furthermore, large-compression-ratio compression can be realized in homogeneous scenarios by using the adaptive bit allocation BAQ method together with the improved bit error control (BEC) algorithm. In this paper, the effectiveness of the 3MBAQ method is verified on GF3 measured data. Moreover, compared to the SQNR and NMSE of the BAQ, A-BAQ, A-BAQ-BEC, MCBAQ-ASQ, and MCBAQ-BEC methods under the same conditions, it is shown that there is an optimal 1.3434 dB improvement in SQNR and a 0.0241 improvement in NMSE using the 3MBAQ algorithm, and the complexity of the algorithm for 3MBAQ is O (N 2 l o g (N)) , which is within acceptable limits, clearly proving the superiority of the 3MBAQ method. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese Algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese Algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese Algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese Algorithm problem-solving capabilities. The primary objective of the Snow Geese Algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese Algorithm is publicly available at https://github.com/stones3421/SGA-project. • Snow Geese Algorithm: A novel meta-heuristic approach inspired by snow geese flight behavior, tackling optimization problems. • Two-stage Strategy: Mimics snow geese exploration and exploitation patterns, setting it apart from traditional methods. • Performance Evaluation: Multiple algorithms are assessed on different problems. • Algorithm Features: Experimental results validate the effectiveness of the Snow Geese Algorithm. • Practical Applications: Snow Geese Algorithm shows potential in engineering optimization, aiding accurate decision-making. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian's gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems. [ABSTRACT FROM AUTHOR]

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting. [ABSTRACT FROM AUTHOR]

Summarizes the construction of paper folding. Method for approximating rational subdivisions or arbitrary angles and line segments; Angle-folding algorithm; Approximating angles, regular polygons and star polygons; Dissection of angles into equal parts.

This paper proposes a cutting head optimization method based on meshing the spatial position of the picks. According to the expanded shape of the spatial mesh composed of four adjacent picks on the plane, a standard mesh shape analysis method can be established with mesh skewness, mesh symmetry, and mesh area ratio as the indicators. The traversal algorithm is used to calculate the theoretical meshing rate, pick rotation coefficient, and the variation of cutting load for the longitudinal cutting head with 2, 3, and 4 helices. The results show that the 3-helix longitudinal cutting head has better performance. By using the traversal result with maximum theoretical meshing rate as the design parameter, the longitudinal cutting head CH51 with 51 picks was designed and analyzed. The prediction model of pick consumption is established based on cutting speed, direct rock cutting volume of each pick, pick rotation coefficient, uniaxial compressive strength, and CERCHAR abrasivity index. And the rock with normal distribution characteristics of Uniaxial Compressive Strength is used for the specific energy calculating. The artificial rock wall cutting test results show that the reduction in height loss suppresses the increase in pick equivalent loss caused by the increase in mass loss, and the pick consumption in this test is only 0.037–0.054 picks/m3. In addition, the correlation between the actual pick consumption and the prediction model, and the correlation between the actual cutting specific energy and the theoretical calculation value are also analyzed. The research results show that the pick arrangement design method based on meshing pick tip spatial position can effectively reduce pick consumption and improve the rock cutting performance. [ABSTRACT FROM AUTHOR]

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. [ABSTRACT FROM AUTHOR]

In this paper, a new appropriate diagonal matrix estimation of the Hessian is introduced by minimizing the Byrd and Nocedal function subject to the weak secant equation. The Hessian estimate is used to correct the framework of a nonmonotone trust region algorithm with the regularized quasi-Newton method. Moreover, to counteract the adverse effect of monotonicity, we introduce a new nonmonotone strategy. The global and superlinear convergence of the suggested algorithm is established under some standard conditions. The numerical experiments on unconstrained optimization test functions show that the new algorithm is efficient and robust. [ABSTRACT FROM AUTHOR]

The quantum alternating operator ansatz (QAOA) and its predecessor, the quantum approximate optimization algorithm, are one of the most widely used quantum algorithms for solving combinatorial optimization problems. However, as there is yet no rigorous proof of convergence for the QAOA, we provide one in this paper. The proof involves retracing the connection between the quantum adiabatic algorithm and the QAOA, and naturally suggests a refined definition of the 'phase separator' and 'mixer' keywords. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive control algorithm for the speed and displacement tracking of urban rail trains. Firstly, the train dynamics model is constructed considering the resistance existing in the actual operational scenarios. Secondly, based on the model predictive control (MPC) framework, a control objective function for tracking the desired speed and displacement and the stable change of control quantities is designed. Finally, combined with constraints of the train operation, the proposed MPC algorithm for the train tracking control problem is transformed into quadratic programming with inequality constraints, thereby facilitating a solution with the commonly-used solvers. Experimental results demonstrate that the proposed algorithm can effectively enhance the speed and displacement control performance while improving energy efficiency, ensuring the safety, stability, and riding comfort of the train. [ABSTRACT FROM AUTHOR]

Abstract-In recent years, electric vehicles have garnered significant attention, with lithium-ion batteries (LIBs) being central to their operation. Researchers and scholars have prioritized the accurate estimation of the state of charge (SOC) within the battery management system (BMS) as a key area of study. In this paper, by analyzing different equivalent circuit models, we choose to use the second-order RC model, elaborate the Kalman filter (KF) principle, and propose the adaptive extended Kalman filter (AEKF) to construct the estimation model of SOC. MATLAB validates the AEKF estimation model under two different operating conditions, UDDS and LA92, and the results show that the designed model can efficiently and accurately estimate the battery charge state with high competitiveness and accurately predict the real SOC direction regardless of the initial state, AEKF is more competitive than KF in terms of SOC prediction accuracy, Despite the different initial values of SOC, the roof-mean-square error of prediction was able to be controlled around one percent. [ABSTRACT FROM AUTHOR]

In this paper, a novel non-convex fractional-order image denoising model is proposed to suppress the staircase effect produced by the TV model while maintaining a neat contour. The model combines ℓ q (0 < q < 1) quasi-norm and fractional-order regularization, and employs a diffusion coefficient with a faster convergence rate to preserve more image edges and details. Additionally, an adaptive regularization parameter is designed to adjust the denoising performance of the algorithm. To obtain the optimal approximate solution of the model, an enhanced primal-dual algorithm is adopted and the complexity and convergence of the algorithm are theoretically analyzed. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. • A new non-convex FOTV model is proposed. • A new diffusion coefficient is introduced to retain more edge details of the image. • The existing primal-dual algorithm is improved, and the convergence of this algorithm is analyzed. • An adaptive regularization parameter is designed. [ABSTRACT FROM AUTHOR]

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

The problem of weight degradation is inevitable in particle filtering algorithms, and the resampling approach is an important method to reduce the particle degradation phenomenon. To solve the problem of particle diversity loss in existing resampling methods, this paper proposes a new digital twin-based resampling algorithm to improve the accuracy of particle filter estimation based on the traditional resampling algorithm. The digital twin-based resampling algorithm continuously improves the resampling process through the data interaction between the data model and the physical model, and realizes the real-time correction capability of particle weights that traditional resampling methods do not have. The new algorithm calibration rules are divided according to the size of particle weights, with particles of large weights retained and particles of small weights selectively processed. Compared with the traditional resampling algorithm, the new resampling algorithm reduces the mean square error of the particle filter estimation results by 16.62 % , 16.49 % , and 13.86 % , and improves the computing speed by 7.67 % , 2.25 % , and 7.54 % , respectively, in the simulation experiments of nonlinear systems with univariate unsteady state growth model. The algorithm is experimentally demonstrated to accurately track a person in motion in an indoor building in a non-rigid target tracking application, which illustrates the effectiveness and reasonableness of the digital twin-based resampling algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we reveal and study a new challenging problem faced by object Re-IDentification (ReID), i.e., Coupled Noisy Labels (CNL) which refers to the Noisy Annotation (NA) and the accompanied Noisy Correspondence (NC). Specifically, NA refers to the wrongly-annotated identity of samples during manual labeling, and NC refers to the mismatched training pairs including false positives and false negatives whose correspondences are established based on the NA. Clearly, CNL will limit the success of the object ReID paradigm that simultaneously performs identity-aware discrimination learning on the data samples and pairwise similarity learning on the training pairs. To overcome this practical but ignored problem, we propose a robust object ReID method dubbed Learning with Coupled Noisy Labels (LCNL). In brief, LCNL first estimates the annotation confidences of samples and then adaptively divides the training pairs into four groups with the confidences to rectify the correspondences. After that, LCNL employs a novel objective function to achieve robust object ReID with theoretical guarantees. To verify the effectiveness of LCNL, we conduct extensive experiments on five benchmark datasets in single- and cross-modality object ReID tasks compared with 14 algorithms. The code could be accessed from https://github.com/XLearning-SCU/2024-IJCV-LCNL. [ABSTRACT FROM AUTHOR]

In this paper, we propose a second-order adaptive DLN algorithm with different subdomain variable time steps for the 3D closed-loop geothermal system. Theoretically, the stability and convergence of the algorithm in the case of variable time steps are analyzed. In numerical experiments, the DLN algorithm is combined with an adaptive algorithm to verify not only its effectiveness and second-order convergence under any arbitrary sequence of time steps but also to improve computational efficiency. We overcome the difficulty of 3D programming, and verify the applicability and accuracy of the proposed algorithm by simulating the flow characteristics and heat transfer of the closed-loop geothermal system with 3D convection. [ABSTRACT FROM AUTHOR]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]

This paper proposes a retinal prosthesis edge detection (RPED) algorithm that can achieve high visual acuity and low power. Retinal prostheses have been used to stimulate retinal tissue by injecting charge via an electrode array, thereby artificially restoring the vision of visually impaired patients. The retinal prosthetic chip, which generates biphasic current pulses, should be located in the foveal area measuring 5 mm × 5 mm. When a high-density stimulation pixel array is realized in a limited area, the distance between the stimulation pixels narrows, resulting in current dispersion and high-power dissipation related to heat generation. Various edge detection methods have been proposed over the past decade to reduce these deleterious effects and achieve high-resolution pixels. However, conventional methods have the disadvantages of high-power consumption and long data processing times because many pixels are activated to detect edges. In this study, we propose a novel RPED algorithm that has a higher visual acuity and less power consumption despite using fewer active pixels than existing techniques. To verify the performance of the devised RPED algorithm, the peak signal-to-noise ratio and structural similarity index map, which evaluates the quantitative numerical value of the image are employed and compared with the Sobel, Canny, and past edge detection algorithms in MATLAB. Finally, we demonstrate the effectiveness of the proposed RPED algorithm using a 1600-pixel retinal stimulation chip fabricated using a 0.35 μm complementary metal-oxide-semiconductor process. [ABSTRACT FROM AUTHOR]

Pinch milling is a new technique for slender and long blade machining, which can simultaneously improve the machining quality and efficiency. However, two-cutter orientation planning is a major challenge due to the irregular blade surfaces and the structural constraints of nine-axis machine tools. In this paper, a method of twin-tool smoothing orientation determination is proposed for a thin-walled blade with pinch milling. Considering the processing status of the two cutters and workpiece, the feasible domain of the twin-tool axis vector and its characterization method are defined. At the same time, an evaluation algorithm of global and local optimization is proposed, and a smoothing algorithm is explored within the feasible domain along the two tool paths. Finally, a set of smoothly aligned tool orientations are generated, and the overall smoothness is nearly globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a turbine blade model and the planning tool orientation is found to be stable, smooth, and well formed, which avoids collision interference and ultimately improves the machining accuracy of the blade with difficult-to-machine materials. [ABSTRACT FROM AUTHOR]