Update Process Research Articles

Design and Implementation of Quality Inspection System for Image on National Platform for Geospatial Information Services in China

Abstract. This article is based on the data update characteristics of the online service for National Platform for Geospatial Information Services (TIANDITU) imagery data, and it designs and implements a full-process imagery data quality inspection system specifically for imagery tile data. The development and application of this system have made the quality inspection and update process of TIANDITU imagery data more scientific and efficient, significantly enhancing the efficiency of the quality inspection and update process for TIANDITU imagery data. The article provides a detailed introduction to the system’s architecture, design, implementation, characteristics and application. It also proposes optimizations to be made in algorithm optimization, user interface, and system stability. This article provides new ideas for quality inspection and publishing based on image tile data.

Improvements to the characterization of Agfa x-ray film for use on opacity spectroscopy diagnostics.

The National Ignition Facility uses a soft x-ray opacity spectrometer for x-ray spectral imaging in high-energy-density experiments. The increased demand for a better spectral resolution prompted the investigation into the Agfa D4 film. Characterization is already under way for the film. A Manson x-ray source using six different anodes was used to expose film to the linear optical density (OD) region. This is a continuation of the previous work, and the updated analysis process is communicated here. The identified uncertainties have been reduced with the updated steps that improve the results of the characterization process. When the Stanford Synchrotron Radiation Lightsource Beamline 16-2 was operational, the film was characterized at that source. Its beam offered a higher fluency with a lower exposure time needed to reach saturation. Results for both sources are compared in this paper.

ANALYSIS OF INTERNATIONAL LEGISLATION REGARDING REGULATION OF THE MANIFESTATIONS OF CORRUPTION IN PUBLIC ADMINISTRATION

The article is devoted to the analysis of international legislation on the regulation of manifestations of corruption in public administration. The introductory part states that corruption affects all spheres of social life, contributes to the spread of organized crime, creates social tension, creates uncertainty among the population in the ability of the authorities to implement organizational and practical measures to overcome the systemic crisis and revive Ukraine. Various approaches to the interpretation of the concept of «corruption» have been presented, in particular that it is both a «struggle for obtaining the maximum benefit» and «a social evil that spreads a negative image, or public crimes that usually intensively spread a negative image and practices», or «abuse state powers to create personal benefits» etc. It is noted that developing countries are more susceptible to the influence of corruption for various reasons, including the following: total poverty and low wages in the public sector; the absence of any risk-sharing mechanisms, such as insurance and a developed market; opportunities provided by complex, ill-defined, constantly changing and inadequate rules and regulations; lack of properly established laws and principles. A number of different international acts (conventions) adopted to combat corruption in a strategically planned manner are analyzed. It was emphasized that the introduction of changes in the legislative sphere took place on the basis of global surveys. It was concluded that the process of systematic updating and analysis of the international legal field contributes to the development of capacity, and also establishes benchmarks for the effectiveness of public administration, which can be monitored and measured. In view of global achievements, there is a need to develop and adopt a national system of integrity, which will provide a basis for analyzing both the causes of corruption and the adequacy and effectiveness of anti-corruption reforms.

Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures

This article proposes a novel reinforcement learning algorithm using an improved Monte Carlo tree search (IMCTS) formulation for the discrete optimum design of truss structures. IMCTS with multiple root nodes includes the update process, best reward, accelerating technique and terminal condition. The update process means that once a final solution is found, it is used as the initial solution for the next search tree. The best reward is used in the backpropagation step. The accelerating technique is introduced by decreasing the width of the search tree and reducing the maximum number of iterations. The agent is trained to minimize the total structural weight under various constraints until the terminal condition is satisfied. The optimal solution is the minimum value of all solutions found by the search trees. Numerical examples show that the agent can find the optimal solution with low computational cost, stably produces an optimal design, and is suitable for multi-objective structural optimization and large-scale structures.

Constitutive modeling of thermo-chemical decomposition and thermo-mechanical deformation, coupled with transient heat conduction, in ablative matrix composite

Several thermal protection systems employ sacrificial composite layer that undergoes thermo-chemical decomposition in high-temperature environment. This results in the pyrolysis gas formation (endothermic reaction) that gets trapped inside the voids generated in the ablative matrix phase. These trapped gases apply pore pressure on the structure, along with the mechanical loading, thus significantly influencing the structure failure. A novel thermo-chemical (TC) decomposition and thermo-mechanical (TM) deformation-based coupled multi-physics formulation, applicable to ablative composite systems, is thus presented. A novel shrinkage expression, due to ablative matrix decomposition, is derived. The TC + TM coupled formulation is converted to stress update process, and its results are validated against the available experimental data. The proposed formulation is also converted to boundary value problem employing non-linear finite element framework (NL-FEM). The Jacobian matrices, for one- and two-dimensional cases, are systematically derived, and the proposed NL-FEM formulation is successfully verified against several benchmark problems.The transient heat conduction equation is finally coupled with the proposed TC + TM formulation (one-way coupling) thus enabling the analysis of more realistic situations where the constant heating rate assumption is not valid. The coupled formulation is finally implemented for several test cases and it is demonstrated that, it has a significant influence on pore pressure and porosity evolution (through pore volumetric strain) within the ablative matrix phase.

Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm

The conventional Ant Colony Optimization (ACO) algorithm, applied to logistics robot path planning in a two-dimensional grid environment, encounters several challenges: slow convergence rate, susceptibility to local optima, and an excessive number of turning points in the planned paths. To address these limitations, an improved ant colony algorithm has been developed. First, the heuristic function is enhanced by incorporating artificial potential field (APF) attraction, which introduces the influence of the target point’s attraction on the heuristic function. This modification accelerates convergence and improves the optimization performance of the algorithm. Second, an additional pheromone increment, calculated from the difference in pheromone levels between the best and worst paths of the previous generation, is introduced during the pheromone update process. This adjustment adaptively enhances the path length optimality. Lastly, a triangle pruning method is applied to eliminate unnecessary turning points, reducing the number of turns the logistics robot must execute and ensuring a more direct and efficient path. To validate the effectiveness of the improved algorithm, extensive simulation experiments were conducted in two grid-based environments of varying complexity. Several performance indicators were utilized to compare the conventional ACO algorithm, a previously improved version, and the newly proposed algorithm. MATLAB simulation results demonstrated that the improved ant colony algorithm significantly outperforms the other methods in terms of path length, number of iterations, and the reduction of inflection points, confirming its superiority in logistics robot path planning.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Multiloss Joint Gradient Control Knowledge Distillation for Image Classification

Knowledge distillation (KD) techniques aim to transfer knowledge from complex teacher neural networks to simpler student networks. In this study, we propose a novel knowledge distillation method called Multiloss Joint Gradient Control Knowledge Distillation (MJKD), which functions by effectively combining feature- and logit-based knowledge distillation methods with gradient control. The proposed knowledge distillation method discretely considers the gradients of the task loss (cross-entropy loss), feature distillation loss, and logit distillation loss. The experimental results suggest that logits may contain more information and should, consequently, be assigned greater weight during the gradient update process in this work. The empirical findings on the CIFAR-100 and Tiny-ImageNet datasets indicate that MJKD generally outperforms traditional knowledge distillation methods, significantly enhancing the generalization ability and classification accuracy of student networks. For instance, MJKD achieves a 63.53% accuracy on Tiny-ImageNet for the ResNet18 MobileNetV2 pair. Furthermore, we present visualizations and analyses to explore its potential working mechanisms.

Have Your Cake and Eat It Too

As datasets have become a more significant aspect of Open Science, attention has turned to the data transformations that drive their creation. Li and Ludäscher have pointed out the importance of identifying data cleaning workflows as a series of modular transformations that can be extracted for reuse. This modular approach aids reproducibility and allows for transparency in data provenance. However, the constantly evolving nature of data science technology means that even once these modules have been identified and implemented, their functionality must be ported to new platforms as old ones become less applicable or less common in a field of study. When these transformations take place, it is important to consider not only practicality and functionality, but also transparency within a data processing team. Clarity of communication within a team is the first step towards providing clear and transparent documentation to the end user. This case study of an updated workflow process for a long-running longitudinal health and well-being study provides practical examples of these principles.

How Culture Affecting English - Vietnamese Book Translation

Translation plays an integral part in both Vietnamese literature and spiritual life. However, to reach well-translated works and documents, translators have to face many difficulties in handling cultural implications. This article discusses the relationship and the cultural factors of the updated translation process. The factors that would make the translated text change are staying the same, changing partially, or changing the whole context. Thanks to the article, the translators would be aware of how to prepare not only their background knowledge but also their translation techniques during the translation process.

A new perspective on optimizers: leveraging moreau-yosida approximation in gradient-based learning

Machine Learning (ML) heavily relies on optimization techniques built upon gradient descent. Numerous gradient-based update methods have been proposed in the scientific literature, particularly in the context of neural networks, and have gained widespread adoption as optimizers in ML software libraries. This paper introduces a novel perspective by framing gradient-based update strategies using the Moreau-Yosida (MY) approximation of the loss function. Leveraging a first-order Taylor expansion, we demonstrate the concrete exploitability of the MY approximation to generalize the model update process. This enables the evaluation and comparison of regularization properties underlying popular optimizers like gradient descent with momentum, ADAGRAD, RMSprop, and ADAM. The MY-based unifying view opens up possibilities for designing new update schemes with customizable regularization properties. To illustrate this potential, we propose a case study that redefines the concept of closeness in the parameter space using network outputs. We present a proof-of-concept experimental procedure, demonstrating the effectiveness of this approach in continual learning scenarios. Specifically, we employ the well-known permuted MNIST dataset, a progressively-permuted MNIST and CIFAR-10 benchmarks, and a non i.i.d. stream. Additionally, we validate the update scheme’s efficacy in an offline-learning scenario. By embracing the MY-based unifying view, we pave the way for advancements in optimization techniques for machine learning.

Decentralized Integrity: Blockchain-Based Frameworks for Enhancing Software Update Security

This article examines the application of blockchain technology as a solution for secure and transparent software update distribution. As cyber threats evolve, traditional centralized update mechanisms face increasing vulnerabilities to tampering and unauthorized modifications. We propose a blockchain-based framework that leverages distributed ledger technology to create an immutable, decentralized environment for software updates. Our article demonstrates how this approach can eliminate single points of failure, ensure update integrity through cryptographic signing and network-wide verification, and enhance transparency in the update process. We discuss the system architecture, including update creation, distribution, and verification mechanisms, and evaluate its integration with existing software ecosystems. While acknowledging challenges such as scalability and implementation costs, we argue that blockchain-based solutions significantly advance securing software distribution. Our findings suggest that this approach has the potential to become a standard practice in the industry, substantially improving trust and reliability in software update systems. This article contributes to the growing body of research on blockchain applications in cybersecurity and provides insights for future developments in secure software distribution methods.

A robust SVD-UKF algorithm and its application in integrated navigation systems

To improve the positioning accuracy in Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation and address the issue of measurement information anomalies, an integrated navigation algorithm based on an improved Huber-M estimation with Singular Value Decomposition Unscented Kalman Filter (SVD-UKF) is proposed. During the iterative update process of the prior information matrix in the UKF filtering, the Singular Value Decomposition (SVD) is introduced. This enhancement optimizes the prior information matrix, thereby enhancing numerical stability. The tail function of the Huber-M estimation is replaced with an exponential square loss function to improve the computational accuracy of the Huber-M estimation. The improved Huber-M estimation is then incorporated into the SVD-UKF framework. This approach reconstructs the UKF prior information and measurement information. The experimental results indicate that, compared to the traditional UKF and the Sage-Husa adaptive SVD-UKF algorithm, the improved algorithm enhances velocity accuracy by 68.7% and 31.2%, respectively, and improves position accuracy by 73.5% and 38.6%, respectively. The positioning accuracy and robustness of the INS/GNSS integrated navigation system were significantly improved, particularly under conditions with abnormal measurement information. The system demonstrated enhanced stability and resistance to interference in these scenarios.

Adaptive Constraint Relaxation-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization

The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors.

A FACILE METHOD TO PRODUCE HIGH-BULK AND HIGH-STRENGTH ROLLED RECONSTITUTED TOBACCO SHEET WITH REFINED TOBACCO CELLULOSE FIBERS

The rolling method is a pivotal means for reconstituted tobacco sheet (RTS) production due to its cost-effectiveness. However, the traditional rolling method is limited by its raw material grinding processes and yields of rolled RTS (RRTS) with subpar strength and bulk. Therefore, it is a challenge to develop a method to produce high-strength and high-bulk RRTS. Here, by replacing traditional raw materials grinding processes with papermaking refining processes, we present a facile and practical method to produce RRTS with elevated bulk and strength. This method separately refines tobacco leaves and stems into long and coarse leave and stem cellulose fibers. These fibers were subsequently reconstructed into RRTS. The detailed process parameters were optimized. The comparative RRTS with flax fibers instead of stem fibers was investigated. The optimal formula of new RRTS was determined. The updated processes, along with the use of refined cellulose fibers, led the RRTS to a significant improvement in strength and bulk, with a 5.3-fold increase in strength, and a 0.7-fold increase in bulk, while smoking qualities were preserved, which surpassed the smoking experience of RRTS with flax fibers. We anticipate this work will enhance the qualities of RTS and facilitate the transition of traditional tobacco industries toward healthier directions.

FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection

Industrial equipment condition monitoring and fault detection are crucial to ensure the reliability of industrial production. Recently, data-driven fault detection methods have achieved significant success, but they all face challenges due to data fragmentation and limited fault detection capabilities. Although centralized data collection can improve detection accuracy, the conflicting interests brought by data privacy issues make data sharing between different devices impractical, thus forming the problem of industrial data silos. To address these challenges, this paper proposes a class prototype guided personalized lightweight federated learning framework(FedCPG). This framework decouples the local network, only uploading the backbone model to the server for model aggregation, while employing the head model for local personalized updates, thereby achieving efficient model aggregation. Furthermore, the framework incorporates prototype constraints to steer the local personalized update process, mitigating the effects of data heterogeneity. Finally, a lightweight feature extraction network is designed to reduce communication overhead. Multiple complex industrial data distribution scenarios were simulated on two benchmark industrial datasets. Extensive experiments have demonstrated that FedCPG can achieve an average detection accuracy of 95% in complex industrial scenarios, while simultaneously reducing memory usage and the number of parameters by 82%, surpassing existing methods in most average metrics. These findings offer novel perspectives on the application of personalized federated learning in industrial fault detection.

The Effectiveness of Updating Data on Small and Medium Industrial Business Actors in the Industrial Sector at the Cirebon City Cooperatives, Small and Medium Enterprises, Trade and Industry Office

The development of Science and Technology (IPTEK) affects various aspects of human life, including in the context of organizations and government agencies. In Cirebon City, the management of Small and Medium Industries (SMEs) by the Trade and Industry Small and Medium Business Cooperative Office faces challenges in updating data that has not been fully computerized. This study uses a descriptive qualitative approach to evaluate the effectiveness of the IKM data update process. The study results show that the number of SMEs is increasing every year, although there are still obstacles, such as incomplete information and manual processes that slow down efficiency. The application of digital technologies, such as applications for data collection and update, is proposed as a solution to improve the efficiency and accuracy of processes. It is hoped that better data updates, facilitated by digital technologies, can significantly support the development of the creative economy of Cirebon City through the identification of more appropriate needs and opportunities, as well as the optimization of government programs in the SME sector.

Probing microstructure evolution of Si/C anode for Li-ion batteries via synchrotron transmission X-ray tomographic microscopy

Holding great potentials as an excellent anode for Li-ion batteries, Si has received enormous attention, and its degradation theory has been enriched extensively based on various characterizations over time. To provide a more comprehensive diagnosis for current Si/C anodes, ex situ phase-contrast synchrotron transmission X-ray tomographic microscopy (TXTM) is applied to study a Si/C electrode sampled at representative cycling numbers. Through a series of data segmentations and identifications, three-dimensional Si/C electrodes are described in details with both global and localized structure features, which are rationally linked to the electrochemical characteristics as well as the previous discoveries. This successfully establishes an updated electrode-level failure process for common Si/C anodes where the electrode degradation is primarily initiated by the nonstop Si-phase pulverization with continually-expanded LixSi/electrolyte interface, and manifested as the internal void space expansion and the ever-growing electrode thickness. Bearing this in mind, multiple complementary and synergistic solutions towards limiting the intrinsic Si rupture and electrolyte decomposition should be developed to ensure a high-performance Si-based anode over long-term cycling for Li-ion battery in the near future.

Crowd Counting Using Meta-Test-Time Adaptation.

Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the specific conditions encountered during testing. The majority of current studies concentrate on unsupervised domain adaptation. These approaches commonly perform hundreds of epochs of training iterations, requiring a sizable number of unannotated data of every new target domain apart from annotated data of the source domain. Unlike these methods, we propose a meta-test-time adaptive crowd counting approach called CrowdTTA, which integrates the concept of test-time adaptation into the meta-learning framework and makes it easier for the counting model to adapt to the unknown test distributions. To facilitate the reliable supervision signal at the pixel level, we introduce uncertainty by inserting the dropout layer into the counting model. The uncertainty is then used to generate valuable pseudo labels, serving as effective supervisory signals for adapting the model. In the context of meta-learning, one image can be regarded as one task for crowd counting. In each iteration, our approach is a dual-level optimization process. In the inner update, we employ a self-supervised consistency loss function to optimize the model so as to simulate the parameters update process that occurs during the test phase. In the outer update, we authentically update the parameters based on the image with ground truth, improving the model's performance and making the pseudo labels more accurate in the next iteration. At test time, the input image is used for adapting the model before testing the image. In comparison to various supervised learning and domain adaptation methods, our results via extensive experiments on diverse datasets showcase the general adaptive capability of our approach across datasets with varying crowd densities and scales.

Deep Hybrid Model for Pest Detection: IoT‐UAV‐Based Smart Agriculture System

ABSTRACTModern technology is revolutionising traditional farming processes by introducing new and streamlined approaches. Despite these advancements, challenges such as disease identification, insect detection and weather forecasting persist. To address these issues, this work proposes a DHMPD‐based IoT‐UAV smart agriculture system focused on pest detection. The method involves several stages: data acquisition, preprocessing, data augmentation, segmentation, feature extraction and classification. During data acquisition, a ‘Pest data set’ is collected. Preprocessing includes Z‐score normalisation to produce better‐normalised images. Data augmentation involves rotating images to create different orientations. The segmentation stage uses an updated HDBSCAN process, which improves the distance calculation between pixels using hybridised Euclidean and Minkowski distances. Feature extraction retrieves various features from segmented images, including modified MBP features, colour‐based features and shape‐based features. After feature extraction, the classification phase is performed by a hybrid technique with DL approaches such as improved DBN and LSTM approaches. Finally, classification results are averaged to predict pest detection accurately. The approach's effectiveness is evaluated through various assessments, aiming to overcome current limitations and enhance smart agriculture systems. The proposed DHMPD method was compared with state‐of‐the‐art approaches and traditional classifiers, achieving a maximum accuracy of 0.936, outperforming conventional methods in accurately detecting pests. Hence, the proposed work holds immense promise to advance the capabilities of smart agriculture systems, offering practical solutions that can benefit farmers, agricultural researchers and industries involved in crop management and food production.