信息工程最新文献

Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO₂ equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.

Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.

To solve the problem of mobile robots needing to adjust their pose for accurate operation after reaching the target point in the indoor environment, a localization method based on scene modeling and recognition has been designed. Firstly, the offline scene model is created by both handcrafted feature and semantic feature. Then, the scene recognition and location calculation are performed online based on the offline scene model. To improve the accuracy of recognition and location calculation, this paper proposes a method that integrates both semantic features matching and handcrafted features matching. Based on the results of scene recognition, the accurate location is obtained through metric calculation with 3D information. The experimental results show that the accuracy of scene recognition is over 90%, and the average localization error is less than 1 meter. Experimental results demonstrate that the localization has a better performance after using the proposed improved method.

This study investigated the usefulness of a two-serotype dengue mathematical model to gain insights into the effects of antibody-dependent enhancement and temperature on dengue transmission dynamics in the presence of vaccination and Wolbachia-carrying mosquitoes. In particular, the effects of temperature on the mosquito death and maturation rates in secondary infections were examined. A deterministic mathematical model was formulated and analysed to address this problem. The results suggest that controlling the population of aquatic mosquitoes is appropriate for reducing the incidence of secondary infections. Furthermore, the wAu Wolbachia strain was more effective in reducing secondary infections.

Disease dynamics are influenced by changes in the environment. In this study, unreported cases (U), environmental perturbations, and exogenous events are included in the epidemic Susceptible–Exposed–Infectious–Unreported–Removed model with time delays. We examine the process of switching from one regime to another at random. Ergodicity and stationary distribution criteria are discussed. A Lyapunov function is used to determine several conditions for disease extinction. The spread of a disease is affected when transitioning from one random regime to another via sudden external events, such as hurricanes. The model and theoretical results are validated using numerical simulations.

Federated learning (FL) allows data owners to train neural networks together without sharing local data, allowing the industrial Internet of Things (IIoT) to share a variety of data. However, traditional FL frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposes a dual-blockchain based multi-layer grouping federated learning (BMFL) architecture. BMFL divides the participant groups based on the training tasks, then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.

The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity and enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. This paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.