Multiple Scheme Research Articles

Multiplication of orbital angular momentum via multi-plane light conversion.

The multiplication of orbital angular momentum (OAM) modes using optical coordinate transformation is useful for OAM optical networks, but the scalability of this scheme is limited by the ray model. Here, we propose an alternative scheme for the scalable multiplication of OAM modes based on modified multi-plane light conversion (MPLC) that can extend azimuthal and radial indices of OAM modes supported by the multipliers and unlock a new degree of freedom for radial high-order OAM states that has been restricted in the zero order. The multiplication for 20 OAM modes with radial index p = 0 and 10 OAM modes with radial index p = 1 is performed in simulation and experiment. The 3-dB optical bandwidth corresponding to the purity of OAM modes covers the entire C-band experimentally. This novel, to the best of our knowledge, approach to manipulating OAM states provides valuable insights and flexible strategies for high-capacity OAM optical communication and high-dimensional optical quantum information processing.

A current injection method for converter power loss representation in electromagnetic transient simulation

The power-electronics converters are increasingly applied in modern power system, which contains various sub-systems, such as renewable energy power plants and high voltage direct current transmission systems, etc. The electromagnetic transient simulation has been one of the important tools for the study of complex system containing large numbers of power converters. The coordination control of the power converters and corresponding power generation and load units is one of the challenges ensuring stability and obtaining optimal efficiency. However, the detailed modeling of the power converters considering the conduction and switching losses can cost a great number of computation resources, which makes it difficult to simulate a large system. This work proposed a current injection method for converter power loss representation, which can accurately consider the conduction and switching losses of the power-electronics switches with low computation complexity. A look-up table (LUT) of the power loss ratio is calculated before the simulation, and a current source representing the power loss is connected in parallel with the converter to track the reference loss generated by the look-up table. The proposed method can be combined with multiple converter modeling schemes, such as the two-resistance model, the switching-function model, and the average value model, etc. The topologies of the two-level converter and the modular multi-level converter (MMC) were used in the case studies to demonstrate the effectiveness of the proposed method. Compared to the device-level model, the simulation power loss deviation is 0.64% and 4.13% applying the current injection method model in the case studies of the two-level converter and the MMC.

Numerical study of interactions between Bingham fluids and flexible structures using the immersed boundary-lattice Boltzmann method

This paper studies the interactions between Bingham fluids and flexible structures under an immersed boundary lattice-Boltzmann framework. The objective of this paper is to understand the rheology effects of Bingham fluids on the structure responses and the flow characteristics within Bingham fluids–structure interactions. To this end, a numerical algorithm that integrates the lattice Boltzmann method and the smoothed point interpolation method is proposed. Alongside this, a hybrid multiple relaxation time scheme in the framework of the immersed boundary-lattice Boltzmann method is used to improve the numerical accuracy and stability. Then, the numerical algorithm is applied to three typical fluid–structure interactions cases in both Newtonian and Bingham fluids regarding an elastic beam, a self-induced elastic beam attached to a circular cylinder and a self-propelled fishlike body. Numerical results highlight that the yield stress of Bingham fluids influences predominantly on the structure responses, such as the solid deformation, the oscillation amplitude, the oscillation period, and the swimming velocity. In addition, the yielded/unyielded zones' evolution process of the Bingham fluids–structure interaction is also identified in this paper.

Multiple-model adaptive explicit predictive control for nonlinear MIMO system

This work is to develop a blending-based multiple model adaptive explicit predictive control scheme for nonlinear MIMO systems that can handle parametric uncertainties. Here, for each identification model, an explicit nonlinear model predictive control (ENMPC) law is computed in advance for the corresponding model. The generated control inputs from the set of ENMPC controllers are being blended online using a weighting vector that is continuously updated by the proposed adaptive identification schemes. The proposed control scheme is used to govern the tracking of a highly nonlinear helicopter model known as the twin rotor MIMO system (TRMS). Here, an extended Kalman filter (EKF) is used to estimate the unavailable states of the TRMS. Finally, simulation and experimental results are presented to prove that the proposed controller gives better performance than some reported works in the literature. The effectiveness of the proposed controller is demonstrated by experimental studies of the TRMS model.

A Novel Tensor Ring Sparsity Measurement for Image Completion.

As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix format. Nevertheless, in practical scenarios, much of the data are intrinsically multi-dimensional, and thus, using a matrix format for data representation will inevitably yield sub-optimal outcomes. Tensor decomposition (TD), as a high-order generalization of matrix decomposition, has been widely used to analyze multi-dimensional data. In a direct generalization to the matrix rank, low-rank tensor modeling has been developed for multi-dimensional data analysis and achieved great success. Despite its efficacy, the connection between TD rank and the sparsity of the tensor data is not direct. In this work, we introduce a novel tensor ring sparsity measurement (TRSM) for measuring the sparsity of the tensor. This metric relies on the tensor ring (TR) Kronecker basis representation of the tensor, providing a unified interpretation akin to matrix sparsity measurements, wherein the Kronecker basis serves as the foundational representation component. Moreover, TRSM can be efficiently computed by the product of the ranks of the mode-2 unfolded TR-cores. To enhance the practical performance of TRSM, the folded-concave penalty of the minimax concave penalty is introduced as a nonconvex relaxation. Lastly, we extend the TRSM to the tensor completion problem and use the alternating direction method of the multipliers scheme to solve it. Experiments on image and video data completion demonstrate the effectiveness of the proposed method.

Energy Consumption Analysis for Continuous Phase Modulation in Smart-Grid Internet of Things of beyond 5G.

Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications. However, the energy consumption (EC) characteristic of sensor nodes is a key factor that affects the operational performance (e.g., lifetime of sensors) and the total cost of ownership of WSNs. In this paper, to find the modulation techniques suitable for WSNs, we investigate the EC characteristic of continuous phase modulation (CPM), which is an attractive modulation scheme candidate for WSNs because of its constant envelope property. We first develop an EC model for the sensor nodes of WSNs by considering the circuits and a typical communication protocol that relies on automatic repeat request (ARQ)-based retransmissions to ensure successful data delivery. Then, we use this model to analyze the EC characteristic of CPM under various configurations of modulation parameters. Furthermore, we compare the EC characteristic of CPM with that of other representative modulation schemes, such as offset quadrature phase-shift keying (OQPSK) and quadrature amplitude modulation (QAM), which are commonly used in communication protocols of WSNs. Our analysis and simulation results provide insights into the EC characteristics of multiple modulation schemes in the context of WSNs; thus, they are beneficial for designing energy-efficient SG-IoT in the beyond-5G (B5G) and the 6G era.

An extended multiplicative error model of allometry: Incorporating systematic components, non-normal distributions, and piecewise heteroscedasticity.

Allometry refers to the relationship between the size of a trait and that of the whole body of an organism. Pioneering observations by Otto Snell and further elucidation by D'Arcy Thompson set the stage for its integration into Huxley's explanation of constant relative growth that epitomizes through the formula of simple allometry. The traditional method to identify such a model conforms to a regression protocol fitted in the direct scales of data. It involves Huxley's formula-systematic part and a lognormally distributed multiplicative error term. In many instances of allometric examination, the predictive strength of this paradigm is unsuitable. Established approaches to improve fit enhance the complexity of the systematic relationship while keeping the go-along normality-borne error. These extensions followed Huxley's idea that considering a biphasic allometric pattern could be necessary. However, for present data composing 10 410 pairs of measurements of individual eelgrass leaf dry weight and area, a fit relying on a biphasic systematic term and multiplicative lognormal errors barely improved correspondence measure values while maintaining a heavy tails problem. Moreover, the biphasic form and multiplicative-lognormal-mixture errors did not provide complete fit dependability either. However, updating the outline of such an error term to allow heteroscedasticity to occur in a piecewise-like mode finally produced overall fit consistency. Our results demonstrate that when attempting to achieve fit quality improvement in a Huxley's model-based multiplicative error scheme, allowing for a complex allometry form for the systematic part, a non-normal distribution-driven error term and a composite of uneven patterns to describe the heteroscedastic outline could be essential.

A Minimized Measurement Scheme for Predicting HbA1c Using Discrete Self-Monitoring Blood Glucose Data Within 4 Weeks for People with Type 2 Diabetes.

Objective: To establish an accurate and robust calculation model for predicting hemoglobin A1c (HbA1c) for people with type 2 diabetes (T2D) by using the fewest discrete blood glucose values according to an irregular data set and propose an appropriate cost-effective and scientific scheme for routine blood glucose monitoring. Methods: By using two data sets obtained from 2017 to 2022, which involved 2432 people with T2D, ∼420,000 irregular blood glucose values, and 10,000 HbA1c values, multiple blood glucose monitoring schemes were designed and compared to find the optimal one. The data were structured and then fitted using a regularized extreme learning machine, and the results were evaluated on the basis of indicators such as mean absolute error (MAE), root mean square error, and the relevance analysis (R) value; the optimal scheme for routine blood glucose monitoring was determined by combining the accuracy and the cost and was compared with previous studies in terms of accuracy and stability. Results: Data fitting results for the chosen scheme: R = 0.8029 (P < 0.001), MAE = 0.3181% (95% confidence interval, 0.2666-0.3695%). Within the last 4 weeks before the prediction of HbA1c, a minimum of only seven fasting and seven postprandial blood glucose values are needed, of which are one fasting and one postprandial blood glucose values per 4 days. Compared with previous studies, the prediction model shows better accuracy and stability (P < 0.05), especially under the great glucose fluctuation group. Conclusion: A minimized calculation model for accurately and robustly predicting HbA1c using discrete self-monitoring of blood glucose data within 4 weeks for people with T2D has been established and provides a new reference for the design of a scheme for blood glucose monitoring. The diabetes care clinic of Peking University First Hospital (Registration Number: ChiCTR2300068139).

Cryptanalysis of cross-coupled chaotic maps multi-image encryption scheme

Recently, Patro et al. proposed a multiple gray-scale image encryption scheme based on cross-coupled chaotic maps. The authors claimed the scheme could withstand known plain-text attacks and chosen-plaintext attacks. In this paper, we have thoroughly analysed the Patro et al. algorithm and reported the crucial loopholes. Patro et al. algorithm uses a row-wise and column-wise scrambling operation based on a secret sequence followed by a feed-forward XOR operation generating the cipher image. Exploiting the loopholes of feed-forward XOR operation and high correlation of pixels in standard images, a cryptanalysis algorithm is developed that deciphers the ciphered images generated by Patro et al. algorithm based on cipher-text only attack. Without using the secret keys, the proposed cryptanalysis algorithm successfully deciphers the cipher image and reveals the secret images.

EFFICIENT SEGMENTATION MODEL USING MRI IMAGES AND DEEP LEARNING TECHNIQUES FOR MULTIPLE SCLEROSIS CLASSIFICATION

The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the magnetic resonance imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the images in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data, and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. In this study, the MRI images are initially gathered. The gathered images are directly given to the image segmentation process, where the Multi-Scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed random opposition of cicada swarm optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process where the hybrid and dilated convolution-based adaptive residual attention network (HDCARAN) is utilized. The HDCARAN categorizes the lesions from the MRI images very effectively and detects the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously developed multiple sclerosis detection schemes to evaluate the effectiveness of the designed model with respect to several functionality measures. The implementation of HDCARAN results in the detection of lesions in the initial stages, with a 94&amp;#37; specificity level, as well as efficiency performance based on existing models. The model also obtains a 94.5&amp;#37; accuracy, as opposed to the other conventional systems. The results show the proposed method performs better than other machine learning models.

Presenting the multi-phase solver implemented in the open source TrioCFD code based on the TRUST HPC platform

A new open source CFD-scale multi-phase flow environment has been developed in the TrioCFD code using the TRUST HPC platform. Mass, momentum and energy equations can be solved for an arbitrary number of phases. This environment is scalable, can use multiple numerical schemes and resolution algorithms, and enables easy modification of the physical closures. It has been verified on adiabatic and boiling two-phase simulations.

Event-Triggered Multiasynchronous H∞ Control for Markov Jump Systems With Transmission Delay.

In this article, the issue of event-triggered multiasynchronous H∞ control for Markov jump systems with transmission delay is concerned. In order to reduce sampling frequency, multiple event-triggered schemes (ETSs) are introduced. Then hidden Markov model (HMM) is employed to describe multiasynchronous jumps among subsystems, ETSs, and controller. Based on the HMM, the time-delay closed-loop model is constructed. In particular, when triggered data are transmitted over networks, a large transmission delay may cause disorder of transmission data such that the time-delay closed-loop model cannot be developed directly. To overcome this difficulty, a packet loss schedule is presented and the unified time-delay closed-loop system is obtained. By the use of the Lyapunov-Krasovskii functional method, sufficient conditions with the controller design are formulated for guaranteeing the H∞ performance of the time-delay closed-loop system. Finally, the effectiveness of the proposed control strategy is demonstrated by two numerical examples.

Multi-color-image compression-encryption scheme in the quaternion discrete Fresnel domain

A multiple color images compression-encryption scheme is designed with compressive sensing in the quaternion discrete Fresnel transform. To tackle multiple color images in a holistic manner, the discrete Fresnel transform is extended into the quaternion domain and the images are encrypted with the quaternion discrete Fresnel transform. In this scheme, the RGB color components of plaintext images are simultaneously compressed and encrypted in three mutually independent channels. Then the red, green and blue components are scrambled respectively by a chaos sequence generated by the 2D logistic-sine-coupling map. Each color component matrix is compressed with sparse representation and matrix measurement. Subsequently, the compressed matrices are integrated into the quaternion algebras and re-encrypted by the defined quaternion discrete Fresnel transform. The devised nonlinear cryptosystem originates from the asymmetric phase truncation operation. In decryption, the original color images are reconstructed by the gradient descent with a sparsification algorithm. The proposed multiple color images compression-encryption algorithm is feasible, effective, secure and robust.

Низкотемпературная сверхпластическая деформация никелевого сплава ЭК79 с ультрамелкозернистой структурой смешанного типа

One of the most effective ways to increase the processing plasticity of advanced superalloys (heat-resistant nickel-based alloys) is the formation of an ultrafine-grained (UFG) microstructure in bulk semi-finished products. Such a microstructure is a necessary condition for the manifestation of the structural superplasticity effect in the technological processes of manufacturing products from such superalloys. One of the most promising methods for producing UFG microstructures is thermomechanical treatment (TMT) according to the multiple isothermal forging scheme. It has been shown that the EK79 superalloy after TMT, with a gradual decrease in the processing temperature from 0.88 to 0.62 Ts (where Ts is the strengthening phase dissolution temperature) leads to the transformation of the initial microduplex fine-grained microstructure into a mixed UFG microstructure. Such a mixed UFG microstructure consists of: 1) relatively coarse (inherited from the fine-grain microstructure) particles – γ'-phase with a size of 3.0±0.8 μm; 2) γ-grains, and incoherent γ'-phase particles with a size of 0.3–0.5 μm; 3) strengthening coherent intragranular γ'-phase particles with a size of 0.05–0.1 μm, released upon cooling from the TMT temperature to room temperature. During uniaxial compression tests, the EK79 superalloy with such microstructure, demonstrates low-temperature superplasticity in the temperature range of 800–1000 °C. It has been found that an increase in the deformation temperature up to 1000 °C, leads to the increase of γ-phase grains to micron size. The maintenance of superplastic properties in the presence of relatively coarse incoherent particles in the microstructure of the second phase (γ'-phase) is apparently related to the fact that the deformation is localised in the UFG component.

Local analog of the Deligne-Riemann-Roch isomorphism for line bundles in relative dimension 1

We prove a local analog of the Deligne-Riemann-Roch isomorphism in the case of line bundles and relative dimension $1$. This local analog consists in computation of the class of $12$th power of the determinant central extension of a group ind-scheme $\mathcal G$ by the multiplicative group scheme over $\mathbb Q$ via the product of $2$-cocyles in the second cohomology group. These $2$-cocycles are the compositions of the Contou-Carrère symbol with the $\cup$-product of $1$-cocycles. The group ind-scheme $\mathcal{G}$ represents the functor which assigns to every commutative ring $A$ the group that is the semidirect product of the group $A((t))^*$ of invertible elements of $A((t))$ and the group of continuous $A$-automorphisms of $A$-algebra $A((t))$. The determinant central extension naturally acts on the determinant line bundle on the moduli stack of geometric data (proper quintets). A proper quintet is a collection of a proper family of curves over $\operatorname{Spec} A$, a line bundle on this family, a section of this family, a relative formal parameter at the section, a formal trivialization of the bundle at the section that satisfy further conditions.

Construction of a Distributed 3D Interior Design System Based on Artificial Intelligence Algorithms

Interior design is a complex system that includes many elements, such as user needs, spatial structure, material selection, color matching, lighting layout, etc. The relationships between them are complex and intricate. How to design space according to user needs is one of the most challenging issues in interior design. A distributed 3D interior design system based on artificial intelligence algorithms was adopted to address this issue. The system divided indoor spatial information into several subsets. It assigned subsets to various professional designers based on user needs, and generated multiple design schemes through artificial intelligence algorithms, which were then screened, modified, and optimized by each professional designer. In this way, designers from different professional backgrounds can personalized design the space according to their own understanding, thereby avoiding issues such as information fragmentation and low efficiency in traditional interior design. The results showed that the distributed 3D interior design system based on artificial intelligence algorithms can improve the overall performance from an average of 82.68% to 94.94%, effectively avoiding the problem of information fragmentation.

A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers

The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well.

Multi-strategy ratiometric luminescent thermometry based on Dy doped CaWO4 phosphors independent of excitation wavelength

This paper explores the detailed photoluminescence (PL) behaviors of Dy3+ doped CaWO4 (Dy:CaWO4) phosphors under multi-ultraviolet wavelength excitation. Moreover, it proposes multi-strategy ratiometric thermometry based on luminescence intensity ratio (LIR) from the transitions of thermally coupled energy levels (TCLs) of Dy3+. Under excitation of multiple ultraviolet wavelengths, the Dy:CaWO4 phosphors exhibited multi-wavelength responsive PL characteristics of Dy3+ with strong blue and yellow emissions from Dy3+ TCLs transitions. The temperature-dependent PL properties of Dy:CaWO4 phosphors were investigated over a temperature range of 300∼650 K. Multi-strategy ratiometric luminescent thermometry was examined under various excitation wavelengths by the thermal equilibrium population of Dy3+ TCLs. The proposed six LIR schemes based on the PL emissions of Dy3+ exhibited a good quantitative relationship with temperature. They showed well-luminescent thermometric behaviors with high absolute and relative sensitivities due to the largest energy gap of TCLs and a relatively larger LIR value. The thermometry properties of the multiple LIR schemes were independent of excitation wavelength, attributed to the stable site symmetry of Dy3+ and the thermal equilibrium population of TCLs under different excitation wavelengths. The multi-strategy ratiometric thermometry of Dy3+ demonstrated the merit of high sensitivity, excellent stability, and repeatability, indicating the potential luminescent thermometric application of Dy:CaWO4 phosphors.

Behavior of the DenStream Clustering Algorithm for Attack Detection in the Internet of Things

Multiple attack detection schemes based on supervised batch learning are presented in the literature as an alternative to improve Internet of Things (IoT) security. These schemes require benign and malicious traffic samples for training and are unable to easily adapt to changes in the analyzed data. In this work, we study how we can use DenStream, an unsupervised stream mining algorithm, to detect attacks in IoT networks. This type of algorithm does not require labeled examples and can learn incrementally, adapting to changes. We aim to investigate whether attacks can be detected by monitoring the behavior of DenStream's clusters. The results showed that DenStream could provide indicators of attack occurrence in TCP, UDP, and ICMP traffic.

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

Neural networks (NNs) often assign high confidence to their predictions, even for points far out of distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation (MVE), deep evidential regression, and Gaussian mixture models (GMM). We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness; MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.