Verification Algorithm Research Articles

Design of a Supercapacitor Module and Control Algorithm for Practical Verification of a Hybrid Energy Storage System

This paper presents an approach to designing a supercapacitor (SC) module according to defined power profiles and providing a control algorithm for sharing the energy from the SC module and accumulator in a hybrid energy storage system (HESS). This paper also presents a view of a printed circuit board (PCB) of the SC module and an interconnection board between the bidirectional converter, accumulator, and SC module. The practical part of the paper presents the measurement of the voltages and currents on the SC module, accumulator, and output of the DC/DC converter to visualize the energy flow between them.

Causal role of the pyrimidine deoxyribonucleoside degradation superpathway mediation in Guillain-Barré Syndrome via the HVEM on CD4 + and CD8 + T cells

Immune system regulation is a key indicator of the gut microbiota (GM) influencing disease development. The causal role of the GM in Guillain-Barré syndrome (GBS) and whether it can be mediated by immune cells is unknown. Genome-wide association study (GWAS) summary statistics for the GM were obtained from the Dutch Microbiota Project (n = 7,738) and the FINRISK 2002 (FR02) cohort (n = 5,959). Inverse variance weighting method (IVW) were used as the main method to evaluate the causal relationship between GM and GBS. Subsequently, the mediating effects of 731 immune traits were evaluated. Additionally, we also executed the Bayesian Weighting algorithm for verification. Mendelian randomization (MR) analysis determined the protective effect of the pyrimidine deoxyribonucleoside degradation superpathway on GBS (IVW: P = 0.0019, OR = 0.4508). It is worth noting that in the causal effects of pyrimidine deoxyribonucleoside degradation superpathway on GBS, the mediated proportions of herpesvirus entry mediator (HVEM) ( HVEM on CM CD4 + , HVEM on naive CD4 + , HVEM on CD45RA − CD4 + , HVEM on CM CD8br) in the T cell maturation stage on GBS were -0.0398, -0.0452, -0.0414, -0.0425, accounting for 5.00%, 5.67%, 5.19% and 5.34% of the total effect. 11 types of intestinal bacteria might be involved in the pyrimidine deoxyriboside degradation superpathway, including Staphylococcus A fleurettii, AR31,CAG-274 sp000432155, Photobacterium, Acetobacteraceae, Dysgonomonadaceae, NK4A144,Leptospirae, CAG-81 sp000435795, Leptospirales and CAG-873 sp001701165. This study suggests that there is a causal relationship between pyrimidine deoxyribonucleoside degradation superpathway and GBS, which may be mediated by HVEM on CD4 + and CD8 + T cells. As a bidirectional molecular switch, HVEM plays an important role in T cell regulation. 11 intestinal flora were found to be involved in pyrimidine deoxyribonucleoside degradation superpathway, and their changes may be related to the occurrence of GBS. However, extensive research is still warranted before microbiome sequencing can be used for prevention and targeted treatment of GBS.

Inner product reduction for fuzzy formal contexts

Formal concept analysis finds application across multiple domains, including knowledge representation, data mining, and decision analysis. Within this framework, the exploration of attribute reduction for fuzzy formal contexts represents a substantial area of research. We introduce a novel form of attribute reduction for fuzzy formal contexts named inner product reduction, and an algorithm for finding all inner product reducts is given by using the indiscernibility matrix, and a calculation example is given. Furthermore, for consistent fuzzy decision formal contexts, the definition and algorithm of inner product reduction are given. Finally, the concept and algorithm of inner product reduction are extended to general fuzzy decision formal contexts. Through experimental verification, the viability and efficacy of the inner product reduction algorithm for fuzzy formal contexts are verified.

Automatic construction and verification algorithm for smart contracts based on formal verification

As an emerging technology, blockchain demonstrates strong potential for applications in digital finance. As a core component of blockchain, the security and reliability of smart contracts is crucial. To ensure the high reliability of smart contracts, this study employs formal construction and verification techniques based on game theory. Initially, the profit function is defined using distortion techniques, and a game model for supply chain participation is designed. However, the equilibrium solution of the two-party game does not represent the optimal solution for the supply chain system. Therefore, the study introduces third-party participation to optimize the equilibrium solution. Finally, a probability model detection method is used to verify the constructed smart contract model. The results show that the supply chain model, analyzed through formal methods, has attributes consistent with theoretical analysis. Consequently, the research on automatic construction and verification algorithms for smart contracts based on formal verification proves to be effective and feasible in practical applications.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net

Abstract Background & Purpose: In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using CT and fluence maps (FMs). 
Methods: We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units (MUs). To effectively encode treatment beam configuration, the constructed FMs were back-projected to 30 cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on 381 prostate cancer cases, with an additional 40 testing cases for independent evaluation of model performance.
Results: The proposed model can estimate dose in ~15 ms for each patient. The average γ passing rate (3%/2mm, 10% threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume (PTV) and organs at risk (OARs) were 0.07%±0.34% and 0.48%±0.72%, respectively.
Conclusion: We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.

Analysis and comparison of encryption and verification algorithms applied to a Fog Computing Architecture

ABSTRACT Due to the Internet of Things (IoT) widespread adoption, data traffic to cloud servers for analysis has surged, impacting IoT system latency. Fog Computing emerged as a solution, bringing processing capabilities closer to data generation points, reducing data sent to the cloud, and improving response times. However, it intensifies security concerns due to the proximity of sensitive data to users and limited node computational resources. This research aims to study encryption and data verification algorithms using various datasets and the cryptography software OpenSSL. Testing is performed on Raspberry Pi hardware, facilitating the implementation of Fog nodes. Metrics such as CPU, memory, and energy consumption are evaluated using scripts. Results show that data type does not influence algorithm choice, and patterns related to data size behaviour are identified, aiding in defining security strategies.

МЕТОДИКА ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ СПРОЩЕНОЇ МОДЕЛІ БПЛА ТИПУ БІКОПТЕР

Modern aircraft are usually statically unstable objects. Due to this, increased maneuverability and controllability is achieved. One of the promising directions is the development and implementation of UAVs of the bicopter or convertible type. Their movement is provided by two screw electric drives. The non-identity of the engine parameters and the peculiarities of the movement of UAVs of this type cause special requirements for the control systems. To ensure sustainability, modeling and conducting research with the help of various tools is necessary. The most available models of unstable movements are pendulum devices of various types, which allow studying stabilization processes. The purpose of the work is to use a computer stabilization system of a simplified bicopter UAV model by changing the thrust of the engines. The tasks of the work are: development of a computer stabilization system for the angle of pitch or roll, creation and verification of stabilization algorithms on a laboratory bench using an ARDUINO controller, research of the functional capabilities of angular stabilization circuits. The object of the study: a computer control system of an aircraft that performs the task of stabilizing a UAV. Subject of research: processes of stabilization of the angular position of a simplified bicopter model. When performing the work, a control law using angular and angular velocity feedback was chosen. A variant of aircraft stabilization when using engine thrust control is proposed. The control law of the computer stabilization system, which is implemented on the ARDUINO controller, was selected and tested. The applied value of the work is the further use of the obtained results in modern samples of bicopters.

Development of a Mathematical Model of a Highly Maneuverable Robot for Simulation of Robots with Different Types of Designs

The article presents the results of developing a mathematical model and simulation modeling of a three-wheeled highly maneuverable mobile robot. A mathematical model of the robot in the state space has been developed. The possibility of using this robot with rotary modules for analyzing and testing control algorithms for robots of various designs has been considered. The relevance of the study is justified by the high costs of creating physical models of robots for experimental verification of control algorithms. The proposed three-wheeled mobile robot with rotary modules, where each wheel is simultaneously both a driving and a steering wheel, allows reducing these costs, while maintaining the ability to simulate part of the real operating conditions. Simplified diagrams of the robot are given, the main parameters and equations characterizing the change in linear and angular velocity are described. Particular attention is paid to modeling various types of robot drives: automotive type and with a differential drive. In the discrete state space, equations describing the dynamics of the robot were presented, which allows them to be used in software products for modeling. The results of simulation modeling obtained in the SimInTech software environment confirm the effectiveness of the proposed model. The analysis of the obtained results showed that the use of a three-wheeled robot with rotary modules allows successfully simulating the behavior of robots with other types of drives and creating conditions close to real ones for testing control algorithms. The proposed model can be used to improve the quality of design and testing of control algorithms in robotics, while reducing the costs of creating physical models of robots, as well as in the educational process.

B-051 Clinical Validation of Vectra® DA: A Multi-Biomarker Based Test for the Objective Measurement of Disease Activity in Patients with Rheumatoid Arthritis

Abstract Background The Vectra® DA test measures serum levels of 12 protein biomarkers associated with rheumatoid arthritis (RA) and uses these levels in an algorithmic calculation to generate a numeric score that represents RA disease activity where established categories are low for scores 1 to 29, moderate for 30 to 44, and high for 45 to 100. LabCorp Specialty Lab in Calabasas, CA completed the quantitative analytical validations of 12 Vectra biomarkers (SAA, CRP, VCAM-1, IL-6, TNF-RI, EGF, VEGF-A, Leptin, Resistin, MMP-1, MMP-3, and YKL-40) and the clinical verification of the Vectra score algorithm. Methods The Vectra test consists of the quantitative measurements of EGF, Leptin, MMP-1, TNF-R1, VEGF, MMP-3, Resistin, VCAM, YKL-40, CRP, SAA and IL-6 using multiplex immunoassay platforms. The data acquired from each biomarker assay is processed through an algorithm where a composite Vectra score adjusted based on age, gender, and adiposity of the patient is computed. Analytical parameters validated for each biomarkers include sensitivity, precision, accuracy, specificity and stability. Precision was validated using pooled human serum controls at low, moderate, and high Vectra score levels. Method comparison was performed using 584 clinically defined serum samples. Quantitatively measured analyte values and computed Vectra scores by the Labcorp Vectra assay were compared with Vectra data produced by Myriad laboratory. Results Intra- and inter-assay sample precision of the final Vectra score was <13% and <18%, respectively. Sample stability using freshly collected serum subject to ambient and refrigerated temperature up to 14 days and frozen up to 364 days and up to 6 freeze-thaw cycles were acceptable. Of 584 clinical Vectra data compared, Labcorp vs Myriad, 96% of data were comparable within the acceptable criteria. Correlation of Vectra score from Labcorp and Myriad was excellent. A linear least square model resulted in a correlation coefficient (R) of 0.981 and a Deming slope of 1.009. Qualitative method comparison of Vectra scores resulted in excellent agreement of risk of radiographic progression and rheumatoid arthritis disease activity. A 91% agreement of risk category match between Labcorp and Myriad and 100% agreement of Vectra scores within 1 risk category was achieved. Conclusions We validated the Vectra® DA multiplex assay for measuring serum levels of 12 protein biomarkers associated with RA. Vectra combines biomarker levels into a personalized disease score that represents a patient’s RA inflammation and predict their risk of radiographic progression. High Vectra® DA scores (> 44) have been associated with a 20% 1-year risk of radiographic progression while low scores (< 30) carry only a 1% risk. Thus, the Vectra score provides an objective measurement of RA disease activity that can be used in combination with existing assessment tools to aid physicians to manage patients with RA.

CSNS-II multi-harmonic LLRF upgrade and commissioning

The second phase of the China Spallation Neutron Source (CSNS-II) will employ a dual-harmonic RF system to mitigate the significant space charge effects associated with 500 kW beam power. To address this, three magnetic alloy loaded cavities, characterized by their wideband and high-gradient properties, will be integrated as multi-harmonic cavities in the upgrade project. Additionally, the current LLRF control system hardware, based on the CPCI platform, is encountering issues related to discontinuation and performance limitations. This paper presents the testing and verification of hardware upgrades and multi-harmonic control system algorithms for the magnetic alloy loaded cavity LLRF control system, ensuring the effective implementation of the multi-harmonic control algorithm within the new hardware framework.

Global integrated navigation position correction algorithm and virtual polar region technology based on normal vector

To achieve the inherent uniformity of all-earth navigation for autonomous underwater vehicles (AUV), a global integrated navigation position correction algorithm based on the normal vector (n-vector) is proposed. First, a four-element vector comprising the non-singular n-vector and height represents the carrier’s position in the Earth-centered Earth-fixed (ECEF) frame. The integrated navigation systems are designed with the strapdown inertial navigation system (SINS), Doppler velocity log (DVL), and other equipment. Secondly, the reverse navigation algorithm in the ECEF mechanization is derived, and the results of forward and reverse navigation are fused to correct the AUV’s underwater position. Additionally, a virtual polar region technology based on the ECEF attitude, velocity, and height invariant method (E-AVHIM) is proposed to convert the actual test data obtained at low and middle latitudes into high latitudes for more convincing polar region algorithm verification. Simulations and lake trial demonstrated the excellence and efficiency of the proposed algorithm.

HAETAE on ARMv8

In this work, we present the highly optimized implementation of the HAETAE algorithm, submitted to the second round of the Korean Post-Quantum Cryptography (KpqC) competition and to the first round of NIST’s additional post-quantum standardization for digital signatures on 64-bit ARMv8 embedded processors. To the best of our knowledge, this is the first optimized implementation of the HAETAE algorithm on 64-bit ARMv8 embedded processors. We apply various optimization techniques to enhance the multiplication operations in the HAETAE algorithm. We utilize parallel operation techniques involving vector registers and NEON (Advanced SIMD technology used in ARM processors) instructions of ARMv8 embedded processors. In particular, we achieved the best performance of the HAETAE algorithm on ARMv8 embedded processors by applying all the state-of-the-art NTT (Number Theoretic Transform) implementation techniques. Performance improvements of up to 3.07×, 3.63×, and 9.15× were confirmed for NTT, Inverse-NTT, and pointwise Montgomery operations (Montgomery multiplication used in modular arithmetic), respectively, by applying the state-of-the-art implementation techniques, including the proposed techniques. As a result, we achieved a maximum performance improvement of up to 1.16× for the key generation algorithm, up to 1.14× for the signature algorithm, and up to 1.25× for the verification algorithm.

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.

FPGA-based implementation and verification of hybrid security algorithm for NoC architecture

FPGA-based implementation and verification of hybrid security algorithm for NoC architecture

Enabling PERK and other MPC-in-the-Head Signatures on Resource-Constrained Devices

One category of the digital signatures submitted to the NIST Post-Quantum Cryptography Standardization Process for Additional Digital Signature Schemes comprises proposals constructed leveraging the MPC-in-the-Head (MPCitH) paradigm. Typically, this framework is characterized by the computation and storage in sequence of large data structures both in signing and verification algorithms, resulting in heavy memory consumption. While some research on the efficiency of these schemes on high-performance machines has been done, studying their performance and optimization on resource-constrained ones still needs to be explored. In this work, we aim to address this gap by (1) introducing a general method to reduce the memory footprint of MPCitH schemes and analyzing its application to several MPCitH proposed schemes in the NIST Standardization Process. Additionally, (2) we conduct a detailed examination of potential memory optimizations in PERK, resulting in a streamlined version of the signing and verification algorithms with a reduced memory footprint ranging from 22 to 85 KB, down from the original 0.3 to 6 MB. Finally, (3) we introduce the first implementation of PERK tailored for Arm Cortex M4 alongside extensive experiments and comparisons against reference implementations.

The Application of Artificial Intelligence in News Communication: Practical Exploration and Ethical Dilemma

The application of artificial intelligence in news communication has evolved from saving labor costs and improving efficiency to extending the perception range of professional journalists and strengthening their judgment ability. Innovative practical explorations have been carried out in news production, presentation, and distribution. However, the application of artificial intelligence has also brought ethical dilemmas of social bias diffusion to news media. In the face of this challenge, the news and communication academic community and industry have worked together to open up innovative paths in eliminating bias and maintaining social equity through technological "algorithm verification evolution" and social "human-machine coupling".

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network’s top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

Enhancement of penetration of electromagnetic waves by field focusing applied to GPR detection

Ground penetrating radar (GPR) is limited by challenges such as signal attenuation and inadequate target detection in lossy dielectrics, prompting research on data processing algorithms to enhance detection capability. In this study, we propose a method for enhancing GPR detection capability based on electromagnetic field focusing (FF). The method involves calculating the phase difference attributable to the range difference between each antenna element and the focal point and compensating its feed phase accordingly. This approach effectively concentrates the energy of electromagnetic waves at the target area. Tapered slot antennas operating within the 2.5–8 GHz frequency band were employed for transmitting and receiving, thus facilitating algorithm verification and target detection. The synthetic aperture radar algorithm executed pulse compression and range migration on the received data, thereby intensifying the signal at the target object’s position and achieving double focusing. Notably, this study uniquely employs FF to enhance the penetrability of electromagnetic waves and augment the detection capability of GPR. Both simulation and experimental results demonstrated the effectiveness of the proposed method in improving the GPR imaging quality.