Design Of Algorithms Research Articles

A Multivariate Student-t Process Model for Dependent Tail-weighted Degradation Data

Traditionally, Gaussian assumption, implied by the Wiener process, is widely admitted for modeling degradation processes. However, when degradation data exhibit heavy tails, this assumption is not suitable. To overcome this limitation, this article proposes a novel class of tail-weighted multivariate degradation model, which is built upon Student-t process. The model is able to account for both between-unit variability and process dependency, while allows adjusting the tail heaviness through tuning the parameter of the degree of freedom. For reliability assessment, we derive the system reliability function and present an efficient Monte Carlo method for its evaluation. Further, we introduce an expectation-maximization algorithm for parameter estimation and design a bootstrap method for interval estimation. Comprehensive simulation studies are conducted to validate the effectiveness of the inference method. Finally, the proposed methodology is applied to analyze two real-world degradation datasets.

Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv.Scientific contributionWe propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.

Analysis of the causes, psychological mechanisms, and coping strategies of short video addiction in China

Short video addiction refers to the uncontrollable desire of users to watch short videos, leading to significant behavioral loss of control or attention disorders, which in turn result in difficulties in social interaction, learning, and work adaptation. With the “invasion” of short videos into people’s daily lives and their spread among underage groups, the issue of short video addiction has attracted widespread social attention. Firstly, based on the causes of short video addiction, this study analyzes it from four levels: algorithm design, content services, platform control, and user experience. Secondly, combining relevant scientific theories, the psychological mechanisms of short video addiction are explained from four levels: cognition, emotion, motivation, and social factors. Finally, in terms of coping strategies, on the theoretical level, further research on the occurrence mechanism of short video addiction should be deepened, and attention should be paid to the influence of recommendation algorithms on short video addiction. On the practical level, the obligations and responsibilities of relevant stakeholders such as short video producers, platforms, and regulators in preventing short video addiction should be clarified, aiming to promote prevention and management of short video addiction.

Simulated annealing algorithm for the optimal design of diffractive structures under fluctuation models

In this paper, to improve the optimization capability of the simulated annealing algorithm (SA) in the optimal design of diffractive structures, the random fluctuation problem in the algorithm is investigated, and a new fluctuation calculation model is proposed. The optimal design of phase-type diffraction gratings and beam-shaping devices verified the fast accuracy of the proposed model. The simulation experimental results show that the model’s introduction is particularly significant in substantially reducing computation time (a threefold increase in computation speed) and improving the algorithm’s optimization finding capability. Finally, the setting of the model parameters is discussed analytically to make the proposed model more applicable and robust. The study results provide guidance and assistance for higher speed and effectiveness in the optimal design using simulated annealing algorithms. It can be applied to any complex and large-scale optimization calculation problem.

Progressive and efficient verification for digital signatures: extensions and experimental results

AbstractDigital signatures are widely deployed to authenticate the source of incoming information, or to certify data integrity. Common signature verification procedures return a decision (accept/reject) only at the very end of the execution. If interrupted prematurely, however, the verification process cannot infer any meaningful information about the validity of the given signature. This limitation is due to the algorithm design solely, and it is not inherent to signature verification. In this work, we provide a formal framework to extract information from prematurely interrupted signature verification, independently of why the process halts: we propose a generic verification procedure that progressively builds confidence on the final decision. Our transformation builds on a simple but powerful intuition and applies to a wide range of existing schemes considered to be post-quantum secure, including some lattice-based and multivariate equations based constructions. We demonstrate the feasibility of our approach through an implementation on off-the-shelf resource-constrained devices. In particular, an intensive testing activity has been conducted measuring the increase of performance on three IoT boards—i.e., Arduino, Raspberry, and Espressif—and a consumer-grade laptop. While the primary motivation of progressive verification is to mitigate unexpected interruptions, we show that verifiers can leverage it in two innovative ways. First, progressive verification can be used to intentionally adjust the soundness of the verification process. Second, our transformation splits verification into a computationally intensive offline set-up (run once), and an efficient online verification that is faster than the original algorithm. We conclude showing how to tweak our compiler for progressive verification to work on a wide range of signatures with properties, on three real-life use cases, and in combination with efficient verification.

State estimation of singularly perturbed Semi-Markov jump coupled neural networks: A two-time-scale event-triggered approach

The state estimation problem of singularly perturbed semi-Markov jump coupled neural networks is investigated in this paper, in which a two-time-scale event-triggered mechanism is proposed to adapt the two-time-scale character of the singularly perturbed coupled neural networks such that mitigate the burden of data communication. A dual-rate sampling strategy is used for slow and fast states such that the proposed two-time-scale event-triggered mechanism contains two independent event-triggered conditions corresponding to the slow and fast states respectively. Based on the two-time-scale event-triggered mechanism, sufficient conditions for the H∞ performance analysis of singularly perturbed semi-Markov jump coupled neural networks are established. Furthermore, under the circumstances that the singularly perturbed parameter does not exceed the presupposed upper bound, a design algorithm is developed for the two-time-scale state estimator to ensure that the error dynamics are stochastically stable. Finally, an example is used to illustrate the effectiveness of this method used in singularly perturbed semi-Markov jump coupled neural networks.

Upgrade in the elastic surface algorithm for airfoil inverse design with variable attack angle via controlled leading-edge movement

Upgrade in the elastic surface algorithm for airfoil inverse design with variable attack angle via controlled leading-edge movement

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

BibPat: Quantum K-means Clustering with Incremental Enhancement

Background: One of the main areas of study within the broader paradigm of quantum machine learning is quantum clustering (QC). Considering the potential time and cost savings that solutions to realworld issues employing QC algorithms bring, in comparison to their classical methods, researchers have recently developed a keen interest in QC. With new algorithms and their applications being invented virtually every other day, this is still a highly young and fascinating area of research. Based on the background information provided, this work aims to analyze research and patent databases spanning twelve years (2010 to mid-2022) to identify and understand the publishing and patent trends in the field of QC. This study aims to study the topological analyses, important study areas, relationships, and collaboration patterns that distinguish traditional and developing research clusters. The graphical representation of the progress of publications and patents over time depends on such rigorous field mapping. This paper presents a comprehensive list of all the sources through the network, bibliometric and patentometric (BibPat) analysis, and future research scope in the QC. The top authors, universities, and research fields were listed after the primary and secondary keywords connected to the quantum K-means clustering algorithm in the analysis design. Reviewing the articles and then delving into the specifics of the patents will help us evaluate the total body of work on the quantum K-means clustering technique. Using the thorough BibPat tools and numerous research and patent databases like Scopus, IEEE, ACM, Google Scholar, Lens, Google Patents, and Espacenet, the analysis design displays the patents and journal papers that have been published. Additionally, it is crucial for later research because it aids in the identification of areas for current research interests and possible avenues for future study. QC offers various studies in disciplines from computer science to psychology. The Ministry of Education, China, produced most publications. Since 2014, the trend has been up, and experts continue studying the issue. The BibPat analysis shows that the Chinese National Natural Science Foundation has facilitated funding for cutting-edge research. In order to open the door for future research and investigation on the substantial amount of unstructured real-time data, the report concluded by proposing an incremental QC approach.

Optimization Algorithm for Green Environment Design Based on Artificial Intelligence

Optimization Algorithm for Green Environment Design Based on Artificial Intelligence

Distributed Event-Triggered Optimal Algorithm Designs for Economic Dispatching of DC Microgrid with Conventional and Renewable Generators: Actuator-Based Control and Optimization

Distributed Event-Triggered Optimal Algorithm Designs for Economic Dispatching of DC Microgrid with Conventional and Renewable Generators: Actuator-Based Control and Optimization

Event-triggered quasi-time-varying [formula omitted] filtering for switched systems via multiple trigger-dependent Lyapunov functionals

This paper investigates the event-triggered filtering problem for switched systems from the perspective of switched impulsive systems (SISs) by exploiting the characteristics of trigger and impulse behavior. By constructing multiple trigger-dependent Lyapunov functionals (MTDLFs), an event-triggered quasi-time-varying (ET-QTV) H∞ filtering scheme is developed. Since the MTDLFs properly capture the event-induced impulse property of SISs, they release less conservatism. The filtering stability in the scenario of frequent plant switching during an interevent interval is also guaranteed by incorporating the average dwell time switching. The filtering synthesis condition is formulated as a set of QTV matrix inequalities defined over a finite time horizon, based on which a sum of squares-based filter design algorithm is presented further. Additionally, the derivation of a lower bound on interevent intervals is clarified for the event-triggering mechanism (ETM) to exclude the Zeno phenomenon. Finally, the effectiveness and potential practicability of the proposed filtering scheme are validated using a morphing aircraft model.

Digital Signal Processing Simulation Based on Matrix-Vector description

Even a small error in the design and implementation of a DSP algorithm can cause complete inoperability, finding and fixing these errors is laborious and time-consuming. Of course, the validation of the correct operation of a DSP algorithm must be confirmed by measurement. In case of a deviation, it is important to find the root of the problem, the simulation of the DSP algorithm is a reliable and efficient task which can be performed by a desktop computer running some mathematical program. The simulation – as an operation check – could be performed in the time domain, or in the frequency domain or in both. The system of equations describing the DSP algorithm can also be written in matrix-vector form, therefore these algorithms can be effectively simulated in the Matlab® environment.

Aerodynamic Characteristics and Its Sensitivity Analysis of a Cylindrical Projectile at Different Incidences

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value.

Design and Implementation of Encryption/ Decryption Architectures for BFV Homomorphic Encryption Scheme

Abstract: Now a days security is the prime part for both, the satellites communication of the electronics data and the stored data, hence encryption is important for information processing system and communication network. The proposed approach is easy to learn due the use of speed efficient Vedic multiplier. Since it minimizes the execution time and area, so the delay and power consumption is further decrease by the compact and flexible approach in the Mix column transform which takes different approach rather than conventional multiplication previously. The structure style of modeling helps to easy understandable the proposed design of algorithm. BFV is the symmetrical has designed and verified in the Verilog HDL in Xilinx tool. In this project we present using kogge-stone adder and Vedic multiplier.

Experimental investigation of the effective parameters on the lead extrusion damper performance

Lead extrusion dampers (LED) are employed for seismic response control of various structures due to their advantages over other energy-dissipative devices. However, a reliable design algorithm and an optimization process are essential to ensure optimal performance within the host structural system. Thus, the influence of device parameters on the damper behavior must be clearly understood. In the first part of this comprehensive study, the effect of different parameters on the LED performance was experimentally investigated. To this end, 23 specimens with different prestressing levels, bulge-to-shaft diameter ratios, shaft-to-bulge transition zone geometries, and device length ratios were cyclically tested under varying displacement amplitudes, i.e., 5 mm, 10 mm, and 20 mm, and loading rates, 0.5 mm/s, 1.0 mm/s, and 2.0 mm/s. Subsequently, they were exposed to a ramp-type loading up to the amplified design displacement. After satisfactorily completing the experimental tests, the behavior of the LEDs was compared in terms of their hysteretic behavior, energy dissipation capacities, temperature change, and stable behavior. It was concluded that the most influential parameters on the LED behavior were the bulge-to-shaft diameter ratio and the prestressing level on the lead. In addition to its effects on improving the hysteretic behavior, having a smooth shaft-to-bulge transition zone geometry was essential to guarantee a stable damper behavior.

A single-individual based variable neighborhood search algorithm for the blocking hybrid flow shop group scheduling problem

The Blocking Hybrid Flow Shop Group Scheduling Problem (BHFGSP) is prevalent within the manufacturing industry, where the ordering of groups poses a significant challenge for dispatchers. Moreover, the blocking constraints associated with jobs significantly influence energy consumption, yet these constraints are often overlooked in algorithm design. To address these issues effectively, a single-individual-based variable neighborhood search strategy is introduced. For the challenge of group ordering, a group-based neighborhood search strategy is proposed. This strategy is complemented by a job-based neighborhood search strategy to tackle the issues of blocking and job sequencing. These two neighborhood search strategies are designed to enhance the performance of the algorithm significantly. Furthermore, to augment the local search abilities of the proposed algorithm, the concept of a single-individual approach from the iterated greedy algorithm is integrated. The performance of the proposed algorithm is validated through 36 instances, demonstrating its efficiency in solving BHFGSPs compared to state-of-the-art algorithms. Notably, the proposed algorithm achieves a reduction in energy consumption by an average of 58% to 63.4% compared to previous best solutions.

Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks

Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate.

Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.

The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively. We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative. Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework. The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies. As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.

Design of Adaptive Target Tracking Algorithm for Robots Based on Visual Attention Mechanism

Adaptive target tracking with visual attention represents a sophisticated approach to object detection and localization in dynamic environments. With principles inspired by human visual perception, this methodology employs mechanisms of selective attention to prioritize relevant visual information for tracking moving targets. By dynamically adjusting attentional focus based on salient visual cues and target motion characteristics, adaptive target tracking enhances the efficiency and accuracy of object localization in cluttered scenes. This research presents a novel adaptive target tracking algorithm designed for robotic systems, integrating a visual attention mechanism with the Fuzzy Clustering Multi-Point Tracking utilizing the Green Channel (FC-MPT-GC) approach. The proposed FC-MPT-GC model comprises of Fuzzy Clustering for the extraction of features in the robots-based environment. The FC-MPT-GC model uses the estimation of green channels in the classification environment. With the estimation of features in the environment with Fuzzy C-means clustering green channels are deployed in the deep learning, The proposed algorithm aims to enhance the adaptability and precision of target tracking in dynamic environments. By incorporating a visual attention mechanism, the algorithm dynamically allocates attentional focus to salient regions of the visual input, optimizing the tracking process for moving targets. The FC-MPT-GC methodology further refines target localization by utilizing fuzzy clustering and multi-point tracking strategies, particularly leveraging information from the Green Channel to improve robustness in various lighting conditions. Simulation analysis demonstrated that the proposed FC-MPT-GC model tracking accuracy is achieved at 95.1% with the minimal computation time of 15.2 ms.