Design Of Algorithms Research Articles

Design of Time Series Fuzzy Segmentation Algorithm for Communication Data Classification

In order to solve the problem that the imbalance of communication data sets leads to a significant increase in classification difficulty, a classification algorithm for fuzzy segmentation of time series is proposed. The principal component analysis method is used to obtain the eigenvector with the largest eigenvalue. The time series of data is established according to the interval number theory. The segmentation target of the communication data time series is characterized by the Langmuir distance measurement function between data and categories. The classification result of fuzzy segmentation is obtained based on the judgment relationship between the difference of fuzzy classification matrix and the closing condition. The experimental results show that the number of data in the three classification situations of the algorithm in this paper is always at the corresponding ideal level, with high accuracy and low error and failure.

Beyond Performance Metrics: The Critical Role of Resource-Based Evaluation in Assessing IoT Attack Detectors

The proliferation of threats within the Internet of Things (IoT) environment is intensifying, largely due to the inherent limitations of this technology. The panoply of anti-threats based on artificial intelligence suffer from the complete embedment of models in limited resources. Tiny Machine Learning (TinyML) is presented as an opportunity in optimizing and selecting machine learning algorithms specifically tailored for intrusion detection systems (IDS) on limited-resource devices. This article addresses the challenges that must be overcome to enable the deployment of machine learning models on devices with constrained resources. In particular, it introduces additional indicators that could influence the algorithmic design of IoT models. Utilizing the PyCaret tool on the TON_IoT dataset, which encompasses nine distinct attacks, we developed and evaluated our approach for selecting the optimal algorithm from fourteen supervised learning models. The proposed tool, beyond the traditional six performance metrics, emphasizes resource consumption metrics, including memory, processor usage, battery life, and execution time – key considerations for TinyML in model refinement and selection. This study has identified less resource-intensive models suitable for developers in the design of IDS for IoT systems. We believe this research offers a foundational framework for the development of lightweight and efficient IoT vulnerability detection solutions.

Lattice metamaterials with controllable mechanical properties inspired by projection of four-dimensional hypercubes

There has been an increasing interest among the material research community in the pursuit of enhancing the designability of mechanical properties. The existing approaches usually resorted to sophisticated algorithms (such as machine learning) for the reverse design of materials with specific properties. Different from these existing approaches, here we propose a new approach to create lattice metamaterials with continuously controllable mechanical properties by continuously adjusting the geometric parameters of a unique cell topology originated from the projection of four-dimensional hypercubes. The cells contain an inner region and an outer region, each with different deformation characteristics. For example, the inner region is a stretching-dominated simple cubic (SC) unit cell, while the outer region is a bending-dominated body-centered cubic (BCC) unit cell. Specifically, both stiffness and strength isotropy can be simultaneously realized. The proposed lattice metamaterial exhibits intriguing feature of dual stress plateaus. These plateaus can be effectively controlled by adjusting the geometric parameters of inner and outer regions, which enables these lattice metamaterials to hold promising application prospects in the energy absorption scenarios, such as vehicle and pedestrian protection. Such lattice metamaterial design can be used to realize the gradient distribution of mechanical properties through continuous transition of cell topology without introduction of inefficient interfaces, providing a new approach for the design of heterogeneous metamaterials used in the scenarios involving non-uniform stress distribution.

A hybrid approach combining UD and GA-CV-SVM to evaluate shear performance in high asphalt concrete core

The shear effect on high asphalt concrete core is significant. However, studies on the reliability of 100-meter-scale cores against shear damage remain limited. A key challenge in this research field is establishing the control criteria for the core and improving the computational efficiency of implicit limit state function (LSF). Additionally, the impact of material parameter uncertainty on the shear failure reliability of the core during the dam construction and impoundment stages remains unclear. To address this, a safety evaluation method based on time discretization was proposed, combining uniform design (UD), K-fold cross-validation (K-CV), and genetic algorithm (GA) to optimize the support vector machines (SVM). The core parameters of 52 asphalt concrete-core rockfill dams (ACCRDs) were analyzed, with the statistical values of the basic variables considered in determining the reliability index. The theoretical derivation of the critical shear failure safety index established a stability formula to assess the safety state of the dam core. The significance parameters were identified, and the sample points were generated at each stage using UD, and the Support Vector Regression (SVR) was applied to reconstruct the LSF, and reliability was calculated through the checking point method.

The impact of quantum computing on the development of algorithms and software

Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions

Optimal estimation of SoC, SoH using machine learning models and design of control algorithm for active cell balancing in lithium ion batteries

Abstract Electric vehicles transform the automotive industry by replacing traditional vehicles powered by fossil fuels with less polluting and efficient vehicles. They are powered by rechargeable Li-ion batteries. While there are drawbacks associated with Li-ion battery technology, it's important to note that these challenges can be addressed or mitigated effectively. A battery management system ensures the tracking of all functions performed by the battery. An advanced battery management system should accurately estimate the battery's state of health and state of charge. This paper aims to develop machine learning models for the estimation of the state of health and state of charge and to implement cell balancing in a battery management system. A dataset of battery charging and discharging profiles was used to train various machine learning models for estimation. These methods are computationally less complex than conventional methods. In addition, a cell balancing algorithm is implemented to control the charging and discharging of individual cells in a battery pack and balance the state of charge of each cell. The machine learning solution is created using several machine learning models, including Gradient Boosting, Random Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models Gradient Boosting and Random Forest provide good MSE and R2 score. The use of machine learning algorithms for the assessment of state of health and charge estimation combined with the design of an efficient control algorithm for active cell balancing offers significant advancements in the battery management system to optimise the performance, reliability, and useful life of Li-ion batteries. The paper also presents a case study utilising a combination of deep learning-based SoC, SoH estimation algorithms in a simulated data set. A well-designed control algorithm for active cell balancing presents a holistic and effective approach to optimise the performance, reliability, and lifespan of Li-ion batteries.

Research on a High-Dynamics Acquisition Algorithm for New Binary Offset Carrier Signal in UAV Communication

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately 10.9×109 and 8.9×109, and the number of real multiplications is approximately 7.6×109 and 6.7×109. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links.

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Advanced swarm intelligence algorithms in quantum circuit design

This study explores the potential of employing algorithms like iSOMA, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimization, and Ant Colony Optimization for the design of quantum computing circuits. Utilizing the Qiskit environment, the research involved simulating a straightforward quantum circuit with variable parameters. To substantiate the effectiveness of these algorithms, three distinct experimental setups were conducted under varying conditions and degrees of freedom. The findings reveal that these algorithms are not only suitable for simulations but also excel in identifying solutions that conserve qubits. A comparative analysis of the methods was performed using the Friedman test, followed by the Nemenyi post-hoc test to evaluate their relative performance.

TeachText: CrossModal Text-Video Retrieval through Generalized Distillation

In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we investigate the design of such algorithms and propose a novel generalized distillation method, TeachText, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the retrieval model. TeachText yields significant gains on a number of video retrieval benchmarks without incurring additional computational overhead during inference and was used to produce the winning entry in the Condensed Movie Challenge at ICCV 2021. We show how TeachText can be extended to include multiple video modalities, reducing computational cost at inference without compromising performance. Finally, we demonstrate the application of our method to the task of removing noisy descriptions from the training partitions of retrieval datasets to improve performance. Code and data can be found at https://www.robots.ox.ac.uk/~vgg/research/teachtext/.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

Revisiting RFID Missing Tag Identification: Theoretical Foundation and Algorithm Design

Revisiting RFID Missing Tag Identification: Theoretical Foundation and Algorithm Design

Validation of an algorithm that separates gaseous micro-embolic signals and artifacts during transcranial Doppler persistent foramen ovale examinations

ObjectivePersistent foramen ovale (PFO) is a risk factor for young stroke. Agitated saline serum is used to deliver small gaseous emboli to the brain in transcranial Doppler ultrasound (TCD) for the detection and grading of PFO. In this study we validated a PFO algorithm that can differentiate between gaseous emboli and artifacts. MethodsThe validation cohort comprised 18 patients with positive PFO examinations. The PFO algorithm uses a binary tree that separates high-intensity transients signals (HITs) into gaseous emboli or artifacts based on intensity, zero-crossing, and velocity parameters. ResultsThe cohort exhibited 385 macroscopic gaseous emboli meeting the >3 dB criterion. An additional 137 gaseous emboli were noticed below the 3-dB intensity cutoff value. The low-intensity gaseous emboli included both macroscopic and microscopic air bubbles observed in curtains. Nearly all emboli (98 %) above the 3-dB level showed overt frequency modulation. The overall accuracy of the PFO algorithm in discriminating macroscopic gaseous emboli and artifacts was 96.4 %, with a similar percentage of sensitivity and specificity (96.4 %). The inter-observer agreement of human experts was excellent (ic-CC 0.989 and 0.953). ConclusionsMacroscopic gaseous emboli and artifacts during PFO exams can be accurately discriminated by the PFO algorithm. The PFO algorithm cannot be used as a standalone system as microscopic air bubbles might escape proper identification. This knowledge will be important in the design of future PFO algorithms which should make it possible to classify the PFO grade without the interference of humans.

Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study.

Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive. In this study, we collaborated with one of the world's largest OMHCs; our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors. We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a "sandbox" to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates. Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker-counselor matches occurred using a "first come, first served" protocol, whereas relatively fewer matches occurred using a "last come, first served" protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group revealed disparities-underaged and gender minority groups had lower average chat ratings and higher blocking rates on the site when compared to their majority counterparts, indicating the potential benefits of algorithmically matching them. We found that some protocols, such as a "filter"-based approach that matched vulnerable support seekers only with a counselor of their same demographic, led to improvements for these groups but resulted in lower satisfaction (-12%) among the overall population. However, this trade-off between minority and majority groups was not observed when using "topic" as a matching criterion. Topic-based matching actually outperformed the filter-based protocol among underaged people and led to significant improvements over the status quo among all minority and majority groups-specifically, a 6% average chat rating improvement and a decrease in blocking incidents from 5.86% to 4.26%. Agent-based modeling can reveal significant design considerations in the OMHC context, including trade-offs in various outcome metrics and the potential benefits of algorithmic matching for marginalized communities.

ID: 325692 Design of a Digitally-triggered Patient Follow-up Algorithm for Change Point Detection After SCS Implant

ID: 325692 Design of a Digitally-triggered Patient Follow-up Algorithm for Change Point Detection After SCS Implant

Design of an FPGA-based short-circuit fault diagnosis algorithm for DC current-limiting fuses

Abstract In recent years, DC current-limiting fuses have played a crucial role in the comprehensive power systems of ships, rail transport, and new energy sectors, ensuring the safe and reliable operation of DC power systems. However, with the increase in the capacity of DC power systems, short-circuit faults can lead to a rise in short-circuit current at rates of up to 40 A/μs, with peak short-circuit currents reaching up to 105KA. This poses higher demands on the accuracy and speed of fuse diagnostics to interrupt short-circuit currents. To address this issue, this paper introduces a fault diagnostic algorithm based on FPGA that can calculate the rise rate of short-circuit currents in real-time: employing FPGA with its high integration, abundant logic resources, and parallel data processing capabilities as the core of the measurement and control system to enhance the speed of short-circuit fault diagnosis from a hardware perspective; replacing the common large current trip protection and DDL protection algorithms with the Newton interpolation algorithm. This improvement calculates the rise rate of each current data point instead of the average rise rate over a period, thus enhancing the speed and accuracy of short-circuit fault diagnosis from a software perspective. The paper also discusses the design of the system’s hardware and software and the construction of the experimental platform. Simulation and experimental results show that the fault diagnosis time of the algorithm has been reduced from 98μs to 36μs, with the numerical error in the short-circuit current rise rate within 2%, and a significant reduction in the peak value of short-circuit current, meeting performance requirements and effectively ensuring the safe and reliable operation of DC power systems.

Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design

This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity.

Quality function deployment approach to urban ecological public art design centred on resident needs

To improve the quality management of urban ecological public art design and the service centered on residents’ needs. Our study introduces a new Quality Function Deployment (QFD)decision-making model called Kano-FAHP-QFD, which applies to urban ecological public art design. Specifically, a new QFD framework is proposed, combining the Kano model and Fuzzy AHP (FAHP). This paper well explains the importance of ranking urban residents’ needs and the impacts of the new framework in defining the quality management of urban ecological public art design and the satisfaction of urban residents, while using the framework as a set of prioritized strategies in urban renewal to guide the design and planning of urban ecological public art. The author combined these three approaches to more accurately capture and address their respective issues and thus better understand the residents’ needs for urban ecological public art design. In essence, this study investigates how urban ecological public art can be designed and planned in urban renewal through a novel QFD algorithm for urban ecological public art design, with application case validation. Thus, the method keeps the satisfaction of urban residents in line with the positioning and strategies of urban ecological public art design, which is dedicated to enhancing urban vitality, promoting urban renaissance, and sustainable development. It also provides new research ideas for the development of urban ecological public art design in the future.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.