Search Speed Research Articles

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

The wheel–rail adhesion is one of the key factors limiting the traction performance of railway vehicles. To meet the adhesion optimization needs and rapidly obtain wheel–rail creep characteristics under specific operating conditions, an engineering identification method for wheel–rail adhesion characteristics based on a nonlinear model is proposed. The proposed method, built upon the traditional Teaching-Learning-Based Optimization (TLBO) algorithm, has been adapted to the specific nature of nonlinear wheel–rail adhesion model parameters identification, enhancing both the search speed in the early stages and the search accuracy in the later stages of the algorithm. The proposed identification algorithm is validated using experimental data from the South African 22E dual-flow locomotive. The validation results demonstrate that the proposed identification algorithm can obtain a nonlinear wheel–rail adhesion characteristics model with an average adhesion coefficient error of around 0.01 within 50 iteration steps. These validation results indicate promising prospects for the engineering practice of the proposed algorithm.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Faster Person Re-Identification: One-Shot-Filter and Coarse-to-Fine Search.

Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g., 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) module together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a Fβ score that can be optimised by Gaussian cumulative distribution functions. Besides, we find even short code (e.g., 32) still takes a long time under large-scale gallery due to the O(n) time complexity. To solve the problem, we propose a gallery-size-free latent-attributes-based One-Shot-Filter (OSF) strategy, that is always O(1) time complexity, to quickly filter major easy negative gallery images, Specifically, we design a Latent-Attribute-Learning (LAL) module supervised a Single-Direction-Metric (SDM) Loss. LAL is derived from principal component analysis (PCA) that keeps largest variance using shortest feature vector, meanwhile enabling batch and end-to-end learning. Every logit of a feature vector represents a meaningful attribute. SDM is carefully designed for fine-grained attribute supervision, outperforming common metrics such as Euclidean and Cosine metrics. Experimental results on 2 datasets show that CtF+OSF is not only 2% more accurate but also 5× faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is 50× faster with comparable accuracy. OSF further speeds CtF by 2× again and upto 10× in total with almost no accuracy drop.

A Sinh-Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems.

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh-Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.

Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm

A ground station uses heterogeneous unmanned aerial vehicles (UAVs) with varying observation resources (primarily airborne cameras) for oblique photography based on mapping task requirements to obtain access to geographic information. In this paper, a cooperative multi-task allocation model is proposed for heterogeneous UAVs based on area segmentation to approximate real-world mapping applications. Specifically, it considers area mapping task requirements, UAV kinematics constraints, resource constraints (maximum endurance and sensor capabilities), and task number ceiling constraints; and optimises the task sequence based on the objective function that considers UAVs coverage path planning characteristics. However, a general genetic algorithm (GA) could undermine the feasibility of the solution in the population, resulting in an undesirable optimisation effect when performing genetic operations on a large-scale task allocation problem with strong coupling constraints and an obscure encoding relation. An improved double-chromosome encoding GA with a conflict-mediation mechanism is proposed to perform genetic operations on the population while meeting these constraints. The proposed GA ensures the universal excellence of population evolution while significantly improving the population optimisation and convergence performance. The simulation results show that the proposed GA has greater global search ability, stability, and speed compared with existing algorithms.

Research on Color Attention Bias Caused by Olfactory Stimulation in Visual Search Task

People often connect and integrate information from different sensory channels to form cross-channel connection. Previous studies have proved that vision, as a dominant perception, will affect the perception and recognition of olfactory channels. This study attempts to prove experimentally that olfactory channels have an influence on the perception and recognition of visual information. Experimental method: Subjects were asked to search the target with a specific shape visually after deeply smelling fragrance card, and their respective reaction time was tested. The indicative of olfactory stimuli is regarded as an intragroup variable, and the forward and reverse correlation between target color and olfactory stimuli is regarded as an inter-group variable. The experimental results show that when the color associated with olfactory stimulation appears on the target pattern, it will speed up the visual search, but when the color associated with olfactory stimulation appears on the interference pattern, it will hinder the visual search speed. Conclusion: It is proved that olfactory cues will lead to visual attention bias to its associated colors, indicating that there is a cross-channel influence of olfactory stimuli on visual attention.

A hybrid particle swarm optimization algorithm for solving engineering problem

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function (f1-f13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${f}_{1}-{f}_{13}$$\\end{document}) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.

Efficient Image Retrieval Using Hierarchical K-Means Clustering.

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

Comparison of cognitive functions in individuals with primary psychosis and substance-induced psychotic disorders

ABSTRACT Background Cognitive dysfunctions are common in patients with primary psychosis disorder (PPD) and substance-induced psychotic disorder (SIPD), and this problem is very important in the course of their treatment and relapse. The present research was done to study the cognitive functions in the patients suffering from PPD compared to the patients with SIPD. Methods In this analytical study, the study population included all patients admitted to Farabi Hospital, Kermanshah, Iran, diagnosed with PPD and SIPD, whose cognitive functions were compared using the Rey Auditory Verbal Learning Test (RAVLT), Symbol Digit Modalities Test (SDMT) and Verbal Fluency Test (VFT). Results Only the mean number of words in the first list that the patient was unable to identify (Miss) distinguished the two groups in terms of the mean RAVLT scores. Due to the number of accurate answers in the SDMT and the speed of visual search, there was no discernible difference between the PPD and SIPD groups. The results showed a significant difference between the two groups due to the total number of correct VFT word in two parts of letters and categories. Conclusion The results of the present study can be used in the treatment and rehabilitation of PPD and SIPD patients.

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

Object Detection in E-Commerce Using YOLO in Real Time

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.

Sensitive remote homology search by local alignment of small positional embeddings from protein language models

Accurately detecting distant evolutionary relationships between proteins remains an ongoing challenge in bioinformatics. Search methods based on primary sequence struggle to accurately detect homology between sequences with less than 20% amino acid identity. Profile- and structure-based strategies extend sensitive search capabilities into this twilight zone of sequence similarity but require slow pre-processing steps. Recently, whole-protein and positional embeddings from deep neural networks have shown promise for providing sensitive sequence comparison and annotation at long evolutionary distances. Embeddings are generally faster to compute than profiles and predicted structures but still suffer several drawbacks related to the ability of whole-protein embeddings to discriminate domain-level homology, and the database size and search speed of methods using positional embeddings. In this work, we show that low-dimensionality positional embeddings can be used directly in speed-optimized local search algorithms. As a proof of concept, we use the ESM2 3B model to convert primary sequences directly into the 3D interaction (3Di) alphabet or amino acid profiles and use these embeddings as input to the highly optimized Foldseek, HMMER3, and HH-suite search algorithms. Our results suggest that positional embeddings as small as a single byte can provide sufficient information for dramatically improved sensitivity over amino acid sequence searches without sacrificing search speed.

Sensitive remote homology search by local alignment of small positional embeddings from protein language models.

Accurately detecting distant evolutionary relationships between proteins remains an ongoing challenge in bioinformatics. Search methods based on primary sequence struggle to accurately detect homology between sequences with less than 20% amino acid identity. Profile- and structure-based strategies extend sensitive search capabilities into this twilight zone of sequence similarity but require slow pre-processing steps. Recently, whole-protein and positional embeddings from deep neural networks have shown promise for providing sensitive sequence comparison and annotation at long evolutionary distances. Embeddings are generally faster to compute than profiles and predicted structures but still suffer several drawbacks related to the ability of whole-protein embeddings to discriminate domain-level homology, and the database size and search speed of methods using positional embeddings. In this work, we show that low-dimensionality positional embeddings can be used directly in speed-optimized local search algorithms. As a proof of concept, we use the ESM2 3B model to convert primary sequences directly into the 3D interaction (3Di) alphabet or amino acid profiles and use these embeddings as input to the highly optimized Foldseek, HMMER3, and HH-suite search algorithms. Our results suggest that positional embeddings as small as a single byte can provide sufficient information for dramatically improved sensitivity over amino acid sequence searches without sacrificing search speed.

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network

BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models.

Smooth and Efficient Path Planning for Car-like Mobile Robot Using Improved Ant Colony Optimization in Narrow and Large-Size Scenes

Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method’s effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios.

DISPERSAL IN PREY–PREDATOR SYSTEMS WITH PREY’S FEAR AND PREDATOR’S ADAPTIVE SEARCH

This paper considers predator–prey systems in which the predator adapts the search speed to prey’s density. The prey admits the cost of fear because of the predator and moves between source–sink patches to escape from predation. Using dynamical systems theory, we demonstrate equilibrium stability, uniform persistence and bifurcations in the system. It is shown that enhancing the search speed of predator and/or decreasing prey’s dispersal from the source to sink could promote persistence of the system, while increasing the fear cost would stabilize the system. Moreover, dispersal could make the prey persist in both source–sink patches, even drive the predator into extinction. The dispersal could also make the prey reach a total population abundance larger than that without dispersal, even larger than the carrying capacity. Asymmetry in dispersal plays a role in the persistence and abundance. A novel finding of this work is that dispersal may lead to results reversing those without dispersal. Numerical simulations illustrate and extend our results. This work is important in understanding complexity in predation systems.

Design a self-controlled high-performance evaluation of content addressable memories using 45 nm technology

A special type of random access memory (RAM) array called as content addressable memory (CAM), in which stored data is compared with the search data which can be returning the address. In the applications of highspeed searching, the CAMs are used. NOR type matchline CAMs are helpful for applications requiring faster search speeds. Because the NOR type match line (ML) CAM consumes a lot of power, therefore many published designs have attempted to reduce power consumption. The design self-controlled high-performance content addressable memories (SC-CAM) using 45-nm technology is presented in this paper. The 6T 4×4 CAM arrays in this paper uses SC logic and Tanner tools 45-nm technology. When compared to the conventional CAM, described SC-CAM architecture reduces the number of voltages sources. Described 6T 4×4 SC-CAM design needs less number of MOSFETs than existed 8T 4×4 CAM array and thus reduces the area with high speed.

Enhanced Efficiency in Permanent Magnet Synchronous Motor Drive Systems with MEPA Control Based on an Improved Iron Loss Model

This paper investigates the efficiency optimization problem in the drive system of Permanent Magnet Synchronous Motors (PMSM) based on the iron loss resistance model. Initially, an efficiency calculation method is proposed, utilizing the direct current (DC) side power based on the iron loss motor model as the input power. This approach determines the system’s input power by measuring DC side voltage and current, accounting for the impacts of temperature, magnetic saturation, and inverter nonlinearity. It avoids the need for intricate nonlinear loss modeling of the inverter, thereby simplifying the computational requirements for system efficiency. Simultaneously, the d-q axis equivalent equations based on the iron loss motor model are employed to calculate the system’s output power, enhancing the accuracy of the system efficiency calculation. An improved discrete gradient descent algorithm is presented based on this system efficiency calculation model, accelerating the search for the optimal current angle and improving its accuracy. In comparison to existing methods, this approach exhibits adaptive step size and provides more precise and higher efficiency calculations, resulting in faster search speeds. Experimental and simulation evaluations are conducted to assess the effectiveness of the proposed methods.

EVALUASI USABILITAS SISTEM MENGGUNAKAN METODE SYSTEM USABILITY SCALE (SUS) PADA APLIKASI AKHLAQU DENGAN PENERAPAN TEKNIK INDEXING MONG

The Akhlaqu Application is a web-based Islamic learning platform used by Al-Falah Integrated Islamic Elementary School to facilitate the recording and reporting of students’ activities in developing their memorization. This research aims to assess the level of user satisfaction with the Akhlaqu Application using the System Usability Scale (SUS). The results showed a high level of usability, measured by a System Usability Scale (SUS) score of 89.7, indicating a “very good” quality application that meets high usability standards. In addition, the implementation of text indexes on MongoDB has a positive impact on the speed of transaction data search, especially on complex data searches, improving the efficiency and responsiveness of the application. The implications of this research involve recommendations to continue to improve the usability and performance of the Akhlaqu Application, by maintaining the use of MongoDB index techniques as a reliable solution for optimizing the database. The study concludes that the Akhlaqu Application is an effective tool for managing students’ memorization and recommends further improvements to enhance its usability and performance.