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Noise control of audio recognition equipment for multimedia system
Get PDFNoise control is one of the most critical technical indicators to improve the performance of intelligent audio recognition system. Based on the noise cancellation technology, a distributed low noise amplification circuit design was proposed, and the PE15-0P technology was applied to realize broadband low noise amplification. The amplifier circuit used diodes and resistors for voltage division, which effectively achieved bias saturation at the transistor and then diode structure. According to the design of low noise amplifier, the noise output characteristics were simulated and analyzed. An audio enhancement method based on noise type recognition was proposed, which can optimize noise estimation by selecting parameter combinations according to noise type, so as to improve the quality and intelligibility of noise frequency signals in various noise environments. From the aspects of hardware and algorithm design, the noise signal was comprehensively reduced, and the accuracy of audio recognition was significantly improved
A one-dimensional high-order dynamic model for twin-cell box girders with deformable cross-section
Get PDFA one-dimensional high-order dynamic model for single-box twin-cell box girders is presented together with the pattern recognition algorithm. The model takes into account the deformable cross-section and can accurately predict its 3D dynamic behaviors. The cross-section deformation is captured by basis functions satisfying displacement continuity condition, which is essential to construct the initial model formulation based on the Hamilton principle. The axial variation patterns of generalized coordinates are decoupled by solving the eigenvalue problem. On this basis, the combinations of basis functions are obtained to bring out cross-section deformation. The cross-section deformation, hierarchically organized and physically meaningful, are used to update the basis functions in the reconstructed high-order model. Numerical analysis has verified the accuracy and applicability of the reconstructed one-dimensional high-order model
Visual reconstruction method of architectural space under laser point cloud big data
Get PDFIn order to solve the problem that the reconstruction accuracy and integrity are affected due to the large amount of point cloud data in the process of building space reconstruction, the visual reconstruction method of building space under laser point cloud big data is studied. The three-dimensional laser scanner is used to collect the laser point cloud big data in the building space, and the laser point cloud big data is organized and processed through three steps: hierarchical calculation of the point cloud pyramid, thinning treatment and block treatment. From the processing results of laser point cloud big data, the line features of building space are extracted based on the improved Mean-shift method, and the continuous broken lines in the point cloud data of building space are extracted by using the double radius threshold line tracing method. According to the feature extraction results of point cloud data in building space, the visual reconstruction of building space is completed through the process of translation matching and space matching. The experimental results show that this method can realize the visual reconstruction of architectural space, and the average reconstruction accuracy is higher than that of 97 %, and the reconstruction completion and smoothness are higher than 95 %
Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection
Get PDFThis research introduces a novel approach for detecting defects in concrete structures. It utilizes the Gramian Angular Difference Field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact-echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1-score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field
Mayfly optimization algorithm: a review
Get PDFThis paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future
A review on positioning techniques of mobile robots
Get PDFIn this article, we have reviewed the available positioning, localization and navigation techniques for mobile robots. Different localization techniques based on diverse technologies are compared with one another, along with diverse algorithms and techniques for analyzing this information. The article highlights algorithms based on odometry, triangulation, visual analysis, and marker detection. The analysis included global, local, and personal location. One acquires knowledge on which method is suitable for indoor use and which for outdoor use, as well as the appropriate environmental conditions for each. The accuracy of the individual methods was compared with that of integrated systems consisting of several methods. For practical knowledge, it is possible to determine whether a particular method is cost-effective for a particular solution and to compare the expenses involved
Integration of robotics and automation in supply chain: a comprehensive review
Get PDFRobotics and automation have developed as key technologies for supply chain management as a result of the increased demand for quicker and more effective supply chains. Robotics and automation improve supply chain management by lowering long-haul expenses, boosting work and usage strength, reducing errors, declining repetitive stock checks, updating orchestrating, taking care of times, and assembling induction to the problematic and hazardous places. Robotics aids in design, creation, etc. Automation helps to do tasks that are often done by people through the use of self-operating physical machines, computer software, and other technology. Despite being widely accepted as a tool to aid in decision-making, supply chain management (SCM) has very seldom used AI and ML. This article investigates several AI and ML sub-fields that are best suited for resolving real-world SCM-related issues in order to fully realize the potential benefits of AI and Ml for SCM. In doing so, this article examines the track record of successful AI and ML applications to supply chain management and highlights the most fruitful SCM domains to apply AI and ML. And also find out the how robotics and automations helps in warehouse management. The most recent developments in robotics and automation for supply chain management are thoroughly reviewed in this paper. We first give a general overview of the difficulties that supply chain management faces before going over the many ways that robotics and automation are used at various points along the supply chain. Additionally, we go over the advantages of robots and automation in supply chain management, including higher efficiency, accuracy, lower costs, and improved safety. Lastly, we discuss some of the present drawbacks and difficulties associated with robots and automation in supply chain management and suggest some possible directions for further investigation
Editor’s Letter. Malocclusion treatment tools evolution – electromagnetic synergism. What is the current status of knowledge? Part I
Get PDFUse of fibre optic systems for detection of small leaks on trunk pipelines
Get PDFThis paper is devoted to the issue of efficiency of application of fibre optic leak detection systems for identification of small leaks on trunk pipelines. The main methods of leak detection currently in use have been considered, and parametric and fibre optic LDS have been selected for comparative analysis. In the course of the research a model of product leakage from an underground oil pipeline equipped with a fibre-optic LDS was built in the COMSOL Multiphysics software package. The result of the simulation was the estimated time of leak identification by the fibre-optic system, which turned out to be much shorter than that of the parametric LDS. Compensable environmental damage for each type of system was then calculated, confirming the effectiveness of fibre optic LDS for detecting small leaks on trunk pipelines due to the significant reduction in compensable damage
Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network
Get PDFThe paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal