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Pareto Genetic algorithm optimization of Broadband Dipole Antenna Elements for Use in a Low Frequency Radio Telescope Array Including Mutual Coupling Effects

Pareto Genetic Algorithm Optimization of Broadband Dipole An...

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2008 IEEE Antennas and Propagation Society International Symposium (APSURSI 2008), vol.6

作者： Aaron Kerkhoff Hao Ling Applied Research Laboratories University of Texas Austin Dept. of Elec. and Comp. Eng. University of Texas Austin

ISBN: (纸本)9781424420414

The Long Wavelength Array (LWA) is a radio telescope currently under development to operate between 20 and 80 MHz. The LWA will consist of 52 stations spread over a diameter of approximately 400 km. Each station is a planar phased array, roughly 100 m in diameter, containing hundreds of crossed dipole antenna elements. An aperiodic arrangement of antenna elements is planned in order to achieve grating lobe-free operation over the entire frequency band while using a minimal number of antenna elements. A problem with aperiodic arrays is that the responses of different antenna elements vary in a random fashion due to mutual coupling interactions. This can lead to problems such as scan angle-dependent changes in the main beam and sidelobes, which can complicate system calibration. Therefore, it is desired to minimize the effects of mutual coupling. These effects can be characterized by calculating the full array response by means of full-wave simulation, but this is computationally intensive for large arrays. In [1], it is shown that a receive-based definition of coupling between two antennas can be used as a simplified means of designing antenna elements which exhibit reduced mutual coupling effects when operated in a large aperiodic array. Besides low mutual coupling, an LWA antenna element should exhibit high Galactic background or "sky" noise reception levels and smoothly varying, wide beamwidth radiation patterns over the entire frequency band. Since it is generally necessary to make some compromise between these three objectives in antenna design, optimization techniques are useful. In [2], Pareto genetic algorithm (GA) optimization is used to characterize performance trade-offs between sky noise response and radiation pattern quality that can be achieved with broadband planar dipole antennas. The present study builds upon these results by including mutual coupling, in addition to sky noise and radiation pattern objectives, in a Pareto GA optimization of antenna e

关键词： antenna element Mutual coupling Frequency bands DIPOLE ANTENNAS algorithm optimization RADIO TELESCOPES Radiation patterns random fashion sidelobe

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A Genetic and Simulated Annealing Combined algorithm for optimization of Wideband Antenna Matching Networks

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INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION 2012年第unknown期2012卷 251624.1-251624.6页

作者： Chen, Aixin Jiang, Tiehua Chen, Zhizhang Zhang, Yanjun Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 2X4 Canada

A genetic and simulated annealing combined algorithm is presented and applied to optimize broadband matching networks for antennas. As a result, advantages of both the genetic algorithm (GA) and simulated annealing (SA) are taken. Effectiveness and efficiency of the presented combined algorithm are demonstrated by optimization of a wideband matching network for a VHF/UHF discone-based antenna. The optimized parameters provide significant improvements of VSWR and transducer power gain for the antenna.

关键词： simulated annealing Genetic algorithm (GA) antenna matching algorithm optimization ANTENNA broadband antennae broadband matching algorithms Power gain broadband antennas matching network Standing wave ratios Network

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Enhanced prediction of total purine content in hyperspectral images of diverse livestock meat samples using optimization algorithm

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FOOD RESEARCH INTERNATIONAL 2025年 205卷 116000页

作者： Liu, Sijia Cui, Jiarui Lv, Yu Wang, Songlei Ningxia Univ Sch Food Sci & Engn Yinchuan 750021 Peoples R China Northwest Univ Sch Food Sci & Engn Xian 710000 Peoples R China

This research looks at rapid detection and algorithm optimization of total purine in livestock meat with the goal of supporting the creation of low-purine diets. First, the prediction models for total purine content of single species were constructed based on chemical and hyperspectral data. Next, the correlation of spectral curves between different species by Hausdorff distance and Pearson correlation coefficient were analyzed to explore differential purine variation in mixed livestock meat. Finally, the optimal prediction models of total purines in mixed livestock meat were obtained, which were the interval-variable-iterative-spatial-contraction-methodsparrow-search-algorithm-bidirectional-long-short-term-memory-mmulti-head-attention (SSA-Bi-LSTM-MHA) in visible near-infrared hyperspectral imaging technology (Vis-NIR HSI) and iteratively-retain-informativevariables-SSA-Bi-LSTM-MHA in NIR-HSI, respectively, with Rp2 of 0.7820 and 0.7766. Totally, the conclusion that model performance of mixed samples due to increased computational complexity was lower than the ideal model of single samples was obtained and validated. This study demonstrated the potential of HSI for rapid detection of purine content, which further promoted the industrialization of online monitoring of livestock meat quality.

关键词： Hyperspectral imaging technology algorithm optimization SSA-Bi-LSTM-MHA Data fusion Correlation analysis

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The Development and optimization of a Textile Image Processing algorithm (TIPA) for Defect Detection in Conductive Textiles

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PROCESSES 2025年第2期13卷 486-486页

作者： Kim, Sang-Un Kim, Joo-Yong Soongsil Univ Dept Smart Wearable Engn Seoul 06978 South Korea Soongsil Univ Dept Mat Sci & Engn Seoul 06978 South Korea

This study introduces a Textile Image Processing algorithm (TIPA) designed to detect defects in conductive textiles, a crucial element of wearable technology. TIPAs employ image preprocessing, filtering, and classification to identify issues like uneven distribution of conductive particles. When applied to fabrics produced via dip-coating, our TIPA was optimized using a threshold ratio, achieving over 85% accuracy, with a maximum of 100% under ideal conditions. However, detection challenges were noted in fabrics with large, diffuse stains, particularly at extreme threshold ratios. This TIPA proves to be a valuable tool for improving quality control in smart textiles, with potential for further optimization.

关键词： conductive textiles textile image processing algorithm (TIPA) defect detection dip-coating process image filtering algorithm optimization

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Parallel CUDA-Based optimization of the Intersection Calculation Process in the Greiner-Hormann algorithm

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algorithmS 2025年第3期18卷 147-147页

作者： Zuo, Jiwei Fan, Junfu Li, Kuan Liu, Qingyun Zhou, Yuke Zhang, Yi Shandong Univ Technol Sch Civil Engn & Geomat Zibo 255000 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Informat Engn Univ Inst Geog Spatial Informat Zhengzhou 450001 Peoples R China Shandong Agr & Engn Univ Coll Land Resources & Surveying Engn Zibo 255300 Peoples R China Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China

The Greiner-Hormann algorithm is a commonly used polygon overlay analysis algorithm. It uses a double-linked list structure to store vertex data, and its intersection calculation step has a significant effect on the overall operating efficiency of the algorithm. To address the time-consuming intersection calculation process in the Greiner-Hormann algorithm, this paper presents two kernel functions that implement a GPU parallel improvement algorithm based on CUDA multi-threading. This method allocates a thread to each edge of the subject polygon, determines in parallel whether it intersects with each edge of the clipping polygon, transfers the number of intersection points back to the CPU for calculation, and opens up corresponding storage space on the GPU side on the basis of the total number of intersection points;then, information such as intersection coordinates is calculated in parallel. In addition, experiments are conducted on the data of eight polygons with different complexities, and the optimal thread mode, running time, and speedup ratio of the parallel algorithm are statistically analyzed. The experimental results show that when a single CUDA thread block contains 64 threads or 128 threads, the parallel transformation step of the Greiner-Hormann algorithm has the highest computational efficiency. When the complexity of the subject polygon exceeds 53,000, the parallel improvement algorithm can obtain a speedup ratio of approximately three times that of the serial algorithm. This shows that the design method in this paper can effectively improve the operating efficiency of the polygon overlay analysis algorithm in the current large-scale data context.

关键词： overlay analysis polygon clipping shape complexity CUDA parallel computing algorithm optimization

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optimization of LED Luminaire Life Prediction algorithm by Integrating Feature Engineering and Deep Learning Models

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 564-574页

作者： Huang, Xiongbo Foshan Vocat & Tech Coll Informat Technol Ctr Foshan 528137 Peoples R China

With the wide application of LED luminaires in various fields, it has become particularly important to accurately predict their lifetime. The lifetimes of LED luminaires are affected by a variety of factors, including temperature, current, voltage, light intensity, and operating time, and there are complex interactions among these factors. Traditional prediction methods are often difficult to capture these nonlinear relationships, so a more powerful prediction model is needed. In this study, we aim to develop an efficient life prediction model for LED luminaires, and propose a hybrid neural network structure that incorporates a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism by combining feature engineering and deep learning techniques. In the research process, we first collected the operation record data provided by a well-known LED lighting manufacturer and performed detailed data preprocessing, including missing value processing, outlier detection, normalization/standardization, data smoothing, and time series segmentation. Then, we designed and implemented several benchmark models (e.g., linear regression, support vector machine regression, random forest regression, and deep learning model using only LSTM) as well as the proposed hybrid neural network model. Through a detailed experimental design including parameter setting, training and testing, we evaluate the performance of these models and analyze the results. The experimental results show that the proposed hybrid neural network model significantly outperforms the conventional model in key performance metrics such as root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2). In particular, the hybrid model outperforms in terms of Mean Absolute Percentage Error (MAPE) and Maximum Absolute Error (Max AE). In addition, through cross-validation and testing on different datasets, the model shows stable performance under

关键词： Feature engineering deep learning LED lamps life prediction algorithm optimization

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Research on Target Detection optimization in Intelligent Solid Waste Classification Based on YOLOv8 algorithm 24

Research on Target Detection Optimization in Intelligent Sol...

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5th International Conference on Computer Science and Management Technology, ICCSMT 2024

作者： Wu, Zixuan Zhan, Xin Shen, Junwei Fu, Zuming Department of Mechanical and Electronic Engineering Zhuhai University of Science and Technology Guangdong Zhuhai China Department of Mechanical and Electronic Engineering Guangdong University of Technology Guangdong Zhuhai China

ISBN: (纸本)9798400709999

Driven by globalization and urbanization, municipal solid waste has *** the front-end link of resource recovery and environmental protection, garbage classification is particularly urgent in order to improve its automation and intelligence *** on the YOLOv8 algorithm, this study proposes a series of optimization strategies such as using GhostBottleneck lightweight module, fusion improvement of CBAM attention mechanism, and using WIoU loss function for target detection tasks in intelligent garbage classification systems, aiming at improving detection accuracy and system *** study considered complex issues such as lighting conditions during garbage classification, the integrity of the garbage itself, the severity of the environment, and the difficulty of *** used multiple shared garbage image data sets on the network to test the garbage classification optimization *** average accuracy rate was significantly improved, reaching 96.12%. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： algorithm optimization

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Research on surface defect detection method and optimization of paper-plastic composite bag based on improved combined segmentation algorithm

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NONLINEAR ENGINEERING - MODELING AND APPLICATION 2025年第1期14卷

作者： Yan, Guoping Han, Yimeng Xiao, Lan Wu, Shiran Zhong, Fei Zhang, Jiansheng Hubei Univ Technol Sch Mech Engn Hubei 430068 Peoples R China Wuhan Inst Shipbldg Technol Sch Econ & Management Wuhan Peoples R China

At present, the detection of surface defects of paper-plastic composite bags still mainly depends on manual visual inspection, which is very inefficient and easy to cause false detection. Based on the algorithm which combines edge detection and adaptive region growth, binary images of the surface defect areas is extracted, six types of shape features and seven types of invariant moment features are defined by calculation as the basis to complete the effective extraction of the surface features, then a paper-plastic composite bags surface defect detection platform is built to complete the defect detection experiment, the defect screening experiment was completed by the rapid screening method of its surface defect image based on gradient projection difference. At the same time, based on Genetic algorithm (GA), Particle Swarm optimization, and Grey Wolf optimization, parameter optimization models of Support Vector Machine were established, respectively. Through experimental comparison, some results show that the defect detection and classification accuracy rate reach 96.83% based on the GA, and the detection speed with the screening method is 3.75 times faster than that without the screening method. Therefore, the reliability of the image classification and screening method for bags surface defects proposed in this study is verified.

关键词： paper-plastic composite bag surface defect detection feature extraction support vector machine algorithm optimization

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The analysis of optimization in music aesthetic education under artificial intelligence

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Peng, Yixuan Hunan Coll Presch Educ Sch Presch Educ Changde 415000 Peoples R China

In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discussion. The aim is to meet to the music aesthetic needs of students across different age groups and levels of musical literacy. In this paper, the concepts of AI and DL algorithm are first introduced, and their algorithm principles and application status are understood. Then, they are integrated into the application of music aesthetic education, and the algorithm principles and running codes are designed. Finally, experiments are carried out to verify the accuracy of music emotion recognition based on DL algorithm in AI environment to verify the effectiveness of music aesthetic education method based on DL. The results show that the algorithm proposed in this paper has higher accuracy, which combines the advantages of AI and DL algorithm, and obtains higher recognition accuracy. It provides more possibilities for future music aesthetic teaching activities. This paper is dedicated to investigating the feasibility and approach to optimizing the method of music aesthetic education through DL. Its objective is to chart a new developmental direction and practical pathway for music aesthetic education in the era of AI.

关键词： Artificial intelligence Deep learning Music aesthetic education algorithm optimization Emotion recognition

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Generative Artificial Intelligence for the Automated Generation of Medical Reports: A Modified Poor and Rich optimization (MPRO)-Based Approach

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TRAITEMENT DU SIGNAL 2025年第1期42卷 409-422页

作者： Aslam, Shabnam Mohamed Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Majmaah 11952 Saudi Arabia

A novel method for the automated generation of medical reports is presented, combining generative Artificial Intelligence (Gen-AI) with optimal feature selection and fusion strategies. The approach employs a pre-trained ResNet architecture for the extraction of visual-semantic features from medical images, including frontal and lateral views, with annotations on key clinical features such as objects, locations, nodules, tumors, and masses. To enhance disease localization and diagnosis, an adaptive attention mechanism is incorporated within a deep recurrent neural network (DRNN), enabling dynamic focus on the most relevant regions of the images. Segmentation is performed using the Modified Poor and Rich optimization (MPRO) method, which is followed by a hybrid feature fusion approach integrating Chaotic Krill Herd optimization (CKHO). The system is further augmented by multimodal data, such as patient demographics and medical history, to ensure the generation of personalized, context-aware reports. The Gen-AI framework employs OpenAI's GPT and advanced natural language processing (NLP) techniques to generate precise and nuanced diagnostic sentences. Benchmark datasets, including CheXpert and IU X-ray, are utilized to evaluate the proposed method and compare it with existing automated report generation techniques. Performance is assessed using standard image captioning evaluation metrics-namely BLEU, METEOR, ROUGE, and CIDER-with the proposed method demonstrating superior performance, achieving the highest values across these metrics. Statistical analysis confirms the robustness, accuracy, and exceptional performance of the model, with a F1 score of 0.93% for automated report generation. The results indicate the significant potential of Gen-AI for improving the accuracy and efficiency of medical reporting.

关键词： generative-AI medical report feature extraction feature optimization feature fusion diagnostic sentence algorithm optimization ChatGPT

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