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Gaussian Distance Weighted algorithm for Geometric Characteristics of Three-Dimensional Discrete Curves

Journal of Applied Mathematics and Physics 2024年第10期12卷 3599-3612页

作者： Liyan Zhang Haiyi Ai Shaohong Yan Haili Chen Jiali Zou Junqing Zhang College of Science North China University of Science and Technology Tangshan China Hebei Provincial Key Laboratory of Data Science and Applications North China University of Science and Technology Tangshan China Key Laboratory of Data Science in Tangshan Tangshan China Tangshan Zhongyu Technology Development Co. Ltd. Tangshan China

Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of any point on the discrete curve, a distance-based Gaussian weighted algorithm is proposed to estimate the geometric characteristics of three-dimensional space discrete curves. According to the definition of discrete derivatives, the algorithm fully considers the relative position difference between a specific point and its neighboring points, introduces the distance weighting idea, and integrates the smoothing strategy. The experiment uses two spatial discrete curves for uniform and non-uniform sampling, and compares them with two commonly used estimation algorithms. The comparative analysis is carried out in terms of sampling density, neighborhood radius and noise resistance. The experimental results show that the Gaussian distance weighted algorithm is effective and provides an efficient algorithm for underground pipeline safety detection.

关键词： Discrete Curve Angle Weight algorithm comparison Underground Pipeline Inspection

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Unsupervised anomaly detection algorithms on real-world data: how many do we need?

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5199-5232页

作者： Roel Bouman Zaharah Bukhsh Tom Heskes Institute for Computing and Information Sciences Radboud University EC Nijmegen The Netherlands Information Systems Industrial Engineering and Innovation Sciences Eindhoven University of Technology AE Eindhoven The Netherlands

In this study we evaluate 33 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular data sets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of data sets, the EIF (Extended Isolation Forest) algorithm significantly outperforms the most other algorithms. Visualizing and then clustering the relative performance of the considered algorithms on all data sets, we identify two clear clusters: one with "local" data sets, and another with "global" data sets. "Local" anomalies occupy a region with low density when compared to nearby samples, while "global" occupy an overall low density region in the feature space. On the local data sets the kNN (k-nearest neighbor) algorithm comes out on top. On the global data sets, the EIF (extended isolation forest) algorithm performs the best. Also taking into consideration the algorithms' computational complexity, a toolbox with these two unsupervised anomaly detection algorithms suffices for finding anomalies in this representative collection of multivariate data sets. By providing access to code and data sets, our study can be easily reproduced and extended with more algorithms and/or data sets.

关键词： unsupervised anomaly detection anomaly analysis algorithm comparison outlier detection outlier analysis

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algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第8期34卷 085404-085404页

作者： Gou, Yi Li, Qingying Yao, Rao Chen, Jianing Bai, Tian Shanghai Univ Engn Sci Sch Air Transportat Shanghai 201620 Peoples R China Beijing Aerosp Inst Metrol & Measurement Technol Beijing 100048 Peoples R China

Active infrared thermography (IRT) has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis (PCA) algorithm with sliding-window mechanism and the one dimensional-residual neural network-convolutional block attention module (1DResnet50_CBAM) model was proposed for the reconstructed image sequence of active IRT to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using PCA algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional defect data were transformed into 1D temporal infrared thermal signal. Moreover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion PCA algorithm with sliding-window mechanism and 1DResnet50_CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and

关键词： infrared thermography flat-bottomed hole defect depth defect extraction 1DResnet50_CBAM model algorithm comparison

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Statistical comparisons of classifiers by generalized stochastic dominance

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10872-10908页

作者： Christoph Jansen Malte Nalenz Georg Schollmeyer Thomas Augustin Department of Statistics Ludwig-Maximilians-Universität Munich Germany

Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect to several criteria. Every comparison framework is confronted with (at least) three fundamental challenges: the multiplicity of quality criteria, the multiplicity of data sets and the randomness of the selection of data sets. In this paper, we add a fresh view to the vivid debate by adopting recent developments in decision theory. Based on so-called preference systems, our framework ranks classifiers by a generalized concept of stochastic dominance, which powerfully circumvents the cumbersome, and often even self-contradictory, reliance on aggregates. Moreover, we show that generalized stochastic dominance can be operationalized by solving easy-to-handle linear programs and moreover statistically tested employing an adapted two-sample observation-randomization test. This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously. We illustrate and investigate our framework in a simulation study and with a set of standard benchmark data sets.

关键词： algorithm comparison statistical test generalized stochastic dominance preference system decision theory

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Comparative analysis of the KL-UCB and UCB algorithms:Delving into complexity and performance

Comparative analysis of the KL-UCB and UCB algorithms:Delvin...

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作者： Chenyue Wu School of Information Science and Technology School of Cyber Security Guangdong University of Foreign Studies

This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound(KL-UCB) and the generic Upper Confidence Bound(UCB) ***, a comprehensive discourse is presented, elucidating the definition, evolution, and realworld applications of both algorithms. The crux of the study then shifts to a side-by-side comparison, weighing the regret performance and time complexities when applied to a quintessential movie rating dataset. In the trenches of practical implementations, addressing multi-armed bandit problems invariably demands extensive training. Consequently, even seemingly minor variations in algorithmic complexity can usher in pronounced differences in computational durations and resource utilization. This inherent intricacy prompts introspection:Is the potency of a given algorithm in addressing diverse practical quandaries commensurate with its inherent complexity. By juxtaposing the KL-UCB and UCB algorithms, this study not only highlights their relative merits and demerits but also furnishes insights that could serve as catalysts for further refinement and optimization. The overarching aim is to cultivate an informed perspective, guiding practitioners in choosing or fine-tuning algorithms tailored to specific applications without incurring undue computational overheads.

关键词： Multi-armed bandit algorithm comparison algorithm complexity regret performance algorithm improvements

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Comparative Breast Cancer Detection with Artificial Neural Networks and Machine Learning Methods 29

Comparative Breast Cancer Detection with Artificial Neural N...

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29th IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Irmak, Muhammed Coskun Tas, Mehmet Bilge Han Turan, Sedat Hasiloglu, Abdulsamet Van Yuzuncu Yil Univ Bilgisayar Muhendisligi Van Turkey Erzincan Binali Yildirim Univ Bilgisayar Muhendisligi Erzincan Turkey Erzincan Binali Yildirim Univ Elekt & Otomasyon Erzincan Turkey Ataturk Univ Bilgisayar Muhendisligi Erzurum Turkey

ISBN: (纸本)9781665436496

The number of cancer patients is increasing all over the world due to reasons such as environmental pollution, excess of technological and biological wastes, climate change, and increased consumption of unnatural foods. According to the data of the World Health Organization in 2020, there are over 19 million cancer patients in the world. The most common type of cancer is breast cancer with 11.7%. Breast cancer is as high as 24.5% in women and 15.5% of these cases result in death. Therefore, it is important to detect cancer at an early stage. The purpose of this study is to detect cancer in the fastest way and with the highest accuracy rates. In the study, high performance results were obtained by comparing artificial neural networks and traditional machine learning methods. In the study;Logistic Regression (LR), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Xgboost (XGB) and Artificial Neural Network (ANN) were used. The highest result obtained using traditional machine learning methods is 98.08% with LR. In addition, using ANN, the highest value was obtained with an accuracy rate of 99.36%.

关键词： artificial neural network machine learning algorithm comparison breast cancer detection logistic regression

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A three-stage optimization method for the classroom envelope in primary and secondary schools in China

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JOURNAL OF BUILDING ENGINEERING 2022年 52卷

作者： Xu, Yizhe Yan, Chengchu Pan, Yan Zhao, Ke Li, Mei Zhu, Faxing Jiang, Yanlong Nanjing Tech Univ Coll Urban Construction 200 North Zhongshan Rd Nanjing 210009 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Aircraft Environm Control & Life Support MIIT 29 Yudao St Nanjing 210016 Peoples R China Technol & Higher Educ Inst Hong Kong Fac Sci & Technol Hong Kong Peoples R China Chuzhou Univ Sch Biol & Food Engn Chuzhou 239000 Peoples R China

The design optimization of primary and secondary school classrooms should consider the effects of energy, thermal comfort, and daylighting. This paper proposes a three-stage optimization design method of classroom envelope based on multi-objective optimization to realize the efficient and accurate optimization of primary and secondary school classrooms. The method is divided into three modules: building modeling and analysis, meta-model training and hyperparameter optimization, and multi-objective optimization algorithm comparison. The application effect of electrochromic technology in typical primary and secondary school classrooms is discussed through sensitivity analysis, and an improved algorithm SEGA-Q based on GA is proposed. In addition, the performance of three typical multi-objective optimization algorithms in three objective optimizations (i.e., energy, thermal comfort, and daylighting) is compared. Finally, two typical school classrooms are taken as cases to verify the effectiveness of the proposed optimization method. The results show that the application significance of the electrochromic window indicates skylights > south side window > north side window. The SEGA-Q algorithm has more accuracy, stability, and convergence speed advantages in meta-model training and hyperparametric optimization, but its calculation time increases significantly. Among the three multiobjective optimization algorithms, the NSGA-II algorithm has the most significant advantages in most aspects. In addition, compared with the benchmark schemes, the optimized common classroom can reduce the TES by 692.5 kWh at most and increase the PT by 41.9% and UDINO by 57.4%. The optimized classroom with skylight can reduce TES by 1261.9 kWh at most and increase PT by 26% and UDINO by 78.5%.

关键词： Envelope optimization Hybrid algorithm MFNN School classroom algorithm comparison

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Maximum number of generations as a stopping criterion considered harmful

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APPLIED SOFT COMPUTING 2022年第0期128卷

作者： Ravber, Miha Liu, Shih-Hsi Mernik, Marjan Crepinsek, Matej Univ Maribor Fac Elect Engn & Comp Sci Koroska cesta 46 Maribor 2000 Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA

Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： Evolutionary algorithms Stopping criteria Benchmarking algorithm termination algorithm comparison

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A new interpolation algorithm based on Hibbard-Laroche algorithm and its superiority

A new interpolation algorithm based on Hibbard-Laroche algor...

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作者： Yuxuan Huang Yiming Ren Shaoguan University University of Shanghai for Science and Technology

In order to optimize the possible problems and improvements in the existing color image restoration interpolation algorithms,we conduct research based on the existing bilinear interpolation method,cok algorithm and Hibbard-Laroche *** method is to use our own comparison method to compare different types of images through three algorithms to find the advantages and disadvantages and to some extent combine the advantages of bilinear interpolation and Hibbard-Laroche algorithm to try to innovate a new algorithm to compare with the existing three *** results show that the existing three algorithms have their own advantages in different scenarios,and the new algorithm is superior to the existing algorithms in terms of clarity and color restoration accuracy in most ***,due to the large computational complexity,the operation speed is slow.

关键词： algorithm comparison Hibbard-Laroche algorithm a new algorithm algorithm improvement

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A comparative study between SA and GA in solving MTSP

A comparative study between SA and GA in solving MTSP

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作者： Wenze Zhang Chenyang Xu Electrical and Computer engineering National University of Singapore Mechanical Engineering Shanghai Institute of Technology

The multiple traveling salesmen problems(MTSP) is a combinatorial optimization and np-hard problem. In practice, the computational resource required to solve such problems is usually prohibitive, and, in most cases, using heuristic algorithms is the only practical *** paper implements genetic algorithms(GA) and simulated annealing(SA) to solve the MTSP and does an experimental study based on a benchmark from the TSPLIB instance to compare the performance of two algorithms in reality. The results show that GA can achieve an acceptable solution in a shorter time for any of the MTSP cases and is more accurate when the data size is small. Meanwhile, SA is more robust and achieves a better solution than GA for complex MTSP cases, but it takes more time to converge. Therefore, the result indicates that it is hard to identify which algorithm is comprehensively superior to the other one. However, It also provides an essential reference to developers who want to choose algorithms to solve MTSP in real life,facilitating them to balance the algorithm's performance on different metrics they value.

关键词： multiple travelling salesmen problem (MTSP) NP-hard optimization Simulated Annealing(SA) genetic algorithms(GAs) algorithm comparison algorithm selection

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