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A matrix effect correction method for fissile nuclear material mass measurement by delayed neutrons

APPLIED RADIATION AND ISOTOPES 2025年 216卷 111600页

作者： Yong, Jinlong Song, Yushou Zhao, Yunlong Hou, Yingwei Liu, Huilan Harbin Engn Univ Coll Nucl Sci & Technol Harbin 150001 Peoples R China

Active neutron interrogation (ANI), extensively employed in nuclear safeguards, is sensitive to the presence of special nuclear materials (SNMs). However, the interaction of the matrix material with neutrons weakens the precision of fissile mass measurements by the ANI system. Therefore, it is paramount to ensure that the evaluation of the fissile mass remains unaffected by the matrix and enhances the assay performance of the ANI system. The present work proposes a matrix correction method based on the neutron flux monitor group (NFMG) response to tackle this issue. Based on the varying influence of different matrices on the neutron energies and fluxes, the NFMG response can be used to quantify the matrix effects. This allows the method to enable the identification of matrices already present in the database and has the potential to compensate for unknown matrix effects. To validate the applicability and accuracy of this method, a Shuffler system model and various matrix compositions were developed using the Geant4 toolkit. The results demonstrate that the improved simulated annealing (SA) algorithm exhibits excellent stability when confronted with varying enrichments and distributions of U3O8 materials. When the threshold is set at alpha >= 0.90, the improved SA algorithm achieves a successful identification rate of 90.4% for matrices. Simultaneously, using the response equation, the average relative deviation of the corrected 235U mass is no more than 7%. For the unknown matrix, the correction capability of this method relies primarily on the construction of the reference database and the response equation. In most cases, the average relative deviation of the corrected 235U mass is less than 28%.

关键词： Nuclear safeguard Delayed neutron count Monte Carlo simulation Matrix effect correction algorithm optimization

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Nanoparticle sensing based on high-Q silicon photonic crystal nanobeam cavity

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OPTICS COMMUNICATIONS 2025年 574卷

作者： Liu, Li Wu, Tao Chen, Dong China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Guilin Univ Elect Technol Guangxi Wireless Broadband Commun & Signal Proc Ke Guilin 541004 Peoples R China Hubei Luojia Lab Wuhan 430072 Peoples R China Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg Qual Engn Wuhan 430072 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Inst Pathol Tongji Med Coll Wuhan 430030 Peoples R China Huazhong Univ Sci & Technol Tongji Med Coll Sch Basic Med Dept Pathol Wuhan 430030 Peoples R China

The ability to detect and accurately measure the size of individual nanoparticles at a high resolution is paramount for comprehending their behavior and harnessing their unique properties for innovative applications. We present a high-resolution particle sensing technology that employs a high quality (Q) one-dimensional photonic crystal nanobeam cavity. By integrating the least squares support vector machine (LSSVM) algorithm model with genetic algorithm optimization, we have significantly enhanced the Q-factor of the photonic crystal, achieving a remarkable value of 1.31 x 10(8). This optimized structure generates an intense evanescent field that interacts precisely with individual nanoparticles, resulting in a sensing resolution of 0.1 nm radius. This advancement enables not only the detection but also the identification of particle materials and variations in their quantity, providing important insights into nanoparticle behavior.

关键词： One-dimensional photonic crystal nanobeam cavity Particle sensing algorithm optimization High Q

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Adaptive DWA algorithm with decision tree classifier for dynamic planning in USV navigation

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OCEAN ENGINEERING 2025年 321卷

作者： Chen, Hui Lin, Zelong Chen, Zhongming Jian, Junkang Liu, Chang Guangdong Univ Technol Sch Automat Guangdong Prov Key Lab Intelligent Decis & Coopera Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Automat Guangdong Hong Kong Joint Lab Intelligent Decis & Guangzhou 510006 Guangdong Peoples R China

Unmanned surface vehicles (USVs) plays a vital role in marine exploration, with obstacle avoidance technology being one of the key techniques for ensuring the safe navigation of USVs. This paper aims to investigate a decision tree-based dynamic window approach (DT-DWA) algorithm to address the obstacle avoidance strategy problem in autonomous navigation of USV. The primary objective of this algorithm is to fuse decision tree classifier into DWA algorithm, and consider some characteristics that affect decision judgment, which can achieve adaptively adjust weights. Secondly, we combine Distance of Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as the basis for USV to make obstacle avoidance decision. Finally, the rationality and effectiveness of the proposed algorithm is demonstrated through simulation experiments.

关键词： Local path planning Unmanned surface vehicles Decision tree classifier Adaptive weights algorithm optimization

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Improved qARM algorithm for frequent itemsets search in large-scale data sets

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-17页

作者： Qi, Han Wang, Liyuan Fu, Dianshuo Gani, Abdullah Gong, Changqing Shenyang Aerosp Univ Sch Comp Sci 37 Daoyi South Ave Shenyang 110000 Peoples R China Univ Malaya Fac Comp & Informat Kuala Lumpur 50603 Malaysia

The existing quantum algorithm for association rules mining (qARM) has a significant limitation: the need for artificial setting of the quantum oracle of data when mining frequent itemsets. This becomes extremely time-consuming when dealing with large-scale data. To overcome this limitation, this study proposes an improved method based on the Cn-X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C<^>{n} -X$$\end{document} gate and the extended Grover algorithm (less than oracle). We abandon the original quantum phase estimation algorithm in the process of calculating the support degree and instead use a combined gate based on the Cn-X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C<^>{n} -X$$\end{document} gate. This is because its linear time complexity is more efficient than the polynomial time complexity of the quantum estimation algorithm. Furthermore, we modify the extended Grover algorithm to screen the support degree. Our method is validated using IBM's Qiskit tool for simulation experiments. We test on data sets with sizes of 2x2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\times 2$$\end{document} and 4x4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4\times 4$$\end{document}, and fragments 29-32 items of data for the dataset Market Basket

关键词： Quantum algorithms Association rules mining Frequent itemsets mining algorithm optimization

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Fast-OverlaPIM: A Fast Overlap-Driven Mapping Framework for Processing In-Memory Neural Network Acceleration

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 130-143页

作者： Wang, Xuan Zhou, Minxuan Rosing, Tajana Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it minimizes data movement and provides large computational parallelism. Similar to machine learning accelerators, application mapping, which determines the operation scheduling and data layout, plays a critical role in the NN acceleration on PIM. The mapping optimization of the previous NN accelerators focused on optimizing the latency of sequential execution. However, PIM accelerators feature a distinct design space of application mapping from conventional NN accelerators, due to the spatial execution of NN layers across different memory locations. This enables opportunities for overlapping execution of consecutive NN layers to improve the latency, where the succeeding layer can start execution before the preceding layer fully completes the computation. In this article, we propose Fast-OverlaPIM framework that incorporates computational overlapping optimization into the deep neural network mapping exploration process on PIM architectures. Fast-OverlaPIM includes analytical algorithms for fast and accurate overlap analysis. Furthermore, it proposes a novel mapping search strategy and a transformation mechanism to enable efficient design space exploration on the overlap-based mapping for the whole network. Our framework demonstrates a significant improvement in runtime performance from 3.4x to 323.1x compared to the previous state-of-the-art overlap-based framework. Our experiments show that Fast-OverlaPIM can efficiently produce mappings that are 4.6x to 18.1x faster than the state-of-the-art mapping optimization framework under the same architecture constraints.

关键词： optimization Computer architecture Artificial neural networks Design automation Random access memory Parallel processing Vectors algorithm optimization deep neural networks (DNNs) processing in-memory (PIM) software-hardware co-design

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A sensitive wavelength-enhanced reconstruction algorithm for track quality assessment based on measured data and vehicle dynamics

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MEASUREMENT 2025年 240卷

作者： Chen, Zelin Cai, Xiaopei Tang, Xueyang Wang, Meng Liu, Weibin Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China State Key Lab Track Technol High Speed Railway Beijing 100081 Peoples R China

High-speed railways are extensively utilized worldwide. However, with prolonged operation, track irregularities increasingly pose significant challenges, adversely affecting the safety and stability of train operations. This paper proposes a sensitive wavelength-enhanced reconstruction algorithm based on the cmplete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to address this issue. This method evaluates the influence of reconstructed track irregularities on vehicle dynamic response, considering sensitive wavelengths and amplification factors. The algorithm effectively enhances these sensitive wavelengths while maintaining the integrity of the input signal, utilizing measured track irregularities. After applying the algorithm, the maximum vertical body acceleration rises by 206.09%. The study validates a coupled train model and analyzes the mapping relationship between single-wave irregularities and vertical body acceleration. Compared to random irregularities, the maximum vertical acceleration can amplify up to 122.80% under various operating conditions. The periodic sensitive wavelengths of track irregularities significantly impact the train's dynamic response.

关键词： High-speed railway Track irregularity Vehicle dynamics simulations algorithm optimization

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Efficient Computation of Collatz Sequence Stopping Times: A Novel algorithmic Approach

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IEEE ACCESS 2025年 13卷 41210-41220页

作者： Getachew, Eyob Solomon Assefa, Beakal Gizachew Addis Ababa Inst Technol Sch Informat Technol & Engn Addis Ababa 1000 Ethiopia

The Collatz conjecture, which posits that any positive integer will eventually reach 1 through a specific iterative process, is a classic unsolved problem in mathematics. This research focuses on designing an efficient algorithm to compute the stopping time of numbers in the Collatz sequence, achieving significant computational improvements. By leveraging structural patterns in the Collatz tree, the proposed algorithm minimizes redundant operations and optimizes computational steps. Unlike prior methods, it efficiently handles extremely large numbers without requiring advanced techniques such as memoization or parallelization. Experimental evaluations confirm computational efficiency improvements of approximately 28% over state-of-the-art methods. These findings underscore the algorithm's scalability and robustness, providing a foundation for future large-scale verification of the conjecture and potential applications in computational mathematics.

关键词： Computational efficiency Encryption Approximation algorithms Visualization Probabilistic logic optimization Complexity theory Watermarking Time complexity Testing algorithm optimization bitwise operations Collatz conjecture Collatz tree computational mathematics high-performance algorithms logarithmic complexity memoization parallel computing stopping time computational complexity sequence analysis and large-scale verification

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Optimizing the compliance third-party supervision workflow of involved enterprises using artificial intelligence ant colony algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Chen, Danqi Jia, Weichen Chen, Qi Chen, Jianing Li, Zhi NingboTech Univ Sch Media & Law Ningbo 315000 Peoples R China

This article optimized the compliance third-party supervision workflow of the involved enterprises based on the artificial intelligence ant colony optimization (ACO) algorithm. The basic principles and application advantages of ACO were introduced, and a heuristic information matrix was defined using ACO to optimize the data collection and analysis stage of the compliance third-party supervision workflow. During the experimental phase, a feasibility analysis was conducted on the optimization of third-party supervision workflows for compliance by ACO involved enterprises through simulation experiments. The experiments were evaluated from four aspects: data quality, model performance, scheme effectiveness, and supervision effectiveness. Among the metrics data for the ACO-optimized test set were 0.03 and 0.025 for MSE (Mean Square Error) and Gamma, 0.8, 0.78, 0.79, and 0.88 for Accuracy, Recall, F1 Score, and AUC-ROC (Area Under the Curve-Receiver Operating Characteristic), and 0.28, 0.4, 0.88, and 0.12 for CER (Cost-Effectiveness Ratio), NPV (Net Present Value), SCR (Supervision Coverage Rate), and CRC (Compliance Rate Change), respectively. The experimental results showed that, in terms of data quality, model performance, scheme effectiveness, and supervision effectiveness, the evaluation indicators of the compliance third-party supervision workflow of the involved enterprises optimized using ACO were superior to those without ACO optimization.

关键词： Ant colony optimization Artificial intelligence Third-party supervision Involved enterprises algorithm optimization

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Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1878-1887页

作者： Zhang, Guoliang Du, Zexu Zhang, Yi Chen, Jiangqi Zhang, Xi State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China

Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search;secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed;then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism;finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

关键词： substation equipment inspection distributed computing performance evaluation algorithm optimization

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optimization of parallel random forest algorithm based on distance weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1951-1963页

作者： Wang, Qinge Chen, Huihua Cent South Univ Sch Civil Engn Changsha Peoples R China

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.

关键词： Distance weights parallel algorithm random forest algorithm algorithm optimization

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