IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030243

Lea Gassab, Onur Pusuluk, Marco Cattaneo, Özgür E Müstecaplıoğlu

引用次数: 0

Improved Variational Bayes for Space-Time Adaptive Processing. 改进变分贝叶斯时空自适应处理。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030242

Kun Li, Jinyang Luo, Peng Li, Guisheng Liao, Zhixiang Huang, Lixia Yang

{"title":"Improved Variational Bayes for Space-Time Adaptive Processing.","authors":"Kun Li, Jinyang Luo, Peng Li, Guisheng Liao, Zhixiang Huang, Lixia Yang","doi":"10.3390/e27030242","DOIUrl":"10.3390/e27030242","url":null,"abstract":"To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Generative Modeling: From Probabilistic Framework to Generative AI. 深度生成建模：从概率框架到生成人工智能。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030238

Jakub M Tomczak

引用次数: 0

Principles Entailed by Complexity, Crucial Events, and Multifractal Dimensionality. 复杂性、关键事件和多重分形维度所包含的原理。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030241

Bruce J West, Senthil Mudaliar

{"title":"Principles Entailed by Complexity, Crucial Events, and Multifractal Dimensionality.","authors":"Bruce J West, Senthil Mudaliar","doi":"10.3390/e27030241","DOIUrl":"10.3390/e27030241","url":null,"abstract":"Complexity is one of those descriptive terms adopted in science that we think we understand until it comes time to form a coherent definition upon which everyone can agree. Suddenly, we are awash in conditions that qualify this or that situation, much like we were in the middle of the last century when it came time to determine the solutions to differential equations that were not linear. Consequently, this tutorial is not an essay on the mathematics of complexity nor is it a rigorous review of the recent growth spurt of complexity science, but is rather an exploration of how physiologic time series (PTS) in the life sciences that have eluded traditional mathematical modeling become less mysterious when certain historical assumptions are discarded and so-called ordinary statistical events in PTS are replaced with crucial events (CEs) using mutifractal dimensionality as the working measure of complexity. The empirical datasets considered include respiration, electrocardiograms (ECGs), and electroencephalograms (EEGs), and as different as these time series appear from one another when recorded, they are in fact shown to be in synchrony when properly processed using the technique of modified diffusion entropy analysis (MDEA). This processing reveals a new synchronization mechanism among the time series which simultaneously measures their complexity by means of the multifractal dimension of each time series and are shown to track one another across time. These results reveal a set of priciples that capture the manner in which information is exchanged among physiologic organ networks.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved GAS Algorithm. 一种改进的GAS算法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030240

Zhijian Wang, Yuchen He, Tian Luan, Yong Long

引用次数: 0

An Innovative Image Encryption Algorithm Based on the DNAS_box and Hyperchaos. 一种基于DNAS_box和超混沌的创新图像加密算法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030239

Da Qiu, Tingting Zhang, Jingyi Liu, Song Liu, Peiyu He

{"title":"An Innovative Image Encryption Algorithm Based on the DNAS_box and Hyperchaos.","authors":"Da Qiu, Tingting Zhang, Jingyi Liu, Song Liu, Peiyu He","doi":"10.3390/e27030239","DOIUrl":"10.3390/e27030239","url":null,"abstract":"This study proposes an innovative image encryption algorithm based on the DNAS_box and hyperchaos. The algorithm dynamically constructs a DNAS_box using 2-bit deoxyribonucleic acid (DNA) coding and 4-bit DNA-like coding, enabling seamless conversion between these two coding schemes. The implementation process comprises four key phases. First, a hyperchaotic system generates chaotic sequences while dynamically encoding the plaintext image according to DNA coding rules. Next, the 2-bit DNA keys undergo encoding before performing XOR operations with the encoded plaintext image. Subsequently, under chaotic sequence control, the DNAS_box transforms 2-bit DNA symbols into 4-bit DNA-like symbols. Finally, XOR operations are executed between the 4-bit DNA-like symbols and corresponding DNA-like keys, producing a 4-bit DNA-like symbol sequence. This sequence is dynamically decoded under chaotic sequence guidance to generate the ciphertext image. The algorithm's effectiveness is validated through MATLAB-based numerical simulations, with experimental results confirming its robust security performance. Notably, the algorithm demonstrates a key space of approximately 10145, an NPCR exceeding 99.5%, and ciphertext entropy surpassing 7.997.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maxwellian Distribution-Based Hall Transport Coefficients for Charged Particles in Magnetic Disk Array. 基于麦克斯韦分布的磁碟阵列中带电粒子的霍尔输运系数。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-26 DOI: 10.3390/e27030244

Linlin An, Peifeng Fan

引用次数: 0

In-Motion Initial Alignment Method Based on Multi-Source Information Fusion for Special Vehicles. 基于多源信息融合的特种车辆运动初始对准方法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-25 DOI: 10.3390/e27030237

Zhenjun Chang, Zhili Zhang, Zhaofa Zhou, Xinyu Li, Shiwen Hao, Huadong Sun

{"title":"In-Motion Initial Alignment Method Based on Multi-Source Information Fusion for Special Vehicles.","authors":"Zhenjun Chang, Zhili Zhang, Zhaofa Zhou, Xinyu Li, Shiwen Hao, Huadong Sun","doi":"10.3390/e27030237","DOIUrl":"10.3390/e27030237","url":null,"abstract":"To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information fusion. First, a federal Kalman filter-based multi-sensor fusion architecture is established to effectively integrate odometer, laser Doppler velocimeter, and SINS data, resolving the challenge of autonomous navigation parameter calculation under GNSS-denied conditions. Second, a dual-mode fault diagnosis and isolation mechanism is developed to enable rapid identification of sensor failures and system reconfiguration. Finally, an environmentally adaptive dynamic alignment strategy is proposed, which intelligently selects optimal alignment modes by real-time evaluation of motion characteristics and environmental disturbances, significantly enhancing system adaptability in complex operational scenarios. The experimental results show that the method proposed in this paper can effectively improve the accuracy of vehicle-mounted alignment in motion, achieve accurate identification, effective isolation, and reconstruction of random incidental faults, and improve the adaptability and robustness of the system. This research provides an innovative solution for the rapid deployment of special-purpose vehicles in GNSS-denied environments, while its fault-tolerant mechanisms and adaptive strategies offer critical insights for engineering applications of next-generation intelligent navigation systems.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ExSPIN: Explicit Feedback-Based Self-Play Fine-Tuning for Text-to-SQL Parsing. ExSPIN：用于文本到sql解析的基于显式反馈的自播放微调。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-25 DOI: 10.3390/e27030235

Liang Yan, Jinhang Su, Chuanyi Liu, Shaoming Duan, Yuhao Zhang, Jianhang Li, Peiyi Han, Ye Liu

{"title":"ExSPIN: Explicit Feedback-Based Self-Play Fine-Tuning for Text-to-SQL Parsing.","authors":"Liang Yan, Jinhang Su, Chuanyi Liu, Shaoming Duan, Yuhao Zhang, Jianhang Li, Peiyi Han, Ye Liu","doi":"10.3390/e27030235","DOIUrl":"10.3390/e27030235","url":null,"abstract":"Recently, self-play fine-tuning (SPIN) has garnered widespread attention as it enables large language models (LLMs) to iteratively enhance their capabilities through simulated interactions with themselves, transforming a weak LLM into a strong one. However, applying SPIN to fine-tune text-to-SQL models presents substantial challenges. Notably, existing frameworks lack clear signal feedback during the training process and fail to adequately capture the implicit schema-linking characteristics between natural language questions and databases. To address these issues, we propose a novel self-play fine-tuning method for text-to-SQL models, termed ExSPIN, which incorporates explicit feedback. Specifically, during fine-tuning, the SQL query execution results predicted by the LLM are fed back into the model's parameter update process. This feedback allows both the main player and the opponent to more accurately distinguish between negative and positive samples, thereby improving the fine-tuning outcomes. Additionally, we employ in-context learning techniques to provide explicit schema hints, enabling the LLM to better understand the schema-linking between the database and natural language queries during the self-play process. Evaluations on two real-world datasets show that our method significantly outperforms the state-of-the-art approaches.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating the Evolution of von Neumann Entropy in Black Hole Hawking Radiation Using Biphoton Entanglement. 利用双光子纠缠模拟黑洞霍金辐射中冯诺依曼熵的演化。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-25 DOI: 10.3390/e27030236

Zhuoying Li, Haoshen Fan, Xingwen Zhao, Qinfei Wu, Ji Bian, Yang Liu, Le Luo

引用次数: 0

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