Entropy最新文献_第10页

Algorithmic Approaches for Assessing Multiscale Irreversibility in Time Series: Review and Comparison.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-25 DOI: 10.3390/e27020126

Massimiliano Zanin, David Papo

引用次数: 0

A Complex Structure for Two-Typed Tangent Spaces.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020117

Jan Naudts

引用次数: 0

A Novel Machine Learning Technique for Fault Detection of Pressure Sensor.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020120

Xiufang Zhou, Aidong Xu, Bingjun Yan, Mingxu Gang, Maowei Jiang, Ruiqi Li, Yue Sun, Zixuan Tang

{"title":"A Novel Machine Learning Technique for Fault Detection of Pressure Sensor.","authors":"Xiufang Zhou, Aidong Xu, Bingjun Yan, Mingxu Gang, Maowei Jiang, Ruiqi Li, Yue Sun, Zixuan Tang","doi":"10.3390/e27020120","DOIUrl":"10.3390/e27020120","url":null,"abstract":"Pressure transmitters are widely used in the process industry for pressure measurement. The sensing line, a core component of the pressure sensor in the pressure transmitter, significantly impacts the accuracy of the pressure transmitter's output. The reliability of pressure transmitters is critical in the nuclear power industry. Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time-Frequency domain characteristics (TFTF) is proposed in this paper. The dataset of pressure transmitters comprises both fault and normal data. This method innovatively integrates multi-scale time series decomposition algorithms with time-domain and frequency-domain feature extraction techniques. Initially, this dataset is decomposed into multi-scale time series to mitigate periodic component interference in diagnosis. Subsequently, via the sliding window algorithm, both the time-domain features and frequency-domain features of the trend components are extracted, and finally, the XGBoost algorithm is used to detect faults. The experimental results demonstrate that the proposed TFTF algorithm achieves superior fault detection accuracy for diagnosing sensing line blockage faults compared with traditional machine learning classification algorithms.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11853850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499893","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

Dynamical Systems on Generalised Klein Bottles.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020119

Peter Grindrod, Ka Man Yim

引用次数: 0

An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020118

Zhen Wang, Jin Duan

引用次数: 0

Thermodynamics-like Formalism for Immiscible and Incompressible Two-Phase Flow in Porous Media.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020121

Alex Hansen, Santanu Sinha

引用次数: 0

Variable-Length Coding with Zero and Non-Zero Privacy Leakage.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020124

Amirreza Zamani, Mikael Skoglund

{"title":"Variable-Length Coding with Zero and Non-Zero Privacy Leakage.","authors":"Amirreza Zamani, Mikael Skoglund","doi":"10.3390/e27020124","DOIUrl":"10.3390/e27020124","url":null,"abstract":"A private compression design problem is studied, where an encoder observes useful data Y, wishes to compress them using variable-length code, and communicates them through an unsecured channel. Since Y are correlated with the private attribute X, the encoder uses a private compression mechanism to design an encoded message C and sends it over the channel. An adversary is assumed to have access to the output of the encoder, i.e., C, and tries to estimate X. Furthermore, it is assumed that both encoder and decoder have access to a shared secret key W. In this work, the design goal is to encode message C with the minimum possible average length that satisfies certain privacy constraints. We consider two scenarios: 1. zero privacy leakage, i.e., perfect privacy (secrecy); 2. non-zero privacy leakage, i.e., non-perfect privacy constraint. Considering the perfect privacy scenario, we first study two different privacy mechanism design problems and find upper bounds on the entropy of the optimizers by solving a linear program. We use the obtained optimizers to design C. In the two cases, we strengthen the existing bounds: 1. |X|≥|Y|; 2. The realization of (X,Y) follows a specific joint distribution. In particular, considering the second case, we use two-part construction coding to achieve the upper bounds. Furthermore, in a numerical example, we study the obtained bounds and show that they can improve existing results. Finally, we strengthen the obtained bounds using the minimum entropy coupling concept and a greedy entropy-based algorithm. Considering the non-perfect privacy scenario, we find upper and lower bounds on the average length of the encoded message using different privacy metrics and study them in special cases. For achievability, we use two-part construction coding and extended versions of the functional representation lemma. Lastly, in an example, we show that the bounds can be asymptotically tight.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499856","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

Entropy and Complexity Tools Across Scales in Neuroscience: A Review.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020115

Rodrigo Cofré, Alain Destexhe

引用次数: 0

Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020123

Chenghao Wei, Chen Li, Yingying Liu, Song Chen, Zhiqiang Zuo, Pukai Wang, Zhiwei Ye

{"title":"Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation.","authors":"Chenghao Wei, Chen Li, Yingying Liu, Song Chen, Zhiqiang Zuo, Pukai Wang, Zhiwei Ye","doi":"10.3390/e27020123","DOIUrl":"10.3390/e27020123","url":null,"abstract":"The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children without imposing any distributional assumptions. In the searching stage of BN structure learning, the causal relationships between variables are achieved by using the conditional entropy scoring function and hill-climbing strategy. To further enhance the computational efficiency of our method, we incorporate a locality sensitive hashing (LSH) function into the KDE process. The method speeds up the calculations of KDE while maintaining the precision of the estimates, leading to a notable decrease in the time required for computing mutual information, conditional mutual information, and conditional entropy. A BN classifier (BNC) is established by using the computationally efficient BN learning method. Our experiments demonstrated that KDE using LSH has greatly improved the speed compared to traditional KDE without losing fitting accuracy. This achievement underscores the effectiveness of our method in balancing speed and accuracy. By giving the benchmark networks, the network structure learning accuracy with the proposed method is superior to other traditional structure learning methods. The BNC also demonstrates better accuracy with stronger interpretability compared to conventional classifiers on public datasets.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499946","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

Mining Suicidal Ideation in Chinese Social Media: A Dual-Channel Deep Learning Model with Information Gain Optimization.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-01-24 DOI: 10.3390/e27020116

Xiuyang Meng, Xiaohui Cui, Yue Zhang, Shiyi Wang, Chunling Wang, Mairui Li, Jingran Yang

{"title":"Mining Suicidal Ideation in Chinese Social Media: A Dual-Channel Deep Learning Model with Information Gain Optimization.","authors":"Xiuyang Meng, Xiaohui Cui, Yue Zhang, Shiyi Wang, Chunling Wang, Mairui Li, Jingran Yang","doi":"10.3390/e27020116","DOIUrl":"10.3390/e27020116","url":null,"abstract":"The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI-BTCNN, which leverages deep learning to discern patterns indicative of suicidal ideation. Our model is designed to process Chinese data and capture the nuances of text locality, context, and logical structure through a fine-grained text enhancement approach. It features a complex parallel architecture with multiple convolution kernels, operating on two distinct task channels to mine relevant features. We propose an information gain-based IDFN fusion mechanism. This approach efficiently allocates computational resources to the key features associated with suicide by assessing the change in entropy before and after feature partitioning. Evaluations on a customized dataset reveal that our method achieves an accuracy of 89.64%, a precision of 92.84%, an F1-score of 89.24%, and an AUC of 96.50%, surpassing TextCNN and BiLSTM models by an average of 4.66%, 12.85%, 3.08%, and 1.66%, respectively. Notably, our proposed model has an entropy value of 81.75, which represents a 17.53% increase compared to the original DSI-BTCNN model, indicating a more robust detection capability. This enhanced detection capability is vital for real-time social media monitoring, offering a promising tool for early intervention and potentially life-saving support.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499996","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