Entropy (Basel, Switzerland)最新文献_第3页

Better Heisenberg Limits, Coherence Bounds, and Energy-Time Tradeoffs via Quantum Rényi Information. 更好的海森堡极限，相干边界，和能量-时间权衡通过量子汇变信息。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-17 DOI: 10.3390/e24111679

Michael J W Hall

引用次数: 1

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes. 连续时间、离散事件过程的推理、预测和熵率估计。

IF 2

Entropy (Basel, Switzerland) Pub Date : 2022-11-17 DOI: 10.3390/e24111675

Sarah E Marzen, James P Crutchfield

引用次数: 0

The κ-Deformed Calogero-Leyvraz Lagrangians and Applications to Integrable Dynamical Systems. 变形Calogero-Leyvraz拉格朗日量及其在可积动力系统中的应用。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-17 DOI: 10.3390/e24111673

Partha Guha

引用次数: 0

Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation. 快速聚类和码本生成的脑启发皮质编码方法。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-17 DOI: 10.3390/e24111678

Meric Yucel, Serdar Bagis, Ahmet Sertbas, Mehmet Sarikaya, Burak Berk Ustundag

{"title":"Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation.","authors":"Meric Yucel, Serdar Bagis, Ahmet Sertbas, Mehmet Sarikaya, Burak Berk Ustundag","doi":"10.3390/e24111678","DOIUrl":"https://doi.org/10.3390/e24111678","url":null,"abstract":"A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy-entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced-seconds versus hours-encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40492957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Numerical Simulation of Heat and Mass Transfer in Sludge Low-Temperature Drying Process. 污泥低温干燥过程传热传质数值模拟。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-17 DOI: 10.3390/e24111682

Zhenyu Wang, Qiang Wang, Ju Lai, Dong Liu, Anjie Hu, Lin Xu, Yongcan Chen

引用次数: 2

Video Action Recognition Using Motion and Multi-View Excitation with Temporal Aggregation. 基于运动和多视点激励的视频动作识别。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111663

Yuri Yudhaswana Joefrie, Masaki Aono

{"title":"Video Action Recognition Using Motion and Multi-View Excitation with Temporal Aggregation.","authors":"Yuri Yudhaswana Joefrie, Masaki Aono","doi":"10.3390/e24111663","DOIUrl":"https://doi.org/10.3390/e24111663","url":null,"abstract":"Spatiotemporal and motion feature representations are the key to video action recognition. Typical previous approaches are to utilize 3D CNNs to cope with both spatial and temporal features, but they suffer from huge computations. Other approaches are to utilize (1+2)D CNNs to learn spatial and temporal features in an efficient way, but they neglect the importance of motion representations. To overcome problems with previous approaches, we propose a novel block which makes it possible to alleviate the aforementioned problems, since our block can capture spatial and temporal features more faithfully and efficiently learn motion features. This proposed block includes Motion Excitation (ME), Multi-view Excitation (MvE), and Densely Connected Temporal Aggregation (DCTA). The purpose of ME is to encode feature-level frame differences; MvE is designed to enrich spatiotemporal features with multiple view representations adaptively; and DCTA is to model long-range temporal dependencies. We inject the proposed building block, which we refer to as the META block (or simply \"META\"), into 2D ResNet-50. Through extensive experiments, we demonstrate that our proposed method architecture outperforms previous CNN-based methods in terms of \"Val Top-1 %\" measure with Something-Something v1 and Jester datasets, while the META yielded competitive results with the Moment-in-Time Mini dataset.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40722014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Personalized Sliding Window Recommendation Algorithm Based on Sequence Alignment. 基于序列比对的个性化滑动窗口推荐算法。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111662

Lei Zhou, Bolun Chen, Hu Liu, Liuyang Wang

引用次数: 0

Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction. 基于混沌时间序列预测的机动目标轨迹在线跟踪。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111668

Qian Wei, Peng Su, Lin Zhou, Wentao Shi

{"title":"Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction.","authors":"Qian Wei, Peng Su, Lin Zhou, Wentao Shi","doi":"10.3390/e24111668","DOIUrl":"https://doi.org/10.3390/e24111668","url":null,"abstract":"Online prediction of maneuvering target trajectory is one of the most popular research directions at present. Specifically, the primary factors balancing, between prediction accuracy and response time, will give the research substance. This paper presents an online trajectory prediction algorithm based on small sample chaotic time series (OTP-SSCT). First, we optimize in terms of data breadth. The dynamic split window is built according to the motion characteristics of the maneuvering target, thus realizing trajectory segmentation and constructing a small sample chaotic time series prediction set. Second, since fully considering the motion patterns of maneuvering targets, we introduce the spatiotemporal features into the particle swarm optimization (PSO) model identification algorithm, which improves the identification sensitivity of key trajectory data points. Furthermore, we propose a feedback optimization strategy of residual compensation to correct the trajectory prediction values to improve the prediction accuracy. For the initial value sensitivity problem of the PSO model identification algorithm, we propose a new initial population strategy, which improves the effectiveness of initial parameters on model identification. Through simulation experiment analysis, it is verified that the proposed OTP-SSCT algorithm achieves better prediction accuracy and faster response time.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Finite-Time Thermodynamic Modeling and Optimization of Short-Chain Hydrocarbon Polymerization-Catalyzed Synthetic Fuel Process. 短链烃聚合催化合成燃料过程的有限时间热力学建模与优化。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111658

Yajie Yu, Shaojun Xia, Qinglong Jin, Lei Rong

{"title":"Finite-Time Thermodynamic Modeling and Optimization of Short-Chain Hydrocarbon Polymerization-Catalyzed Synthetic Fuel Process.","authors":"Yajie Yu, Shaojun Xia, Qinglong Jin, Lei Rong","doi":"10.3390/e24111658","DOIUrl":"https://doi.org/10.3390/e24111658","url":null,"abstract":"The short-chain hydrocarbon polymerization-catalyzed synthetic fuel technology has great development potential in the fields of energy storage and renewable energy. Modeling and optimization of a short-chain hydrocarbon polymerization-catalyzed synthetic fuel process involving mixers, compressors, heat exchangers, reactors, and separators are performed through finite-time thermodynamics. Under the given conditions of the heat source temperature of the heat exchanger and the reactor, the optimal performance of the process is solved by taking the mole fraction of components, pressure, and molar flow as the optimization variables, and taking the minimum entropy generation rate (MEGR) of the process as the optimization objective. The results show that the entropy generation rate of the optimized reaction process is reduced by 48.81% compared to the reference process; among them, the component mole fraction is the most obvious optimization variable. The research results have certain theoretical guiding significance for the selection of the operation parameters of the short-chain hydrocarbon polymerization-catalyzed synthetic fuel process.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems. OFDM系统信道和PDP的迭代联合估计方法。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111664

Ruixuan He, Xiaoran Liu, Kai Mei, Guangwei Gong, Jun Xiong, Jibo Wei

{"title":"Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems.","authors":"Ruixuan He, Xiaoran Liu, Kai Mei, Guangwei Gong, Jun Xiong, Jibo Wei","doi":"10.3390/e24111664","DOIUrl":"https://doi.org/10.3390/e24111664","url":null,"abstract":"The power-delay profile (PDP) estimation of wireless channels is an important step to generate a channel correlation matrix for channel linear minimum mean square error (LMMSE) estimation. Estimated channel frequency response can be used to obtain time dispersion characteristics that can be exploited by adaptive orthogonal frequency division multiplexing (OFDM) systems. In this paper, a joint estimator for PDP and LMMSE channel estimation is proposed. For LMMSE channel estimation, we apply a candidate set of frequency-domain channel correlation functions (CCF) and select the one that best matches the current channel to construct the channel correlation matrix. The initial candidate set is generated based on the traditional CCF calculation method for different scenarios. Then, the result of channel estimation is used as an input for the PDP estimation whereas the estimated PDP is further used to update the candidate channel correlation matrix. The enhancement of LMMSE channel estimation and PDP estimation can be achieved by the iterative joint estimation procedure. Analysis and simulation results show that in different communication scenarios, the PDP estimation error of the proposed method can approach the Cramér-Rao lower bound (CRLB) after a finite number of iterations. Moreover, the mean square error of channel estimation is close to the performance of accurate PDP-assisted LMMSE.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0