Entropy (Basel, Switzerland)最新文献_第4页

Underwater Acoustic Target Recognition Based on Attention Residual Network. 基于注意残差网络的水声目标识别。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111657

Juan Li, Baoxiang Wang, Xuerong Cui, Shibao Li, Jianhang Liu

{"title":"Underwater Acoustic Target Recognition Based on Attention Residual Network.","authors":"Juan Li, Baoxiang Wang, Xuerong Cui, Shibao Li, Jianhang Liu","doi":"10.3390/e24111657","DOIUrl":"https://doi.org/10.3390/e24111657","url":null,"abstract":"Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.","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/PMC9688950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704371","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: 3

Variable Selection of Spatial Logistic Autoregressive Model with Linear Constraints. 线性约束空间Logistic自回归模型的变量选择。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111660

Yunquan Song, Yuqi Su, Zhijian Wang

{"title":"Variable Selection of Spatial Logistic Autoregressive Model with Linear Constraints.","authors":"Yunquan Song, Yuqi Su, Zhijian Wang","doi":"10.3390/e24111660","DOIUrl":"https://doi.org/10.3390/e24111660","url":null,"abstract":"In recent years, spatial data widely exist in various fields such as finance, geology, environment, and natural science. These data collected by many scholars often have geographical characteristics. The spatial autoregressive model is a general method to describe the spatial correlations among observation units in spatial econometrics. The spatial logistic autoregressive model augments the conventional logistic regression model with an extra network structure when the spatial response variables are discrete, which enhances classification precision. In many application fields, prior knowledge can be formulated as constraints on the parameters to improve the effectiveness of variable selection and estimation. This paper proposes a variable selection method with linear constraints for the high-dimensional spatial logistic autoregressive model in order to integrate the prior information into the model selection. Monte Carlo experiments are provided to analyze the performance of our proposed method under finite samples. The results show that the method can effectively screen out insignificant variables and give the corresponding coefficient estimates of significant variables simultaneously. As an empirical illustration, we apply our method to land area data.","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/PMC9689031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704305","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

Optimization and Benefit Analysis of Grain Trade in Belt and Road Countries. “一带一路”沿线国家粮食贸易优化与效益分析

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111667

Ruijin Du, Yang Chen, Gaogao Dong, Lixin Tian, Jing Zhang, Nidan Zhang

{"title":"Optimization and Benefit Analysis of Grain Trade in Belt and Road Countries.","authors":"Ruijin Du, Yang Chen, Gaogao Dong, Lixin Tian, Jing Zhang, Nidan Zhang","doi":"10.3390/e24111667","DOIUrl":"https://doi.org/10.3390/e24111667","url":null,"abstract":"Grain trade in Belt and Road (B&R) countries shows a mismatch between the volume and direction of grain flows and actual demand. With economic and industrial development, the water crisis has intensified, which poses a great challenge to the security of world grain supply and demand. There are few studies on the reconstruction of grain trade relations from the perspective of grain economic value. In this paper, a linear optimization model considering opportunity cost is proposed to fill the gap, and it is compared and analyzed with the optimization model considering only transportation cost. The grain supply and demand structures in both optimization results show characteristics of geographical proximity and long-tail distribution. Furthermore, the economic and water resource benefits resulting from the two optimal configurations are compared and analyzed. It is found that the economic benefits generated by grain trade in B&R countries with the consideration of opportunity cost not only cover transportation costs but also generate an economic value of about 130 trillion US dollars. Therefore, considering opportunity cost in grain trade is of great significance for strengthening cooperation and promoting the economic development of countries under the B&R framework. In terms of resource benefits, the grain trade with consideration of opportunity cost saves nearly 28 billion cubic meters of water, or about 5% of the total virtual water flow. However, about 72 billion cubic meters of water is lost for the grain trade with consideration of transportation cost. This study will help to formulate and adjust policies related to the \"Belt and Road Initiative\" (B&R Initiative), so as to maximize the economic benefits while optimizing the structure of grain trade and alleviating water scarcity pressures.","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/PMC9689001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704311","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

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation. 神经网络与重心在介子命中位置估计中的比较。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111659

Kadir Aktas, Madis Kiisk, Andrea Giammanco, Gholamreza Anbarjafari, Märt Mägi

{"title":"A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation.","authors":"Kadir Aktas, Madis Kiisk, Andrea Giammanco, Gholamreza Anbarjafari, Märt Mägi","doi":"10.3390/e24111659","DOIUrl":"https://doi.org/10.3390/e24111659","url":null,"abstract":"The performance of cosmic-ray tomography systems is largely determined by their tracking accuracy. With conventional scintillation detector technology, good precision can be achieved with a small pitch between the elements of the detector array. Improving the resolution implies increasing the number of read-out channels, which in turn increases the complexity and cost of the tracking detectors. As an alternative to that, a scintillation plate detector coupled with multiple silicon photomultipliers could be used as a technically simple solution. In this paper, we present a comparison between two deep-learning-based methods and a conventional Center of Gravity (CoG) algorithm, used to calculate cosmic-ray muon hit positions on the plate detector using the signals from the photomultipliers. In this study, we generated a dataset of muon hits on a detector plate using the Monte Carlo simulation toolkit GEANT4. We demonstrate that two deep-learning-based methods outperform the conventional CoG algorithm by a significant margin. Our proposed algorithm, Fully Connected Network, produces a 0.72 mm average error measured in Euclidean distance between the actual and predicted hit coordinates, showing great improvement in comparison with CoG, which yields 1.41 mm on the same dataset. Additionally, we investigated the effects of different sensor configurations on performance.","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/PMC9689399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704373","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

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms. 基于NISQ算法的金融领域量子机器学习分类优势预处理研究。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111656

Javier Mancilla, Christophe Pere

引用次数: 2

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data. 基于深度学习的临床部分标记数据头颈部危险器官分割。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-15 DOI: 10.3390/e24111661

Lucía Cubero, Joël Castelli, Antoine Simon, Renaud de Crevoisier, Oscar Acosta, Javier Pascau

{"title":"Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.","authors":"Lucía Cubero, Joël Castelli, Antoine Simon, Renaud de Crevoisier, Oscar Acosta, Javier Pascau","doi":"10.3390/e24111661","DOIUrl":"https://doi.org/10.3390/e24111661","url":null,"abstract":"Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.","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/PMC9689629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704374","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: 4

A Nonbinary LDPC-Coded Probabilistic Shaping Scheme for a Rayleigh Fading Channel. 一种非二进制ldpc编码的瑞利衰落信道概率整形方案。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-14 DOI: 10.3390/e24111649

Weimin Kang

引用次数: 0

Universal Single-Mode Lasing in Fully Chaotic Billiard Lasers. 全混沌台球激光器中的通用单模激光。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-14 DOI: 10.3390/e24111648

Mengyu You, Daisuke Sakakibara, Kota Makino, Yonosuke Morishita, Kazutoshi Matsumura, Yuta Kawashima, Manao Yoshikawa, Mahiro Tonosaki, Kazutaka Kanno, Atsushi Uchida, Satoshi Sunada, Susumu Shinohara, Takahisa Harayama

引用次数: 2

Global Stability of the Curzon-Ahlborn Engine with a Working Substance That Satisfies the van der Waals Equation of State. 具有满足范德华状态方程的工作物质的Curzon-Ahlborn发动机的全局稳定性。

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-14 DOI: 10.3390/e24111655

Juan Carlos Pacheco-Paez, Juan Carlos Chimal-Eguía, Ricardo Páez-Hernández, Delfino Ladino-Luna

引用次数: 0

3D Simulations of Freezing Characteristics of Double-Droplet Impact on Cold Surfaces with Different Wettability. 不同润湿性冷表面双液滴撞击冻结特性的三维模拟

IF 2.7

Entropy (Basel, Switzerland) Pub Date : 2022-11-14 DOI: 10.3390/e24111650

Anjie Hu, Qiaowei Yuan, Kaiyue Guo, Zhenyu Wang, Dong Liu

{"title":"3D Simulations of Freezing Characteristics of Double-Droplet Impact on Cold Surfaces with Different Wettability.","authors":"Anjie Hu, Qiaowei Yuan, Kaiyue Guo, Zhenyu Wang, Dong Liu","doi":"10.3390/e24111650","DOIUrl":"https://doi.org/10.3390/e24111650","url":null,"abstract":"In this work, the freezing characteristics of double-droplet impact on three typical wettability surfaces were investigated by coupling the solidification and melting VOF models. Different temperature conditions were adopted to study the influence of icing speed on droplet behavior. Simulation results show that the motion of the double-droplet impact is consistent with that of a single droplet in the early spreading stage but behaves differently in the retraction stage. The wetting area evolution during the impact-freezing process shows different tendency for hydrophilic and hydrophobic surfaces: Compared with single droplets, double droplets have a smaller wetting area factor on hydrophilic surfaces but a larger one on superhydrophobic surfaces. In addition, three typical impact results are observed for the double-droplet impact on a superhydrophobic cold surface: full rebound, adhesive avulsion, and full adhesion, which reflects the interaction of droplet merging and solidification during the impact freezing of the double droplet. These findings may deepen our understanding of the mechanism of impact freezing on a cold surface, it provides reference for the associated applications and technologies in icing/anti-icing.","PeriodicalId":520609,"journal":{"name":"Entropy (Basel, Switzerland)","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40703422","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