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1
Kong, Tianjiao, Jie Shao, Jiuyuan Hu, Xin Yang, Shiyiling Yang, and Reza Malekian. "EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph." Sensors 21, no. 5 (March 7, 2021): 1870. http://dx.doi.org/10.3390/s21051870.
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Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.
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Ma, Zhi-Yi, Xiao-Dong Yang, Ai-Jun He, Lu Ma, and Jun Wang. "Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph." Acta Physica Sinica 71, no. 5 (2022): 050501. http://dx.doi.org/10.7498/aps.71.20211656.
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The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure. Now, visibility graph analysis of univariate time series has become mature gradually. However, most of complex systems in real world are multi-dimensional, so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series. In this paper, a novel method of analyzing the multivariate time series is proposed. For patients with myocardial infarction and healthy subjects, the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series, which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network. Each node of the network corresponds to a lead, and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges. Owing to the fully connected network of different groups showing an identical topological structure, the dynamic characteristics of different individuals cannot be uniquely represented. Therefore, we reconstruct the fully connected network according to inter-layer mutual information, and when the value of inter-layer mutual information is less than the threshold we set, the edge corresponding to the inter-layer mutual information is deleted. We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients. Moreover, multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result. Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects, their reconstructed networks show a more regular structure, higher complexity and connectivity, and the healthy subjects can be distinguished from patients with myocardial infarction, whose reconstructed networks are sparser. Experimental results show that the identification accuracy of both parameters, average weighted degree and average weighted clustering coefficient, reaches 93.3%, which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction, and realize the automatic detection of myocardial infarction.
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Zhu, Guohun, Yan Li, and Peng (Paul) Wen. "Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm." Computer Methods and Programs in Biomedicine 115, no. 2 (July 2014): 64–75. http://dx.doi.org/10.1016/j.cmpb.2014.04.001.
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Zhao, Zhi-Qin, Liang Luo, and Xiao-Yan Liu. "Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network." American Journal of Biochemistry and Biotechnology 14, no. 1 (January 1, 2018): 67–75. http://dx.doi.org/10.3844/ajbbsp.2018.67.75.
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Gao, Yiyuan, Dejie Yu, and Haojiang Wang. "Fault diagnosis of rolling bearings using weighted horizontal visibility graph and graph Fourier transform." Measurement 149 (January 2020): 107036. http://dx.doi.org/10.1016/j.measurement.2019.107036.
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Supriya, Supriya, Siuly Siuly, Hua Wang, Jinli Cao, and Yanchun Zhang. "Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy." IEEE Access 4 (2016): 6554–66. http://dx.doi.org/10.1109/access.2016.2612242.
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Xu, Hengyu, Yu Fei, Chun Li, Jiajuan Liang, Xinan Tian, and Zhongjie Wan. "The North–South Asymmetry of Sunspot Relative Numbers Based on Complex Network Technique." Symmetry 13, no. 11 (November 22, 2021): 2228. http://dx.doi.org/10.3390/sym13112228.
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Solar magnetic activity exhibits a complex nonlinear behavior, but its dynamic process has not been fully understood. As the complex network technique can better capture the dynamics of nonlinear system, the visibility graphs (VG), the horizontal visibility graphs (HVG), and the limited penetrable visibility graphs (LPVG) are applied to implement the mapping of sunspot relative numbers in the northern and southern hemispheres. The results show that these three methods can capture important information of nonlinear dynamics existing in the long-term hemispheric sunspot activity. In the presentation of the results, the network degree sequence of the HVG method changes preferentially to the original data series as well as the VG and the LPVG, while both the VG and the LPVG slightly lag behind the original time series, which provides some new ideas for the nonlinear dynamics of the hemispheric asymmetry in the two hemispheres. Meanwhile, the use of statistical feature-skewness values and complex network visibility graphs can yield some complementary information for mutual verification.
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Gómez-Gómez, Javier, Rafael Carmona-Cabezas, Elena Sánchez-López, Eduardo Gutiérrez de Ravé, and Francisco José Jiménez-Hornero. "Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs." Entropy 23, no. 2 (February 8, 2021): 207. http://dx.doi.org/10.3390/e23020207.
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The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.
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Wang, Hongping, Hongming Mo, Rehan Sadiq, Yong Hu, and Yong Deng. "Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis." Computers & Industrial Engineering 88 (October 2015): 181–90. http://dx.doi.org/10.1016/j.cie.2015.06.021.
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Yoshimura, Takaaki, Kentaro Nishioka, Takayuki Hashimoto, Takashi Mori, Shoki Kogame, Kazuya Seki, Hiroyuki Sugimori, et al. "Prostatic urinary tract visualization with super-resolution deep learning models." PLOS ONE 18, no. 1 (January 6, 2023): e0280076. http://dx.doi.org/10.1371/journal.pone.0280076.
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In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen’s weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.
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Hu, Bo, Xingying Zhang, Rui Sun, and Xianchun Zhu. "Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach." Atmosphere 10, no. 12 (November 25, 2019): 740. http://dx.doi.org/10.3390/atmos10120740.
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Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias.
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Zhao, Zhi-Qin, Zu-Guo Yu, Vo Anh, Jing-Yang Wu, and Guo-Sheng Han. "Protein Folding Kinetic Order Prediction from Amino Acid Sequence Based on Horizontal Visibility Network." Current Bioinformatics 11, no. 2 (April 1, 2016): 173–85. http://dx.doi.org/10.2174/1574893611666160125221326.
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Muñoz, Víctor, and N. Elizabeth Garcés. "Analysis of pulsating variable stars using the visibility graph algorithm." PLOS ONE 16, no. 11 (November 17, 2021): e0259735. http://dx.doi.org/10.1371/journal.pone.0259735.
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We study the light curves of pulsating variable stars using a complex network approach to build visibility graphs. We consider various types of variables stars (e.g., Cepheids, δ Scuti, RR Lyrae), build two types of graphs (the normal visibility graph (VG) and the horizontal visibility graph (HVG)), and calculate various metrics for the resulting networks. We find that all networks have a power-law degree distribution for the VG and an exponential distribution for the HVG, suggesting that it is a universal feature, regardless of the pulsation features. Metrics such as the average degree, the clustering coefficient and the transitivity coefficient, can distinguish between some star types. We also observe that the results are not strongly affected by the presence of observation gaps in the light curves. These findings suggest that the visibility graph algorithm may be a useful technique to study variability in stars.
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Chen, Luyuan, Yong Deng, and Kang Hao Cheong. "Probability transformation of mass function: A weighted network method based on the ordered visibility graph." Engineering Applications of Artificial Intelligence 105 (October 2021): 104438. http://dx.doi.org/10.1016/j.engappai.2021.104438.
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HU, LI-DAN, NING-DE JIN, and ZHONG-KE GAO. "CHARACTERIZATION OF HORIZONTAL GAS–LIQUID TWO-PHASE FLOW USING MARKOV MODEL-BASED COMPLEX NETWORK." International Journal of Modern Physics C 24, no. 05 (May 2013): 1350028. http://dx.doi.org/10.1142/s0129183113500289.
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Horizontal gas–liquid two-phase flow widely exists in many physical systems and chemical engineering processes. Compared with vertical upward gas–liquid two-phase flow, investigations on dynamic behavior underlying horizontal gas–liquid flows are quite limited. Complex network provides a powerful framework for time series analysis of complex dynamical systems. We use a network generation method based on Markov transition probability to infer directed weighted complex networks from signals measured from horizontal gas–liquid two-phase flow experiment and find that the networks corresponding to different flow patterns exhibit different network structure. To investigate the dynamic characteristics of horizontal gas–liquid flows, we construct a number of complex networks under different flow conditions, and explore the network indices for each constructed network. In addition, we investigate the sample entropy of different flow patterns. Our results suggest that the network statistic can well represent the complexity in the transition among different flow patterns and further allows characterizing the interface fluctuation behavior in horizontal gas–liquid two-phase flow.
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Karimimoshaver, Mehrdad, Hatameh Hajivaliei, Manouchehr Shokri, Shakila Khalesro, Farshid Aram, and Shahab Shamshirband. "A Model for Locating Tall Buildings through a Visual Analysis Approach." Applied Sciences 10, no. 17 (September 2, 2020): 6072. http://dx.doi.org/10.3390/app10176072.
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Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.
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Ding, Mei-Shuang, Ning-De Jin, and Zhong-Ke Gao. "Modality transition-based network from multivariate time series for characterizing horizontal oil–water flow patterns." International Journal of Modern Physics C 26, no. 03 (February 25, 2015): 1550034. http://dx.doi.org/10.1142/s0129183115500345.
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The simultaneous flow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the flow behavior underlying horizontal oil–water flows is a challenging problem of significant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from different flow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of flow conditions and further allow quantitatively distinguishing and uncovering the dynamic flow behavior underlying different horizontal oil–water flow patterns.
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Mali, P., A. Mukhopadhyay, S. K. Manna, P. K. Haldar, and G. Singh. "Multifractal analysis of charged particle distributions using horizontal visibility graph and sandbox algorithm." Modern Physics Letters A 32, no. 08 (March 7, 2017): 1750024. http://dx.doi.org/10.1142/s0217732317500249.
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Horizontal visibility graphs (HVGs) and the sandbox (SB) algorithm usually applied for multifractal characterization of complex network systems that are converted from time series measurements, are used to characterize the fluctuations in pseudorapidity densities of singly charged particles produced in high-energy nucleus–nucleus collisions. Besides obtaining the degree distribution associated with event-wise pseudorapidity distributions, the common set of observables, typical of any multifractality measurement, are studied in [Formula: see text]O-Ag/Br and [Formula: see text]S-Ag/Br interactions, each at an incident laboratory energy of 200 GeV/nucleon. For a better understanding, we systematically compare the experiment with a Monte Carlo model simulation based on the Ultra-relativistic Quantum Molecular Dynamics (UrQMD). Our results suggest that the HVG-SB technique is an efficient tool that can characterize multifractality in multiparticle emission data, and in some cases, it is even superior to other methods more commonly used in this regard.
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Sengupta, Anwesha, Subhadeep Datta, Sibsambhu Kar, and Aurobinda Routray. "EEG Synchronization and Brain Networks." International Journal of Biomedical and Clinical Engineering 4, no. 2 (July 2015): 1–11. http://dx.doi.org/10.4018/ijbce.2015070101.
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Synchronization measures between Electro-encephalograph (EEG) signals from different regions of the brain are often employed to characterize the interaction of brain areas during mental and physical activity. The present work examines the variation of alertness of human subjects due to fatigue in a simulated driving task induced by loss of sleep using a Horizontal Visibility Graph (HVG)-based EEG synchronization measure. A brain network is formed at each stage of the experiment using synchronization values and network parameter values from various brain regions are compared to study the variation in connectivity between brain regions along successive stages of the experiment.
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Cai, Qing, Zhong-Ke Gao, Yu-Xuan Yang, Wei-Dong Dang, and Celso Grebogi. "Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection." International Journal of Neural Systems 29, no. 05 (May 29, 2019): 1850057. http://dx.doi.org/10.1142/s0129065718500570.
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Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.
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Craciunescu, Teddy, Andrea Murari, and Michela Gelfusa. "Improving Entropy Estimates of Complex Network Topology for the Characterization of Coupling in Dynamical Systems." Entropy 20, no. 11 (November 20, 2018): 891. http://dx.doi.org/10.3390/e20110891.
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A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems.
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Lee, Giljae, Hwunjae Lee, and Gyehwan Jin. "Analysis of Fitting Degree of MRI and PET Images in Simultaneous MRPET Images by Machine Learning Neural Networks." ScholarGen Publishers 3, no. 1 (December 28, 2020): 43–61. http://dx.doi.org/10.31916/sjmi2020-01-05.
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Simultaneous MR-PET imaging is a fusion of MRI using various parameters and PET images using various nuclides. In this paper, we performed analysis on the fitting degree between MRI and simultaneous MR-PET images and between PET and simultaneous MR-PET images. For the fitness analysis by neural network learning, feature parameters of experimental images were extracted by discrete wavelet transform (DWT), and the extracted parameters were used as input data to the neural network. In comparing the feature values extracted by DWT for each image, the horizontal and vertical low frequencies showed similar patterns, but the patterns were different in the horizontal and vertical high frequency and diagonal high frequency regions. In particular, the signal value was large in the T1 and T2 weighted images of MRI. Neural network learning results for fitting degree analysis were as follows. 1. T1-weighted MRI and simultaneous MR-PET image fitting degree: Regression (R) values were found to be Training 0.984, Validation 0.844, and Testing 0.886. 2. Dementia-PET image and Simultaneous MR-PET Image fitting degree: R values were found to be Training 0.970, Validation 0.803, and Testing 0.828. 3. T2-weighted MRI and concurrent MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.908, and Testing 0.766. 4. Brain tumor-PET image and Simultaneous MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.983, and Testing 0.876. An R value closer to 1 indicates more similarity. Therefore, each image fused in the simultaneous MR-PET images verified in this study was found to be similar. Ongoing study of images acquired with pulse sequences other than the weighted images in the MRI is needed. These studies may establish a useful protocol for the acquisition of simultaneous MR-PET images.
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Tian, Feng, Dan Wang, Qin Wu, and Daijun Wei. "An empirical study on network conversion of stock time series based on STL method." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 10 (October 2022): 103111. http://dx.doi.org/10.1063/5.0089059.
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A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the [Formula: see text] method and the visibility graph further highlights the advantages of the [Formula: see text] method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.
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Im, Chan-Gi, Dong-Min Son, Hyuk-Ju Kwon, and Sung-Hak Lee. "Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion." Entropy 24, no. 10 (October 9, 2022): 1435. http://dx.doi.org/10.3390/e24101435.
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In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.
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Alenezi, Fayadh. "RGB-Based Triple-Dual-Path Recurrent Network for Underwater Image Dehazing." Electronics 11, no. 18 (September 13, 2022): 2894. http://dx.doi.org/10.3390/electronics11182894.
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In this paper, we present a powerful underwater image dehazing technique that exploits two image characteristics—RGB color channels and image features. In using RGB color channels, each color channel is decomposed into two units based on the similarities via the k-mean. This markedly improves the adaptability and identification of similar pixels, and thus reduces pixels with a weak correlation, leaving only pixels with a higher correlation. We use an infinite impulse response (IIR) in the triple-dual and parallel interaction structure to suppress hazed pixels via a pixel comparison and amplification to increase the visibility of even very minor features. This improves the visual perception of the final image, thus improving the overall usefulness and quality of the image. The softmax-weighted fusion is finally used to fuse the output color channel features to attain the final image. This preserves the color, leaving our proposed method’s output very true to the original scene’s. This is accomplished by taking advantage of adaptive learning based on the confidence levels of the pixel contribution variation in each color channel during subsequent fuses. The proposed technique both visually and objectively outperforms the existing methods in several rigorous tests.
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Acosta-Tripailao, Belén, Denisse Pastén, and Pablo S. Moya. "Applying the Horizontal Visibility Graph Method to Study Irreversibility of Electromagnetic Turbulence in Non-Thermal Plasmas." Entropy 23, no. 4 (April 16, 2021): 470. http://dx.doi.org/10.3390/e23040470.
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One of the fundamental open questions in plasma physics is the role of non-thermal particles distributions in poorly collisional plasma environments, a system that is commonly found throughout the Universe, e.g., the solar wind and the Earth’s magnetosphere correspond to natural plasma physics laboratories in which turbulent phenomena can be studied. Our study perspective is born from the method of Horizontal Visibility Graph (HVG) that has been developed in the last years to analyze time series avoiding the tedium and the high computational cost that other methods offer. Here, we build a complex network based on directed HVG technique applied to magnetic field fluctuations time series obtained from Particle In Cell (PIC) simulations of a magnetized collisionless plasma to distinguish the degree distributions and calculate the Kullback–Leibler Divergence (KLD) as a measure of relative entropy of data sets produced by processes that are not in equilibrium. First, we analyze the connectivity probability distribution for the undirected version of HVG finding how the Kappa distribution for low values of κ tends to be an uncorrelated time series, while the Maxwell–Boltzmann distribution shows a correlated stochastic processes behavior. Subsequently, we investigate the degree of temporary irreversibility of magnetic fluctuations that are self-generated by the plasma, comparing the case of a thermal plasma (described by a Maxwell–Botzmann velocity distribution function) with non-thermal Kappa distributions. We have shown that the KLD associated to the HVG is able to distinguish the level of reversibility that is associated to the thermal equilibrium in the plasma, because the dissipative degree of the system increases as the value of κ parameter decreases and the distribution function departs from the Maxwell–Boltzmann equilibrium.
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Yetkin Ekren, Banu, and Bartu Arslan. "Simulation-based lateral transshipment policy optimization for s, S inventory control problem in a single-echelon supply chain network." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 10, no. 1 (September 16, 2019): 9–16. http://dx.doi.org/10.11121/ijocta.01.2020.00789.
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Since it affects the performance of whole supply chain significantly, definition of correct inventory control policy in a supply chain is critical. Recent technological development enabled real time visibility of a supply network by horizontal integration of each node in a supply network. By this opportunity, inventory sharing among stocking locations is also possible in the effort of cost minimization in supply chain management. Hence, lateral transshipment gained popularity and studies seeking the best lateral-transshipment policy is still under research. In this study, we aim to compare different lateral-transshipment policies for an s, S inventory control problem for a single-echelon supply chain network system. In this work, we consider a supply network with three stocking locations which may perform lateral transshipment among them when backorder takes place. We develop the simulation models of the systems in ARENA 14.5 commercial software and compare the performance of the policies by minimizing the total cost under a pre-defined fill rate constraint by using an optimization tool, OptQuest, integrated in that software. The results show that lateral transshipment works well compared to the scenario when there is no lateral transshipment policy in the network.
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Guo, Yinggang, and Zongchun Li. "Weighted quasi-stable combined adjustment of two-order horizontal control network using posteriori variance to determine the weight." Measurement Science and Technology 32, no. 11 (August 18, 2021): 115027. http://dx.doi.org/10.1088/1361-6501/ac17f8.
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Huang, Yu, Qimin Deng, and Zuntao Fu. "Could network analysis of horizontal visibility graphs be faithfully used to infer long-term memory properties in real-world time series?" Communications in Nonlinear Science and Numerical Simulation 79 (December 2019): 104908. http://dx.doi.org/10.1016/j.cnsns.2019.104908.
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Sainaghi, Ruggero, and Rodolfo Baggio. "Destination Events, Stability, and Turning Points of Development." Journal of Travel Research 60, no. 1 (December 9, 2019): 172–83. http://dx.doi.org/10.1177/0047287519890927.
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This study investigates the effects generated by tourism events and investments to improve destination development (dynamics) and stability (topology). The horizontal visibility graph framework (a technique able to transform a time series into a network) was used. Two hypotheses were tested: the first was the ability of these events and investments to generate a turning point, and the second was their ability to increase the system’s stability. The findings are based on a longitudinal analysis of three different destinations in terms of size, type of destination events and investments, and the prevalent market segment. For each case, a daily longitudinal time series was considered, and the empirical evidence confirmed both hypotheses. In the concluding remarks, the theoretical and empirical implications are reported, and some future research avenues are discussed.
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Bakhtavar, Ezzeddin, Shahab Hosseini, Kasun Hewage, and Rehan Sadiq. "Green blasting policy: Simultaneous forecast of vertical and horizontal distribution of dust emissions using artificial causality-weighted neural network." Journal of Cleaner Production 283 (February 2021): 124562. http://dx.doi.org/10.1016/j.jclepro.2020.124562.
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Tian-Qi, Wu, and Yao Lu. "Research on Quality Management Evaluation Method of Intelligent Robot Upgrade and R&D Project." E3S Web of Conferences 253 (2021): 01023. http://dx.doi.org/10.1051/e3sconf/202125301023.
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In order to promote the high-quality development of intelligent robot upgrade R&D projects, promote the progress of social and economic intelligence, and improve production efficiency, a dynamic weighted evaluation method based on network cohesion of balanced groups is proposed. This method is based on balanced grouping of evaluation groups to ensure the relative homogeneity of evaluation groups. It uses dynamic interactive evaluation to construct a network adjacency matrix based on the consistency of pairwise evaluations within the group. And it collects Horizontal information based on the concept of network cohesion. A stage planning function is constructed to calculate the weights of multiple rounds of evaluation, Finally, a comprehensive evaluation result is obtained after vertical merging. This paper provides a new evaluation method for improving the quality management of intelligent robot upgrade R&D projects.
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Shutters, Shade T., and Keith Waters. "Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure." Entropy 22, no. 10 (September 25, 2020): 1078. http://dx.doi.org/10.3390/e22101078.
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Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system “tightness” of a city’s labor force, capturing the degree of integration or internal connectedness of a city’s economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city’s resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies.
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Li, Tianjie, Yan Huang, Chaoguang Gu, and Fangbo Qiu. "Application of Geodesign Techniques for Ecological Engineered Landscaping of Urban River Wetlands: A Case Study of Yuhangtang River." Sustainability 14, no. 23 (November 24, 2022): 15612. http://dx.doi.org/10.3390/su142315612.
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Although geodesign techniques have been studied and developed worldwide, there is still a lack of in-depth application of geodesign workflows for redesigning urban river wetlands with characteristics of ecologically engineered landscaping (EEL). The study mainly aims at putting forward a proper approach in the methodological foundation for EEL practices in river wetlands. A typical EEL-oriented project of river restoration in Hangzhou, China, was conducted in this study. Based on in-situ geodata and tools within QGIS, individual geological factors analysis, with the hierarchical analysis method (AHP) and ecological vulnerability evaluation (EVE), was conducted by experts’ voting and the weighted linear combination (WLC) method. Analysis of hydrological-related factors proceeded. This GIS-based analysis with expert knowledge provided comprehensive redesign solutions for the redesign project, i.e., restoration of the riverbed, spatial restoration in the horizontal and vertical dimensions, and integration with the multifunctional design. Detailed three-dimensional models for design practices were developed to present redesigned topology and space accordingly. Terrain, inundation, and visibility analysis proceeded with parametric mapping programs within Grasshopper to check the feasibility. The adapted geodesign-based workflow in the study also applies to the site analysis, sustainable assessment and landscape planning for urban wetlands EEL projects.
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Visirov, Yu V. "Creation of linear-angular constructions in a closed area using satellite geodetic equipment." Geodesy and Cartography 960, no. 6 (July 20, 2020): 13–19. http://dx.doi.org/10.22389/0016-7126-2020-960-6-13-19.
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Invisibility between points can occur due to dense building location, terrain irregularities, intensive construction, and under polar night conditions. Forest thickets (the distance between the trunks up to 5 m, the closeness of the crowns start at 0,5 m) and undergrowth require cutting and clearing glades; terrain elevations, dense building setting, traffic flows and construction machines complicate the development of geodetic reference networks, the implementation of detailed surveys and construction layout. In the absence of visibility when the sides of the geodetic network make 200–500 m, it is recommended that two or more GPS receivers should work simultaneously under the same weather conditions, which compensates for errors at receiving radio signals, except for multipath and noise. With synchronous operation of the receivers, accurate coordinates and orientations for the upcoming electronic geodetic surveys are at mutually visible neighboring points obtained even without post-processing. Simultaneous GPS measurements enable finding the distance between them and horizontal angles in a closed area over the nearest points of the base and moving antennas in the building network for geodetic planning justification of detailed surveys and construction layout.
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Dawidowicz, Karol, Grzegorz Krzan, and Krzysztof Świątek. "Urban area GPS positioning accuracy using ASG-EUPOS POZGEO service as a function of session duration." Artificial Satellites 49, no. 1 (March 1, 2014): 33–42. http://dx.doi.org/10.2478/arsa-2014-0003.
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ABSTRACT GNSS observations carried out in a network of Continuously Operating Reference Station (CORS) are a complex systems which offer post-processing as well as corrections sent in realtime. In Poland, such a system has been in operation since June 2008, known as the Polish Active Geodetic Network (ASG-EUPOS). Usually the measurements performed in real time characterized lower accuracy than static measurements. For users who demand the highest precision results the post-processing services are provided. The paper presents an analysis of the position determination accuracy using ASG-EUPOS POZGEO service. It is well known that the final accuracy is e.g. the measuring conditions, time of observations or number of measured frequencies dependent. We processed 4 consecutive days of GPS data to determine how the accuracy of derived positional coordinates depends on the length of the observing session, the characteristics of horizon visibility on points and the used in post-processing observations (L1 or L1+L2). The POZGEO results show that horizontal accuracies of about 1-2 cm and vertical accuracies of 4 cm are achievable provided 0.5 hours dual frequency GPS data. The accuracy clearly decreases for point measured under conditions of strongly limited satellite availability
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Thurai, Merhala, Kumar Vijay Mishra, V. N. Bringi, and Witold F. Krajewski. "Initial Results of a New Composite-Weighted Algorithm for Dual-Polarized X-Band Rainfall Estimation." Journal of Hydrometeorology 18, no. 4 (April 1, 2017): 1081–100. http://dx.doi.org/10.1175/jhm-d-16-0196.1.
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Abstract Data analyses for the mobile Iowa X-band polarimetric (XPOL) radar from a long-duration rain event that occurred during the NASA Iowa Flood Studies (IFloodS) field campaign are presented. A network of six 2D video disdrometers (2DVDs) is used to derive four rain-rate estimators for the XPOL-5 radar. The rain accumulation validations with a collocated network of twin and triple tipping-bucket rain gauges have highlighted the need for combined algorithms because no single estimator was found to be sufficient for all cases considered. A combined version of weighted and composite algorithms is introduced, including a new R(Ah, Zdr) rainfall estimator for X band, where Ah is the specific attenuation for horizontal polarization and Zdr is the differential reflectivity. Based on measurement and algorithm errors, the weights are derived to be as piecewise constant functions over reflectivity values. The weights are later turned into continuous functions using smoothing splines. A methodology to derive the weights in near–real time is proposed for the composite-weighted algorithm. Comparisons of 2-h accumulations and 8-h event totals obtained from the XPOL-5 with 12 rain gauges have shown 10%–40% improvement in normalized bias over individual rainfall estimators. The analyses have enabled the development of rain-rate estimators for the Iowa XPOL.
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Golard, A., P. Witkovsky, and D. Tranchina. "Membrane currents of horizontal cells isolated from turtle retina." Journal of Neurophysiology 68, no. 2 (August 1, 1992): 351–61. http://dx.doi.org/10.1152/jn.1992.68.2.351.
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1. Membrane currents of horizontal cells isolated from the retina of the turtle, Pseudemys, were characterized by the whole-cell patch-clamp technique. 2. Four membrane currents were identified: an anomalous rectifier blocked by barium, a transient A-current, a sustained L-type calcium current enhanced by Bay K 8644, and a fast, tetrodotoxin-sensitive sodium current. Each of these four currents was found in both horizontal cell somata and axon terminals. 3. The current-voltage relations of axon terminals and somata were similar, but, in the normal operating range of the cell (-30 to -50 mV), the mean slope resistance of the axon terminal was higher (1.38 G omega) than that of the soma (0.26 G omega). 4. Exposure to either glutamate, kainate, or quisqualate induced a sustained inward current in horizontal cell axon terminals. The reversal potential for this current was -3 mV when tested with voltage steps and +9.1 mV when measured by a voltage ramp. The same horizontal cells were insensitive to N-methyl-D-aspartate. 5. A continuum model was developed to compute the degree of signal transfer between a horizontal cell body and its axon terminal. The model consisted of a network of electrically coupled somata that communicates with a network of electrically coupled axon terminals through the connecting axons. The specific membrane resistances used for the model derived from the patch-clamp measures. 6. We computed the voltage change elicited in either the layer of somata or of axon terminals by a static light stimulus of arbitrary dimensions. The amplitude of a spot response as a function of its radius was given by the weighted sum of two Bessel functions with different space constants. 7. The computed responses of the cell body were dominated by the Bessel function with the smaller space constant, whereas those of the axon terminal depended primarily on the Bessel function with the larger space constant. 8. The model predicts that, in contrast to the findings in teleost retina, there is little signal transfer between the somata and axon terminals of horizontal cell in the turtle retina.
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Ding, Dan, Ye Li, Peng Zhao, Kaitai Li, Sheng Jiang, and Yanxiu Liu. "Single Infrared Image Stripe Removal via Residual Attention Network." Sensors 22, no. 22 (November 11, 2022): 8734. http://dx.doi.org/10.3390/s22228734.
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The non-uniformity of the readout circuit response in the infrared focal plane array unit detector can result in fixed pattern noise with stripe, which seriously affects the quality of the infrared images. Considering the problems of existing non-uniformity correction, such as the loss of image detail and edge blurring, a multi-scale residual network with attention mechanism is proposed for single infrared image stripe noise removal. A multi-scale feature representation module is designed to decompose the original image into varying scales to obtain more image information. The product of the direction structure similarity parameter and the Gaussian weighted Mahalanobis distance is used as the similarity metric; a channel spatial attention mechanism based on similarity (CSAS) ensures the extraction of a more discriminative channel and spatial feature. The method is employed to eliminate the stripe noise in the vertical and horizontal directions, respectively, while preserving the edge texture information of the image. The experimental results show that the proposed method outperforms four state-of-the-art methods by a large margin in terms of the qualitative and quantitative assessments. One hundred infrared images with different simulated noise intensities are applied to verify the performance of our method, and the result shows that the average peak signal-to-noise ratio and average structural similarity of the corrected image exceed 40.08 dB and 0.98, respectively.
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Liu, Jinhua, Mulian Xu, Xinye Xu, and Yuanyuan Huang. "Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy." Entropy 21, no. 11 (October 31, 2019): 1070. http://dx.doi.org/10.3390/e21111070.
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The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.
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Luo, Xiaomin, Yidong Lou, Xiaopeng Gong, Shengfeng Gu, and Biyan Chen. "Benefit of Sparse Reference Network in BDS Single Point Positioning with Single-Frequency Measurements." Journal of Navigation 71, no. 2 (November 23, 2017): 403–18. http://dx.doi.org/10.1017/s0373463317000765.
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The current positioning accuracy of the BeiDou Navigation Satellite System (BDS) Single Point Positioning (SPP) with code measurement is in the order of several metres due to systematic errors. To further reduce the systematic errors in SPP, this contribution develops a new strategy to BDS SPP with a sparse reference network, named Augmented SPP (A-SPP). In this method, the Combined Residual Errors (CRE) products of BDS B1I measurement are integrated with three optional base stations that are close to the rover station. Based on the Satellite Elevation Angle Weighted (SEAW) average technique, the code residual errors of each BDS satellite observed by the rover station can be acquired epoch-by-epoch. Finally, the corrected code observations for the rover station can be utilised to achieve an A-SPP solution. The validation of this method is confirmed by both static and kinematic tests. Results clearly show that the accuracies of the A-SPP solution for horizontal and vertical directions are better than 0·5 m and 1·0 m. This study suggests that the proposed A-SPP solution is a good option for single-frequency GNSS users to improve their positioning performance.
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Zhang, Zhaoyue, An Zhang, Cong Sun, Shuaida Xiang, and Shanmei Li. "Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow." Journal of Advanced Transportation 2020 (September 18, 2020): 1–17. http://dx.doi.org/10.1155/2020/8830731.
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Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.
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Blumthaler, Mario. "UV Monitoring for Public Health." International Journal of Environmental Research and Public Health 15, no. 8 (August 11, 2018): 1723. http://dx.doi.org/10.3390/ijerph15081723.
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Overexposure to solar ultraviolet (UV) radiation is a risk for public health. Therefore, it is important to provide information to the public about the level of solar UV. The UV-Index (UVI) is the relevant quantity, expressing the erythemally weighted irradiance to a horizontal plane on a simple scale. As solar UV irradiance is strongly variable in time and space, measurements within a network provide the best source of information, provided they can be made available rapidly. However, to ensure the information is reliable, strict quality assurance/quality control (QA/QC) procedures for the monitoring networks are necessary. Near real time presentation of the measured UVI on web-pages is the best way to inform the public. The interpretation of the data in terms of the individual ‘allowable’ exposure time is heavily impacted by skin type, behavior, and clothing, and must be learned for each person through experience and guidance. Nonetheless, reliable knowledge of the actual level of the intensity of erythemally weighted irradiance and its variability forms the basis of education and public awareness. The challenges and requirements in providing comprehensive UVI data for public health guidance are here considered.
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Ishikawa, Kiichiro, Yoshiharu Amano, Takumi Hashizume, Jun-ichi Takiguchi, and Naoyuki Kajiwara. "A Mobile Mapping System for Precise Road Line Localization Using a Single Camera and 3D Road Model." Journal of Robotics and Mechatronics 19, no. 2 (April 20, 2007): 174–80. http://dx.doi.org/10.20965/jrm.2007.p0174.
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Precise highway alignment data to be used in car navigation and ITS to increase driving safety must be kept up-to-date and accurate. A Mobile Mapping System (MMS) provides a highway alignment database and offers unparalleled productivity when combined with navigation and videogrammetry tools. The MMS we propose features a GPS/Dead Reckoning (DR) combined navigation system, a three-axis GPS-Gyro/Inertial Measurement Unit (IMU), laser scanners, nearly horizontal cameras, and network-based Positioning Augmentation Services (PASTM) (Mitsubishi Electric Corporation) and measures center-line and side-line locations precisely based on a 3D road surface model. The carrier-phased D-GPS/DR navigation system and GPS-Gyro/IMU conducts highly accurate positioning in centimeters and posture estimation at 0.073° rms for heading, 0.064° rms for pitch, and 0.116° for roll. It provides 0.095 m rms accuracy for both center-line and side-line measurement when GPS visibility is sufficient. A comparison of accuracy between static RTK-GPS measurement and MMS measurement on the Tateyama Kurobe alpine route confirmed MMS dynamic measurement accuracy and effectiveness.
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Baek, Yoonjee, and Heesun Joo. "A Study on the Spatial Structure of the Bu-Ul-Gyeong Megacity Using the City Network Paradigm." Sustainability 14, no. 23 (November 28, 2022): 15845. http://dx.doi.org/10.3390/su142315845.
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Developing bidirectional urban networks within areas in megacities is an essential spatial strategy across regions today. In 2018, Korea began its Bu-Ul-Gyeong (BUG) megacity project. Today, Korea is working to improve functional polycentric urban networks within the BUG megacity. To uncover insights useful for this project, this study sought to examine urban network patterns (e.g., network asymmetries and imbalances in the sizes and directions of their weighted flows) and identify the primary and secondary centers of the BUG megacity using mobile flow data from 2019 to 2020. Specifically, a three-step social network analysis was conducted across different geographical scales; namely: (1) the BUG megacity, (2) South Gyeongsang Province (SGP), and (3) every community in SGP. Eigenvector centrality and flow betweenness centrality revealed two primary centers (Changwon and Jinju) and four secondary centers (Haman, Sacheon, Tongyeong, and Geochang). Unidirectional and hierarchical connections were evident between the primary and secondary centers. In response to these findings, this paper proposes some beneficial strategies for the region’s public transportation networks to prevent small- and medium-sized cities from being marginalized and to enhance horizontal urban connectivity in megacities.
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Chen, Biyan, Wujiao Dai, Zhizhao Liu, Lixin Wu, Cuilin Kuang, and Minsi Ao. "Constructing a precipitable water vapor map from regional GNSS network observations without collocated meteorological data for weather forecasting." Atmospheric Measurement Techniques 11, no. 9 (September 11, 2018): 5153–66. http://dx.doi.org/10.5194/amt-11-5153-2018.
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Abstract. Surface pressure (Ps) and weighted mean temperature (Tm) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) zenith total delay (ZTD) estimates. The lack of Ps or Tm information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate Ps and Tm at the GNSS station using nearby synoptic observations. Ps and Tm obtained at the nearby synoptic sites are interpolated onto the location of the GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in Ps and Tm calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high-quality PWV maps through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the Ps and Tm retrieval, and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate the PWV maps. The retrieved Ps and Tm and constructed PWV maps were assessed by the results derived from radiosonde and the ERA-Interim reanalysis. The results show that (1) accuracies of Ps and Tm derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA-Interim reanalysis data with the overall accuracy being better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.
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Xu, Zhijing, Rui Gao, Kan Huang, and Qihui Xu. "Triangle Distance IoU Loss, Attention-Weighted Feature Pyramid Network, and Rotated-SARShip Dataset for Arbitrary-Oriented SAR Ship Detection." Remote Sensing 14, no. 18 (September 19, 2022): 4676. http://dx.doi.org/10.3390/rs14184676.
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In synthetic aperture radar (SAR) images, ship targets are characterized by varying scales, large aspect ratios, dense arrangements, and arbitrary orientations. Current horizontal and rotation detectors fail to accurately recognize and locate ships due to the limitations of loss function, network structure, and training data. To overcome the challenge, we propose a unified framework combining triangle distance IoU loss (TDIoU loss), an attention-weighted feature pyramid network (AW-FPN), and a Rotated-SARShip dataset (RSSD) for arbitrary-oriented SAR ship detection. First, we propose a TDIoU loss as an effective solution to the loss-metric inconsistency and boundary discontinuity in rotated bounding box regression. Unlike recently released approximate rotational IoU losses, we derive a differentiable rotational IoU algorithm to enable back-propagation of the IoU loss layer, and we design a novel penalty term based on triangle distance to generate a more precise bounding box while accelerating convergence. Secondly, considering the shortage of feature fusion networks in connection pathways and fusion methods, AW-FPN combines multiple skip-scale connections and attention-weighted feature fusion (AWF) mechanism, enabling high-quality semantic interactions and soft feature selections between features of different resolutions and scales. Finally, to address the limitations of existing SAR ship datasets, such as insufficient samples, small image sizes, and improper annotations, we construct a challenging RSSD to facilitate research on rotated ship detection in complex SAR scenes. As a plug-and-play scheme, our TDIoU loss and AW-FPN can be easily embedded into existing rotation detectors with stable performance improvements. Experiments show that our approach achieves 89.18% and 95.16% AP on two SAR image datasets, RSSD and SSDD, respectively, and 90.71% AP on the aerial image dataset, HRSC2016, significantly outperforming the state-of-the-art methods.
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Psychas, Dimitrios, and Sandra Verhagen. "Real-Time PPP-RTK Performance Analysis Using Ionospheric Corrections from Multi-Scale Network Configurations." Sensors 20, no. 11 (May 26, 2020): 3012. http://dx.doi.org/10.3390/s20113012.
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The long convergence time required to achieve high-precision position solutions with integer ambiguity resolution-enabled precise point positioning (PPP-RTK) is driven by the presence of ionospheric delays. When precise real-time ionospheric information is available and properly applied, it can strengthen the underlying model and substantially reduce the time required to achieve centimeter-level accuracy. In this study, we present and analyze the real-time PPP-RTK user performance using ionospheric corrections from multi-scale regional networks during a day with medium ionospheric disturbance. It is the goal of this contribution to measure the impact the network dimension has on the ambiguity-resolved user position through the predicted ionospheric corrections. The user-specific undifferenced ionospheric corrections are computed at the network side, along with the satellite phase biases needed for single-receiver ambiguity resolution, using the best linear unbiased predictor. Such corrections necessitate the parameterization of an estimable user receiver code bias, on which emphasis is given in this study. To this end, we process GPS dual-frequency data from four four-station evenly distributed CORS networks in the United States with varying station spacings in order to evaluate if and to what extent the ionospheric corrections from multi-scale networks can improve the user convergence times. Based on a large number of samples, our experimental results showed that sub-10 cm horizontal accuracy can be achieved almost instantaneously in the ionosphere-weighted partially-ambiguity-fixed kinematic PPP-RTK solutions based on corrections from a network with 68 km spacing. Most of the solutions (90%) were shown to require less than 6.0 min, compared to the ionosphere-float PPP solutions that needed 68.5 min. In case of sparser networks with 115, 174 and 237 km spacing, 50% of the horizontal positioning errors are shown to become less than one decimeter after 1.5, 4.0 and 7.0 min, respectively, while 90% of them require 10.5, 16.5 and 20.0 min. We also numerically demonstrated that the user’s convergence times bear a linear relationship with the network density and get shorter as the density increases, for both full and partial ambiguity resolution.
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Jing, Xueping, Monique D. Dorrius, Mirjam Wielema, Paul E. Sijens, Matthijs Oudkerk, and Peter van Ooijen. "Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information." Cancers 14, no. 8 (April 18, 2022): 2042. http://dx.doi.org/10.3390/cancers14082042.
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Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.
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Shi, Ge, Jie Shan, Liang Ding, Peng Ye, Yang Li, and Nan Jiang. "Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City." International Journal of Environmental Research and Public Health 16, no. 13 (June 30, 2019): 2318. http://dx.doi.org/10.3390/ijerph16132318.
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Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development.