Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde

The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-v

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde's observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde's data in the polar regions. The differences between ANN-based maps and references show that the d

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD

OBJECTIVE: We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application. METHODS: A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups. RESULTS: Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory. CONCLUSIONS: Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD

Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region

The height of F2 peak (hmF2) is an essential ionospheric parameter and its variations can reflect both the earth magnetic and solar activities. Therefore, reliable prediction of hmF2 is important for the study of space, such as solar wind and extreme weather events. However, most current models are unable to forecast the variation of the ionosphere effectively since real-time measurements are required as model inputs. In this study, a new Australian regional hmF2 forecast model was developed by using ionosonde measurements and the bidirectional Long Short-Term Memory (bi-LSTM) method. The hmF2 value in the next hour can be predicted using the data from the past five hours at the same location. The inputs chosen from a location of interest include month of the year, local time (LT), K p , F 10 . 7 and hmF2 as an independent variable vector. The independent variable vectors in the immediate past five hours are considered as an independent variable set, which is used as an input of the new Australian regional hmF2 forecast model developed for the prediction of hmF2 in the hour to come. The performance of the new model developed is evaluated by comparing with those from other popular models, such as the AMTB, Shubin, ANN and LSTM models. Results showed that: (1) the new model can substantially outperform all the other four models. (2) Compared to the LSTM model, the new model is proven to be more robust and rapidly convergent. The mew model also outperforms that of the ANN model by around 30%. (3) the minimum sample number for the bi-LSTM method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000). (4) Compared to the Shubin model, the bi-LSTM method can effectively forecast the hmF2 values up to 5 h. This research is a first attempt at using the deep learning-based method for the application of the ionospheric prediction

Ultrasonic vibration assisted grinding of CFRP composites: Effect of fiber orientation and vibration velocity on grinding forces and surface quality

Ultrasonic vibration assisted grinding (UVAG) of carbon fiber reinforced plastic (CFRP) composites was carried out using a monolayer brazed diamond tool. Effects of the fiber orientation and vibration velocity on grinding forces and surface quality were analyzed. The maximum vibration velocity was varied by adjusting the grinding tool length. The results reveals that the grinding force is decreased with the increase of vibration velocity. The maximal grinding force is obtained with the fiber orientation at 135°, while the minimum grinding force is produced when the fiber orientation is 45°. Surface damages, such as groove scratch, fiber breakage are reduced with the increase of vibration velocity. Additionally, the fiber orientation plays a critical role in the surface morphology. Keywords: Vibration velocity, Fiber orientation, Grinding force, Surface quality, Carbon fiber reinforced plastic composites, Ultrasonic vibration assisted grindin

How Diversity and Accessibility Affect Street Vitality in Historic Districts?

The loss of traditional features and place memory, and ultimately vibrancy in historic districts, has attracted substantial attention in today’s urban design. Most conventional theories are of the consensus that diversity and accessibility characteristics play important roles in creating street vitality, whereas how these characteristics influence street vitality in historic districts has not been thoroughly explored. Furthermore, it is less clear as to which characteristics exert greater influence. Taking the Drum Tower Muslim District, a historical neighborhood in Xi’an, China, as a case study, this paper employs geospatial data to examine how diversity and accessibility influence street vitality. This study identifies seven factors of diversity and accessibility, and incorporates them into a spatial multivariate regression model for analysis. The results indicate that accessibility makes a stronger impact on the street vitality than diversity does. Furthermore, the closeness of streets, the functional density, the intersection density, the location of public transportation and the density of public infrastructure are the top five factors influencing street vitality. The outcome of this study will shed light on what constitutes a vibrant historic district and will help to inform us as to where and how we can improve street vitality

A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System

This paper proposes a scheme for indoor positioning by fusing floor map, WiFi and smartphone sensor data to provide meter-level positioning without additional infrastructure. A topology-constrained K nearest neighbor (KNN) algorithm based on a floor map layout provides the coordinates required to integrate WiFi data with pseudo-odometry (P-O) measurements simulated using a pedestrian dead reckoning (PDR) approach. One method of further improving the positioning accuracy is to use a more effective multi-threshold step detection algorithm, as proposed by the authors. The “go and back” phenomenon caused by incorrect matching of the reference points (RPs) of a WiFi algorithm is eliminated using an adaptive fading-factor-based extended Kalman filter (EKF), taking WiFi positioning coordinates, P-O measurements and fused heading angles as observations. The “cross-wall” problem is solved based on the development of a floor-map-aided particle filter algorithm by weighting the particles, thereby also eliminating the gross-error effects originating from WiFi or P-O measurements. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that the proposed scheme can reliably achieve meter-level positioning

Fast and Accurate Temperature-Dependent Current Modeling of HBTs Using the Dimension Reduction Method

10.1109/ACCESS.2019.2950179IEEE Access7160858-16086

All-Biobased Hydrovoltaic-Photovoltaic Electricity Generators for All-Weather Energy Harvesting

Hygroelectricity generators (HEGs) utilize the latent heat stored in environmental moisture for electricity generation, but nevertheless are showing relatively low power densities due to their weak energy harvesting capacities. Inspired by epiphytes that absorb ambient moisture and concurrently capture sunlight for dynamic photosynthesis, we propose herein a scenario of all-biobased hydrovoltaic-photovoltaic electricity generators (HPEGs) that integrate photosystem II (PSII) with Geobacter sulfurreducens (G.s) for simultaneous energy harvesting from both moisture and sunlight. This proof of concept illustrates that the all-biobased HPEG generates steady hygroelectricity induced by moisture absorption and meanwhile creates a photovoltaic electric field which further strengthens electricity generation under sunlight. Under environmental conditions, the synergic hydrovoltaic-photovoltaic effect in HPEGs has resulted in a continuous output power with a high density of 1.24 W/m2, surpassing all HEGs reported hitherto. This work thus provides a feasible strategy for boosting electricity generation via simultaneous energy harvesting from ambient moisture and sunlight