Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy

Grow and Merge: A Unified Framework for Continuous Categories Discovery

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories. In this work, we focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD}) problem, which is significantly more challenging than the static setting. A common challenge faced by novel category discovery is that different sets of features are needed for classification and category discovery: class discriminative features are preferred for classification, while rich and diverse features are more suitable for new category mining. This challenge becomes more severe for dynamic setting as the system is asked to deliver good performance for known classes over time, and at the same time continuously discover new classes from unlabeled data. To address this challenge, we develop a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating between a growing phase and a merging phase: in the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining, and in the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes. Our extensive studies verify that the proposed GM framework is significantly more effective than the state-of-the-art approaches for continuous category discovery.Comment: This paper has already been accepted by 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Structural transitions in two-dimensional modulated systems under triangular confinement

We study numerically the structural transitions of two-dimensional systems of classic particles with competing interactions under a triangular confinement with two different types of soft-wall potentials. We observe a variety of novel confinement-induced equilibrium configurations as a function of particle density and confinement steepness for each considered confinement potential. The specific role played by the confining potentials on the ordering of the particle clusters is revealed. These findings allow us to control the self-organization of modulated systems through using external confinements

Improving Temporal Event Scheduling through STEP Perpetual Learning

Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods

Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms

The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short-term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively

Nanostrip-Induced High Tunability Multipolar Fano Resonances in a Au Ring-Strip Nanosystem

Surface plasmon resonances of a Au ring-strip nanosystem with tunable multipolar Fano resonances have been investigated based on the finite-difference time-domain (FDTD) method. Abundant plasmon properties of a Au ring-strip nanosystem can be obtained on the basis of the unique electronic properties of different geometry parameters. In our research models, these multipolar Fano resonances are induced and can be tuned independently by changing the geometry parameters of the Au ring-strip nanosystem. Complex electric field distributions excited by the Au ring-strip nanosystem provide possibility to form dark plasmonic modes. Multipolar Fano resonances display strong light extinction in the Au ring-strip nanosystem, which can offer a new approach for an optical tunable filter, optical switching, and advanced biosensing

Genetic Diversity Assessment of Sweetpotato Germplasm in China Using InDel Markers

Sweetpotato (Ipomoea batatas (L.) Lam.), whose roots are rich in starch, is widely grown around the world and plays a prominent role in ensuring food security. At present, there are no reports on the genetic diversity of sweetpotato germplasm revealed by InDel markers. In this study, we developed a set of 30 InDel markers to evaluate the genetic diversity and relationships of 240 accessions, comprising 77 landraces, 80 introduced accessions, 82 improved varieties released in China, and a diploid wild relative Ipomoea trifida. A total of 94 reliable loci were obtained, with a mean of 3.13 loci per primer, and the PIC value ranged from 0.143 to 0.821. The whole population could be divided into three sub-populations according to a structure analysis based on the Bayesian model, which was consistent with the results of principal component analysis (PCA). A neighbor-joining tree was constructed based on Nei’s genetic distance ranging from 0 to 0.556 and discriminated the panel of the population into three main groups (Ⅰ, Ⅱ, Ⅲ). Group Ⅲ was further split into seven subgroups (ⅢA–ⅢG). The clustering pattern of the 240 accessions was unrelated to their geographic origins. Most of the accessions, whether landraces, improved varieties released in China or introduced germplasm, were mixed, which revealed the high level of genetic similarity among accessions from different regions. There was little difference in the level of genetic diversity between landraces and improved varieties, which was probably due to the exchange and utilization of accessions from different regions. More efforts should be made to collect and utilize sweetpotato germplasm resources and further broaden the genetic basis of sweetpotato cultivars

Source Profiles of Volatile Organic Compounds from Biomass Burning in Yangtze River Delta, China

The volatile organic compounds (VOCs) associated with biomass burning were characterized in the Yangtze River Delta of China, including two types of burning conditions (stove burning and field burning) and five typical kinds of biomass (straws of rice, wheat, bean and rape, and wood). According to the results, the VOC emission factors of straw burning ranged from 2.08 g/kg to 6.99 g/kg with an average of (4.89 +/- 1.70) g/kg, compared to 0.98 g/kg for wood burning. Some differences in VOC composition were observed with the burning of different biomasses. Oxygenated VOC (o-VOC) were the largest contributors to the mass concentration of measured VOCs from straw burning, with a proportion of 49.4%, followed by alkenes 21.4%, aromatics 13.5%, alkanes 10.6% and halogenated VOC (x-VOC) 5.0%. More aromatics and x-VOC were emitted from wood burning compared with straw burning. Field burning emitted more o-VOC due to more air being supplied during the burning test compared with stove burning. Further examination of the detailed VOC species showed the most abundant VOC species from biomass burning were o-VOC, C2-C3 alkenes and C6-C7 aromatics. The ozone formation potential (OFP) of VOCs from straw burning was in the range of 13.92-33.24 g/kg, which was much higher than that of wood burning (4.30 g/kg). Alkenes and o-VOC were the largest contributors to OFP of VOCs from biomass burning. The top five contributors of OFP were ethene, n-hexanal, propylene, acetaldehyde and methyl vinyl ketone, the sum of which accounted for 77% of total OFP. The ratio of ethylbenzene to m,p-xylenes from biomass burning was significantly higher than those from other VOC sources, and thus this could be seen as the fingerprint of biomass burning.Environmental SciencesSCI(E)[email protected]