A Robust Recursive Filter for Nonlinear Systems with Correlated Noises, Packet Losses, and Multiplicative Noises

A robust filtering problem is formulated and investigated for a class of nonlinear systems with correlated noises, packet losses, and multiplicative noises. The packet losses are assumed to be independent Bernoulli random variables. The multiplicative noises are described as random variables with bounded variance. Different from the traditional robust filter based on the assumption that the process noises are uncorrelated with the measurement noises, the objective of the addressed robust filtering problem is to design a recursive filter such that, for packet losses and multiplicative noises, the state prediction and filtering covariance matrices have the optimized upper bounds in the case that there are correlated process and measurement noises. Two examples are used to illustrate the effectiveness of the proposed filter

Optimal configuration of hybrid AC/DC urban distribution networks for high penetration renewable energy

Existing AC medium-voltage distribution networks are facing challenges on handling increasing loads and renewable energy integrations. However, it is very difficult to build new distribution lines in urban areas. This study proposes a configuration method of hybrid AC/DC medium-voltage distribution networks, in which some existing AC lines are converted to DC operation. Existing topologies and dispatching scenarios are considered during configuration because the overall power flow can be rescheduled in the hybrid AC/DC distribution network. Therefore, transfer capacities of the lines are fully utilised, and more renewable energies are accommodated. A bi-level programming model is established embedding chance constraint programming to consider the intermittent output of renewable energy. In the upper level, a multiple objective optimal model is proposed in order to balance investments, power losses, and the maximum load level and renewable energy capacity. In the lower level, daily operations of the newly installed VSCs are optimised by a chance constraint programming. The influences of energy storage systems on the configuration are also analysed. Simulation studies are performed to verify the proposed method

Numerical study on the internal characteristics of single screw expanders used in organic Rankine cycle systems

Single screw expanders have been widely studied in middle-low temperature heat recovery power system of organic Rankine cycle, which is significant for the energy-conservation and environment-protection. However, internal irreversible loss including leakage, heat transfer and friction power loss has great influence on the performances of single screw expanders. Although some present researches of single screw expanders have been carried out, few can directly reflect the effect of internal irreversible loss on the performance of expanders. Therefore, it is necessary to research the internal characteristics of the single screw expanders. In this paper, a numerical study of a single screw expander was carried out to analyze its internal irreversible loss. Based on the theory of engineering thermodynamics and hydrodynamics, a thermodynamics working process mathematical model was presented to calculate the flow rate and efficiency of a single screw expander. A separation approach was proposed to solve the above coupling problem, which could be solved by classical fourth-order Runge-Kutta method through MATLAB language programming. Take the organic working fluid R123 for example, the numerical results were verified by experimental results. The numerical results of flow rate, volumetric and isentropic efficiency were in good agreement with the experimental results, and the average relative error are 4.9%, 4.4% and 0.3% at the rotation speed of 3000±10rpm under different intake pressure. Then three organic working fluids R123, R245fa and R134a were chose to simulate the characteristics of single screw expanders. Results show that the highest efficiency is R123, followed by R245fa and the last is R134a at the same rotational speed and intake conditions. The maximum volumetric and isentropic efficiency of R123 is 81.73% and 78.41%, respectively at the rotational speed of 3000rpm. The maximum volumetric and isentropic efficiency of R245fa is 80.63% and 69.67%, respectively at the rotational speed of 3000rpm. The maximum volumetric and isentropic efficiency of R134a is 78.04% and 54.50%, respectively at the rotational speed of 3000rpm

4,4′-{[4-(2,2′:6′,2′′-Terpyridin-4′-yl)phen­yl]imino}­dibenzaldehyde

The central pyridine ring of the 2,2′:6′,2′′-terpyridine fragment of the title compound, C35H24N4O2, forms dihedral angles of 8.3 (2), 10.6 (3) and 39.4 (3)°, respectively, with the two outer pyridine rings and the attached benzene ring. In the crystal, weak C—H⋯O inter­actions link the mol­ecules into chains in [010]

Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities. Document type: Articl

Molecular Cloning and Analysis of the Tryptophan oxygenase Gene in the Silkworm, Bombyx mori

A Bombyx mori L. (Lepidoptera: Bombycidae) gene encoding tryptophan oxygenase has been molecularly cloned and analyzed. The tryptophan oxygenase cDNA had 1374 nucleotides that encoded a 401 amino acid protein with an estimated molecular mass of 46.47 kDa and a PI of 5.88. RT-PCR analysis showed that the B. mori tryptophan oxygenase gene was transcribed in all examined stages. Tryptophan oxygenase proteins are relatively well conserved among different orders of arthropods