Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction

Recent works have demonstrated that deep learning (DL) based compressed sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by reconstructing MR images from sub-sampled k-space data. However, network architectures adopted in previous methods are all designed by handcraft. Neural Architecture Search (NAS) algorithms can automatically build neural network architectures which have outperformed human designed ones in several vision tasks. Inspired by this, here we proposed a novel and efficient network for the MR image reconstruction problem via NAS instead of manual attempts. Particularly, a specific cell structure, which was integrated into the model-driven MR reconstruction pipeline, was automatically searched from a flexible pre-defined operation search space in a differentiable manner. Experimental results show that our searched network can produce better reconstruction results compared to previous state-of-the-art methods in terms of PSNR and SSIM with 4-6 times fewer computation resources. Extensive experiments were conducted to analyze how hyper-parameters affect reconstruction performance and the searched structures. The generalizability of the searched architecture was also evaluated on different organ MR datasets. Our proposed method can reach a better trade-off between computation cost and reconstruction performance for MR reconstruction problem with good generalizability and offer insights to design neural networks for other medical image applications. The evaluation code will be available at https://github.com/yjump/NAS-for-CSMRI.Comment: To be appear in Computerized Medical Imaging and Graphic

Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task

Characterizing users' interests accurately plays a significant role in an effective recommender system. The sequential recommender system can learn powerful hidden representations of users from successive user-item interactions and dynamic users' preferences. To analyze such sequential data, conventional methods mainly include Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Recently, the use of self-attention mechanisms and bi-directional architectures have gained much attention. However, there still exists a major limitation in previous works that they only model the user's main purposes in the behavioral sequences separately and locally, and they lack the global representation of the user's whole sequential behavior. To address this limitation, we propose a novel bidirectional sequential recommendation algorithm that integrates the user's local purposes with the global preference by additive supervision of the matching task. We combine the mask task with the matching task in the training process of the bidirectional encoder. A new sample production method is also introduced to alleviate the effect of mask noise. Our proposed model can not only learn bidirectional semantics from users' behavioral sequences but also explicitly produces user representations to capture user's global preference. Extensive empirical studies demonstrate our approach considerably outperforms various state-of-the-art models.Comment: Accepted by ICONIP202

Design and implementation of a loss optimization control for electric vehicle in-wheel permanent-magnet synchronous motor direct drive system

As a main driving force of electric vehicles (EVs), the losses of in-wheel permanent-magnet synchronous motor (PMSM) direct drive system can seriously affect the energy consumption of EVs. This paper proposes a loss optimization control strategy for in-wheel PMSM direct drive system of EVs which optimizes the losses of both the PMSM and the inverter. The proposed method adjusts the copper losses and iron losses by identifying the optimal flux-weakening current, which results in the PMSM achieving the lower losses in the whole operational range. Moreover there are strongly nonlinear characteristics for the power devices, this paper creates a nonlinear loss model for three-phase half-bridge inverters to obtain accurate inverter losses under space vector pulse width modulation (SVPWM). Based on the inverter loss model and double Fourier integral analysis theory, the PWM frequency is optimized by the control strategy in order to maximize the inverter efficiency without affecting the operational stability of the drive. The proposed loss optimization control strategy can quickly find the optimum flux-weakening current and PWM frequency, and as a result, significantly broaden the high efficiency area of the PMSM direct drive system. The effects of the aforementioned strategy are verified by both theoretical analysis and experimental results

Uncertainty-driven Trajectory Truncation for Model-based Offline Reinforcement Learning

Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics model are reliable (e.g., some synthetic samples may lie outside of the support region of the static dataset). To address this issue, we propose Trajectory Truncation with Uncertainty (TATU), which adaptively truncates the synthetic trajectory if the accumulated uncertainty along the trajectory is too large. We theoretically show the performance bound of TATU to justify its benefits. To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark show that TATU significantly improves their performance, often by a large margin

Damage detection on railway bridges using Artificial Neural Network and train induced vibrations

A damage detection approach based on Artificial Neural Network (ANN), using the statistics of structural dynamic responses as the damage index, is proposed in this study for Structural Health Monitoring (SHM). Based on the sensitivity analysis, the feasibility of using the changes of variances and covariance of dynamic responses of railway bridges under moving trains as the indices for damage detection is evaluated.   A FE Model of a one-span simply supported beam bridge is built, considering both single damage case and multi-damage case. A Back-Propagation Neural Network (BPNN) is designed and trained to simulate the detection process. A series of numerical tests on the FE model with different train properties prove the validity and efficiency of the proposed approach. The results show not only that the trained ANN together with the statistics can correctly estimate the location and severity of damage in the structure, but also that the identification of the damage location is more difficult than that of the damage severity. In summary, it is concluded that the use of statistical property of structural dynamic response as damage index with the Artificial Neural Network as detection tool for damage detection is reliable and effective

Stock market and its future development's'in Latvia

Bakalaura darbā „Vērtspapīru tirgus un tā attīstības tendences Latvijā” ir izpētīti vērtspapīru tirgus funkcionēšanas teorētiskie aspekti, Latvijas vērtspapīru tirgus veidošanās un funkcionēšana, ir veikta Latvijas vērtspapīru tirgus attīstību ietekmējošo faktoru analīze, izvērtētas attīstības tendences, kā arī izstrādāti priekšlikumi vērtspapīru tirgus turpmākai attīstībai. Darba izstrādē pielietotas ekonomiskās un ekonometriskās analīzes, datu grupēšanas, grafiskās attēlošanas un salīdzināšanas metodes. Bakalaura darba apjoms ir 81 lappuse, ir ievietotas 12 tabulas, 8 attēli, 4 pielikumi. Darba izstrādē ir izmantoti 16 literatūras avoti un 15 interneta resursi.Stock market and its future development in Latvia is bachelor’s research which is focused on theoretical studies of stock market, its development and operation in Latvia. The major factors and trends, which affects the development of stock market in Latvia are explored and analysed, suggestions for its further development are drawn up in this paper. Economic and econometric analysis, data grouping, graphs and comparison of facts are the main methods used in the study. The paper contains 82 pages, 12 tables, 8 graphs, 4 attachments. There are 16 literar resources an 15 Internet resources used in the research

Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model

Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow

Numerical and experimental study on high-speed hydrogen-oxygen combustion gas flow and aerodynamic heating characteristics

The need to increase the payload capacity of the rockets motivates the development of high-power rocket engines. For a chemical propulsion system, this results in an increasing thermal load on the structure, especially the combustion chamber and nozzle must be able to withstand the extreme thermal load caused by high-temperature and high-pressure combustion gas. In order to protect the structure from the effect of increasing heat flux, it is necessary to counteract such effect with more advanced thermal management technology. This requires us to accurately predict the aerodynamic heating of the structure by high-temperature and high-speed combustion gas. In this study, a high-temperature combustion gas tunnel developed in the laboratory is used to produce high-speed combustion gas. Combined with the results of numerical calculation, the flow and aerodynamic heating characteristics of air and hydrogen-oxygen combustion gas under the same total temperature and pressure are analyzed and compared. The comparison revealed that the combustion gas flow in the nozzle has higher static temperature, velocity, and smaller Mach number. When the combustion gas flows around the sphere, the shock standoff distance and stagnation pressure are smaller than those of air, and the wall heat flux is much larger than that of air. The active chemical reaction in the combustion gas makes the aerodynamic heating of the structure more severe. Finally, through the analysis of a large amount of data, a semi-empirical formula for the heat flux of the stagnation point heated by a high-speed hydrogen and oxygen equivalent ratio combustion gas is obtained. Published under an exclusive license by AIP Publishing

Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model

Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow

Numerical and Experimental Study on the Duration of Nozzle Starting of the Reflected High-Enthalpy Shock Tunnel

The starting process of the flow in the nozzle of the JF-14 shock tunnel (1.6 m in length, 500 mm in outlet diameter) in the State Key Laboratory of High Temperature Gas Dynamics is analyzed by calculation and experiment. Two key factors which directly affect the duration of the nozzle starting are the velocity of the expansion wave and the low-velocity zone generated by the interaction between the secondary shock wave and boundary layer on the wall surface. In the process of the nozzle starting, the flow field stabilizes at the center of the nozzle outlet first, and then gradually stabilizes along the radius direction, thus defining the central startup and complete startup of the nozzle. It is found that there is a critical initial pressure. When the initial pressure is lower than the critical pressure, the airflow can reach stability in the nozzle outlet center with the shortest time, otherwise, the time required is much longer. The time required for the airflow to stabilize in the whole outlet section is mainly affected by the size of the low-velocity zone. It is also found that only at a very low initial pressure can the airflow simultaneously reach stability at the entire outlet of the nozzle