• 제목/요약/키워드: Chongqing model

검색결과 227건 처리시간 0.027초

Common Model EMI Prediction in Motor Drive System for Electric Vehicle Application

  • Yang, Yong-Ming;Peng, He-Meng;Wang, Quan-Di
    • Journal of Electrical Engineering and Technology
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    • 제10권1호
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    • pp.205-215
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    • 2015
  • Common mode (CM) conducted interference are predicted and compared with experiments in a motor drive system of Electric vehicles in this study. The prediction model considers each part as an equivalent circuit model which is represented by lumped parameters and proposes the parameter extraction method. For the modeling of the inverter, a concentrated and equivalent method is used to process synthetically the CM interference source and the stray capacitance. For the parameter extraction in the power line model, a computation method that combines analytical method and finite element method is used. The modeling of the motor is based on measured date of the impedance and vector fitting technique. It is shown that the parasitic currents and interference voltage in the system can be simulated in the different parts of the prediction model in the conducted frequency range (150 kHz-30 MHz). Experiments have successfully confirmed that the approach is effective.

Tumor Necrosis Factor-α Gene Polymorphisms and Risk of Oral Cancer: Evidence from a Meta-analysis

  • Chen, Fang-Chun;Zhang, Fan;Zhang, Zhi-Jiao;Meng, Si-Ying;Wang, Yang;Xiang, Xue-Rong;Wang, Chun;Tang, Yu-Ying
    • Asian Pacific Journal of Cancer Prevention
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    • 제14권12호
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    • pp.7243-7249
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    • 2013
  • Numerous studies have been conducted regarding association between TNF-${\alpha}$ and oral cancer risk, but the results remain controversial. The present meta-analysis is performed to acquire a more precise estimation of relationships. Databases of Pubmed, the Cochrane library and the China National Knowledge Internet (CNKI) were retrieved until August 10, 2013. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated with fixed- or random-effect models. The heterogeneity assumption was assessed by I-squared test. Among the eight included case-control studies, all were focused on TNF-${\alpha}$-308G>A and four also concerned the TNF-${\alpha}$-238G>A polymorphism. It was found that oral cancer risk were significant decreased with the TNF-${\alpha}$-308G>A polymorphism in the additive genetic model (GG vs. AA, OR=0.19, 95% CI: [0.04, 1.00], P=0.05, I2=68.9%) and the dominant genetic model (GG+GA vs. AA, OR=0.22, 95% CI: [0.06, 0.82], P=0.03, I2=52.4%); however, no significant association was observed in allele contrast (G vs. A, OR=0.70, 95% CI: [0.23, 2.16], P=0.54, I2=95.9%) and recessive genetic models (GG vs. GA+AA, OR=0.72, 95% CI: [0.33, 1.57], P=0.41, I2=93.1%). For the TNF-${\alpha}$-238G>A polymorphism, significant associations with oral cancer risk were found in the allele contrast (G vs. A, OR=2.75, 95% CI: [1.25, 6.04], P=0.01, I2=0.0%) and recessive genetic models (GG vs. GA+AA, OR=2.23, 95%CI: [1.18, 4.23], P=0.01, I2=0.0%). Conclusively, this meta-analysis indicates that TNF-${\alpha}$ polymorphisms may contribute to the risk of oral cancer. Allele G and the GG+GA genotype of TNF-${\alpha}$-308G>A may decrease the risk of oral cancer, while allele G and the GG genotype of TNF-${\alpha}$-238G>A may cause an increase.

Dynamic analysis of metro vehicle traveling on a high-pier viaduct under crosswind in Chongqing

  • Zhang, Yunfei;Li, Jun;Chen, Zhaowei;Xu, Xiangyang
    • Wind and Structures
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    • 제29권5호
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    • pp.299-312
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    • 2019
  • Due to the rugged terrain, metro lines in mountain city across numerous wide rivers and deep valleys, resulting in instability of high-pier bridge and insecurity of metro train under crosswind. Compared with the conditions of no-wind, crosswind triggers severer vibration of the dynamic system; compared with the short-pier viaduct, the high-pier viaduct has worse stability under crosswind. For these reasons, the running safety of the metro vehicle traveling on a high-pier viaduct under crosswind is analyzed to ensure the safe operation in metro lines in mountain cities. In this paper, a dynamic model of the metro vehicle-track-bridge system under crosswind is established, in which crosswind loads model considering the condition of wind zone are built. After that, the evaluation indices and the calculation parameters have been selected, moreover, the basic characteristics of the dynamic system with high-pier under crosswind are analyzed. On this basis, the response varies with vehicle speed and wind speed are calculated, then the corresponding safety zone is determined. The results indicate that, crosswind triggers drastic vibration to the metro vehicle and high-pier viaduct, which in turn causes running instability of the vehicle. The corresponding safety zone for metro vehicle traveling on the high-pier is proposed, and the metro traffic on the high-pier bridge under crosswind should not exceed the corresponding limited vehicle speed to ensure the running safety.

Hot Spot Detection of Thermal Infrared Image of Photovoltaic Power Station Based on Multi-Task Fusion

  • Xu Han;Xianhao Wang;Chong Chen;Gong Li;Changhao Piao
    • Journal of Information Processing Systems
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    • 제19권6호
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    • pp.791-802
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    • 2023
  • The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

Group Power Constraint Based Wi-Fi Access Point Optimization for Indoor Positioning

  • Pu, Qiaolin;Zhou, Mu;Zhang, Fawen;Tian, Zengshan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권5호
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    • pp.1951-1972
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    • 2018
  • Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

Multi-scale heat conduction models with improved equivalent thermal conductivity of TRISO fuel particles for FCM fuel

  • Mouhao Wang;Shanshan Bu;Bing Zhou;Zhenzhong Li;Deqi Chen
    • Nuclear Engineering and Technology
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    • 제55권3호
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    • pp.1140-1151
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    • 2023
  • Fully Ceramic Microencapsulated (FCM) fuel is emerging advanced fuel material for the future nuclear reactors. The fuel pellet in the FCM fuel is composed of matrix and a large number of TRistructural-ISOtopic (TRISO) fuel particles which are randomly dispersed in the SiC matrix. The minimum layer thickness in a TRISO fuel particle is on the order of 10-5 m, and the length of the FCM pellet is on the order of 10-2 m. Hence, the heat transfer in the FCM pellet is a multi-scale phenomenon. In this study, three multi-scale heat conduction models including the Multi-region Layered (ML) model, Multi-region Non-layered (MN) model and Homogeneous model for FCM pellet were constructed. In the ML model, the random distributed TRISO fuel particles and coating layers are completely built. While the TRISO fuel particles with coating layers are homogenized in the MN model and the whole fuel pellet is taken as the homogenous material in the Homogeneous model. Taking the results by the ML model as the benchmark, the abilities of the MN model and Homogenous model to predict the maximum and average temperature were discussed. It was found that the MN model and the Homogenous model greatly underestimate the temperature of TRISO fuel particles. The reason is mainly that the conventional equivalent thermal conductivity (ETC) models do not take the internal heat source into account and are not suitable for the TRISO fuel particle. Then the improved ETCs considering internal heat source were derived. With the improved ETCs, the MN model is able to capture the peak temperature as well as the average temperature at a wide range of the linear powers (165 W/cm~ 415 W/cm) and the packing fractions (20%-50%). With the improved ETCs, the Homogenous model is better to predict the average temperature at different linear powers and packing fractions, and able to predict the peak temperature at high packing fractions (45%-50%).

Aqueous Extract of Lysimachia christinae Hance Prevents Cholesterol Gallstone in Mice by Affecting the Intestinal Microflora

  • Liu, Shijia;Luorong, Quji;Hu, Kaizhi;Cao, Weiguo;Tao, Wei;Liu, Handeng;Zhang, Dan
    • Journal of Microbiology and Biotechnology
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    • 제31권9호
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    • pp.1272-1280
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    • 2021
  • With changes in human dietary patterns, the proportion of high-fat and high-cholesterol foods in the daily diet has increased. As a result, the incidence rate of cholelithiasis is increasing rapidly. Many studies have reported on the crucial role that the intestinal microflora plays in the progression of gallstones. Although the whole herb of Lysimachia christinae, a traditional Chinese medicine, has long been extensively used as a remedy for cholelithiasis in China, its effects on the intestinal microflora remain unknown. Hence, in this study, we investigated the ability of the aqueous extract of L. christinae (LAE) to prevent cholesterol gallstones (CGSs) in model animals by affecting the intestinal microflora. The effects of LAE on body weight, serum lipid profile, visceral organ indexes, and histomorphology were studied in male C57BL/6J mice, which were induced by a lithogenic diet. After the 8-week study, CGSs formation was greatly reduced after LAE treatment. LAE also reduced body weight gain and hyperlipidemia and restored the histomorphological changes. Moreover, the intestinal microflora exhibited significant variation. In the model group fed the lithogenic diet, the abundances of the genera unclassified Porphyromonadaceae, Lactobacillus and Alloprevotella decreased, but in contrast, Akkermansia dramatically increased compared with the control check group, which was fed a normal diet; the administration of LAE reversed these changes. These results imply that L. christinae can be considered an efficient therapy for eliminating CGSs induced by a high-fat and high-cholesterol diet, which may be achieved by influencing the intestinal microflora.

Load and Mutual Inductance Identification Method for Series-Parallel Compensated IPT Systems

  • Chen, Long;Su, Yu-Gang;Zhao, Yu-Ming;Tang, Chun-Sen;Dai, Xin
    • Journal of Power Electronics
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    • 제17권6호
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    • pp.1545-1552
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    • 2017
  • Identifying the load and mutual inductance is essential for improving the power transfer capability and power transfer efficiency of Inductive Power Transfer (IPT) systems. In this paper, a steady-state load and mutual inductance identification method focusing on series-parallel compensated IPT systems is proposed. The identification model is established according to the steady-state characteristics of the system. Furthermore, two sets of identification results are obtained, and then they are analyzed in detail to eliminate the untrue one. In addition, the identification method can be achieved without extra circuits so that it does not increase the complexity of the system or the control difficulty. Finally, the feasibility of the proposed method has been verified by simulation and experimental results.

A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

  • Liu, Ganghua;Tian, Wei;Luo, Yushun;Zou, Juncheng;Tang, Shu
    • Journal of Information Processing Systems
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    • 제18권1호
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    • pp.48-58
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    • 2022
  • Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

Deep learning-based sensor fault detection using S-Long Short Term Memory Networks

  • Li, Lili;Liu, Gang;Zhang, Liangliang;Li, Qing
    • Structural Monitoring and Maintenance
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    • 제5권1호
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    • pp.51-65
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    • 2018
  • A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.