Search

[Objective] The T2T (train to train) communication train control system in urban rail transit is a new type of signal system, and it manages and controls trackside equipment through resource management. To verify the superiority of the system, it is necessary to analyze the train operation efficiency under this system. [Method] In view of the characteristics of the resource management and safety protection principles for T2T train control system, the originally interlocking controlled double action switch is transformed into two single action switches, and ATP (automatic train protection) is achieved based on train precise self-positioning under reversal working conditions. The utilization capacity of line resources in the turnout area is improved. Under tracking conditions in line sections and stations, the train tracking interval is further reduced by utilizing the principle of relative speed safety protection and by inter-transmitting the real-time information of speed, acceleration, and position between the front and rear trains. Through the technological innovation in the above two aspects and based on the actual data from urban rail transit lines, operation scenarios such as reversal on scissors crossover behind the station, reversal on single crossover in front of the station, tracking in line sections and stations are simulated and calculated. The capacity of the T2T train control system in train reversal and line section tracking is determined. [Result & Conclusion] Compared to the traditional CBTC (communication-based train control system), the T2T has significant improvement in reversal and tracking capacity and other aspects, which enables it to be more suitable for urban rail transit lines with super large traffic volume. The T2T train control system is also particularly suitable for urban rail transit renovation projects as it has a simple architecture and requires less trackside equipment.

The number of cables in the distribution system is extremely huge, but there are some cables with thinner conductors in the middle section, which may threaten the safety of the system. At present, there is no effective detection method for such problems. Based on the broadband impedance spectrum, this paper proposes an algorithm to identify and locate the cables with discontinuous conductor diameters in the middle using the integral transformation with Chebyshev window. The simulation and experimental results show that the algorithm proposed in this paper can realize the identification and location of the conductor thinning cable in the middle section, and the location error is less than 0.76%.

Introduction: POLD2 is an indispensable subunit of DNA polymerase δ, which is responsible for the synthesis of the backward accompanying strand in eukaryotic organisms. Current studies have found an association between POLD2 and the development of a variety of cancers. However, its value in cancer immunotherapy has not been fully established.Methods: POLD2 expression was analyzed using RNA expression and clinical data from TCGA and GTEx databases. The prognostic impact of POLD2 on tumor patients was analyzed using clinical survival data from TCGA. Gene enrichment analysis was performed using the R package “cluster analyzer” to explore the role of POLD2. We used the TIMER2 database to analyze the relationship between immune cell infiltration and POLD2 expression in TCGA. We downloaded relevant data from TCGA and analyzed the relationship between POLD2 and immune checkpoints, immunosuppressive genes, immune activating genes, chemokines and chemokine receptors.Results: POLD2 was significantly overexpressed in most tumors compared to normal tissue. High POLD2 expression was significantly associated with advanced tumor stage, significantly shorter overall survival and progression-free survival. Also, we found that POLD2 expression correlated strongly with immunomodulatory genes, and significantly negatively with most immune checkpoints (PD-L1, CTLA4, TIM3, and CD28). Pathway enrichment analysis suggests that low expression of POLD2 promotes immune regulation-related pathways and high expression promotes metabolic and DNA repair-related pathways. Furthermore, tumor microenvironment analysis suggests that high POLD2 expression inhibits infiltration of CD8+ T cells and CD4+ memory T cells.Discussion: In conclusion, POLD2 may be a molecular biomarker for pan-cancer prognosis and immunotherapy. It may serve as a potential target for new insights in human tumor prognosis prediction and immunotherapy assessment.

The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.

Models of electrical equipment components are the basis of the transient stability studies of power system with multi-microgrids. Microgrid is a small local power system contains different electrical components which connected into distribution network through the Point of Common Couple(PCC). In order to simplify the grid-connect model of microgrid in power system stability study, a data-driven equivalent modeling method for microgrid based on Long Short-Term Memory(LSTM) recurrent neural network is proposed in this paper. LSTM recurrent neural network is the state-of-the-art models for a variety machine learning problems, and the dynamic behaviors of microgrid under grid-connect mode are presented by the LSTM recurrent neural network. The training data set should contain the dynamic behaviors of all components, the current and power of PCC are collected as the training data set during the faults for the parameter estimation of LSTM. Based on the measurement data set, a LSTM recurrent neural network structure with four inputs and two outputs is designed. During the training process, the real and imaginary parts of the current at present and previous time are taken as the input of the network, and errors of the output active and reactive power between equivalent model and detailed model of microgrid is taken as the evaluation index of the equivalent model method. An AC microgrid contains different distributed generations and load is built in DIgSILENT, and the accuracy of the proposed method is verified by comparing the equivalent model with the original detailed system.

Photovoltaic (PV) power generation has been heavily connected into distribution network recently. However, the intermittent and uncertainty of PV generation system will significantly affect the security operation of distribution network. Therefore, the planning of PV system shows urgency in the extensive planning of distribution network. Herein, a multi-objective PV planning model is proposed in this paper, active power losses, voltage deviation, voltage harmonic distortion and static voltage steady index are considered and discussed in the operation of distribution network. In order to dispose the complex coupling of these different objectives, multi-attribution decision making theory method combined with the game theory is proposed for coordinating the different objectives. The comprehensive weight model of the different objectives is used to present the combined effect of PV system toward distribution network. Furthermore, an improved PSO algorithm is developed for the solution of the multi-objective problem. Simulation results and the application in practical system show that the proposed PV generation system planning model and solution method are effective.

Separatrix crossing stands for the continuation of the modulation instability into the strong depleted regime and is responsible for the symmetry breaking nature of the recurrence phenomenon. The near-separatrix dynamic for recurrent modulational instability is of great importance because it is closely associated with the formation of rogue breather structures. Here, we have developed the underlying phase-space structure of nonlinear modulation instability by treating the three-mode truncate model as a simple oscillator. This allowed us to reveal the high sensitivity of the switching dynamics characterized by inner or outer trajectories to the initial condition around a separatrix. Our results present a major step forward towards the complete understanding of recurrent modulational instability in a truly conservative setting and can provide a guidance to experimentally measure the involved recursive behaviors.

A stabilization scheme is proposed for the dynamics of breather waves induced by the coherent-seed modulation instability based on manipulation of phase-space trajectory. Theoretical and numerical analysis show that carefully dispersion- and nonlinearity-managed cascades of fiber configuration allows the system evolution to be stabilized around an elliptic center point, forming stable pulse trains with ultrahigh contrast efficiently. We also demonstrate that the scheme proposed works equally well for near-separatrix dynamics. Our results provide an alternative means to control the unsteady nonlinear waves by abruptly changing the waveguide properties.

Ceramic flash sintering with a strong electric field at room temperature is the most attractive method. This paper presents the flash sintering of ZnO ceramics at room temperature by the application of a 3-kV/cm electric field after a dropwise addition of ethanol. This method is simple and easy to control. The density of the specimen exceeded 96% after 30 s of sintering. No significant difference was observed in the initiation voltage of flash sintering with and without the dropwise addition of ethanol. Ethanol burns upon dropwise addition, causing a discharge to first occur far from the location of the dropwise addition, followed by glowing and heating up, which causes the temperature of the entire specimen to rise.

Cognitive radio (CR) provides a radio-access method for unlicensed/secondary users to share the spectrum of licensed/primary users. To improve the energy efficiency of CR networks, we propose an energy-harvesting-aided spectrum sensing and data transmission scheme in this paper. In the proposed scheme, the individual sensing and the cooperative sensing of multi-users are combined corresponding to the strong and weak signal of primary users. Meanwhile, the transmission signal of local decisions in the cooperative sensing and the strong signal of primary users are harvested as RF energy by secondary users. In order to improve the performance of spectrum sensing and energy harvesting, we formulate a multi-objective optimization problem that jointly maximizes the detection probability of the presence of primary user and minimizes the false alarm probability while limiting the least harvested energy and the interference from secondary users on the primary receiver. The multi-objective optimization problem is solved by transferring it into a single objective problem. The single objective problem on the parameters without certain range is transferred into the problem on the parameters with certain range to obtain the final solution. Numerical results prove the effectiveness of our proposed scheme, that is, the interference from primary users on secondary users can improve the global detection probability and the harvested energy at secondary users.

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

The absence of Raman and unique pressure-tunable dispersion is the characteristic feature of gas-filled photonic crystal fiber (PCF), and its zero dispersion points can be extended to the near-infrared by increasing gas pressure. The generation of dispersive wave (DW) in the normal group velocity dispersion (GVD) region of PCF is investigated. It is demonstrated that considering the self-steepening (SS) and introducing the chirp of the initial input pulse are two suitable means to control the DW generation. The SS enhances the relative average intensity of blue-shift DW while weakening that of red-shift DW. The required propagation distance of DW emission is markedly varied by introducing the frequency chirp. Manipulating DW generation in gas-filled PCF by the combined effects of either SS or chirp and three-order dispersion (TOD) provides a method for a concentrated transfer of energy into the targeted wavelengths.

Microgrids can significantly improve the utilization of distributed generation (DG) and the reliability of the power supply. However, in the grid-tied operational mode, the interaction between the microgrid and the distribution network cannot be ignored. The paper proposes an equivalent modeling method for the microgrid under grid-tied mode based on a characteristic model. It can simplify the microgrid model in the numerical simulation of the distribution network. The proposed equivalent model can present the dynamic response of a microgrid but not miss any of its primary characteristics. The characteristic model is represented by a low-order time-varying differential equation with the same characteristics of the original microgrid system. During the modeling process, the voltage and the power exchanged between the microgrid and distribution network are collected as the training data for the identification of model parameters. A recursive damped least squares algorithm (RDLS) is used for the parameter identification. A microgrid system containing different DGs is built to test the proposed modeling method in DIgSILENT, and the results show that the proposed dynamic equivalent modeling method is effective and the characteristic model can present the dynamic behaviors of the detailed model of a microgrid.

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)