Hasil untuk "Electricity and magnetism"

Ning WEI, Minglei LI, Guangyong CHEN et al.

The airport docking guidance system is essential for enhancing airport safety and operational efficiency. This study introduces a deep learning-based point cloud completion network designed for accurate aircraft localization using LiDAR technology. Initially, the aircraft parking process is simulated in a realistic virtual environment to generate complete point cloud data. Subsequently, partial point clouds caused by occlusions or sensor limitations are processed through the proposed network to reconstruct their complete geometric structures. Then the restored point cloud is aligned with a predefined aircraft model, enabling precise calculation of the aircraft’s center coordinates in the simulated coordinate system through spatial transformation. Experimental results demonstrate that the network effectively recovers structural details from incomplete point clouds, enabling accurate computation of aircraft centroid coordinates. This approach achieves high-precision position detection for aircraft during docking, showing significant potential for practical airport applications. The codes are available at: https://www.scidb.cn/anonymous/UXZFZkFm.

Chengshuai HUANG, Jian LIANG, Bo LI et al.

In order to achieve low-carbon and high-efficiency operation of natural gas stations driven by hydrogen, a novel integrated energy system is proposed in this paper. The steam cycle is used to recover the waste heat generated by gas turbines. The electrical energy is used to drive the solid oxide electrolysis hydrogen production system to produce hydrogen, and then the mixture of methane and hydrogen is used as the fuel of gas turbine, and the compressed air energy storage technology is used to convert renewable energy into stable electrical energy output. The calculation results indicate that under design conditions, the energy efficiency, exergy efficiency and levelized cost of energy are 85.66%, 41.37% and 294.70 Yuan·(MW·h)–1, respectively. Parameter sensitivity analysis shows that the operating parameters of gas turbine pressure ratio, gas turbine hydrogen blending ratio, steam cycle low-pressure boiler pressure, steam cycle extraction coefficient, compressed air energy storage technology energy release power have significant impact on system thermodynamic performance, while the operating parameters of gas turbine pressure ratio, gas turbine hydrogen blending ratio, and steam cycle extraction coefficient have significant impact on system economic performance.The multi-objective optimization results indicate that the optimal exergy efficiency and standardized unit energy cost of the system are 42.31% and 284.33 Yuan·(MW·h)–1, respectively.

Yufei WANG, Tong DU, Weiguo BIAN et al.

Multi-user power load forecasting refers to the power load forecasting of multiple users or regions based on historical loads data，which can make the grid companies understand the power demands of different users or regions，so as to better carry out the planning and scheduling optimization of the power system. However, different users have complex and diverse power consumption behaviors, so it is difficult to use traditional methods to universally model different power users' loads and achieve accurate prediction. Therefore, a new multi-user short-term load prediction model based on DTW K-medoids and VMD-multi-branch neural network is established. Firstly, in order to improve the clustering performance of traditional clustering methods, the DTW K-medoids method is used to cluster users' load data, and the distance between loads data is calculated using the dynamic time warping (DTW) instead of the traditional Euclidean distance measurement method in K-medoids to improve the clustering effects of multiple users' load. Secondly, in order to fully characterize the long short-term time series-dependent characteristics of load history data, a parallel load forecasting method based on VMD-multi-branch neutral network model is established for multi-user short-term load forecasting. Finally, the 365-day load data of 20 users in a region is used for clustering, training and experiment, and the results show that the MAE and RMSE indexes of the proposed model significantly decrease compared with that of the comparative models, indicating that the proposed method can effectively characterize the power consumption behaviors of multiple users and improve the prediction efficiency and accuracy of multi-user loads.

Xiang WANG, Yumiao WANG, Xingyu CHEN et al.

Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.

Dan LI, Shiyao QIN, Shaolin LI et al.

The high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration. However, the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain, and the accuracy of model simulation is difficult to guarantee. In order to address the fault transient modeling problems of doubly-fed wind turbines, a measured data-based modeling and parameter identification method of doubly-fed wind turbines is proposed. Firstly, based on the DFIG model and control structure of the Power System Integrated Stability Program (PSASP), a low voltage ride through (LVRT) control mathematical model is established to analyze the fault transient process, and the LVRT transient control core parameters are clarified. Secondly, based on part of the field measured LVRT data of doubly-fed wind turbines, the fault transient parameters are identified with the chaotic particle swarm optimization algorithm. Finally, the accuracy of the identification parameters are analyzed and verified based on the remaining measured data. The simulation results have verified the effectiveness and accuracy of the proposed parameter identification method. The proposed method has strong generalization ability and high accuracy of identification results, and is of great engineering application value.

Alice Perrin, Dai Hamaguchi, Josina W. Geringer et al.

High strength, high conductivity copper alloys that can resist creep at high temperatures are one of the primary candidates for efficient heat exchangers in fusion reactors. Cu–Cr–Nb–Zr (CCNZ) alloys, which were designed to improve the strength and creep life of ITER Cu–Cr–Zr (CCZ) reference alloys, have been found to have comparable electrical conductivity and tensile properties to CCZ alloys. The measured creep rupture times for these improved alloys is about ten times higher than the ITER reference alloys at 90–125 MPa at 500 °C. However, the effects of neutron irradiation on these alloys, and the ensuing material properties, have not been studied; thus, their utility in a fusion reactor environment is not well understood. This study characterizes the room temperature mechanical and electrical properties of a neutron-irradiated CCNZ alloy and compares them to a neutron-irradiated ITER reference heat sink CCZ alloy. Tensile specimens were neutron irradiated in the High Flux Isotope Reactor (HFIR) to 5 dpa between 250 °C and 325 °C. Post-irradiation characterization included electrical resistivity measurements, hardness, and tensile tests. Microstructural evaluation used scanning electron microscopy, energy dispersive x-ray spectroscopy, and atom probe tomography to characterize the irradiation-produced changes in the microstructure and investigate the mechanistic processes leading to post-irradiation properties. Transmutation calculations were validated with composition measurements from atom probe data and used to calculate contributions to the increased electrical resistivity measured after irradiation. Comparisons with CCZ alloys in the same irradiation heat found that the post-irradiated CCNZ and CCZ alloys had comparable electrical resistivity. Although CCNZ alloys suffered more irradiation hardening than CCZ, the overall tensile behavior deviated very little from non-irradiated values in the temperature range studied.

Roya Aghsaee, Christopher Hecht, Felix Schwinger et al.

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.

Shunya Hashimoto, Hideo Fukuhara, Endre J. Szili et al.

Low-temperature atmospheric-pressure plasma jets are generally considered a safe medical technology with no significant long-term side effects in clinical studies reported to date. However, there are studies emerging that show plasma jets can cause significant side effects in the form of skin burns under certain conditions. Therefore, with a view of developing safer plasma treatment approaches, in this study we have set out to provide new insights into the cause of these skin burns and how to tailor plasma treatments to mitigate these effects. We discovered that joule heating by the plasma bullet currents is responsible for creating skin burns during helium plasma jet treatment of live mice. These burns can be mitigated by treating the mice at a further distance so that the visible plasma plume does not contact the skin. Under these treatment conditions we also show that the plasma jet treatment still retains its medically beneficial property of producing reactive oxygen species in vivo. Therefore, treatment distance is an important parameter for consideration when assessing the safety of medical plasma treatments.

WANG Yuanfang, WANG Xiaohua, SHU Chang et al.

ATPase family AAA domain-containing protein 2 (ATAD2) is a chromatin regulator, also known as an oncogenic transcription cofactor. Its abnormal expression is closely related to the occurrence and development of various malignant tumors. ATAD2 consists of two domains: the ATPase domain and the bromodomain. The bromodomain can specifically recognize and interact with the acetylated lysines in proteins, which regulates the refactoring and transcription of chromosomes. In this work, we found that ATAD2 bromodomains are aggregated under normal solution conditions. Considering the possible impact of aggregation on the interaction between ATAD2 bromodomain and acetylated histone tail, we preliminarily investigated the aggregation of ATAD2 bromodomains mainly by nuclear magnetic resonance (NMR) and circular dichroism (CD) spectra. The results suggested that the aggregation is accompanied with structure alteration and possibly related to the physiological functions of cells. This study may provide new clues for the development of ATAD2 bromodomain inhibitors.

Kai Wang, Liu Liu, Jiachi Zhang et al.

Abstract A reliable train‐to‐ground wireless communication system is proposed for the foundation of the safe operation of Hyperloop. The wireless channel characteristics of Hyperloops at millimetre‐wave (mmWave) bands are analysed based on the ray‐tracing method. Considering the reflection paths and line of sight path, the channel transfer function expression is derived for each multipath and then the channel impulse response is obtained. On this basis, the investigation and analysis of the large‐scale and small‐scale channel characteristics, including the path loss, shadow fading, delay spread, and angular spread of 4 different mmWave frequency carriers, that is, 28/38/45/60 GHz, are conducted. Simulation results show that the path loss increases as the frequency increases while the time delay spread and angular spread almost keep unchanged. The relevant research results will contribute to the design of future Hyperloop wireless communication systems.

Jinli WANG, Fengsheng LI, Fang XIE et al.

In the context of “carbon peaking and carbon neutrality”, the construction of a new type of clean and low-carbon power system with high penetration of renewable energies is moving forward at a fast pace, which has profoundly changed the behavior and functional roles of power distribution system. However, the current standard system is hardly capable of meeting the requirements of the development of distribution network due to the absence of corresponding critical supportive standard in addition to its lack of compatibility, coordination and integrity in the entire power distribution business. By taking the technical development and business needs into full consideration, this paper puts forward the principles of systematic, coordinated, dynamic and prospective standard system construction. Based on the theory of multidimensional model, the snowflake multidimensional structure model is established, which lays the foundation for the information management of standard system. With the power distribution as the main technical direction, a multi-level standard architecture covering the whole life cycle is also designed. Finally with regards to the key technical fields, this paper analyzes the requirements for standards and implements the planning and layout of key standards, so as to provide effective standards and direction guidance for the promotion of the green and low-carbon transformation of smart distribution network.

Seyyed Abbas Sahafi, Mohsen Ghaffari‐Miab, Saeed Souri

Abstract An efficient and highly accurate method for extracting the singularity of scalar potential time‐domain Green's function (TDGF) of layered media is presented. The proposed method is based on the Taylor series expansion and is exploited in the finite‐difference (FD) technique. The presented method is accurate for the singularity behavior of the potential near the source. Also, the effect of singularity extraction box size on the calculation error of Green's functions is explained. Numerical results demonstrating the accuracy of the proposed singularity extraction method for calculating the TDGF of a PEC‐backed dielectric slab are presented. It is shown that the accuracy of the evaluated TDGF is improved by an order of magnitude using the proposed singularity extraction technique. Also, the effect of changing the spatial and temporal steps of the FD scheme on the calculation error is studied.

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Zhongling HUANG, Xiwen YAO, Junwei HAN

Deep learning technologies have been developed rapidly in Synthetic Aperture Radar (SAR) image interpretation. The current data-driven methods neglect the latent physical characteristics of SAR; thus, the predictions are highly dependent on training data and even violate physical laws. Deep integration of the theory-driven and data-driven approaches for SAR image interpretation is of vital importance. Additionally, the data-driven methods specialize in automatically discovering patterns from a large amount of data that serve as effective complements for physical processes, whereas the integrated interpretable physical models improve the explainability of deep learning algorithms and address the data-hungry problem. This study aimed to develop physically explainable deep learning for SAR image interpretation in signals, scattering mechanisms, semantics, and applications. Strategies for blending the theory-driven and data-driven methods in SAR interpretation are proposed based on physics machine learning to develop novel learnable and explainable paradigms for SAR image interpretation. Further, recent studies on hybrid methods are reviewed, including SAR signal processing, physical characteristics, and semantic image interpretation. Challenges and future perspectives are also discussed on the basis of the research status and related studies in other fields, which can serve as inspiration.

Fedir O. Yevtushenko, Sergii V. Dukhopelnykov, Tatiana L. Zinenko et al.

Abstract Scattering and absorption of the H‐polarized plane wave by the infinite grating of flat graphene strips are considered in the environment met most frequently—on or at the surface of a dielectric‐slab substrate. The full‐wave meshless code is based on the analytical semi‐inversion using the Riemann–Hilbert problem solution. This leads to a Fredholm second‐kind matrix equation for the Floquet harmonic amplitudes that guarantees code convergence and provides easy control of computational error, which can be reduced to machine precision. The matrix elements are combinations of elementary functions, and therefore, the code is accurate and quite economical. This enables computation of the reflectance, transmittance, and absorbance as a function of the frequency in the wide band from static case to 10 THz. Numerical results show that such a metasurface with micrometre‐sized strips is a composite periodic open resonator. It is highly frequency‐selective thanks to the interplay of three types of natural modes—low‐Q slab, moderate‐Q plasmon strip, and ultra‐high‐Q lattice—that do not exist in the absence of the substrate. Varying the chemical potential of graphene, one can manipulate the electromagnetic characteristics of the metasurface at a fixed frequency from almost total transmission to almost total reflection.

Gulab Shah, Qunsheng Cao, Muhammad Azeem et al.

Abstract An innovative four‐bit second order multifunction active frequency selective structure controlled via four PIN diodes with nine different combinations is introduced . The proposed design is equipped with electromagnetic switching, frequency switching and polarization selection at two passbands. The coding states 0000 and 1111 offer transmission to the passbands centred at 1.96 and 4.98 GHz frequencies and shield to the bands centred at 4.98 and 1.96 GHz for transverse electric (TE)/transverse magnetic (TM) waves, respectively. The coding state 1010 offers transmission to the TE and TM waves at 1.96 GHz and 4.98 GHz, respectively and shields the TM and TE waves at 1.96 GHz and 4.98 GHz, respectively. Contrarily, the coding state 0101 offers transmission to the TE and TM waves at 4.98 GHz and 1.96 GHz, respectively and shields the TE and TM waves at 1.96 GHz and 4.98 GHz, respectively. The coding states 1000/0100 and 1101/1110 allow transmission to the TE wave at 1.96 and 4.98 GHz frequencies, respectively and shield to both the passbands at TM wave. Conversely, the coding states 0001/0100 and 1011/0111 allow transmission to the TM wave at 1.96 and 4.98 GHz frequencies, respectively and shields to both the passbands at TE wave. The coding state 1100/0011 provides shield to all the frequencies below 7.7 GHz. The designed structure is validated through the measured response of the fabricated prototype.

Ying LIU, Yi-yun GUO, Jing-cong CHEN et al.

To meet the demands in surgical treatment and radiotherapy, this work combines the faster region convolutional neural network (Faster-RCNN) and Level-Set methods to segment cerebellopontine angle (CPA) tumors automatically and precisely. T1WI-SE magnetic resonance images from 317 CPA tumor patients were collected. Features extracted by VGG16 were combined with the region proposal network (RPN) for training. A CPA tumor localization model was then established, before the Level-Set method was applied to accurately segment the tumor. The segmentation results of different CPA tumor regions were compared in terms of precision, recall, mean average precision (mAP) and Dice coefficient. The results showed that the method proposed can effectively and precisely segment CPA tumors, thereby capable of reducing the burden on clinicians and improving the treatment effect.

BI Hui, ZHANG Bingchen, HONG Wen et al.

Sparse signal processing-based Synthetic Aperture Radar (SAR) imaging, also known as sparse SAR imaging, is the main research direction of sparse microwave imaging theory. Compared with a conventional SAR system, sparse SAR imaging radar has significant potential to improve imaging performance. However, because it requires heavy computations, the application of sparse SAR imaging in large-scene recovery has become difficult, which restricts its further applications. Additionally, complex SAR images, rather than raw data, are usually used for data archiving due to a number of reasons such as data copyright and system confidentiality. Therefore, it is worthwhile to study how sparse imaging can be achieved using only Matched Filtering (MF) recovered complex images with less computational cost. GaoFen-3 is China’s first 1-m resolution multi-polarization C-band satellite. It has a high-resolution, wide swath imaging ability and hence plays an important role in disaster monitoring and ocean surveillance applications. In this paper, we introduce a complex image-based sparse SAR imaging method to process GaoFen-3 complex image data and improve image performance. Experimental results show that the sparse imaging results have lower sidelobes, higher signal-toclutter and noise ratio, and better target distinguishing ability compared with inputted images. Additionally, sparse imaging can effectively preserve the statistical distribution and phase information of images that makes the recovered GaoFen-3 sparse image-based applications such as interferometric synthetic aperture radar and constant false alarm ratio detection possible.

Arthur F. Andrade, Edson G. Costa, Edson G. Costa et al.

In this article, an analysis of heat transfer in a porcelain-housed ZnO surge arrester is presented. Due to the fact that ZnO-based surge arresters are key components for protection and reliability of electrical systems, it is necessary to seek techniques to assure correct assessment. In porcelain-housed arresters, the air gap leads to an increased thermal resistance between the ambient and the varistor column. Consequently, a thermal analysis must consider convection and radiation heat transfer mechanisms, besides heat conduction. This study proposes the use of computational simulations in combination with thermography as a tool for a temperature-based estimation of the varistor state. For the analysis, temperature rise tests were performed in a 69-kV ceramic-housed arrester to measure material emissivity and produce data to fit the simulated response. Furthermore, the thermal response of the surge arresters subject to an impulsive energy input was evaluated. The simulations allow to relate varistors temperature and housing temperature with regard to a specified amount of dissipated power. The obtained results show that the used technique can be an effective method for arrester monitoring and may allow to define assessing criteria for a porcelain-housed arrester based on housing temperature measurements.

Li Lei, Li Guo-lin, Liu Run-jie

Based on coherent accumulation matrix reconstruction, a novel Direction Of Arrival (DOA) estimation decorrelation method of coherent signals is proposed using a small sample. First, the Signal to Noise Ratio (SNR) is improved by performing coherent accumulation operation on an array of observed data. Then, according to the structure characteristics of the accumulated snapshot vector, the equivalent covariance matrix, whose rank is the same as the number of array elements, is constructed. The rank of this matrix is proved to be determined just by the number of incident signals, which realize the decorrelation of coherent signals. Compared with spatial smoothing method, the proposed method performs better by effectively avoiding aperture loss with high-resolution characteristics and low computational complexity. Simulation results demonstrate the efficiency of the proposed method.