Our world is composed of various materials with different structures,where spin structures have been playing a pivotal role in spintronic devices of the contemporary information technology.Apart from conventional collinear spin materials such as collinear ferromagnets and collinear antiferromagnetic ally coupled materials,noncollinear spintronic materials have emerged as hot spots of research attention due to exotic physical phenomena.In this review,we first introduce two types of noncollinear spin structures,i.e.,the chiral spin structure that yields real-space Berry phases and the coplanar noncollinear spin structure that could generate momentum-space Berry phases,and then move to relevant novel physical phenomena including topological Hall effect,anomalous Hall effect,multiferroic,Weyl fermions,spin-polarized current and spin Hall effect without spin-orbit coupling in these noncollinear spin systems.Afterward,we summarize and elaborate the electric-field control of the noncollinear spin structure and related physical effects,which could enable ultralow power spintronic devices in future.In the final outlook part,we emphasize the importance and possible routes for experimentally detecting the intriguing theoretically predicted spin-polarized current,verifying the spin Hall effect in the absence of spin-orbit coupling and exploring the anisotropic magnetoresistance and domain-wall-related magnetoresistance effects for noncollinear antiferromagnetic materials.
This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.
The estimation of sequence or symmetrical components and frequency in three-phase unbalanced power system is of great importance for protection and relay.This paper proposes a new H∞filter based on sparse model to track the sequence components and the frequency of three-phase unbalanced power systems.The inclusion of sparsity improves the error convergence behavior of estimation model and hence short-duration non-stationary PQ events can easily be tracked in the time domain.The proposed model is developed using l1 norm penalty in the cost function of H∞filter,which is quite suitable for estimation across all the three phases of an unbalanced system.This model uses real state space modeling across three phases to estimate amplitude and phase parameters of sequence components.However,frequency estimation uses complex state space modeling and Clarke transformation generates a complex measurement signal from the unbalanced three-phase voltages.The state vector used for frequency estimation consists of two state variables.The proposed sparse model is tested using distorted three-phase signals from IEEE-1159-PQE database and the data generated from experimental laboratory setup.The analysis of absolute and mean square error is presented to validate the performance of the proposed model.
