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Based on electromechanical impedance (EMI) technology, we studied the indication effect of each characterization index on flange bolt looseness under different noise levels. A new evaluation index T is constructed to solve the problem that the minor looseness and noise under the actual monitoring environment cannot be distinguished with existing indexes. The results show that among the five characterization indexes, the root mean square deviation (RMSD) and the correlation coefficient deviation (CCD) can reflect the minor looseness sensitively and are positively correlated with the degree of looseness. They also have good performance of looseness identification under the noise of 30 dBW. However, the indexes' indication range of the case under 1 dBW-6 dBW noise overlaps with the case under 36 N·m-39.5 N·m torque. So it is impossible to judge the structure state with mere characterization indexes. Evaluation index T can extract the correlation difference of the conductance spectrum under the noise conditions and looseness conditions with the benchmark respectively. And there are different indication ranges of T on the noise and minor looseness. By determining a threshold and comparing T with it, whether bolt looseness occurs in an unknown condition can be effectively judged. Finally, T is applied to other flange structure with 8 bolts to verify the effectiveness and general applicability of the method. The evaluation index plays an important role in avoiding misjudgment of flange bolt caused by noise influence on minor looseness and improving the ability of identification when the bolt is at the critical state.

In global climate researches, marine ecosystem researches, and ocean-related applications, it is of considerable significance to accurately observe and predict sea surface temperature (SST). However, various physical and environmental factors affect the changes in SST, making it highly random and uncertain. Therefore, it is still a challenge to propose a highly accurate SST prediction method. SST prediction methods based on the temporal information usually focus on capturing the temporal influence of the historical SST but ignore the spatial influence in the sea area, so these methods meet the performance bottlenecks. To fuse the multidimensional spatiotemporal influence and further improve the accuracy of the SST prediction, this letter proposed the convolutional gated recurrent unit (GRU) with multilayer perceptron (CGMP) to predict SST in the Bohai Sea and the South China Sea. The convolutional layer of CGMP can capture the neighbor influence effectively in the spatial dimension, making up for the shortcomings of methods that are based on the temporal information and do not consider the spatial information. The GRU layer and the MLP layer of CGMP can process historical information effectively in the temporal dimension. Experiments showed that the prediction performance of CGMP was better than those of other comparison methods in different sea areas, different schemas, and different prediction scales. Besides, the error distribution law in the Bohai Sea daily mean SST prediction was explored. [ABSTRACT FROM AUTHOR]

With the feedback rotor speed outer-loop PI control, the mechanism analysis of SMES-based power oscillation damping is derived by the small signal and classical electric torque method in this research. Theoretical analysis shows that adjusting the proportional and integral can equivalently dominate the damping ability and synchronization level. However, the conventional PI may cause the deep charging-discharging so that the power oscillation damping failure. To this end, the proportional integral controller with integral alleviation (PI-IA) controller is designed to address this issue, which not only ensures energy deviation-free of SMES, but also holds the advantage of PI controller. Moreover, the effectiveness of the proposed controller and the validity of the mechanism analysis are performed by the time-domain simulation. [ABSTRACT FROM AUTHOR]

Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Comparison States (PreEGS). To capture the alteration of the network in comparison states, PreEGS extracts topological and gene expression features of each gene in a five-dimensional vector. PreEGS also recruits a positive sample expansion method to address the problem of unbalanced positive and negative samples, which is often encountered in practical applications. Different classifiers are applied to the simulated datasets, and the PreEGS based on the random forests model (PreEGSRF) was chosen for optimal performance. PreEGSRF was then compared with six other methods, including three machine learning methods, to predict EGS in a specific state. On real datasets with four gene regulatory networks, PreEGSRF predicted five essential genes related to leukemia and five enriched KEGG pathways. Four of the predicted essential genes and all predicted pathways were consistent with previous studies and highly correlated with leukemia. With high prediction accuracy and generalization ability, PreEGSRF is broadly applicable for the discovery of disease-causing genes, driver genes for cell fate decisions, and complex biomarkers of biological systems. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) prediction plays an important role in ocean-related fields. It is challenging due to the nonlinear temporal dynamics with changing complex factors and the inherent difficulties in long-scale predictions. Conventional models often lack efficient information extraction and cannot meet the requirements of long-scale predictions. Therefore, the gate recurrent unit (GRU) encoder–decoder with SST codes and dynamic influence link (DIL), GRU encoder–decoder (GED), which considered both the static and dynamic influence, is proposed in this letter. Each SST code, capturing the static information more effectively, was computed by all hidden states of the encoder and was individually associated with each predicted SST. The DIL, capturing the dynamic influence, connected the SST code with the early predicted future SST for solving the long-scale dependence problem. GED was tested on the Bohai Sea SST data sets and South China Sea SST data sets and compared with full-connected long-short term memory (FC-LSTM) and support vector regression. The results demonstrated that GED outperformed others on different prediction scales and different prediction terms (daily, weekly, and monthly), especially in terms of long-scale and long-term predictions. In addition, attention relationships between historical and future SSTs were further explored, and there was a meaningful finding that each future daily mean SST of Bohai Sea most strongly correlated with the past 27th to 29th historical values. [ABSTRACT FROM AUTHOR]

With the development of autonomous vehicular technologies, the execution tasks become more memory-consuming and computation-intensive. Simultaneously, a certain portion of tasks are latency-sensitive, such as collaborative perception, path planning, collaborative simultaneous localization and mapping, real-time pedestrian detection, etc. Because of the limited computation resources inside vehicles and restricted transmission bandwidth, edge computing can be an effective way to assist with the tasks execution. Considering from the perspective of business, the reservation or subscription cost is cheaper than real time requests. In order to minimize the expense of consuming edge services, the desirable situation is to reserve the resources as much as needed. However, the configuration of vehicular network is variational in practice due to the diversity of road maps, different time range like peak time and off-peak time, and the various task types, which makes it challenging to figure out a general machine learning model that is suitable for any case. Therefore, to predict the resource consumption in edge nodes accurately in different scenarios, we propose a two-stage meta-learning based approach to adaptively choose the appropriate machine learning algorithms based on the meta-features extracted on database. Besides, due to the deficiency of dataset for edge resource consumption, we program in game engine unity to generate the 3D model of Manhattan area. Meanwhile, we change the factors like different road maps and number of vehicles so as to get closer to practices. In the evaluation part, we adopt root mean square error, mean absolute percentage, and mean GEH as evaluation metrics to assess the performance of each model. Also, a quantitative analysis for the total cost and waste is also conducted. Eventually, we can find that the proposed meta-learning based method outperforms the non-meta ones. [ABSTRACT FROM AUTHOR]