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The scientific literature on Al-Si alloy materials is a rich source of factual information about various classes of entities (e.g., alloys, properties, and compositions) as well as various relationships between these entities. Automatically extracting this information using natural language processing (NLP) techniques to generate a knowledge graph for Al-Si alloy materials is a challenging task. In this paper, we propose ASaRE-Net (Al-Si alloy Relationship Extraction Network) to extract entities and relationships from Chinese scientific literature on Al-Si alloy materials, thus forming a triple (Entity 1, Relation, Entity 2) as the basic unit for constructing a knowledge graph. Due to the complexity of overlapping triples and many triples in a text sentence, this paper designs a triple-aware module based on knowledge representation learning, which is used to mine the correlation between entities and relations, and uses a simple and efficient labeling strategy to decode, so as to alleviate the problem of cascading errors. This paper defines 11 types of entity and 13 types of relationship for Al-Si alloy materials, and constructs ASaIED (Al-Si alloy Information Extraction Dataset). Experimental results show that compared to the strongest baseline, the proposed model improves the F1 score by 2.34% on the ASaIED dataset, and demonstrates better performance in scenarios involving SEO overlapping types and many triples. The proposed model can continuously provide credible data for constructing knowledge graphs in the field of Al-Si alloy materials. [ABSTRACT FROM AUTHOR]

A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability. [ABSTRACT FROM AUTHOR]

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%. [ABSTRACT FROM AUTHOR]

In recent years, Material Genome Initiative (MGI) has become a global hot spot. The lack of data sources and irregular data storage methods have led to a lack of structured data that can be used for machine learning model training in the materials field, which has become a bottleneck for researchers in predicting material performance. With the continuous development of materials science, a large amount of information contained in the materials field text has become the focus of attention for researchers, and has become the main data sources for materials field personnel to apply machine learning. How to obtain a large amount of effective materials data is a new challenge at this stage. This article uses natural language processing technology to obtain valid data from the aluminum-silicon alloy materials literature. Named entity recognition is an important subtask in natural language processing, which aims to identify entities with meaning in text. In this paper, five types of entities are selected from the material science literature, and an aluminum-silicon alloy material entity recognition data set is constructed by hand annotation, which includes 5 347 sentences and 2 835 entities. In order to reduce the dependence of natural language processing tasks on annotation expectations, transfer learning is used to pre-train the language model and apply it to specific domain tasks. Combining entity characteristics, joint modeling is carried out based on ALBERT (A lite BERT) pre-training language model and conditional random fields (CRF), and the pre-training model is applied to alloy material entity recognition based on active learning. Based on a small number of labeled training set samples, combined with active learning, the F1 value, accuracy rate, and recall rate of the model are increased by 0.61%, 2.68%, and 0.29%, respectively. Experiments prove that combining pre-training and active learning can further reduce the dependence of entity recognition task models on labeled data and the cost of manual labeling. The research results of this paper can solve the problem of material data islands and improve the problem of material genome machine learning, which has been in the dilemma of small-scale data sets. It will promote the development of aluminum-silicon alloys and provide a scientific basis for the design of new materials for material genomes. [ABSTRACT FROM AUTHOR]

With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data. [ABSTRACT FROM AUTHOR]

Recent works suggest that using knowledge transfer strategies to tackle cross-domain diagnosis problems is promising for achieving engineering diagnosis. This article presents a diagnosis scheme for rolling bearing under a challenging domain generalization scenario, in which more potential discrepancies among multiple source domains are eliminated and only normal samples of the target domain are available during the training stage. To achieve sufficient generalization performance, a diagnosis scheme combining some a priori diagnosis knowledge and a deep domain generalization network for fault diagnosis (DDGFD) is elaborated. Through signal preprocessing steps guided by the a priori diagnosis knowledge, the inputs of DDGFD with a primary consistent meaning across domains are constructed from the vibration signal. On this basis, DDGFD would intently release its talent on learning discriminative and domain-invariant fault features from source domains, and then generalize the learned knowledge to identify unseen target samples. On cross-domain tasks organized using broad bearing data sets, the superiority of DDGFD is validated by comparing its performance with various data-driven diagnosis methods. [ABSTRACT FROM AUTHOR]

The transient impact components in vibration signal, which are the major information for bearing fault severity recognition, are often interfered with by ambient noise. Meanwhile, for bearing fault severity recognition, the frequency band selection methods which are employed to pre-process the contaminated vibration signal only select the partial frequency band of the vibration signal and cause information loss of other frequency band. Aiming at this issue, this paper proposes a novel fault severity recognition method based on Huffman coding, which can retain all the information of the frequency band, and is applied for the first time to bearing fault severity recognition. Specifically, the average coding length of Huffman coding (ACLHC) of the original vibration signal is first calculated to reduce the noise and highlight the impact components of the signal. Then, the ACLHC is encoded by symbolic aggregate approximation (SAX) to reflect the modulation information of bearing. Finally, the Lempel‑Ziv indicator (LZ indicator) of the symbol sequence is calculated to reflect the fault severity. The proposed method is verified by the bearing datasets under different working conditions. Compared with the methods based on frequency band selection, the proposed method effectively recognizes the fault severity of bearing for more working conditions. [ABSTRACT FROM AUTHOR]

A regional control constrained spray deposition model was established to study the flow fields and deposition of atomized droplets in a semienclosed space. The FLUENT software was used to simulate the changing rules of the pressure field, velocity field, and temperature field under different atomization gas pressures (AGPs), aiming to optimize the equipment and parameters of spray Conform. The results show that the flow fields are prone to be disturbed by the controller of the two rotating disks when the AGP is low, which makes it difficult to control the droplet deposition. Besides, excessive AGP will bring a robust reverse airflow, which is not conducive to control droplet deposition either. However, the outlet pressure of the diversion tube is about 2.5 × 104 Pa when the AGP is between 2.5 and 3.0 × 105 Pa. In this condition, the melt will exit the nozzle without blockage. Moreover, the pressure near the basement is about 5 × 104 Pa at this point, making the droplet deposition controllable. [ABSTRACT FROM AUTHOR]

Potassium (K+) ion is an important biological substance in the human body and plays a critical role in the maintenance of transmembrane potential and hormone secretion. Several detection techniques, including fluorescent, electrochemical, and electrical methods, have been extensively investigated to selectively recognize K+ ions. In this work, a highly sensitive and selective biosensor based on single-layer graphene has been developed for K+ ion detection under Van der Pauw measurement configuration. With pre-immobilization of guanine-rich DNA on the graphene surface, the graphene devices exhibit a very low limit of detection (≈1 nM) with a dynamic range of 1 nM-10 µM and excellent K+ ion specificity against other alkali cations, such as Na+ ions. The origin of K+ ion selectivity can be attributed to the fact that the formation of guanine-quadruplexes from guanine-rich DNA has a strong affinity for capturing K+ ions. The graphene-based biosensors with improved sensing performance for K+ ion recognition can be applied to health monitoring and early disease diagnosis. [ABSTRACT FROM AUTHOR]