Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review. Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information. And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context. In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts. It is capable of extracting the syntactic structure features and syntax information of aspect and context. In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model.

A novel runway detection algorithm for PolSAR (Polarimetric Synthetic Aperture Radar) images based on optimized polarimetric features and local spatial information is proposed. Existing methods for runway detection for PolSAR images always utilize the parallel line as the primary feature. However, many other ground objects such as rivers and roads also have parallel structures thus affect the performance of these detection methods. The proposed method is based on two stages of classification with polarimetric features and the HOG (Histogram of Oriented Gradient) feature, while avoiding the interference due to the similar morphological features among different ground objects. An FCBF (Fast Correlation Based Filter) is firstly used for optimizing and selecting of the ground objects’ polarimetric features of ground targets. Then RF (Random Forest) classifier is employed for extracting ROIs (Region of Interest) which may contain runways. Then HOG features are extracted from these ROIs for further classification with SVM (Support Vector Machines) to detect the runway area. Experimental results with the measured PolSAR data provided by NASA UAVSAR project show that the proposed method can detect runway regions effectively without using the parallel line. Comparative analysis is also conducted on parallel line pattern based algorithms. And the results suggest the effectiveness and performance enhancement of this method.

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels).

To improve feature learning ability and accurately diagnose the faults of rolling bearings under a strong background noise environment, we present a new shrinkage function named leaky thresholding to replace the soft thresholding in the deep residual shrinkage networks (DRSNs). In this work, we discover that such improved deep residual shrinkage networks (IDRSNs) can be realized by using a group searching method to optimize the slope value of leaky thresholding, and leaky thresholding in the IDRSNs can more effectively eliminate the noise of signal features. We highlight that our techniques can significantly improve the performance on various fundamental tasks. Experimental results show that IDRSNs achieve better fault diagnosis results on noised vibration signals compared with DRSNs. Moreover, we also provide a normalized processing to further improve the fault diagnosing accuracy of rolling bearing under a strong background noise environment.

In this article, a method to fast classify (intradural hemorrhage, epidural hemorrhage, and cerebral parenchymal hemorrhage) and locate the bleeding points by using the singularity expansion method (SEM) and backpropagation (BP) neural network optimized by genetic algorithm (GA) and sparrow search algorithm (SSA) is proposed. In the simulation model, the bleeding spot with a radius of 3 mm is successfully identified by the approach. The test accuracy in the simulation for both the bleeding’s localization and classification are 98.0% and 97.4%, respectively. Head phantoms that have all been improved over the previous phantom established are used for experiments. A bleeding target with a volume of 3 mL can be identified in the microwave detection system. In the experiment, the accuracy of classification and localization of the bleeding type are 90% and 94.7%, respectively. The final results demonstrate the capability and effectiveness of the method. Faster determination of bleeding point type and orientation means that patients can be provided with different rescue measures accordingly.

A novel signal processing method to directly extract the hemorrhage response signals using the maximum correlation kurtosis deconvolution (MCKD) technique and complete ensemble empirical mode decomposition (CEEMDAN) algorithm is proposed in this article. This method only needs to acquire the raw data measured one time, which saves a lot of time in data acquisition. In emergency cases, a shorter time in detection means a better prognosis and better life for the patients. A portable microwave brain hemorrhage imaging system comprising a portable vector network analyzer (VNA), a small-sized antenna array (30 mm $\times$ 30.5 mm of each antenna), a flexible substrate, simple structure, and wideband (0.5–6 GHz) is established by applying this proposed signal processing method. The preliminary results from the experiments on the head phantom demonstrate the capability and effectiveness of this proposed method. Final results show that the proposed portable microwave imaging system can successfully detect brain hemorrhage with a size of 17 mm in diameter. The robustness of the proposed method is verified by successfully detecting the brain hemorrhage with a size of 20 mm in diameter.