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Online product reviews are exploring on e-commerce platforms, and mining aspect-level product information contained in those reviews has great economic benefit. The aspect category classification task is a basic task for aspect-level sentiment analysis which has become a hot research topic in the natural language processing (NLP) field during the last decades. In various e-commerce platforms, there emerge various user-generated question-answering (QA) reviews which generally contain much aspect-related information of products. Although some researchers have devoted their efforts on the aspect category classification for traditional product reviews, the existing deep learning-based approaches cannot be well applied to represent the QA-style reviews. Thus, we propose a 4-dimension (4D) textual representation model based on QA interaction-level and hyperinteraction-level by modeling with different levels of the text representation, i.e., word-level, sentence-level, QA interaction-level, and hyperinteraction-level. In our experiments, the empirical studies on datasets from three domains demonstrate that our proposals perform better than traditional sentence-level representation approaches, especially in the Digit domain.

Jian-Pi-Yi-Shen (JPYS), the traditional Chinese medicine (TCM) decoction, has been commonly used to treat chronic kidney disease (CKD) and its complications such as anemia. JPYS has been previously found to induce erythropoietin (EPO) production in HEK293T cells and CKD rats. However, the mechanism of JPYS in treating anemia of CKD rats has remained largely unknown. Here, we further extend our effort to investigate the translational control of hypoxia inducible factor- (HIF-) α protein via ERK signaling and the effect on iron recycling-related protein expression by JPYS, thus revealing the mechanism of JPYS in correcting anemia in CKD. Experimental CKD rats with anemia were induced by 5/6 nephrectomy. Rats were administrated orally with high dose (6.0 g/kg/d) and low dose (1.5 g/kg/d) of JPYS for 90 days. Serum hepcidin level was determined to evaluate iron homeostasis. The protein expressions of HIF-2α, erythropoietin (EPO), ferritin, and ferroportin (FPN) and the phosphorylation level of extracellular signal-regulated kinase 1/2 (ERK1/2) were detected by Western blot. The results showed that JPYS treatment significantly ameliorated kidney function by reducing increased levels of blood urea nitrogen (BUN), serum creatinine (Scr), and urine protein (UPRO). Periodic acid-Schiff (PAS) and Masson staining observation showed that the renal pathological damage was restored in JPYS-treated CKD rats. In parallel, JPYS markedly improved CKD anemia through upregulation of red blood cell (RBC), hemoglobin (HGB), and hematocrit (HCT). JPYS stimulated EPO and HIF-2α protein expressions in both the kidney and liver of CKD rats. Furthermore, JPYS induced the phosphorylation of ERK1/2 protein. In addition, JPYS regulated protein expression of ferritin and FPN in both the liver and spleen of CKD rats and the serum level of hepcidin. In conclusion, JPYS induces the expression of EPO through ERK-mediated HIF-2α protein accumulation and regulates systemic iron recycling, supporting its role in promoting erythropoiesis and improvement of anemia in CKD.

UAVs communicate with each other by simple commands using sound, which has the advantages of small interference, autonomous attenuation and proximity interconnection. It can provide emergency support in case of failure of other means of communication such as electromagnetic wave. However, the microphone on the drone acquires a signal from other drones will suffer severe distortion, because of the high-speed motion. That further results in the transmitted signaling information cannot be resolved. In this paper, a distorted signal correction scheme based on generalized S-transform and morphological filtering is proposed to recover the distorted signal. Because the proposed method does not need to know the trajectory of UAV, and does not need to establish geometric model, it has strong practicability. The effectiveness of the proposed method is verified by a simulation case.

The wayside Acoustic Defective Bearing Detector (ADBD) system plays an important role in ensuring the safety of railway transportation. However, Doppler distortion and multi-bearing source aliasing in the acquired acoustic bearing signals significantly decrease the accuracy of bearing diagnosis. Traditional multisource separation schemes using time-frequency filters constructed by a single microphone signal always show poor performance on weak signal separation. Inspired by an assumption that the spatial location of different sources is different, this paper proposes a novel time-varying spatial filtering rearrangement (TSFR) scheme based on a microphone array to overcome current difficulties. In the scheme, a zero-angle spatial filter and peak searching are proposed to obtain the time-centers of corresponding sources. Based on these time-centers, several time-varying spatial filters are designed to extract different source signals. Then interpolation and rearrangement are used to correct the Doppler distortion and reconstruct the corresponding separated signals. Finally, the train bearing fault diagnosis is implemented by analyzing the envelope spectrum of the corrected signals. Because the time-varying spatial filter construction is only dependent on the source location and has little relationship with the signal energy, the proposed TSFR scheme has significant advantages in weak signal separation and diagnosis in comparison with traditional ones. With the verifications by both simulation and experiment cases, the proposed array-based TSFR scheme shows a good performance on multiple fault source separation and is expected to be used in the ADBD system.

In the e-commerce websites, such as Taobao and Amazon, interactive question-answering (QA) style reviews usually carry rich aspect information of products. To well automatically analyze the aspect information inside QA style reviews, it’s worthwhile to perform aspect classification on them. Unfortunately, until now, there are few papers that focus on performing aspect classification on the QA style reviews. For short, we referred to this novel task as QA aspect classification (QA-AC). In this study, we model this task as a multi-label classification problem where each QA style review is explicitly mapped to multiple aspect categories instead of only one aspect category. To solve this issue, we propose a contextualized attention-based neural network approach to capture both the contextual information and the QA matching information inside QA style reviews for the task of QA-AC. Specifically, we first propose two aggregating strategies to integrate multi-layer contextualized word embeddings of the pre-trained language representation model (i.e., BERT) so as to capture contextual information. Second, we propose a bidirectional attention layer to capture the QA matching information. Experimental results demonstrate the effectiveness of our approach to QA-AC.

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

The nonlinear stochastic resonance system possesses the ability of taking advantage of background noise to enhance the weak signal. It provides a new approach to detect the weak signal embedded with heavy noise. This study proposes a new varying parameter stochastic resonance employing the fourth-order Runge–Kutta numerical method as well as the normalized transformation of a bistable stochastic resonance system. The model performs well in the detection of a time-varying signal with background noise for denoising and signal recovery. We take the fitness coefficient and cross-correlation coefficient as the criteria and analyze the influence of different parameters. The simulating results indicate its availability, validity and that it generates a better performance than the traditional stochastic resonance. The method develops the area of time-varying signal detection with stochastic resonance and presents new strategy for detection and denoising of a time-varying signal. It can be expected to be widely used in the areas of aperiodic signal processing, radar communication, etc.

The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.

Wayside acoustic condition monitoring and fault diagnosis for train bearings depend on acquired acoustic signals, which consist of mixed signals from different train bearings with obvious Doppler distortion as well as background noises. This study proposes a novel scheme to overcome the difficulties, especially the multi-source problem in wayside acoustic diagnosis system. In the method, a time–frequency data fusion (TFDF) strategy is applied to weaken the Heisenberg׳s uncertainty limit for a signal׳s time–frequency distribution (TFD) of high resolution. Due to the Doppler Effect, the signals from different bearings have different time centers even with the same frequency. A Doppler feature matching search (DFMS) algorithm is then put forward to locate the time centers of different bearings in the TFD spectrogram. With the determined time centers, time–frequency filters (TFF) are designed with thresholds to separate the acoustic signals in the time–frequency domain. Then the inverse STFT (ISTFT) is taken and the signals are recovered and filtered aiming at each sound source. Subsequently, a dynamical resampling method is utilized to remove the Doppler Effect. Finally, accurate diagnosis for train bearing faults can be achieved by applying conventional spectrum analysis techniques to the resampled data. The performance of the proposed method is verified by both simulated and experimental cases. It shows that it is effective to detect and diagnose multiple defective bearings even though they produce multi-source acoustic signals.

The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

Weak signal detection has attracted many researchers attention all over the world. Most of the methods focus on the single frequency signal. This paper introduces a new method to separate the weak double-frequency signal overwhelmed in heavy background noise. The cascaded stochastic resonance (SR) system (CSRS) is composed of a bistable SR model in the first stage and a tristable one in the second. Based on the characteristic of the SR system, we can amplify the useful signal of high frequency using twice sampling technic to make its parameters matching the requirements of the system. In our proposed cascade stochastic resonance system, we highlight the appointed frequency from high to low successfully with adjusting the twice sampling multiple and the high pass filter band. The signal composition of different frequency can be obtained from the systems two stage output. Simulated experiment validates the CSRSs availability in weak double-frequency signal separation and also its promising application in a more complex mixed signal.

This paper depicts the system design of an automatic golf collection robot. The robot is an intelligent system that consisted of three modules: the hardware module includes the mechanical structure and the electrical system; the control module processes the signals from the multiple sensors and outputs the instructions to navigate the robot; the vision module is used for recognition the golf balls from the grass background. The robot has been tested on the driving range, the basic functions of the robot have been achieved. The golf collection robot supersedes the human labor and shows a good application prospect.

The wayside Acoustic Defective Bearing Detector (ADBD) system plays an important role to maintain the safety of the railway transport. Due to the system acquired acoustic signal from a passing train by stationary microphones, it should be noted that the acquired signal is severely distorted by the Doppler Effect, which is an obstacle for defective bearings detection. This paper proposes a hardly needing prior knowledge method, called angle interpolation resampling (AIR) based on a microphone array, to remove the Doppler distortion embedded in the acoustic signal, and verifies it by means of simulation case and experiment case. The results indicate that the proposed AIR method has the superior performance in removing the Doppler distortion and has obvious advantages for ADBD system.

A novel control strategy of Fuzzy-Immune PID based on GA is proposed in this paper. The strategy learns from the immune feedback principle of biological immune system and takes advantage of the good approaching ability of fuzzy control to achieve nonlinear function of the immune controller, and then the GA algorithm is used to optimize parameters of PID controller and immune controller. Apply the strategy into the bending control system of rolling mills with characteristics of high nonlinearity, time-varying and time-delay. The simulation results indicate that the strategy has strong adaptability to the transformation of the system parameters and has advantages of low overshoot, short adjusting time, strong anti-disturbance ability and great robustness.

In view of the characteristics of nonlinearity, time-varying and time-delay in hydraulic AGC control system, a Smith fuzzy-PID complex control strategy is proposed. Smith predictor algorithm is used to compensate for the time delay and fuzzy control is used to enhance the robustness of the system, and then PID algorithms are used to improve the static precision of the system. The simulation results indicate that the method has characteristics of small overshoot, short adjusting time and strong anti-interference ability and robustness. It can further improve the quality of the strip thickness.

To explore the effect of jianxin pinglu pill (JPP) on arrhythmia and aquaporin 4 (AQP4) in rats with myocardial ischemia/reperfusion (I/R) injury.The effects of JPP on arrhythmia, mortality and AQP4 on I/R injured rats model induced by blocking left coronary artery were observed using II lead of ECG, HE stain and AQP4 immunohistochemical stain.JPP showed significant effect in lowering the arrhythmia occurrence and mortality, reducing myocardial ischemic edema and injury, strengthening AQP4 expression in myocardial tissue.JPP has the effect of preventing I/R induced arrhythmia, it might be related with its action in up-regulating AQP4 expression level in myocardium and reducing the intracellular edema.

This Note reports a tristable cantilever that exploits stochastic resonance (SR) phenomenon for a study of signal amplification and filtering. The tristable device system combines the benefits of bistable system (wide interwell spacing) and monostable system (smooth motion in potential). The prototype tristable cantilever exhibits 42 times root-mean-square amplitude, 35.86 dB power gain, advance of 15 dB signal-to-noise ratio, and twice fidelity at around 7.6 Hz as compared to the input signal. In a wide operating bandwidth [5.5 Hz, 8.2 Hz], the tristable SR cantilever outperforms the traditional monostable cantilever and bistable SR cantilever in these characteristics.