Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.     

Meaningful Secret Image Sharing with Saliency Detection

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.     

Intelligent Reflecting Surface–Assisted Wireless Secret Key Generation against Multiple Eavesdroppers

In this paper, we propose an improved physical layer key generation scheme that can maximize the secret key capacity by deploying intelligent reflecting surface (IRS) near the legitimate user aiming at improving its signal-to-noise ratio (SNR). We consider the scenario of multiple input single output (MISO) against multiple relevant eavesdroppers. We elaborately design and optimize the reflection coefficient matrix of IRS elements that can improve the legitimate user’s SNR through IRS passive beamforming and deteriorate the channel quality of eavesdroppers at the same time. We first derive the lower bound expression of the achievable key capacity, then solve the optimization problem based on semi-definite relaxation (SDR) and the convex–concave procedure (CCP) to maximize the secret key capacity. Simulation results show that our proposed scheme can significantly improve the secret key capacity and reduce hardware costs compared with other benchmark schemes.     

Learning Visible Thermal Person Re-Identification via Spatial Dependence and Dual-Constraint Loss

Visible thermal person re-identification (VT Re-ID) is the task of matching pedestrian images collected by thermal and visible light cameras. The two main challenges presented by VT Re-ID are the intra-class variation between pedestrian images and the cross-modality difference between visible and thermal images. Existing works have principally focused on local representation through cross-modality feature distribution, but ignore the internal connection of the local features of pedestrian body parts. Therefore, this paper proposes a dual-path attention network model to establish the spatial dependency relationship between the local features of the pedestrian feature map and to effectively enhance the feature extraction. Meanwhile, we propose cross-modality dual-constraint loss, which adds the center and boundary constraints for each class distribution in the embedding space to promote compactness within the class and enhance the separability between classes. Our experimental results show that our proposed approach has advantages over the state-of-the-art methods on the two public datasets SYSU-MM01 and RegDB. The result for the SYSU-MM01 is Rank-1/mAP 57.74%/54.35%, and the result for the RegDB is Rank-1/mAP 76.07%/69.43%.     

Nanomaterials in Cerebrovascular Disease Diagnose and Treatment

Cerebrovascular diseases (CVDs) are among the most serious diseases with high mortality and disability rates. The prevalent diagnosis and treatment methods of CVDs include imaging and interventional therapy. With the development of nanotechnology, large numbers of nanomaterials have been applied to the diagnosis and treatment of CVDs, mainly including carbon nanotubes, quantum dots, fullerenes, and dendrimers. In this review, the applications of nanomaterials in the field of diagnosis and treatment of CVDs, mainly including drug target delivery, imaging, therapy, endovascular treatment, and angiogenesis, are summarized. The applications of nanomaterials in the field of CVD are almost in the laboratory, and more effort is needed for clinical translation. The aim of this review is to provide useful information for future research and equipment development.     

Synthesizing Red Fluorescent Carbon Dots from Rigid Polycyclic Conjugated Molecules: Dual-Mode Sensing and Bioimaging in Biochemical Applications

Red fluorescent carbon dots (R-CDs) are special desirable for biochemical analysis due to good biological compatibility and deep penetration; however, they remain as bottlenecks due to difficulties in expanding the sp² domain, especially those are fused from rigid polycyclic conjugated molecules (RPCMs) with heteroatom substituents due to huge steric hindrance and heteroatom blockage toward graphic lattice. Here, an RPCM with heteroatom substituents, 1,5-diamino-4,8-dihydroxyanthraquinone (DDAQ), based self-doped R-CDs with PL emission at 635 nm is reported. Further investigations reveal that the expanding, hybrid sp² domain with indanthrone tannin structure from DDAQ is mainly responsible for the obtained red fluorescence of R-CDs. Taking advantage of optical properties, R-CDs are considered to construct a colorimetric/fluorescent dual mode sensing array for quantifying trace levels of Fe³⁺ and glyphosate based on the static quenching, and a biomarker for cell imaging. The CD-based sensors exhibit outstanding recovery, high selectivity, and sensitivity, also facilitated dual-mode detection with the naked-eye. The R-CDs have low cytotoxicity, good cell membrane penetration for rapid cell entry, and high resolution, demonstrating their potential for biolabeling and bioanalytic applications.     

Amorphous High-Entropy Hydroxides of Tunable Wide Solar Absorption for Solar Water Evaporation

The high-entropy materials have raised much attention in recent years due to their extraordinary performances in mechanical, catalysis, energy storage fields. Herein, a new type of high-entropy hydroxides (e.g., NiFeCoMnAl(OH)_x) that are amorphous and capable of broad solar absorption is reported. A facile one-pot co-precipitation method is employed to synthesize these amorphous high-entropy hydroxides (a-HEHOs) under ambient conditions. The a-HEHOs thus obtained display widely tunable bandgap (e.g., from 2.6 to 1.1 eV) due to their high-entropy and amorphous characteristics, enabling efficient light absorbance and photothermal conversion in the solar regime. Further solar water evaporation measurements show that the a-HEHOs delivered a considerable energy conversion efficiency of 55%, comparable to black titanium oxides that are synthesized using more complex and expensive methods.     

Tantalum Nitride Nanosheets for Photoacoustic Imaging-Guided Photothermal Cancer Therapy

In this study, the synthesis of TaN nanosheets and their application in theranostic agents is reported. After coating polyethylene glycol (PEG) on the TaN nanosheets, the as-synthesized PEG-modified TaN nanosheets (TaN-PEG) show good stability and biocompatibility. Because of their high absorbance in the near-IR region, TaN-PEG can be utilized as photoacoustic imaging contrast agents for tumor imaging. Moreover, TaN-PEG has significant photothermal conversion performance, exhibiting effective laser-induced tumor ablation capability. The TaN-PEG possessing excellent photoacoustic contrast effect and photothermal properties thus have great promise in theranostic applications, especially imaging-guided cancer treatment.     

Thermally Evaporated Ag–Au Bimetallic Catalysts for Efficient Electrochemical CO2 Reduction

CO₂ reduction reaction (CO₂RR) has indispensable significance for carbon recycling and renewable energy production. As typical electrochemical catalysts, Au and Ag show relatively high reaction activity and selectivity in CO₂RR. In this study, a series of Ag–Au bimetallic catalysts are designed and synthesized through the thermal evaporation method for efficient yet massive production of electrochemical catalysts. The Ag–Au catalysts show significantly enhanced activity and selectivity in CO₂RR, which is mainly attributed to the increased grain boundaries with well-dispersed single Ag atoms. After the optimization, Au20Ag10 exhibits the best performance with a CO Faraday efficiency of 89% at −0.9 V (vs the reversible hydrogen electrode) with good stability.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.