Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Computer science. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Analysis of a Surface Plasmon Resonance Probe Based on Photonic Crystal Fibers for Low Refractive Index Detection

Abstract: A photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) probe with gold nanowires as the plasmonic material is proposed in this work. The coupling characteristics and sensing properties of the probe are numerically investigated by the finite element method. The probe is designed to detect low refractive indices between 1.27 and 1.36. The maximum spectral sensitivity and amplitude sensitivity are 6 × 103 nm/RIU and 600 RIU−1, respectively, corresponding to a resolution of 2.8 × 10−5 RIU for the overall refractive index range. Our analysis shows that the PCF-SPR probe can be used for lower refractive index detection.

Boosting the supercapacitor performance of activated carbon by constructing overall conductive networks using graphene quantum dots

Abstract: Although high-surface-area activated carbons have been widely used for supercapacitors, they usually have limited capacitive and rate performances primarily because of the low conductivity and sluggish electrochemical kinetics caused by their amorphous microporous structure. Here, we report a simple strategy to boost the electrochemical performance of activated carbons by embedding highly crystallized graphene quantum dots. Benefiting from the formation of the overall conductive networks, the charge-transfer and ion migration kinetics of the activated carbon are significantly improved, facilitating electrolyte ion transport and storage in deep and branched micropores. As a result, the graphene quantum dot embedded activated carbon, possessing a microporous structure with a specific surface area of 2829 m2 g−1, achieves a remarkably high electric double-layer capacitance of 388 F g−1 at 1 A g−1 as well as excellent rate performance with 60% capacitance retention at 100 A g−1 in a two-electrode system. The capacitive and rate performances are much higher than not only those of the activated carbon without graphene quantum dots, but also those of most porous carbons reported in the literatures. This strategy provides a new route for designing advanced porous carbon materials for high performance energy storage.

Ethanol sensing characteristics of ambient temperature sonochemically synthesized ZnO nanotubes

Abstract: ZnO nanotubes were synthesized by a sonochemical method at low temperature. The length and the diameter of the obtained nanotubes were 1.5–2 μm and 250 nm, respectively, and the walls were about 30 nm in thickness. The sensors fabricated from the nanotubes exhibited excellent ethanol sensing properties at a working temperature of 300 °C. The nanotubes can detect ethanol vapor with concentration down to 1 ppm and also showed good sensing characteristics at relatively low temperature. Our results demonstrated that the ZnO nanotubes were very promising for gas sensors with good sensing characteristics.

Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark

Abstract: The emergence of Internet connectivity has led to a significant increase in the volume and complexity of cyber attacks. Abnormal behavior detection systems are valuable tools for ensuring the security in computer networks. However, due to the huge amount and ever increasing diversity of the intrusions, the existing intrusion detection systems, which use machine learning techniques to learn a classifier based on a handcrafted feature vector, are not robust enough to detect sophisticated attacks which cause a high false alarm rate. Therefore, building a flexible in-depth defense system to detect abnormal behavior requires an ability to automatically learn powerful features and analyze large amounts of network traffic. To address these concerns, this paper proposes a novel distributed approach for the detection of abnormal behavior in large-scale networks. The developed model discovers the abnormal behavior from large-scale network traffic data using a combination of a deep feature extraction and multi-layer ensemble support vector machines (SVMs) in a distributed way. First, we perform a non-linear dimensionality reduction, achieved through a distributed deep belief networks on large-scale network traffic data. Then, the obtained features are fed to the multi-layer ensemble SVM. The construction of the ensemble is accomplished through the iterative reduce paradigm based on Spark. Empirical results show a promising gain in performance compared with other existing models.