Electrode erosion and lifetime performance of a compact and repetitively triggered field distortion spark gap switch

© 1973-2012 IEEE. The electrode erosion and lifetime performance of a compact and repetitively triggered field distortion spark gap switch were studied at a repetitive frequency rate of 30 Hz, a peak current of 8.5 kA, and a working voltage of ±35 kV when the switch was filled with a gas mixture of 30% SF6 and 70% N2 at a pressure of 0.3 MPa. The variations of the time-delay jitter and the self-breakdown voltage were both studied for the whole service lifetime of the spark gap switch. The morphology of both the electrodes and the plate insulator, before and after the service lifetime tests, is also analyzed. The results show that during these tests, the time-delay jitter is basically synchronized with the self-breakdown voltage jitter, and both undergo firstly a process of rapidly decreasing their values, then remaining stable, and finally and gradually increasing after 70 000 pulses. The change in the electrode surface roughness (i.e., surface profile) is caused by erosion and chemical deposits in the switch cavity, which are mainly the two factors that affect the time-delay jitter of the switch. Tip protrusions on the electrode surface, due to electrode erosion, contribute to reducing the time-delay jitter. However, due to chemical reactions, fluorides and sulfides are deposited on the switch components, as well as metal particles caused by electrode erosion sputtering. Slowly, after a large number of shots, all these phenomena affect the self-breakdown performance resulting in an increased self-breakdown voltage jitter, which also causes the time-delay jitter to increase. Although there are a number of reasons that contribute to the deterioration of the performance of the switch, it is fortunate that if a switch suffering a degraded performance is reassembled, with the electrodes mechanically polished and all the components cleaned, the optimal performance of the switch can be restored. If maintenance work is carried out regularly to preserve the condition of the switch's inner components, the service lifetime of the switch can be prolonged

Time series analysis and modelling of the freezing of gait phenomenon

Freezing of Gait (FOG) is one of the most debilitating symptoms of Parkinson’s Disease and is associated with falls and loss of independence. The patho-physiological mecha- nisms underpinning FOG are currently poorly understood. In this thesis we combine time series analysis and mathematical modelling to study the FOG phenomenon’s dynamics. We focus on the transition from stepping in place into freezing and treat this phenomenon in the context of an escape from an oscillatory attractor into an equilibrium attractor state. We analyze the experimental data by two different approaches. In the first approach we use a stochastic Hopf bifurcation normal form model to study the escape time from oscillatory behavior to small-amplitude fluctuations. For the other approach we extract a discrete-time discrete-space Markov chain from experimental data and divide its state space into communicating classes to identify the transition into freezing. This allows us to develop a methodology for computationally estimating the time to freezing as well as the phase along the oscillatory (stepping) cycle of a patient experiencing Freezing Episodes (FE). The developed methodology is general and could be applied to any time series featuring transitions between different dynamic regimes including time series data from forward walking in people with FOG

Identification and Characterisation of Alternative Splice Variants of Hoxb9 and Their Correlation with Melanogenesis in the Black-Boned Chicken

ABSTRACT Homeobox B9 (HOXB9) gene has been demonstrated to be associated with melanogenesis in chicken plumage by high-throughput RNA sequencing. In this study, we cloned and characterised HOXB9 in black-boned chickens. Two alternative splice variants (HOXB9-1 and HOXB9-2) were identified in chicken feather bulbs. Expression analysis of HOXB9 in 11 different chicken tissues by RT-PCR indicated that the two transcripts were only expressed in the kidney, abdominal fat, feather bulbs, skin, and small intestine. No HOXB9-1 or HOXB9-2 transcripts were detected in the breast muscle or the ovary. The two HOXB9 variants were expressed at significantly different levels in black feather bulbs and white feather bulbs (p</div

Message from the Symposium Chairs: UbiSafe 2014

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record

GATCluster: Self-supervised Gaussian-Attention Network for Image Clustering

We propose a self-supervised Gaussian ATtention network for image Clustering (GATCluster). Rather than extracting intermediate features first and then performing traditional clustering algorithms, GATCluster directly outputs semantic cluster labels without further post-processing. We give a Label Feature Theorem to guarantee that the learned features are one-hot encoded vectors and the trivial solutions are avoided. Based on this theorem, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping. Specifically, the transformation invariance and separability maximization tasks learn the relations between samples. The entropy analysis task aims to avoid trivial solutions. To capture the object-oriented semantics, we design a self-supervised attention mechanism that includes a Gaussian attention module and a soft-attention loss. Moreover, we design a two-step learning algorithm that is memory-efficient for clustering large-size images. Extensive experiments demonstrate the superiority of our proposed method in comparison with the state-of-the-art image clustering benchmarks

Asymmetric Similarity-Preserving Discrete Hashing for Image Retrieval

Hashing methods have been widely studied in the image research community due to their low storage and fast computation. However, generating compact hash codes is still a challenging task. In this paper, we propose a novel Asymmetric Similarity-Preserving Discrete Hashing (ASPDH) method to learn compact binary codes for image retrieval. Specifically, the pairwise similarity matrix is approximated in the asymmetric learning manner with two different real-valued embeddings. In addition, ASPDH constructs two distinct hash functions from the kernel feature and label consistency embeddings. Therefore, similarity preservation and hash code learning can be simultaneously achieved and interactively optimized, which further improves the discriminative capability of the learned binary codes. Then, a well-designed iterative algorithm is developed to efficiently solve the optimization problem, resulting in high-quality binary codes with reduced quantization errors. Extensive experiments on three public datasets show the rationality and effectiveness of our proposed method.</p

Asymmetric Similarity-Preserving Discrete Hashing for Image Retrieval

Hashing methods have been widely studied in the image research community due to their low storage and fast computation. However, generating compact hash codes is still a challenging task. In this paper, we propose a novel Asymmetric Similarity-Preserving Discrete Hashing (ASPDH) method to learn compact binary codes for image retrieval. Specifically, the pairwise similarity matrix is approximated in the asymmetric learning manner with two different real-valued embeddings. In addition, ASPDH constructs two distinct hash functions from the kernel feature and label consistency embeddings. Therefore, similarity preservation and hash code learning can be simultaneously achieved and interactively optimized, which further improves the discriminative capability of the learned binary codes. Then, a well-designed iterative algorithm is developed to efficiently solve the optimization problem, resulting in high-quality binary codes with reduced quantization errors. Extensive experiments on three public datasets show the rationality and effectiveness of our proposed method.</p

Stochastic systems 2013

Stochastic systems 2013</p

Temperature Sensor of MoS2 Based on Hybrid Plasmonic Waveguides

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. In order to overcome the challenge of traditional sensors, including large size, complex preparation process, and difficult filling and sensing media, a long-range dielectric-loaded hybrid plasmonic waveguide (LRDLHPW) based on MoS2 is proposed and numerically studied. The propagation length is as high as 4.85 cm, and the corresponding mode width is 981 nm. A near-infrared plasmonic sensor based on the hybrid plasmonic waveguide-nanocavity system achieves a refractive index sensitivity of 787.5 nm/RIU and line width and figure of merit of 30 nm and 26.3, respectively; at the same time, the temperature sensitivity is as high as 2.775 nm/ ° C. Compared with other researches, the sensor proposed in this paper improves the adaptability and sensitivity of the device, and the ultracompact structure combined with the planar waveguide structure makes it easy to integrate to chip. In addition, the device can also be used as an adjustable surface plasmon polaritons band-pass filter. Note that increasing sensitivity is accompanied by decreasing resolution, and vice versa. The trade-off between sensitivity and resolution is important in order to achieve a larger figure of merit. In general, the structure designed by us achieves good optical and sensing characteristics and is widely used in nanophotonic circuits, environmental monitoring, and even drug research

AppTCP: The design and evaluation of application-based TCP for e-VLBI in fast long distance networks

Electric Very Long Baseline Interferometry (e-VLBI) is a typical astronomical interferometry used in radio astronomy. It allows observations of an object that are made simultaneously by many radio telescopes to be combined, emulating a telescope with the size equal to the maximum separation between the telescopes. The main requirements of transporting e-VLBI data are the high and constant transmission rate. However, the traditional TCP and its variants cannot meet these requirements. In an effort to solve the problem of transporting e-VLBI data in fast long distance networks, we propose an application-based TCP (AppTCP) congestion control algorithm, using Closed-Loop Control theory to keep the stable and constant transmission rate. AppTCP can swiftly reach the required transmission rate by increasing the congestion control window, and keep the transmission rate and allows the other TCP flows to share the remaining bandwidth. We further conduct extensive experiments in both fast long distance network test-bed and actual national networks (i.e., from Beijing to Shanghai in China) and international networks (i.e., from Hongkong in China to Chicago in USA) to evaluate and verify the performance and effectiveness of AppTCP. The results show that the AppTCP can effectively utilize the link capacity and maintain the constant rate during the data transmission, and its performance significantly outperforms that of the existing TCP variants