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

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

Negative Deterministic Information based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation

Weakly  supervised  object  detection  and  semanticsegmentation  with  image-level  annotations  have  attracted  ex-tensive   attention   due   to   their  high   label   efficiency.   Multipleinstance  learning  (MIL)  offers  a  feasible  solution  forthe  twotasks by treating each image as a bag with a series of instances(object  regions  or  pixels)  and  identifying  foreground  instancesthat contribute to bag classification. However, conventional MILparadigms  often  suffer  from  issues,  e.g.,  discriminative  instancedomination  and  missing  instances.In  this  paper,  weobservethat  negative  instances  usually  contain  valuable  deterministicinformation, which is the key to solving the two issues. Motivatedby  this,  we  proposea  novel  MIL  paradigm  based  on  negativedeterministic   information   (NDI),   termed   NDI-MIL,   whichisbased  on  two  core  designs  with  a  progressive  relation:  NDIcollection  and  negative  contrastive  learning.  In  NDI  collection,we  identify  and  distill  NDI  from  negative  instances  online  bya  dynamic  feature  bank.  The  collected  NDI  is  then  utilized  ina  negative  contrastive  learning  mechanism  to  locate  and  punishthose discriminative regions, by which the discriminative instancedomination and missing instances issues are effectively addressed,leading to improved object- and pixel-level localization accuracyand completeness. In addition, we design an NDI-guided instanceselection strategy to further enhance the systematic performance.Experimental  results  on  several  public  benchmarks,  includingPASCAL VOC 2007, PASCAL VOC 2012, and MS COCO, showthat  our  method  achieves  satisfactory  performance.  The  code  isavailable at: https://github.com/GC-WSL/NDI.</p

High-Quality Angle Prediction for Oriented Object Detection in Remote Sensing Images

Oriented object detection is a challenging task in remote sensing, where the detected objects can be represented by oriented bounding boxes (OBBs). Angle prediction in oriented object detection has been widely studied, due to its crucial role in object detection. However, the precision of angle prediction is severely limited by misalignments in most of the existing methods, including representation-, evaluation-, and optimization-based misalignments. To alleviate these misalignments, this paper presents a novel angle prediction method, called Angle Quality Estimation (AQE). Specifically, our proposed AQE transforms the angle prediction task into a distribution estimation task to address the representation misalignment problem and implicitly measure the quality of the predicted angles. Based on the estimated angle quality, we then propose a new metric to comprehensively evaluate the quality of OBBs. Then we propose an object aspect ratio based loss function to optimize angle prediction for addressing the optimization misalignment. Our proposed AQE is a plug-and-play method, which can be embedded on any existing oriented object detector. Experimental results on three public benchmarks, including DOTA, HRSC2016, and ICDAR2015 datasets, show that our method achieves better performance than the other state-of-the-art.</p

Negative Deterministic Information based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation

Weakly  supervised  object  detection  and  semanticsegmentation  with  image-level  annotations  have  attracted  ex-tensive   attention   due   to   their  high   label   efficiency.   Multipleinstance  learning  (MIL)  offers  a  feasible  solution  forthe  twotasks by treating each image as a bag with a series of instances(object  regions  or  pixels)  and  identifying  foreground  instancesthat contribute to bag classification. However, conventional MILparadigms  often  suffer  from  issues,  e.g.,  discriminative  instancedomination  and  missing  instances.In  this  paper,  weobservethat  negative  instances  usually  contain  valuable  deterministicinformation, which is the key to solving the two issues. Motivatedby  this,  we  proposea  novel  MIL  paradigm  based  on  negativedeterministic   information   (NDI),   termed   NDI-MIL,   whichisbased  on  two  core  designs  with  a  progressive  relation:  NDIcollection  and  negative  contrastive  learning.  In  NDI  collection,we  identify  and  distill  NDI  from  negative  instances  online  bya  dynamic  feature  bank.  The  collected  NDI  is  then  utilized  ina  negative  contrastive  learning  mechanism  to  locate  and  punishthose discriminative regions, by which the discriminative instancedomination and missing instances issues are effectively addressed,leading to improved object- and pixel-level localization accuracyand completeness. In addition, we design an NDI-guided instanceselection strategy to further enhance the systematic performance.Experimental  results  on  several  public  benchmarks,  includingPASCAL VOC 2007, PASCAL VOC 2012, and MS COCO, showthat  our  method  achieves  satisfactory  performance.  The  code  isavailable at: https://github.com/GC-WSL/NDI.</p

Predication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithm

Efficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional (S˙fr) and thermal (S˙th) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate

Endovascular thrombectomy versus standard bridging thrombolytic with endovascular thrombectomy within 4·5 h of stroke onset: an open-label, blinded-endpoint, randomised non-inferiority trial

Background: The benefit of combined treatment with intravenous thrombolysis before endovascular thrombectomy in patients with acute ischaemic stroke caused by large vessel occlusion remains unclear. We hypothesised that the clinical outcomes of patients with stroke with large vessel occlusion treated with direct endovascular thrombectomy within 4·5 h would be non-inferior compared with the outcomes of those treated with standard bridging therapy (intravenous thrombolysis before endovascular thrombectomy). Methods: DIRECT-SAFE was an international, multicentre, prospective, randomised, open-label, blinded-endpoint trial. Adult patients with stroke and large vessel occlusion in the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or basilar artery, confirmed by non-contrast CT and vascular imaging, and who presented within 4·5 h of stroke onset were recruited from 25 acute-care hospitals in Australia, New Zealand, China, and Vietnam. Eligible patients were randomly assigned (1:1) via a web-based, computer-generated randomisation procedure stratified by site of baseline arterial occlusion and by geographic region to direct endovascular thrombectomy or bridging therapy. Patients assigned to bridging therapy received intravenous thrombolytic (alteplase or tenecteplase) as per standard care at each site; endovascular thrombectomy was also per standard of care, using the Trevo device (Stryker Neurovascular, Fremont, CA, USA) as first-line intervention. Personnel assessing outcomes were masked to group allocation; patients and treating physicians were not. The primary efficacy endpoint was functional independence defined as modified Rankin Scale score 0–2 or return to baseline at 90 days, with a non-inferiority margin of –0·1, analysed by intention to treat (including all randomly assigned and consenting patients) and per protocol. The intention-to-treat population was included in the safety analyses. The trial is registered with ClinicalTrials.gov, NCT03494920, and is closed to new participants. Findings: Between June 2, 2018, and July 8, 2021, 295 patients were randomly assigned to direct endovascular thrombectomy (n=148) or bridging therapy (n=147). Functional independence occurred in 80 (55%) of 146 patients in the direct thrombectomy group and 89 (61%) of 147 patients in the bridging therapy group (intention-to-treat risk difference –0·051, two-sided 95% CI –0·160 to 0·059; per-protocol risk difference –0·062, two-sided 95% CI –0·173 to 0·049). Safety outcomes were similar between groups, with symptomatic intracerebral haemorrhage occurring in two (1%) of 146 patients in the direct group and one (1%) of 147 patients in the bridging group (adjusted odds ratio 1·70, 95% CI 0·22–13·04) and death in 22 (15%) of 146 patients in the direct group and 24 (16%) of 147 patients in the bridging group (adjusted odds ratio 0·92, 95% CI 0·46–1·84). Interpretation: We did not show non-inferiority of direct endovascular thrombectomy compared with bridging therapy. The additional information from our study should inform guidelines to recommend bridging therapy as standard treatment. Funding: Australian National Health and Medical Research Council and Stryker USA

Longitudinal double-spin asymmetry and cross section for inclusive jet production in polarized proton collisions at square root of s = 200 GeV

We report a measurement of the longitudinal double-spin asymmetry A(LL) and the differential cross section for inclusive midrapidity jet production in polarized proton collisions at s=200 GeV. The cross section data cover transverse momenta 5 < p(T)< 50 GeV/c and agree with next-to-leading order perturbative QCD evaluations. The A(LL) data cover 5 < p(T)< 17 GeV/c and disfavor at 98% C.L. maximal positive gluon polarization in the polarized nucleon