249 research outputs found
sj-docx-1-wmr-10.1177_0734242X231187578 – Supplemental material for Practice and performance of domestic waste source segregation in Chinese universities: A case study in Shanghai
No full textSupplemental material, sj-docx-1-wmr-10.1177_0734242X231187578 for Practice and performance of domestic waste source segregation in Chinese universities: A case study in Shanghai by Guangyu Cui, Weiping Ren, Fan Lü, Hua Zhang, Hui Xue and Pinjing He in Waste Management & Research</p
sj-pdf-2-wmr-10.1177_0734242X231187578 – Supplemental material for Practice and performance of domestic waste source segregation in Chinese universities: A case study in Shanghai
No full textSupplemental material, sj-pdf-2-wmr-10.1177_0734242X231187578 for Practice and performance of domestic waste source segregation in Chinese universities: A case study in Shanghai by Guangyu Cui, Weiping Ren, Fan Lü, Hua Zhang, Hui Xue and Pinjing He in Waste Management & Research</p
Effective deep leaning methodologies for salient object detection
No full textSalient object detection has achieved great improvement by using deep neural
networks. Diverse challenging problems arise from this computer vision task and
attract more attention from researchers. This thesis investigates the issues in specific
tasks and aims at proposing effective methodologies to tackle the problems and
improve detection performance.
More specifically, existing network architectures may cause dilution problems in highlevel
semantic information during up-sample operations in the top-down pathway
of the Feature Pyramid Network (FPN). In order to overcome this limitation, we
propose a novel pyramid self-attention module (PSAM) and the adoption of an
independent feature-complementing strategy. In addition, a channel-wise attention
module is also employed to reduce redundant features of the feature pyramid network
and provide refined results.
Introducing depth information in a suboptimal fusion strategy may have a negative
influence on the performance of SOD. We discuss the advantages of the so-called
progressive multi-scale fusion method and propose a mask-guided feature aggregation
module (MGFA). We also introduce a mask-guided refinement module (MGRM) to
complement the high-level semantic features and reduce the irrelevant features from
multi-scale fusion, leading to an overall refinement of detection.
RGB-D methods sacrifice the model size to improve the detection accuracy, which
may impede the practical application of SOD problems. To tackle this dilemma,
we propose a dynamic distillation method along with simple noise elimination. To
this end, the final model can significantly reduce the computational burden while
maintaining the validity and mitigating the impact of distorted training data caused
by low-quality depth maps.
Furthermore, in cosaliency detection, we propose a novel adaptive intra-group aggregation
(AIGA) method to model the relationship between individual feature
representation in a single image and group feature representation. This proposed
AIGA can effectively improve the performance without increasing extra network
parameters.Open Acces
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Trust filter for disease surveillance : Identity
No full textA flexible and extensible mobile application was delivered for evaluation and optimal inclusion of NextGen (Next Generation) data sources into biosurveillance for early detection, situational awareness and prediction. We present trust analysis of NextGen data sources to increase data confidence. One of the trust filters is the Identity filter, which helps us determine the degree of separation between the sender and the subject of a sentence. In this thesis, the author presents the definition of Identity. To help us distinguish different degrees of separation, the author uses relation distance along with a family tree to weight different relationships. Then the author compares a discriminative algorithm and a generative algorithm to calculate a user's Identity score. In the end, the author concludes that it is a good choice to apply a binary classification algorithm combined with a Natural Language Processing algorithm because of the trade-off between accuracy and runtime complexity.Electrical and Computer Engineerin
On New Media Art, Its Development and Achievement in China
No full textThis essay is aimed to introduce the development status of the new media art in China; the author gave its definition based on his own understanding and observation, which included various forms. Moreover, the developing environment of the new media art in the world is presented systematically in the essay. The author combed the art history and technology history which are closely connected with the birth of the new media art. China has achieved many accomplishments in new media art forms, such as the successful hosting of 2008 World Olympic Games and 2010 World expo. In the opening ceremony of 2008 World Olympic Games, interactive art, installation art and virtual space are in perfect use. Especially, “scroll” has combined all the above technological methods into display, which brought about stunning sensory impact to the audiences. After two years, Shanghai was the focus of the world, 242 countries and international organizations that have attended 2010 Shanghai World expo. The new media art forms are bloomed and flourished, China pavilion’s Qingming Riverside was endowed with new vitality via these new art media forms
Quantitative observations of a deep-sea hydrothermal plume using an acoustic imaging sonar
No full textThe Cabled Observatory Vent Imaging Sonar (COVIS) is used to quantitatively monitor the hydrothermal discharge from the Grotto mound, a venting sulfide structure on the Endeavour Segment of the Juan de Fuca Ridge. Since its deployment in September 2010, COVIS has recorded a multi-year long, near-continuous acoustic backscatter dataset. Further analysis of this dataset sheds light on the backscattering mechanisms within the buoyant plumes above Grotto and yields quantitative information on the influences of oceanic, atmospheric, and geological processes on the dynamics and heat source of the plumes. An investigation of the acoustic scattering mechanisms within the buoyant plumes issuing from Grotto suggests the dominant scattering mechanism within the plumes is the temperature fluctuations caused by the turbulent mixing of the buoyant plumes with the ambient seawater. In comparison, the backscatter from plume particles is negligible at lower levels of the plume but can potentially be signi cant at higher levels. Furthermore, this finding demonstrates the potential of inverting the acoustic backsatter to estimate the temperature fluctuations within the plumes. Processing the backscatter dataset recorded by COVIS yields time-series measurements of the vertical flow rate, volume transport, expansion rate of the largest buoyant plume above Grotto. Further analysis of those time-series measurements suggests the rate at which the ambient seawater is entrained into the plume increases with the magnitude of the ambient ocean currents---the current-driven entrainment. Furthermore, the oscillations in the ambient ocean currents that are driven by tidal and atmospheric forcing are introduced into the flow field within the plume through the current-driven entrainment. An inverse method has been developed to estimate the source heat transport driving the largest plume above Grotto from its volume transport estimates. The result suggests the heat transport driving the plume was steady over the 41-month period between October 2011 and February 2015. Comparing the current and historical heat transport measurements with contemporaneous seismic data suggests the evolution of the heat transport since 1988 correlates with the rate of local seismicity with a short episode of increased heat transport following pronounced seismic events and reduced steady heat transport during time periods of quiescent seismicity.Ph.D.Includes bibliographical referencesby Guangyu X
Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity
No full textTruck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin
Parallel Elite Genetic Algorithm for Test Scheduling of SoC
No full textTest scheduling is an important issue for testing the SoC (system-on-chip). This work uses a parallel elite genetic algorithm for test scheduling to reduce the test application time under the peak power constraint. It is applied to the 2D SoC and experimental results on benchmark circuits show that it is one of the most effective algorithms in solving the problem.Engineering, Electrical & ElectronicEICPCI-S(ISTP)
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