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The Application of Object-Oriented Views to an Engineering Environment.
With the increasing popularity of object-oriented technology, object-oriented database systems are being used in design environments as central repositories. In this thesis, we investigate the role of versioning and the characteristics of design databases in design environments. In an effort to improve the configuration management scheme in a design environment, we also investigate the use of database views as a possible configuration tool.
We propose a unified version management scheme that facilitates cooperative team work and show that the use of database views provides a powerful configuration management scheme for a design environment
Region-Object Relation-Aware Dense Captioning via Transformer
Dense captioning provides detailed captions of complex visual scenes. While a number of successes have been achieved in recent years, there are still two broad limitations: 1) most existing methods adopt an encoder-decoder framework, where the contextual information is sequentially encoded using long short-term memory (LSTM). However, the forget gate mechanism of LSTM makes it vulnerable when dealing with a long sequence and 2) the vast majority of prior arts consider regions of interests (RoIs) equally important, thus failing to focus on more informative regions. The consequence is that the generated captions cannot highlight important contents of the image, which does not seem natural. To overcome these limitations, in this article, we propose a novel end-to-end transformer-based dense image captioning architecture, termed the transformer-based dense captioner (TDC). TDC learns the mapping between images and their dense captions via a transformer, prioritizing more informative regions. To this end, we present a novel unit, named region-object correlation score unit (ROCSU), to measure the importance of each region, where the relationships between detected objects and the region, alongside the confidence scores of detected objects within the region, are taken into account. Extensive experimental results and ablation studies on the standard dense-captioning datasets demonstrate the superiority of the proposed method to the state-of-the-art methods
Research Trends in Non Point Source during 1975-2010
AbstractAccording to the samples of 2924 articles about non point source of SCI and SSCI databases from 1975 to 2010, this study analysed the articles in the growth trend of article outputs, subject categories and journals, international collaborations, geographic distribution and scientific research issues by using bibliometric analysis. The results showed that non point source research steadily increased over the past 35 years and the annual number of articles published in 2010 was 79 times of that in 1975. Non point source was involved into 67 kinds of subjects and appeared in 451 journals. The main study area was concentrated in North America and Europe, following by East Asia. There were 79 countries/territories participated in non point source research, and USA was the largest contributor in non point source research and had a central position in collaboration networks. A keyword analysis indicated that water quality, non point pollutions, and watershed were the hottest issues of non point source research; “GIS, “watershed management”, “modeling”, “simulation”, “monitoring”, and “remote sensing” were the most popular research methods; and “agriculture”, “land use”, “runoff”, and “pollution” were the leading causes of non point pollution
Liquid Crystal-Solid Interface Structure at the Antiferroelectric-Ferroelectric Phase Transition
Total Internal Reflection (TIR) is used to probe the molecular organization
at the surface of a tilted chiral smectic liquid crystal at temperatures in the
vicinity of the bulk antiferroelectric-ferroelectric phase transition. Data are
interpreted using an exact analytical solution of a real model for
ferroelectric order at the surface. In the mixture T3, ferroelectric surface
order is expelled with the bulk ferroelectric-antiferroelectric transition. The
conditions for ferroelectric order at the surface of an antiferroelectric bulk
are presented
Optimization of isolation and purification of total flavonoids from Ardisia mamillata Hance roots using macroporous resins, and determination of their antioxidant activity
Purpose: To isolate, purify and determine the antioxidant property of total flavonoids from the roots of Ardisia mamillata, so as to provide a theoretical basis for development of natural antioxidants.Methods: Macroporous resin was used to optimize the isolation and purification of total flavonoids, taking adsorption rate and resolution rate as evaluation indices. The antioxidant property of the purified total flavonoids was determined using 1,1-diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6- trinitrophenyl)hydrazyl (DPPH) radical scavenging activity.Results: The best conditions for separation and purification of total flavonoids from Ardisia mamillata roots were: use of ADS-7 resin, loading total flavonoid concentration of 0.8896 mg/mL, loading buffer flow rate of 1.5 mL/min, loading buffer pH of 4.48, elution ethanol concentration of 60 %, and flow rate of 2.5 mL/min. Under these conditions, the degree of purification of total flavonoids of Ardisia mamillata root was 76.43 ± 0.36 %, adsorption rate was 96.52 ± 0.19 %, while resolution rate was 99.31 ± 0.27 %. When the concentration of the purified total flavonoids was 4.0 mg/mL, its DPPH radical scavenging activity was stronger than that of the standard, butylated hydroxytoluene (BHT), but lower than that of vitamin C.Conclusion: ADS-7 resin is the best macroporous resin for the purification of total flavonoids from the radix of Ardisia mamillata Hance, under the optimized conditions. The purified total flavonoids of Ardisia mamillata root have stronger DPPH radical scavenging ability than the standard, BHT.Keywords: Szechwan raspberry root, Flavonoids, Macroporous adsorption resin, ADS-7 resin, Purification, Antioxidan
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
Pretrained vision-language models, such as CLIP, have demonstrated strong
generalization capabilities, making them promising tools in the realm of
zero-shot visual recognition. Visual relation detection (VRD) is a typical task
that identifies relationship (or interaction) types between object pairs within
an image. However, naively utilizing CLIP with prevalent class-based prompts
for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish
between different fine-grained relation types and it neglects essential spatial
information of two objects. To this end, we propose a novel method for
zero-shot VRD: RECODE, which solves RElation detection via COmposite
DEscription prompts. Specifically, RECODE first decomposes each predicate
category into subject, object, and spatial components. Then, it leverages large
language models (LLMs) to generate description-based prompts (or visual cues)
for each component. Different visual cues enhance the discriminability of
similar relation categories from different perspectives, which significantly
boosts performance in VRD. To dynamically fuse different cues, we further
introduce a chain-of-thought method that prompts LLMs to generate reasonable
weights for different visual cues. Extensive experiments on four VRD benchmarks
have demonstrated the effectiveness and interpretability of RECODE
ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation
Pervasive mobile AI applications primarily employ one of the two learning
paradigms: cloud-based learning (with powerful large models) or on-device
learning (with lightweight small models). Despite their own advantages, neither
paradigm can effectively handle dynamic edge environments with frequent data
distribution shifts and on-device resource fluctuations, inevitably suffering
from performance degradation. In this paper, we propose ECLM, an edge-cloud
collaborative learning framework for rapid model adaptation for dynamic edge
environments. We first propose a novel block-level model decomposition design
to decompose the original large cloud model into multiple combinable modules.
By flexibly combining a subset of the modules, this design enables the
derivation of compact, task-specific sub-models for heterogeneous edge devices
from the large cloud model, and the seamless integration of new knowledge
learned on these devices into the cloud model periodically. As such, ECLM
ensures that the cloud model always provides up-to-date sub-models for edge
devices. We further propose an end-to-end learning framework that incorporates
the modular model design into an efficient model adaptation pipeline including
an offline on-cloud model prototyping and training stage, and an online
edge-cloud collaborative adaptation stage. Extensive experiments over various
datasets demonstrate that ECLM significantly improves model performance (e.g.,
18.89% accuracy increase) and resource efficiency (e.g., 7.12x communication
cost reduction) in adapting models to dynamic edge environments by efficiently
collaborating the edge and the cloud models
Meson Mixing in Pion Superfluid
We investigate meson mixing and meson coupling constants in pion superfluid
in the framework of two flavor NJL model at finite isospin density. The mixing
strength develops fast with increasing isospin chemical potential, and the
coupling constants in normal phase and in the pion superfluid phase behave very
differently.Comment: 6 pages, 4 figures. Updates from version 2: 1, Correct Some language
mistakes and Some errors in the cited references. 2, Rewrite the last
sentence in the summary to indicate a possible way to measure the
isospin-asymmetry related meson propertie
Investigation of the Free-Fall Dynamic Behavior of a Rectangular Wing with Variable Center of Mass Location and Variable Moment of Inertia
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).In recent years, the air-drop launch technology of near-space UAVs has attracted much attention. Between downfall from the carrier and the flight control system’s initiation, the UAV presents free-fall movement. This free-fall process is very important for the control effect of the flight control system and is also crucial for the safety of the UAV and the carrier. Focus is required on two important dynamic parameters of the UAV: the moment of inertia and the center of mass position. In this paper, we used a quasi-steady model proposed by predecessors to address the flat-plate falling problem with modifications to describe the freely falling motion of the wing. Computational fluid dynamics (CFD) were used to simulate the free-fall movement of the wing with various parameters, and the wing release behavior was analyzed to check the quasi-steady model. Research shows that the movement characteristics of the falling wing are mostly reflected in the longitudinal plane, and the developed quasi-steady analytical model can more accurately describe the dynamic behavior of free-fall to some extent. By using CFD methods, we further investigated the aerodynamic performance of the free-fall wing. The results show that the wing mainly presents tumbling and fluttering motion. Changing the moment of inertia around the tumbling axis changes the tumbling frequency and the time point as the wing enters tumbling. In contrast, changing the position of the center of mass significantly changes the form of falling and makes the free-fall motion more complex. Therefore, it is necessary to carefully configure the center of mass in the UAV design process.Peer reviewe
Early and late rice identification from Tiangong- 2 wide band images based on CNN
The wide band images acquired from the Tiangong-2 space laboratory covers many spectral bands such as visible light, shortwave infrared and thermal infrared. These high-quality images can be used for space science experiments such as earth observation. In this paper, we use CNN (convolutional neural networks) to extract the spectral features of different landcover from the wide band images, then identify the early rice and the late rice accurately in Huarong County, Hunan Province, China. With advanced techniques such as deep learning, the spatial distribution information of crops can be effectively obtained from the wide band images which can provide data services for agricultural production management
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