543 research outputs found
Improved Multi-Population Differential Evolution for Large-Scale Global Optimization
Differential evolution (DE) is an efficient population-based search algorithm with good robustness, however, it is challenged to deal with high-dimensional problems. In this paper, we propose an improved multi-population differential evolution with best-and-current mutation strategy (mDE-bcM). The population is divided into three subpopulations based on the fitness values, each of subpopulations uses different mutation strategy. After crossover, mutation and selection, all subpopulations are updated based on the new fitness values of their individuals. An improved mutation strategy is proposed, which uses a new approach to generate base vector that is composed of the best individual and current individual. The performance of mDE-bcM is evaluated on a set of 19 large-scale continuous optimization problems, a comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bcM has a competitive performance compared to the contestant algorithms and better efficiency for large-scale optimization problems
Particle shape effect on heat transfer performance in an oscillating heat pipe
The effect of alumina nanoparticles on the heat transfer performance of an oscillating heat pipe (OHP) was investigated experimentally. A binary mixture of ethylene glycol (EG) and deionized water (50/50 by volume) was used as the base fluid for the OHP. Four types of nanoparticles with shapes of platelet, blade, cylinder, and brick were studied, respectively. Experimental results show that the alumina nanoparticles added in the OHP significantly affect the heat transfer performance and it depends on the particle shape and volume fraction. When the OHP was charged with EG and cylinder-like alumina nanoparticles, the OHP can achieve the best heat transfer performance among four types of particles investigated herein. In addition, even though previous research found that these alumina nanofluids were not beneficial in laminar or turbulent flow mode, they can enhance the heat transfer performance of an OHP
Dolphins: Multimodal Language Model for Driving
The quest for fully autonomous vehicles (AVs) capable of navigating complex
real-world scenarios with human-like understanding and responsiveness. In this
paper, we introduce Dolphins, a novel vision-language model architected to
imbibe human-like abilities as a conversational driving assistant. Dolphins is
adept at processing multimodal inputs comprising video (or image) data, text
instructions, and historical control signals to generate informed outputs
corresponding to the provided instructions. Building upon the open-sourced
pretrained Vision-Language Model, OpenFlamingo, we first enhance Dolphins's
reasoning capabilities through an innovative Grounded Chain of Thought (GCoT)
process. Then we tailored Dolphins to the driving domain by constructing
driving-specific instruction data and conducting instruction tuning. Through
the utilization of the BDD-X dataset, we designed and consolidated four
distinct AV tasks into Dolphins to foster a holistic understanding of intricate
driving scenarios. As a result, the distinctive features of Dolphins are
characterized into two dimensions: (1) the ability to provide a comprehensive
understanding of complex and long-tailed open-world driving scenarios and solve
a spectrum of AV tasks, and (2) the emergence of human-like capabilities
including gradient-free instant adaptation via in-context learning and error
recovery via reflection.Comment: The project page is available at https://vlm-driver.github.io
Human exposure to halogenated and organophosphate flame retardants through informal e-waste handling activities - A critical review
A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers
With the wide application of electric energy storage component arrays, such
as battery arrays, capacitor arrays, inductor arrays, their potential safety
risks have gradually drawn the public attention. However, existing technologies
cannot meet the needs of non-contact and real-time diagnosis for faulty
components inside these massive arrays. To solve this problem, this paper
proposes a new method based on the beamforming spatial filtering algorithm to
precisely locate the faulty components within the arrays in real-time. The
method uses highly sensitive magnetometers to collect the magnetic signals from
energy storage component arrays, without damaging or even contacting any
component. The experimental results demonstrate the potential of the proposed
method in securing energy storage component arrays. Within an imaging area of
80 mm 80 mm, the one faulty component out of nine total components can
be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for
battery arrays
- …