Decision making with both diversity supporting and opposing membership information

Online big data provides large amounts of decision information to decision makers, but supporting and opposing information are present simultaneously. Dual hesitant fuzzy sets (DHFSs) are useful models for exactly expressing the membership degree of both supporting and opposing information in decision making. However, the application of DHFSs requires an improved distance measure. This paper aims to improve distance measure models for DHFSs and apply the new distance models to generate a technique for order preference by similarity to an ideal solution (TOPSIS) method for multiple attribute decision making (MADM)

Inverse design of artificial skins

Mimicking the perceptual functions of human cutaneous mechanoreceptors, artificial skins or flexible pressure sensors can transduce tactile stimuli to quantitative electrical signals. Conventional methods to design such devices follow a forward structure-to-property routine based on trial-and-error experiments/simulations, which take months or longer to determine one solution valid for one specific material. Target-oriented inverse design that shows far higher output efficiency has proven effective in other fields, but is still absent for artificial skins because of the difficulties in acquiring big data. Here, we report a property-to-structure inverse design of artificial skins based on small dataset machine learning, exhibiting a comprehensive efficiency at least four orders of magnitude higher than the conventional routine. The inverse routine can predict hundreds of solutions that overcome the intrinsic signal saturation problem for linear response in hours, and the solutions are valid to a variety of materials. Our results demonstrate that the inverse design allowed by small dataset is an efficient and powerful tool to target multifarious applications of artificial skins, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces

Changing pattern and driving factors of ecosystem service value of the lakes in Northern China since 1990

Lakes in Northern China are widely distributed with large surface areas, and play a crucial role in maintaining ecological security in the northern regions of China. In this study, based on the relationship between ecosystem services (ES) value and lake key indicators, including lake area, potential evapotranspiration, comprehensive trophic level index (TLI), precipitation, and lake volume, the lake ecoservice production functions (LEPFs) were constructed to evaluate lake ecosystem service value (LESV) in Northern China. Subsequently, the driving factors influencing LESV were identified at the lake-basin scale. The results showed that the total LESV in Northern China increased from 5,088.7 billion yuan in 1990 to 5,112.9 billion yuan in 2020, by increasing 0.47%. The total LESV of Xinjiang (XJ) and Tibetan Plateau (TP) lake regions showed an increasing trend, with rates of 5.39% and 2.32%, respectively. However, those of Inner Mongolia Plateau (IMP), Northeast Plain and Mountains (NPM), and Eastern Plain (EP) lake regions showed a decrease, with rates of 19.83%, 6.29%, and 1.72%, respectively. The changing rate in LESV varied significantly among different lake regions. Approximately 30% and 40% of the lakes in XJ and TP lake regions had a growth rate exceeding 0.3 billion yuan, while 86% and 14% of lakes in NPM and IMP lake regions experienced a decline exceeding 0.3 billion yuan, respectively. 40% of the lakes in EP lake region had a growth rate of less than 0.05 billion yuan, and 60% of the lakes had a decline rate of less than 0.05 billion yuan. The average temperature, precipitation, impervious area, and water area within the lake-basins had a significant impact on LESV. Among them, the effect of climate change on LESV was higher than that of the anthropogenic factors. These findings can provide helpful references for the assessing methods of the LESV at a large regional scale and developing lake conservation policies

Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

Elevation resolution is an important indicator in tomographic SAR imaging as it represents the ability to discriminate closed targets in elevation. In general, the elevation resolution is proportional to the length of the elevation aperture. However, as the elevation aperture increases, the geometric consistency of the image will undesirably deteriorate and hence fails the image coregistration approach required by the traditional super-resolution tomographic imaging. In this paper, a new super-resolution tomographic imaging method is proposed to overcome the inconsistency problem caused by the large elevation aperture. The core strategy is to get rid of two-dimensional image coregistration by applying a three-dimensional (3D) back projection like imaging manner: the 3D space is firstly divided into a 3D imaging grid, each of which is individually imaged via compressive sensing for super-resolution. The effectiveness of the proposed approach is evaluated by both computer simulations and real P-band UAV SAR data.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.Microwave Sensing, Signals & System