Animals living in the extremely cold environment, such as polar bears, have shown amazing capability to keep warm, benefiting from their hollow hairs. Mimicking such a strategy in synthetic fibers ...would stimulate smart textiles for efficient personal thermal management, which plays an important role in preventing heat loss and improving efficiency in house warming energy consumption. Here, a “freeze‐spinning” technique is used to realize continuous and large‐scale fabrication of fibers with aligned porous structure, mimicking polar bear hairs, which is difficult to achieve by other methods. A textile woven with such biomimetic fibers shows an excellent thermal insulation property as well as good breathability and wearability. In addition to passively insulating heat loss, the textile can also function as a wearable heater, when doped with electroheating materials such as carbon nanotubes, to induce fast thermal response and uniform electroheating while maintaining its soft and porous nature for comfortable wearing.
A textile with excellent thermal insulation capability, mimicking the porous structure of polar bear hair, is fabricated by a “freeze‐spinning” technique to continuously spin silk fibroin solution into aligned porous fibers. Doped with electroheating materials, this type of textile is beneficial for personal thermal management, thermal stealth in military applications, and wearable electronics.
The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in serious cases, and these faults can ...affect the cooling effect of the air conditioner and even affect the safety of using the air conditioner in serious cases. Therefore, it is necessary to study the fault diagnosis system of the refrigeration unit. The traditional fault diagnosis system for refrigeration units, which generally uses wireless sensor networks to transmit data, is a challenge because of its slow diagnosis speed and the low accuracy rate of fault diagnosis for refrigeration units. This paper uses a probabilistic neural network (PNN) to debug a blockage fault diagnosis algorithm system for simulated fault training. The system successfully achieves four levels of pre-warning for blockage faults: “uncertainty, micro-blockage, blockage, and severe blockage”, with the fastest pre-warning time of “uncertainty, micro-blockage” being less than 2 seconds. The experimental results of this paper show that the PNN system can take timely measures against blockage faults, and the fault diagnosis accuracy of the model reaches 97.97%, which is 13.34% higher than the traditional fault identification algorithm. The PNN model can pinpoint the blockage faults of the refrigeration unit, which enables the working state of the refrigeration unit to be continuously optimized, and the air conditioner to maintain energy-efficient operation. The fault simulation of the air conditioning refrigeration unit in automatic control mode verifies the blockage fault diagnosis’s accuracy and the algorithm’s feasibility, providing reference and learning for the development of fault diagnosis algorithms.
This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) ...Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.
An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the ...different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.
•A novel battery pack SOH definition is proposed.•A PSO-GA estimator is applied in parameters identification.•The accuracy and robustness of the method is verified by different profiles.•The influential battery pack SOH factors are performed.
Liquid absorption and recycling play a crucial role in many industrial and environmental applications, such as oil spill cleanup and recovery, hemostasis, astronauts' urine recycling, and so on. ...Although many liquid absorbing materials have been developed, it still remains a grand challenge to achieve both fast absorption and efficient recycling in a cost‐effective and energy‐saving manner, especially for viscous liquids such as crude oil. A smart polyurethane‐based porous sponge with aligned channel structure is prepared by directional freezing. Compared to common sponges with random porous structure, the as‐prepared smart sponge has larger liquid absorption speed due to its lower tortuosity and stronger capillary (“tortuosity effect”). More importantly, the absorbed liquid can be remotely squeezed out due to a thermally responsive shape memory effect when the sponge is heated up. Such smart sponges with well‐defined porous structure and thermal responsive self‐squeezing capability have great potential in efficient liquid absorption and recycling.
A smart porous sponge with aligned channel structure is fabricated by directional freezing. Compared to sponges with random porous structure, the as‐prepared sponge has larger liquid absorption speed due to its lower tortuosity. The absorbed liquid can be remotely squeezed out due to thermally responsive shape memory effect. Such sponges have great potential in efficient liquid absorption and recycling.
Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare ...poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID 33, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released.
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•Build a method for joint estimation of both state-of-charge and state-of-energy.•Data of an IFP1865140-type battery have been analyzed comprehensively in order to better understand ...the cell character.•The particle filter is used for simultaneous SOC and SOE estimation to improve the accuracy.•Dynamic temperature experiments are performed to verify the robustness of the new method.
The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.
•The equivalent circuit model is estimated for battery states estimation.•Battery peak current is analyzed by multi-constrained conditions.•A novel multi-time-scale observer is used to estimate SOE ...and SOP concurrently.•The accuracy of the proposed method is verified under different conditions.
The battery state of energy and state of power are two important parameters in battery usage. The state of energy represents the residual energy storage in battery and the state of power represents the ability of battery discharge/charge. To estimate the two states with high accuracy, the characteristics of battery maximum available capacity and open-circuit voltage are analyzed under different working temperatures. Meanwhile, the equivalent circuit model of the battery is employed to embody the battery dynamic performance. To improve the accuracy of the battery states estimation, the multi-time-scale filter is applied in battery model parameters identification and battery states prediction. Besides, the state of power is analyzed by multi-constrained conditions to ensure battery work with safety. The proposed approach is verified by experiments operated on lithium-ion battery under new European driving cycle profiles and dynamic test profiles. The experimental results indicate the proposed method can estimate the battery states with high accuracy for actual application. In addition, the factors affecting the change of battery states are analyzed.
Abstract
One long-lasting puzzle in amorphous solids is shear localization, where local plastic deformation involves cooperative particle rearrangements in small regions of a few inter-particle ...distances, self-organizing into shear bands and eventually leading to the material failure. Understanding the connection between the structure and dynamics of amorphous solids is essential in physics, material sciences, geotechnical and civil engineering, and geophysics. Here we show a deep connection between shear localization and the intrinsic structures of internal stresses in an isotropically jammed granular material subject to shear. Specifically, we find strong (anti)correlations between the micro shear bands and two polarized stress fields along two directions of maximal shear. By exploring the tensorial characteristics and the rotational symmetry of force network, we reveal that such profound connection is a result of symmetry breaking by shear. Finally, we provide the solid experimental evidence of long-range correlated inherent shear stress in an isotropically jammed granular system.
Recently, understanding of the extracellular matrix (ECM) has expanded rapidly due to the accessibility of cellular and molecular techniques and the growing potential and value for hydrogels in ...tissue engineering. The fabrication of hydrogel-based cellular scaffolds for the generation of bioengineered tissues has been based on knowledge of the composition and structure of ECM. Attempts at recreating ECM have used either naturally-derived ECM components or synthetic polymers with structural integrity derived from hydrogels. Due to their increasing use, their biocompatibility has been questioned since the use of these biomaterials needs to be effective and safe. It is not surprising then that the evaluation of biocompatibility of these types of biomaterials for regenerative and tissue engineering applications has been expanded from being primarily investigated in a laboratory setting to being applied in the multi-billion dollar medicinal industry. This review will aid in the improvement of design of non-invasive, smart hydrogels that can be utilized for tissue engineering and other biomedical applications. In this review, the biocompatibility of hydrogels and design criteria for fabricating effective scaffolds are examined. Examples of natural and synthetic hydrogels, their biocompatibility and use in tissue engineering are discussed. The merits and clinical complications of hydrogel scaffold use are also reviewed. The article concludes with a future outlook of the field of biocompatibility within the context of hydrogel-based scaffolds.
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