Collaborative robots (cobots) have been increasingly adopted in industries to facilitate human–robot collaboration. Despite this, it is challenging to program cobots for collaborative industrial ...tasks as the programming has two distinct elements that are difficult to implement: (1) an intuitive element to ensure that the operations of a cobot can be composed or altered dynamically by an operator, and (2) a human-aware element to support cobots in producing flexible and adaptive behaviours dependent on human partners. In this area, some research works have been carried out recently, but there is a lack of a systematic summary on the subject. In this paper, an overview of collaborative industrial scenarios and programming requirements for cobots to implement effective collaboration is given. Then, detailed reviews on cobot programming, which are categorised into communication, optimisation, and learning, are conducted. Additionally, a significant gap between cobot programming implemented in industry and in research is identified, and research that works towards bridging this gap is pinpointed. Finally, the future directions of cobots for industrial collaborative scenarios are outlined, including potential points of extension and improvement.
We use the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST) to determine the Hubble constant from optical and infrared observations of over 600 Cepheid variables in the host galaxies of ...eight recent Type Ia supernovae (SNe Ia), providing the calibration for a magnitude-redshift relation based on 253 SNe Ia. Increased precision over past measurements of the Hubble constant comes from five improvements: (1) more than doubling the number of infrared observations of Cepheids in the nearby SN hosts; (2) increasing the sample size of ideal SN Ia calibrators from six to eight; (3) increasing by 20% the number of Cepheids with infrared observations in the megamaser host NGC 4258; (4) reducing the difference in the mean metallicity of the Cepheid comparison samples between NGC 4258 and the SN hosts from Delta *Dlog O/H = 0.08 to 0.05; and (5) calibrating all optical Cepheid colors with a single camera, WFC3, to remove cross-instrument zero-point errors. The result is a reduction in the uncertainty in H 0 due to steps beyond the first rung of the distance ladder from 3.5% to 2.3%. The measurement of H 0 via the geometric distance to NGC 4258 is 74.8 ? 3.1 km s--1 Mpc--1, a 4.1% measurement including systematic uncertainties. Better precision independent of the distance to NGC 4258 comes from the use of two alternative Cepheid absolute calibrations: (1) 13 Milky Way Cepheids with trigonometric parallaxes measured with HST/fine guidance sensor and Hipparcos and (2) 92 Cepheids in the Large Magellanic Cloud for which multiple accurate and precise eclipsing binary distances are available, yielding 74.4 ? 2.5 km s--1 Mpc--1, a 3.4% uncertainty including systematics. Our best estimate uses all three calibrations but a larger uncertainty afforded from any two: H 0 = 73.8 ? 2.4 km s--1 Mpc--1 including systematic errors, corresponding to a 3.3% uncertainty. The improved measurement of H 0, when combined with the Wilkinson Microwave Anisotropy Probe (WMAP) 7 year data, results in a tighter constraint on the equation-of-state parameter of dark energy of w = --1.08 ? 0.10. It also rules out the best-fitting gigaparsec-scale void models, posited as an alternative to dark energy. The combined H 0 + WMAP results yield N eff = 4.2 ? 0.7 for the number of relativistic particle species in the early universe, a low-significance excess for the value expected from the three known neutrino flavors.
We analyse the observed fractions of core-collapse supernova (SN) types from the Lick Observatory Supernova Search (LOSS), and we discuss the corresponding implications for massive star evolution. ...For a standard initial mass function, observed fractions of SN types cannot be reconciled with the expectations of single-star evolution. The mass range of Wolf-Rayet (WR) stars that shed their hydrogen envelopes via their own mass-loss accounts for less than half of the observed fraction of Type Ibc supernovae (SNe Ibc). The true progenitors of SNe Ibc must extend to a much lower range of initial masses than classical WR stars, and we argue that most SN Ibc and SN IIb progenitors must arise from binary Roche lobe overflow. In this scenario, SNe Ic would still trace higher initial mass and metallicity, because line-driven winds in the WR stage remove the helium layer and propel the transition from SN Ib to Ic. Less massive progenitors of SNe Ib and IIb may not be classical WR stars; they may be underluminous with weak winds, possibly hidden by overluminous mass-gainer companions that could appear as Be supergiants or related objects having aspherical circumstellar material. The remaining SN types (II-P, II-L and IIn) need to be redistributed across the full range of initial masses, so that even some very massive single stars retain H envelopes until explosion. We consider the possibility of direct collapse to black holes without visible SNe, but find this hypothesis difficult to accommodate in most scenarios. Major areas of remaining uncertainty are (1) the detailed influence of binary separation, rotation and metallicity; (2) mass differences in progenitors of SNe IIn compared to SNe II-L and II-P; and (3) the fraction of SNe Ic arising from single stars with the help of eruptive mass-loss, how this depends on metallicity and how it relates to diversity within the SN Ic subclass. Continued studies of progenitor stars and their environments in nearby galaxies, accounting for SN types, may eventually test these ideas.
Fatigue resistance is a crucial requirement to the novel high-entropy alloys when they come to engineering applications, as many metal structures used in practice are failed by cyclic loading. Here, ...a thorough analysis of the information on the low-cycle fatigue, high-cycle fatigue, crack-growth rates, and fatigue mechanisms in the high-entropy alloy literature unveils a guideline through which the discovery and design of fatigue-resistant high-entropy alloys can be facilitated. Overall, multi-phase alloys, particularly the metastable ones, are favorable to fatigue resistance over single-phase alloys. Suggestions are proposed in the end to accelerate the discovery and design of candidates for fatigue-resistant applications.
A challenging but pressing task to design and synthesize novel, efficient, and robust pH‐universal hydrogen evolution reaction (HER) electrocatalysts for scalable and sustainable hydrogen production ...through electrochemical water splitting. Herein, we report a facile method to prepare an efficient and robust Ru‐M (M=Ni, Mn, Cu) bimetal nanoparticle and carbon quantum dot hybrid (RuM/CQDs) for pH‐universal HER. The RuNi/CQDs catalysts exhibit outstanding HER performance at all pH levels. The unexpected low overpotentials of 13, 58, and 18 mV shown by RuNi/CQDs allow a current density of 10 mA cm−2 in 1 m KOH, 0.5 m H2SO4, and 1 m PBS, respectively, for Ru loading at 5.93 μgRu cm−2. This performance is among the best catalytic activities reported for any platinum‐free electrocatalyst. Theoretical studies reveal that Ni doping results in a moderate weakening of the hydrogen bonding energy of nearby surface Ru atoms, which plays a critical role in improving the HER activity.
How low can Ru go: A scalable and general synthetic method for the preparation of transition‐metal‐doped RuM/carbon quantum dots (CQDs; M=Ni, Mn, Cu) has been developed through metal‐mediated CQD condensation and carbonization. The low‐ruthenium‐content RuM/CQD catalysts exhibit outstanding activity and stability in catalyzing hydrogen evolution at all pH values.
To enhance production quality, productivity and energy consumption, it is paramount to predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep learning ...algorithm-driven approaches have been actively explored in the research field though there are still potential areas to further enhance the performance of the approaches. In this research, to improve accuracy and expedite computational efficiency for predicting the RUL of cutting tools, a novel systematic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analysing multi-sourced sensor signals collected throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two sub-systems: (i) a Hurst exponent–based method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution, and (ii) a hybrid CNN-LSTM algorithm is designed to combine feature extraction, fusion and regression in a systematic means to facilitate the prediction based on segmented signals. The system was validated using a case study with a large set of databases with multiple cutting tools and multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other mainstream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and unpartitioned signals. Benchmarks showed that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which is significantly better than those of the comparative algorithms.
Designing bifunctional catalysts capable of driving the electrochemical hydrogen evolution reaction (HER) and also H2 evolution via the hydrolysis of hydrogen storage materials such as ammonia borane ...(AB) is of considerable practical importance for future hydrogen economies. Herein, we systematically examined the effect of tensile lattice strain in CoRu nanoalloys supported on carbon quantum dots (CoRu/CQDs) on hydrogen generation by HER and AB hydrolysis. By varying the Ru content, the lattice parameters and Ru‐induced lattice strain in the CoRu nanoalloys could be tuned. The CoRu0.5/CQDs catalyst with an ultra‐low Ru content (1.33 wt.%) exhibited excellent catalytic activity for HER (η=18 mV at 10 mA cm−2 in 1 M KOH) and extraordinary activity for the hydrolysis of AB with a turnover frequency of 3255.4 mol(H2)
mol−1(Ru) min−1 or 814.7 mol(H2)
mol−1(cat) min−1 at 298 K, respectively, representing one of the best activities yet reported for AB hydrolysis over a ruthenium alloy catalyst. Moreover, the CoRu0.5/CQDs catalyst displayed excellent stability during each reaction, including seven alternating cycles of HER and AB hydrolysis. Theoretical calculations revealed that the remarkable catalytic performance of CoRu0.5/CQDs resulted from the optimal alloy electronic structure realized by incorporating small amounts of Ru, which enabled fast interfacial electron transfer to intermediates, thus benefitting H2 evolution kinetics. Results support the development of new and improved catalysts HER and AB hydrolysis.
Ultra‐low Ru induced CoRu nanoalloy lattice strain for robust hydrogen evolution reaction (HER) and hydrolysis of ammonia borane (AB) bifunctional hydrogen production is introduced. The CoRu0.5/CQDs displayed excellent stability during each reaction, including seven alternating cycles of HER and AB hydrolysis.
Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make ...accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.
Highly active, stable, and cheap Pt‐free catalysts for the hydrogen evolution reaction (HER) are facing increasing demand as a result of their potential use in future energy‐conversion systems. ...However, the development of HER electrocatalysts with Pt‐like or even superior activity, in particular ones that can function under alkaline conditions, remains a significant challenge. Here, the synthesis of a novel carbon‐loaded ruthenium nanoparticle electrocatalyst (Ru@CQDs) for the HER, using carbon quantum dots (CQDs), is reported. Electrochemical tests reveal that, even under extremely alkaline conditions (1 m KOH), the as‐formed Ru@CQDs exhibits excellent catalytic behavior with an onset overpotential of 0 mV, a Tafel slope of 47 mV decade−1, and good durability. Most importantly, it only requires an overpotential of 10 mV to achieve the current density of 10 mA cm−2. Such catalytic characteristics are superior to the current commercial Pt/C and most noble metals, non‐noble metals, and nonmetallic catalysts under basic conditions. These findings open a new field for the application of CQDs and add to the growing family of metal@CQDs with high HER performance.
The ruthenium@carbon quantum dots (Ru@CQDs) electrocatalyst is very robust for the hydrogen evolution reaction in alkaline media, with an onset overpotential of 0 mV and low overpotentials at 10 mA cm−2 (10 mV). More importantly, after 10 000 cycles, the current density at 10 mA cm−2 of the Ru@CQDs catalyst increases merely 4 mV at 10 mA cm−2.
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