Automatic inspection of analog and digital meters in a robot vision system

A critical limitation of most of the robots utilized in industrial environments arises due to their inability to utilize sensory feedback. This forces robot operation into totally preprogrammed or teleoperation modes. In order to endow the new generation of robots with higher levels of autonomy techniques for sensing of their work environments and for accurate and efficient analysis of the sensory data must be developed. In this paper detailed development of vision system modules for inspecting various types of meters, both analog and digital, encountered in a robotic inspection and manipulation tasks are described. These modules are tested using industrial robots having multisensory input capability

A general strategy for synthesis of metal oxide nanoparticles attached on carbon nanomaterials

We report a general strategy for synthesis of a large variety of metal oxide nanoparticles on different carbon nanomaterials (CNMs), including single-walled carbon nanotubes, multi-walled carbon nanotubes, and a few-layer graphene. The approach was based on the π-π interaction between CNMs and modified aromatic organic ligands, which acted as bridges connecting metal ions and CNMs. Our methods can be applicable for a large variety of metal ions, thus offering a great potential application

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Estimates of Europa’s ice shell thickness and strain rate from flanking cracks and bulge along Ridge R

Europa, the second Galilean satellite outward from Jupiter, has an outer layer of water of about 100 km thick and an outmost ice shell. The thickness of the ice shell is very important in understanding Europa’s habitability and thermal history, but estimates from different studies are very inconsistent, ranging from 0.2 to 30 km. Here we obtain an estimate of the ice shell thickness from locations of flanking crack and forebulge along Ridge R. Considering the water’s heating process to nearby ice shell in the crack, a flexure model is applied and it suggests the thickness of an ice shell to be 500–1500 m without a convective layer. Compared with previous studies using the same method but ignoring the water’s heating process, the rationality and accuracy have been improved dramatically in our results. We also get some constraints on the strain rate ε and the characteristic temperature T_c, which defines the base of the elastic layer

Learning Representations for Incomplete Time Series Clustering

Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missing values. Traditional strategy (imputing first and then clustering) does not optimize the imputation and clustering process as a whole, which not only makes per- formance dependent on the combination of imputation and clustering methods but also fails to achieve satisfactory re- sults. How to best improve the clustering performance on incomplete time series remains a challenge. This paper pro- poses a novel unsupervised temporal representation learning model, named Clustering Representation Learning on Incom- plete time-series data (CRLI). CRLI jointly optimizes the im- putation and clustering process to impute more discrimina- tive values for clustering and make the learned representa- tions possessed good clustering property. Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner. An experiment conducted on eight real-world in- complete time-series datasets shows that CRLI outperforms existing methods. We demonstrates the effectiveness of the learned representations and the convergence of the model through visualization analysis. Moreover, we reveal that the joint training strategy can impute values close to the true ones in those important sub-sequences, and impute more discrim- inative values in those less important sub-sequences at the same time, making the imputed sequence cluster-friendly

The Influence of Negative Workplace Gossip on Knowledge Sharing: Insight from the Cognitive Dissonance Perspective

Increasing attention is drawn to the effect of workplace gossip on the organization. Negative workplace gossip is a negative evaluation of others behind their back in the workplace. Based on the cognitive dissonance theory, the study explored the relationship between negative workplace gossip and knowledge sharing, through the mediation of organizational trust and the moderation of self-efficacy. The regression results of a two-stage questionnaire survey on 173 Chinese employees suggested that negative workplace gossip negatively influenced employees’ knowledge sharing through organizational trust. Additionally, findings also showed that self-efficacy moderated the mediation of organizational trust in the relationship between negative workplace gossip and knowledge sharing. This research provided a new theoretical perspective on the impact of workplace gossip, which has management implications for informal communication and team-building

A systematic COSMO-RS study on mutual solubility of ionic liquids and C6-hydrocarbons

When considering the usage of ionic liquids (ILs) for reactions and separations involving non-polar or weak-polar hydrocarbons, the knowledge of the mutual solubility behaviors of ILs and hydrocarbons is of the utmost importance. In this work, taking four typical C6-hydrocarbons namely benzene, cyclohexene, cyclohexane, and hexane as representatives, the mutual solubility of ILs and non-polar or weak-polar hydrocarbons are systematically studied based on the COSMO-RS model. The reliability of COSMO-RS for these systems is first evaluated by comparing experimental and predicted hydrocarbon-in-IL activity coefficient at infinite dilution and binary/ternary liquid-liquid equilibria of related systems. Then, the mutual solubility of the four hydrocarbons and 13,650 ILs (composed by 210 cations and 65 anions) are predicted. The effect of different IL structural characteristics including alkyl chain length, cation family/symmetry/functional group, and anion on the IL-hydrocarbon mutual solubility behaviors are further analyzed by the analyses of interaction energy and screen charge distribution. The mutual solubility databases and the structural effects identified thereon could provide useful guidance for IL selection in related applications

State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning

Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries based on ultrasonic guided wave (UGW) scanning is carried out. The laser Doppler vibrometer (LDV) and the X-Y stage are used to obtain the surface scanning UGW signal and the line scanning UGW signal of lithium-ion batteries under different states of charge and different aging degrees. The propagation law of UGWs in the battery is analyzed by surface scanning signals, then the energy spectrum of the signals is calculated, showing that the aging of the battery attenuates the transmission energy of UGWs. The “point” parameters are extracted from the scanning point signals. On this basis, the “line” parameters composed of line scanning multi-point signals are extracted. By analyzing the changing law of parameters during the charge–discharge process of batteries, several characteristic parameters that can be used to characterize the battery state of charge and state of health are obtained. The method has good consistency in the state characterization of the three batteries and provides a new approach for non-destructive testing and evaluation of battery states