Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning

Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly changing task demands, making it difficult to obtain sufficient and diverse defect data. This paper proposes a parallel solution that uses a human-machine knowledge hybrid augmentation method to help the model extract unknown important features. Specifically, by incorporating experts' knowledge of abnormality to create data with rich features, positions, sizes, and backgrounds, we can quickly accumulate an amount of data from scratch and provide it to the model as prior knowledge for few-data learning. The proposed method was evaluated on the magnetic tile dataset and achieved F1-scores of 60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images, respectively. Compared to the traditional augmentation method's F1-score of 64.59%, the proposed method achieved an 18.22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection.Comment: 24 pages, 15 figure

Biostratigraphy of a Paleocene–Eocene Foreland Basin boundary in southern Tibet

AbstractThis study of the Paleocene–Eocene boundary within a foreland basin of southern Tibet, which was dominated by a carbonate ramp depositional environment, documents more complex environmental conditions than can be derived from studies of the deep oceanic environment. Extinction rates for larger foraminiferal species in the Zongpu-1 Section apply to up to 46% of the larger foraminiferal taxa. The extinction rate in southern Tibet is similar to rates elsewhere in the world, but it shows that the Paleocene fauna disappeared stepwise through the Late Paleocene, with Eocene taxa appearing abruptly above the boundary. A foraminifera turnover was identified between Members 3 and 4 of the Zongpu Formation—from the Miscellanea–Daviesina assemblage to an Orbitolites–Alveolina assemblage. The Paleocene and Eocene boundary is between the SBZ 4 and SBZ 5, where it is marked by the extinction of Miscellanea miscella and the first appearance of Alveolina ellipsodalis and a large number of Orbitolites. Chemostratigraphically, the δ13C values from both the Zongpu-1 and Zongpu-2 Sections show three negative excursions in the transitional strata, one in Late Paleocene, one at the boundary, and one in the early Eocene. The second negative excursion of δ13C, which is located at the P–E boundary, coincides with larger foraminifera overturn. These faunal changes and the observed δ13C negative excursions provide new evidence on environmental changes across the Paleocene–Eocene boundary in Tibet

The response of sea ice and high-salinity shelf water in the Ross Ice Shelf Polynya to cyclonic atmosphere circulations

Coastal polynyas in the Ross Sea are important source regions of high-salinity shelf water (HSSW) - the precursor of Antarctic Bottom Water thatsupplies the lower limb of the thermohaline circulation. Here, the responseof sea ice production and HSSW formation to synoptic-scale and mesoscalecyclones was investigated for the Ross Ice Shelf Polynya (RISP) using acoupled ocean-sea ice-ice shelf model targeted on the Ross Sea. Whensynoptic-scale cyclones prevailed over RISP, sea ice production (SIP)increased rapidly by 20 %-30 % over the entire RISP. During the passage of mesoscale cyclones, SIP increased by about 2 times over the western RISP but decreased over the eastern RISP, resulting respectively from enhancement inthe offshore and onshore winds. HSSW formation mainly occurred in thewestern RISP and was enhanced responding to the SIP increase under bothtypes of cyclones. Promoted HSSW formation could persist for 12-60 h after the decay of the cyclones. The HSSW exports across the DrygalskiTrough and the Glomar Challenger Trough were positively correlated with themeridional wind. Such correlations are mainly controlled by variations ingeostrophic ocean currents that result from sea surface elevation change and density differences.Peer reviewe

Impacts of strong wind events on sea ice and water mass properties in Antarctic coastal polynyas

Strong offshore wind events (SOWEs) occur frequently near the Antarctic coast during austral winter. These wind events are typically associated with passage of synoptic- or meso-scale cyclones, which interact with the katabatic wind field and affect sea ice and oceanic processes in coastal polynyas. Based on numerical simulations from the coupled Finite Element Sea-ice Ocean Model (FESOM) driven by the CORE-II forcing, two coastal polynyas along the East Antarctica coast––the Prydz Bay Polynya and the Shackleton Polynya are selected to examine the response of sea ice and oceanic properties to SOWEs. In these polynyas, the southern or western flanks of cyclones play a crucial role in increasing the offshore winds depending on the local topography. Case studies for both polynyas show that during SOWEs, when the wind speed is 2–3 times higher than normal values, the offshore component of sea ice velocity can increase by 3–4 times. Sea ice concentration can decrease by 20–40%, and sea ice production can increase up to two to four folds. SOWEs increase surface salinity variability and mixed layer depth, and such effects may persist for 5–10 days. Formation of high salinity shelf water (HSSW) is detected in the coastal regions from surface to 800 m after 10–15 days of the SOWEs, while the HSSW features in deep layers exhibit weak response on the synoptic time scale. HSSW formation averaged over winter is notably greater in years with longer duration of SOWEs

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolution Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

An ordinary state-based peridynamic model for the fracture of zigzag graphene sheets

This study develops an ordinary state-based peridynamic coarse-graining (OSPD-CG) model for the investigation of fracture in single-layer graphene sheets (SLGS), in which the peridynamic (PD) parameters are derived through combining the PD model and molecular dynamics (MD) simulations from the fully atomistic system via energy conservation. The fracture failure of pre-cracked SLGS under uniaxial tension is studied using the proposed PD model. And the PD simulation results agree well with those from MD simulations, including the stress–strain relations, the crack propagation patterns and the average crack propagation velocities. The interaction effect between cracks located at the centre and the edge on the crack propagation of the pre-cracked SLGS is discussed in detail. This work shows that the proposed PD model is much more efficient than the MD simulations and, thus, indicates that the PD-based method is applicable to study larger nanoscale systems