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Abstract We report observations of direct evidence of energetic protons being accelerated above ∼400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA’s Parker Solar Probe (PSP) at a distance of ∼16.25 solar radii (R_s) from the Sun. Inside the exhaust, both the reconnection-generated plasma jet and the accelerated protons up to ∼400 keV propagated toward the Sun, unambiguously establishing their origin from HCS reconnection sites located antisunward of PSP. Within the core of the exhaust, PSP detected stably trapped energetic protons up to ∼400 keV, which is ≈1000 times greater than the available magnetic energy per particle. The differential energy spectrum of the accelerated protons behaved as a pure power law with spectral index of ∼−5. Supporting simulations using thekglobalmodel suggest that the trapping and acceleration of protons up to ∼400 keV in the reconnection exhaust are likely facilitated by merging magnetic islands with a guide field between ∼0.2 and 0.3 of the reconnecting magnetic field, consistent with the observations. These new results, enabled by PSP’s proximity to the Sun, demonstrate that magnetic reconnection in the HCS is a significant new source of energetic particles in the near-Sun solar wind. Our findings of in situ particle acceleration via magnetic reconnection at the HCS provide valuable insights into this fundamental process, which frequently converts the large magnetic field energy density in the near-Sun plasma environment and may be responsible for heating the Sun’s atmosphere, accelerating the solar wind, and energizing charged particles to extremely high energies in solar flares. 

Abstract We report observations of multiple subscale reconnecting current sheets embedded inside a large-scale heliospheric current sheet (HCS) reconnection exhaust. The discovery was made possible by the unusual skimming trajectory of Parker Solar Probe through a sunward-directed HCS exhaust, sampling structures convecting with the exhaust outflows for more than 3 hr during Encounter 14, at a radial distance of ∼17 solar radii. A large number of subscale current sheets (SCSs) were detected inside the HCS exhaust. Remarkably, five SCSs showed direct evidence for reconnection, displaying near-Alfvénic outflow jets and bifurcated current sheets. The reconnecting SCSs all had small magnetic shears (27°–81°), i.e., strong guide fields. The thickness of the subscale reconnecting current sheets ranged from ∼60 km to ∼5000 km (∼20–2000 ion inertial lengths). The SCS exhausts were directed predominantly in the normal or out-of-plane direction of the HCS, i.e., nearly orthogonal to the HCS exhaust direction. The presence of multiple low-magnetic-shear reconnecting current sheets inside a large-scale exhaust could be associated with coalescence of multiple large flux ropes inside the HCS exhaust. The orientation of some SCS exhausts was partly in the ecliptic plane of the HCS, which may indicate that the coalescence process is highly three-dimensional. Since the coalescence process is likely short-lived, the detection of five such events inside a single HCS crossing could imply the common occurrence of flux rope coalescence in large-scale HCS reconnection exhausts. 

This study aims at sensing and understanding the worker’s activity in a human-centered intelligent manufacturing system. We propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armband and a visual camera are applied to capture Inertial Measurement Unit (IMU) signals and videos, respectively. For the IMU signals, we design two novel feature transform mechanisms, in both frequency and spatial domains, to assemble the captured IMU signals as images, which allow using convolutional neural networks to learn the most discriminative features. Along with the above two modalities, we propose two other modalities for the video data, i.e., at the video frame and video clip levels. Each of the four modalities returns a probability distribution on activity prediction. Then, these probability distributions are fused to output the worker activity classification result. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab a tool/part, hammer a nail, use a power-screwdriver, rest arms, turn a screwdriver, and use a wrench. The developed multi-modal approach is evaluated on this dataset and achieves recognition accuracies as high as 97% and 100% in the leave-one-out and half-half experiments, respectively. 

The state-of-the-art of fully-supervised methods for temporal action localization from untrimmed videos has achieved impressive results. Yet, it remains unsatisfactory for the weakly-supervised temporal action localization, where only video-level action labels are given without the timestamp annotation on when the actions occur. The main reason comes from that, the weakly-supervised networks only focus on the highly discriminative frames, but there are some ambiguous frames in both background and action classes. The ambiguous frames in background class are very similar to the real actions, which may be treated as target actions and result in false positives. On the other hand, the ambiguous frames in action class which possibly contain action instances, are prone to be false negatives by the weakly-supervised networks and result in a coarse localization. To solve these problems, we introduce a novel weakly-supervised Action Completeness Modeling with Back- ground Aware Networks (ACM-BANets). Our Background Aware Network (BANet) contains a weight-sharing two-branch architecture, with an action guided Background aware Temporal Attention Module (B-TAM) and an asymmetrical training strategy, to suppress both highly discriminative and ambiguous background frames to remove the false positives. Our action completeness modeling contains multiple BANets, and the BANets are forced to discover different but complementary action instances to completely localize the action instances in both highly discriminative and ambiguous action frames. In the 𝑖-th iteration, the 𝑖-th BANet discovers the discriminative features, which are then erased from the feature map. The partially-erased feature map is fed into the (i+1)-th BANet of the next iteration to force this BANet to discover discriminative features different from the 𝑖-th BANet. Evaluated on two challenging untrimmed video datasets, THUMOS14 and ActivityNet1.3, our approach outperforms all the current weakly-supervised methods for temporal action localization. 

In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power to the data source, to achieve real-time recognition. The operator’s activity is captured using visual cameras. Instead of directly training a deep learning model from scratch, transfer learning is applied to transfer the learning abilities to our application. A worker assembly operation dataset is established, which at present contains 10 sequential operations in an assembly task of installing a desktop CNC machine. The developed model is evaluated on this dataset and achieves a recognition accuracy of 95% in the testing experiments. 

Training and on-site assistance is critical to help workers master required skills, improve worker productivity, and guarantee the product quality. Traditional training methods lack worker-centered considerations that are particularly in need when workers are facing ever changing demands. In this study, we propose a worker-centered training & assistant system for intelligent manufacturing, which is featured with self-awareness and active-guidance. Multi-modal sensing techniques are applied to perceive each individual worker and a deep learning approach is developed to understand the worker’s behavior and intention. Moreover, an object detection algorithm is implemented to identify the parts/tools the worker is interacting with. Then the worker’s current state is inferred and used for quantifying and assessing the worker performance, from which the worker’s potential guidance demands are analyzed. Furthermore, onsite guidance with multi-modal augmented reality is provided actively and continuously during the operational process. Two case studies are used to demonstrate the feasibility and great potential of our proposed approach and system for applying to the manufacturing industry for frontline workers.