Lithium batteries are one class of key components in new‐energy vehicles, and surface defects are easily generated during production, causing serious threats to safety. Most deep learning methods of surface defect detection heavily rely on lots of high‐quality labeled samples. Unfortunately, it is very difficult and expensive to prepare defect datasets of lithium batteries in practice. To deal with this issue, this paper presents cross‐object transfer learning (COTL)–based few‐shot surface defect detection of lithium batteries by resort to massive defect samples of other objects. The COTL model is composed of image preprocessing, feature extraction, feature fusion, and contrastive learning‐based defect detection modules. The ResNeXt‐101 network is used as the backbone to enhance feature extraction capability. The path aggregation feature pyramid network (PAFPN) is used to realize multiscale feature fusion. The contrastive learning branch is added to improve the discrimination ability among different categories of region proposals under few defect samples and increase the generalization ability. Then, experiments are done to testify the proposed method, where base‐class defect dataset from other objects and new‐class defect dataset from soft‐pack lithium batteries are adopted for training and testing. Furthermore, model comparison and ablation studies are performed. The results show that the recall rate, the AP50, the mAP, and the F1 values of the COTL model are much better than those of other existing models when only using few defect samples. In particular, when there are only 30 new‐class defect samples, the above four metrics of the COTL model are already larger than 0.90. The results testify that the proposed COTL model provides a more effective solution for few‐shot surface defect detection of lithium batteries.

In this study, we proposed a network structure based on the shifted attention mechanism, namely U-Shiftformer, to overcome the limitation of existing convolution neural networks (CNNs) in brain tumor segmentation that lacks multimodal information interaction. The U-Shiftformer takes the U-shape encoder-decoder structure as the backbone and embeds the proposed Shiftformer module to exchange the information between modalities in the downsampling process. The Shiftformer module contains one standard attention, and three proposed shifted attention modules, in which the shifted attention module considers the information exchange by constructing the relationship between adjacent modalities. In the experiments, we compare the proposed U-Shiftformer with SOTA networks in the dice, precision, and Hausdorff metrics. Its average accuracies of these metrics surpass all the comparison networks and achieve 0.8424, 0.8675, 0.9244, and 1.2961 in dice, precision, sensitivity, and Hausdorff metrics, respectively.

This paper presents a boost converter for thermo-electric energy harvesting with photovoltaic (PV)-assisted startup. The converter employs a new two-phase startup architecture and the PV cell is used in the first phase to provide an initial high voltage for startup. This high voltage drives the boost converter to charge a startup capacitor, which powers the main control block to continue self-startup in phase 2. The proposed system is designed and simulated in a $0.18\mu{\mathrm{m}}$ BCD process. The simulations show successful cold-start from 10 mV thermoelectric voltage. In addition, maximum power point tracking and zero current switching techniques are adopted in the system to achieve 91% peak efficiency. The proposed system can finish the cold-start within 250 ms.

Very-large-scale network-on-chip (VLS-NoC) has become a promising fabric for supercomputers, but this fabric may encounter the many-fault problem. This article proposes a deterministic routing algorithm to tolerate the effects of many faults in VLS-NoCs. This approach generates routing tables offline using a breadth-first traversal algorithm and stores a routing table locally in each switch for online packet transmission. The approach applies the Tarjan algorithm to degrade the faulty NoC and maximizes the number of available nodes in the reconfigured NoC. In 2D NoCs, the approach updates routing tables of some nodes using the deprecated channel/node rules and avoids deadlocks in the NoC. In 3D NoCs, the approach uses a forbidden-turn selection algorithm and detour rules to prevent faceted rings and ensures the NoC is deadlock-free. Experimental results demonstrate that the proposed approach provides fault-free communications of 2D and 3D NoCs after injecting 40 faulty links. Meanwhile, it maximizes the number of available nodes in the reconfigured NoC. The approach also outperforms existing algorithms in terms of average latency, throughput, and energy consumption.

Wafer-level NoC has emerged as a promising fabric to further improve supercomputer performance, but this new fabric may suffer from the many-fault problem. This paper presents a deterministic-path routing algorithm for tolerating many faults on wafer-level NoCs. The proposed algorithm generates routing tables using a breadth-first traversal strategy, and stores one routing table in each NoC switch. The switch will then transmit packages according to its routing table online. We use the Tarjan algorithm to dynamically reconfigure the routes to avoid the faulty nodes and develop the deprecated link/node rules to ensure deadlock-free communication of the NoCs. Experimental results demonstrate that the proposed algorithm does not only tolerate the effects of many faults, but also maximizes the available nodes in the reconfigured NoC. The performance of the proposed algorithm in terms of average latency, throughput, and energy consumption is also better than those of the existing solutions.