A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

Structure and photoluminescence properties of red-emitting apatite-type phosphor NaY9(SiO4)6O2:Sm3+ with excellent quantum efficiency and thermal stability for solid-state lighting.

A novel red-emitting phosphor NaY9(SiO4)6O2:Sm3+ (NYS:Sm3+) was synthesized and the X-ray diffraction and high-resolution TEM testified that the NYS compound belongs to the apatite structure which crystallized in a hexagonal unit cell with space group P63/m. The novel phosphor boasts of such three advantageous properties as perfect compatible match with the commercial UV chips, 73.2% quantum efficiency and 90.9% thermal stability at 150 °C. Details are as follows. NYS:Sm3+ phosphor showed obvious absorption in the UV regions centered at 407 nm, which can be perfectly compatible with the commercial UV chips. The property investigations showed that NYS:Sm3+ phosphor emitted reddish emission with CIE coordination of (0.563, 0.417). The optimum quenching concentration of Sm3+ in NYS phosphor was about 10%mol, and the corresponding concentration quenching mechanism was verified to be the electric dipole-dipole interaction. Upon excitation at 407 nm, the composition-optimized NYS:0.10Sm3+ exhibited a high quantum efficiency of 73.2%, and its luminescence intensity at 150 °C decreased simply to 90.9% of the initial value at room temperature. All of the results indicated that NYS:Sm3+ is a promising candidate as a reddish-emitting UV convertible phosphor for application in white light emitting diodes (w-LEDs)

MorphStream: Scalable Processing of Transactions over Streams on Multicores

Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream applications handling shared mutable states. Yet, the full potential of these systems, specifically in exploiting parallelism and implementing dynamic scheduling strategies, is largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables dynamic scheduling and parallel processing in TSPEs. Our experiment showcased MorphStream outperforms current TSPEs across various scenarios and offers support for windowed state transactions and non-deterministic state access, demonstrating its potential for broad applicability

CGraph : a correlations-aware approach for efficient concurrent iterative graph processing

With the fast growing of iterative graph analysis applications, the graph processing platform has to efficiently handle massive Concurrent iterative Graph Processing (CGP) jobs. Although it has been extensively studied to optimize the execution of a single job, existing solutions face high ratio of data access cost to computation for the CGP jobs due to significant cache interference and memory wall, which incurs low throughput. In this work, we observed that there are strong spatial and temporal correlations among the data accesses issued by different CGP jobs because these concurrently running jobs usually need to repeatedly traverse the shared graph structure for the iterative processing of each vertex. Based on this observation, this paper proposes a correlations-aware execution model, together with a core-subgraph based scheduling algorithm, to enable these CGP jobs to efficiently share the graph structure data in cache/memory and their accesses by fully exploiting such correlations. It is able to achieve the efficient execution of the CGP jobs by effectively reducing their average ratio of data access cost to computation and therefore delivers a much higher throughput. In order to demonstrate the efficiency of the proposed approaches, a system called CGraph is developed and extensive experiments have been conducted. The experimental results show that CGraph improves the throughput of the CGP jobs by up to 2.31 times in comparison with the existing solutions

RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u27 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u27 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u27 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139× and 84.7× speedups for DGNN inference, with average 2242× and 234.2× energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1×, 11.7×, 10.4×, and 7.9× speedup and 14.8×, 12.9×, 11.5×, and 8.9× energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively

Genome-resolved metagenomics provides insights into the microbial-mediated sulfur and nitrogen cycling in temperate seagrass meadows

The presence of seagrasses facilitates numerous microbial-mediated biogeochemical cycles, with sulfur- and nitrogen-cycling microorganisms playing crucial roles as regulators. Despite efforts to comprehend the diversity of microbes in seagrass ecosystems, the metabolic functions of these benthic microorganisms in seagrass sediments remain largely unknown. Using metagenomics, we provide insights into the sulfur- and nitrogen-cycling pathways and key metabolic capacities of microorganisms in both Z. japonica-colonized and unvegetated sediments over a seasonal period. Taxonomic analysis of N and S cycling genes revealed that δ- and γ- proteobacteria dominated the benthic sulfate-reducing bacteria, while α- and γ-proteobacteria played a significant role in the sulfur-oxidation processes. The proteobacterial lineages were also major contributors to the benthic nitrogen cycling. However, at a finer taxonomic resolution, microbial participants in different processes were observed to be highly diverse and mainly driven by environmental factors such as temperature and salinity. The gene pools of sulfur and nitrogen cycles in the seagrass sediments were dominated by genes involved in sulfide oxidation (fccA) and hydroxylamine oxidation (hao), respectively. Seagrass colonization elevated the relative abundance of genes responsible for sulfite production (phsC), hydroxylamine oxidation (hao), and nitrogen fixation (nifK), but suppressed sulfur oxidation (soxXYZ) and denitrification (nosZ and nirS). The prevalence of proteobacterial lineages functioned with versatile capabilities in both sulfur and nitrogen cycles in seagrass ecosystems, highlighting tight couplings between these processes, which was further supported by the recovery of 83 metagenome-assembled genomes (MAGs). These findings broaden our understanding of the biogeochemical processes that are mediated by microorganisms in seagrass ecosystems

Saturable Absorption and Modulation Characteristics of Laser with Graphene Oxide Spin Coated on ITO Substrate

The graphene oxide (GO) thin film has been obtained by mixture of GO spin coated on substrate of indium tin oxide (ITO). The experiment has shown that continuous-wave laser is modulated when the graphene oxide saturable absorber (GO-SA) is employed in the 1064 nm laser cavity. The shortest pulse width is 108 ns at the pump power of 5.04 W. Other output laser characteristics, such as the threshold pump power, the repetition rate, and the peak power, have also been measured. The results have demonstrated that graphene oxide is an available saturable absorber for 1064 nm passive Q-switching laser