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"Ang, Li"
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Anomaly detection of hyperspectral images based on improved isolation forest algorithm
Hyperspectral images (HIs) include roughly continuous spectral and spatial data pertaining to land cover, enhancing the ability to identify land cover. Therefore, HIs have significant application value in fields such as remote sensing and environmental monitoring. At present, HI anomaly detection has problems such as low computational efficiency, low detection accuracy and poor adaptability. To solve these problems, the research introduces the Godec method based on the Isolation Forest (IForest) algorithm and combines the methods of global and local evaluation for improvement, addressing the issues of local false detecations and false alarms in traditional algorithms. Meanwhile, based on multi-scale spatial constraints combined with Gabor filters and the Entropy Rate Superpixel (ERS) algorithm, spatial features are extracted from multiple scales, and the abnormal scores in the spatial domain and spectral domain are comprehensively calculated to achieve abnormal target detection. The research conducts experiments using two sets of standard hyperspectral datasets, namely San Diego and HYDICE. The results show that the area values under the curve of the research method on the training set and the test set reach 0.973 and 0.967 respectively, the F1 score is 0.937, and the false alarm suppression rate reaches 92.14%. The detection rate and missed detection rate of the research method in the monitoring of mining area collapse are 95.28% and 4.08% respectively. The detection rate and missed detection rate in military camouflage reconnaissance are 93.79% and 3.82%. The method proposed in the research provides a highly reliable and low-false alarm rate detection tool for fields such as mining area collapse monitoring and military camouflage reconnaissance.
Journal Article
On-chip spectrometers using stratified waveguide filters
We present an ultra-compact single-shot spectrometer on silicon platform for sparse spectrum reconstruction. It consists of 32 stratified waveguide filters (SWFs) with diverse transmission spectra for sampling the unknown spectrum of the input signal and a specially designed ultra-compact structure for splitting the incident signal into those 32 filters with low power imbalance. Each SWF has a footprint less than 1 µm × 30 µm, while the 1 × 32 splitter and 32 filters in total occupy an area of about 35 µm × 260 µm, which to the best of our knowledge, is the smallest footprint spectrometer realized on silicon photonic platform. Experimental characteristics of the fabricated spectrometer demonstrate a broad operating bandwidth of 180 nm centered at 1550 nm and narrowband peaks with 0.45 nm Full-Width-Half-Maximum (FWHM) can be clearly resolved. This concept can also be implemented using other material platforms for operation in optical spectral bands of interest for various applications.
Compact spectrometers that are simple and scalable in design can enable many applications. Here the authors demonstrate a silicon photonics based single-shot spectrometer that uses a group of waveguide frequency filters to construct the spectrum.
Journal Article
Research on the Impact of Scheduling Efficiency on Production Costs in Pharmaceutical Intelligent Manufacturing Workshops Based on Improved Particle Swarm Optimization Algorithm
The intelligent transformation of the pharmaceutical industry is of great significance for improving production efficiency and reducing costs, and workshop scheduling optimization is a key approach to achieving this goal. This study proposes a pharmaceutical intelligent manufacturing workshop scheduling method based on a hybrid particle swarm optimization algorithm. By integrating three well-established mechanisms including elite learning mechanism, dynamic inertia weight adjustment, and spiral contraction search strategy, the algorithm’s solution quality and convergence speed are improved. A multi-objective optimization model considering pharmaceutical-specific constraints such as batch tracing, cleaning validation, and quality inspection is constructed, with coordinated optimization aimed at minimizing completion time and production costs. Validation based on actual production data from a large pharmaceutical enterprise shows that the hybrid algorithm increases equipment utilization by 20.1%, shortens average flow time by 18.1%, achieves an on-time delivery rate of 91.5%, and reduces total production costs by 6.3%, with energy costs and inventory costs decreasing by 14.9% and 36.8% respectively. The research finds that the impact of scheduling efficiency on indirect costs is significantly greater than on direct costs, with a comprehensive indirect cost reduction rate of 42.3%. Statistical significance tests and ablation studies validate the algorithm’s effectiveness and the contribution of each component. Sensitivity analysis validates the algorithm’s robustness and parameter stability. The contributions of this study lie in three aspects: constructing an integrated scheduling model with pharmaceutical-specific constraints, developing an effective hybrid optimization approach, and establishing a systematic cost-efficiency analytical framework. This study provides theoretical support and practical guidance for pharmaceutical enterprises to implement intelligent manufacturing and holds important value for promoting digital transformation in the pharmaceutical industry.
Journal Article
4D Printing of Recyclable Lightweight Architectures Using High Recovery Stress Shape Memory Polymer
High-performance lightweight architectures, such as metallic microlattices with excellent mechanical properties have been 3D printed, but they do not possess shape memory effect (SME), limiting their usages for advanced engineering structures, such as serving as a core in multifunctional lightweight sandwich structures. 3D printable self-healing shape memory polymer (SMP) microlattices could be a solution. However, existing 3D printable thermoset SMPs are limited to either low strength, poor stress memory, or non-recyclability. To address this issue, a new thermoset polymer, integrated with high strength, high recovery stress, perfect shape recovery, good recyclability, and 3D printability using direct light printing, has been developed in this study. Lightweight microlattices with various unit cells and length scales were printed and tested. The results show that the cubic microlattice has mechanical strength comparable to or even greater than that of metallic microlattices, good SME, decent recovery stress, and recyclability, making it the first multifunctional lightweight architecture (MLA) for potential multifunctional lightweight load carrying structural applications.
Journal Article
Multiple testing with the structure-adaptive Benjamini–Hochberg algorithm
In multiple-testing problems, where a large number of hypotheses are tested simultaneously, false discovery rate (FDR) control can be achieved with the well-known Benjamini–Hochberg procedure, which adapts to the amount of signal in the data, under certain distributional assumptions. Many modifications of this procedure have been proposed to improve power in scenarios where the hypotheses are organized into groups or into a hierarchy, as well as other structured settings. Here we introduce the ‘structure-adaptive Benjamini–Hochberg algorithm’ (SABHA) as a generalization of these adaptive testing methods. The SABHA method incorporates prior information about any predetermined type of structure in the pattern of locations of the signals and nulls within the list of hypotheses, to reweight the p-values in a data-adaptive way. This raises the power by making more discoveries in regions where signals appear to be more common. Our main theoretical result proves that the SABHA method controls the FDR at a level that is at most slightly higher than the target FDR level, as long as the adaptive weights are constrained sufficiently so as not to overfit too much to the data—interestingly, the excess FDR can be related to the Rademacher complexity or Gaussian width of the class from which we choose our data-adaptive weights. We apply this general framework to various structured settings, including ordered, grouped and low total variation structures, and obtain the bounds on the FDR for each specific setting. We also examine the empirical performance of the SABHA method on functional magnetic resonance imaging activity data and on gene–drug response data, as well as on simulated data.
Journal Article
A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN’s monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.
Journal Article
Direct observation of noble metal nanoparticles transforming to thermally stable single atoms
Single noble metal atoms and ultrafine metal clusters catalysts tend to sinter into aggregated particles at elevated temperatures, driven by the decrease of metal surface free energy. Herein, we report an unexpected phenomenon that noble metal nanoparticles (Pd, Pt, Au-NPs) can be transformed to thermally stable single atoms (Pd, Pt, Au-SAs) above 900 °C in an inert atmosphere. The atomic dispersion of metal single atoms was confirmed by aberration-corrected scanning transmission electron microscopy and X-ray absorption fine structures. The dynamic process was recorded by in situ environmental transmission electron microscopy, which showed competing sintering and atomization processes during NP-to-SA conversion. Further, density functional theory calculations revealed that high-temperature NP-to-SA conversion was driven by the formation of the more thermodynamically stable Pd-N4 structure when mobile Pd atoms were captured on the defects of nitrogen-doped carbon. The thermally stable single atoms (Pd-SAs) exhibited even better activity and selectivity than nanoparticles (Pd-NPs) for semi-hydrogenation of acetylene.
Journal Article
Engineering unsymmetrically coordinated Cu-S1N3 single atom sites with enhanced oxygen reduction activity
Atomic interface regulation is thought to be an efficient method to adjust the performance of single atom catalysts. Herein, a practical strategy was reported to rationally design single copper atoms coordinated with both sulfur and nitrogen atoms in metal-organic framework derived hierarchically porous carbon (S-Cu-ISA/SNC). The atomic interface configuration of the copper site in S-Cu-ISA/SNC is detected to be an unsymmetrically arranged Cu-S
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moiety. The catalyst exhibits excellent oxygen reduction reaction activity with a half-wave potential of 0.918 V vs. RHE. Additionally, through in situ X-ray absorption fine structure tests, we discover that the low-valent Cuprous-S
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moiety acts as an active center during the oxygen reduction process. Our discovery provides a universal scheme for the controllable synthesis and performance regulation of single metal atom catalysts toward energy applications.
Engineering the coordination environment of single atom catalysts offers to opportunity to optimize electrocatalytic activity. In this work, the authors prepare an unsymmetrical Cu-S
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single atom site on porous carbon with high performance in the oxygen reduction reaction.
Journal Article
Improved GOA-based fuzzy PI speed control of PMSM with predictive current regulation
To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm’s ability to escape local optima. The improved GOA is then employed to optimize the contraction-expansion factor of the variable universe fuzzy PI controller, achieving enhanced control performance for PMSMs. Additionally, to address the high torque and current ripple issues commonly associated with traditional PI controllers in the current loop, Model Predictive Control (MPC) is adopted to further improve control performance. Finally, experimental results validate the effectiveness of the proposed control scheme, demonstrating precise motor speed control, rapid and stable current tracking, as well as improved system robustness.
Journal Article