Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel Imbalance Knowledge-Driven Multi-modal Network (IKD-Net) to extract features from raw multi-modal heterogeneous data directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized using a holistic loss function. While we were developing IKD-Net, we also established a new dataset called the National Agriculture Imagery Program and 3D Elevation Program Combined dataset in California (N3C-California), which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission

A neural network-based adaptive power-sharing strategy for hybrid frame inverters in a microgrid

The capacitive-coupling inverter (CCI) is more cost-effective in reactive power conditioning and enhanced reactive power regulation ability when compared with the inductive-coupling inverter (ICI). As power conditioning capability is vital for a microgrid (MG) system, a new MG frame with hybrid parallel-connected ICIs and CCIs was proposed in this paper. With lower DC-link voltage for the CCI, an adaptive power sharing method was proposed for reducing total rated power and losses. A power-sharing control layer based on a back-propagation neural network that guarantees rapid and accurate sharing ratio computation was investigated as well. The results of simulations and experiments were used to verify the effectiveness of the proposed method

Hyperspectral imaging with a band matrix reduction method to detect early drought stress in tomato

Abstract Drought stress is one of the key abiotic stresses affecting plant growth, crop yield and food quality. The main objective of this study is to investigate the potential effectiveness of hyperspectral imaging with band selection method for the rapid detection of the early drought stress of tomatoes. First, the unsupervised algorithm - K-means and statistical histogram are used to extract samples representing each experimental treatment group. Then, to solve problems related to the high redundancy and correlation of hyperspectral data, band matrix reduction method (BMRM) based on recursive feature elimination theory is proposed to determine the optimal band subset. The band matrix is constructed according to the band ranking obtained by the discrimination coefficient - C o e f i, which is calculated from the average spectral curve and the first-derivative spectrum. Finally, the effectiveness of waveband selection algorithms was validated by comparison with successive projections algorithm, competitive adaptive reweighted sampling, recursive feature elimination with cross-validation and full spectrum. The results demonstrated that BMRM achieved higher classification accuracy with fewer bands selected, and the amount of calculation is not greatly improved. The proposed method provides a more accurate, and effective way of detecting early drought stress

Monte Carlo simulation-based defect ratio estimation approach for a chemical materials stockpile reliability program

A chemical material stockpile reliability program (CSRP) that determines the usability, safety, reliability, and performance of chemical equipment and materials is developed to determine the storage or disposal of chemical material stockpile (Storage Chemical Equipment and Material Reliability Evaluation Instruction, 2019). However, current inspection for current CSRP depend on test and evaluation of criteria for level of importance, and so the number of samples and acceptance quality limit (AQL) are presented based on the lot size. All the processes are conducted under KS Q ISO 2859-1, and the defect rate of the entire lot of CSRP items is generally assumed to be a distribution that is similar to a binomial distribution. However, the pass-fail test for CSRP items is based on approximately 10 test items, and the factors that cause defects in these items are also heterogeneous. We propose a new methodology for estimating the defect rates of CSRP items based on Monte Carlo simulations, which are widely used in various academic fields. In addition, we show the future applicability of the methodology by applying it to the K1 gas mask case and revealing the results of the defect rate estimation. We also present future work, including the need for a standard sample of CSRP items

Monte Carlo simulation-based defect ratio estimation approach for a chemical materials stockpile reliability program

A chemical material stockpile reliability program (CSRP) that determines the usability, safety, reliability, and performance of chemical equipment and materials is developed to determine the storage or disposal of chemical material stockpile (Storage Chemical Equipment and Material Reliability Evaluation Instruction, 2019). However, current inspection for current CSRP depend on test and evaluation of criteria for level of importance, and so the number of samples and acceptance quality limit (AQL) are presented based on the lot size. All the processes are conducted under KS Q ISO 2859-1, and the defect rate of the entire lot of CSRP items is generally assumed to be a distribution that is similar to a binomial distribution. However, the pass-fail test for CSRP items is based on approximately 10 test items, and the factors that cause defects in these items are also heterogeneous. We propose a new methodology for estimating the defect rates of CSRP items based on Monte Carlo simulations, which are widely used in various academic fields. In addition, we show the future applicability of the methodology by applying it to the K1 gas mask case and revealing the results of the defect rate estimation. We also present future work, including the need for a standard sample of CSRP items

Depositing boron on Cu(111): Borophene or boride?

Large-area single-crystal surface structures were successfully prepared on Cu(111) substrate with boron deposition, which is critical for prospective applications. However, the proposed borophene structures do not match the scanning tunneling microscopy (STM) results very well, while the proposed copper boride is at odds with the traditional knowledge that ordered copper-rich borides normally do not exist due to small difference in electronegativity and large difference in atomic size. To clarify the controversy and elucidate the formation mechanism of the unexpected copper boride, we conducted systematic STM, X-ray photoelectron spectroscopy and angle-resolved photoemission spectroscopy investigations, confirming the synthesis of two-dimensional copper boride rather than borophene on Cu(111) after boron deposition under ultrahigh vacuum. First-principles calculations with defective surface models further indicate that boron atoms tend to react with Cu atoms near terrace edges or defects, which in turn shapes the intermediate structures of copper boride and leads to the formation of stable Cu-B monolayer via large-scale surface reconstruction eventually.Comment: 15 pages, 4 figure

Anisotropic c-f hybridization in the ferromagnetic quantum critical metal CeRh6_6Ge4_4

Heavy fermion compounds exhibiting a ferromagnetic quantum critical point have attracted considerable interest. Common to two known cases, i.e., CeRh$_6$Ge$_4$ and YbNi$_4$P$_2$, is that the 4f moments reside along chains with a large inter-chain distance, exhibiting strong magnetic anisotropy that was proposed to be vital for the ferromagnetic quantum criticality. Here we report an angle-resolved photoemission study on CeRh6Ge4, where we observe sharp momentum-dependent 4f bands and clear bending of the conduction bands near the Fermi level, indicating considerable hybridization between conduction and 4f electrons. The extracted hybridization strength is anisotropic in momentum space and is obviously stronger along the Ce chain direction. The hybridized 4f bands persist up to high temperatures, and the evolution of their intensity shows clear band dependence. Our results provide spectroscopic evidence for anisotropic hybridization between conduction and 4f electrons in CeRh$_6$Ge$_4$, which could be important for understanding the electronic origin of the ferromagnetic quantum criticality

Quasi-Two-Dimensional Fermi Surface and Heavy Quasiparticles in CeRh2As2

The recent discovery of multiple superconducting phases in CeRh2As2 has attracted considerable interest. These rich phases are thought to be related to the locally noncentrosymmetric crystal structure, although the possible role of a quadrupole density wave preceding the superconductivity remains an open question. While measurements of physical properties imply that the Ce 4f electrons could play an essential role, the momentum-resolved electronic structure remains hitherto unreported, hindering an in-depth understanding of the underlying physics. Here, we report a high-resolution angle-resolved photoemission study of CeRh2As2. Our results reveal fine splittings of conduction bands, which are directly related to the locally noncentrosymmetric structure, as well as a quasi-two-dimensional Fermi surface, implying weak interlayer hopping and possible nesting instabilities. Our experiments also uncover the fine structures and pronounced temperature evolution of the Kondo peak, demonstrating strong Kondo effect facilitated by excited crystal electric field states. Our results unveil the salient electronic features arising from the interplay between the crystal structure and strong electron correlation, providing spectroscopic insight for understanding the heavy fermion physics and unconventional quadrupole density wave in this enigmatic compound

Cathepsin B-Mediated NLRP3 Inflammasome Formation and Activation in Angiotensin II -Induced Hypertensive Mice: Role of Macrophage Digestion Dysfunction

Background/Aims: Angiotensin II (Ang II) is an octapeptide hormone that plays a significant role in mediating hypertension. Although hypertension is considered a chronic inflammatory disease, the molecular basis of the sterile inflammatory response involved in hypertension remains unclear. Methods: We investigated the role of macrophage NLRP3 inflammasomes in engulfing and digesting microbes, a key macrophage function, and in early onset of hypertension-associated macrophage injury using biochemical analyses, gene silencing, molecular biotechnology, immunofluorescence, and microbiology. Results: Ang II stimulation decreased nitric oxide (NO) release and macrophage digestion in cultured THP-1 cells and markedly increased NLRP3 inflammasome formation and activation. NO release and macrophage digestion were restored by NLRP3 inflammasome inhibition with isoliquiritigenin and gene silencing. This Ang II-induced upregulation of NLRP3 inflammasomes in macrophages was attributed to lysosomal damage and release of cathepsin B. Mechanistically, losartan, a nonpeptide Ang II receptor antagonist, decreased Ang II-induced NLRP3 inflammasome activation, lysosomal membrane permeability, lysosomal cathepsin B release, and macrophage digestion dysfunction. Similarly, Ang II-induced macrophage microbe digestion and NO production, which were blocked by ATI gene silencing. In addition, in vivo experiments showed that the bacteria scavenging function was clearly decreased in macrophages from Ang II-induced hypertensive mice. Conclusion: Angiotensin II enhances lysosomal membrane permeabilization and the consequent release of lysosomal cathepsin B, resulting in activation of the macrophage NLRP3 inflammasome. This may contribute to NO mediation of dysfunction in digesting microbes