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Aeromagnetic compensation is important in aeromagnetic detection. Traditionally, a calibration flight would be implemented to estimate the interference model. However, the magnetic interference is not constant during the mission flight, which makes the interference model constructed in the calibration flight inapplicable in the mission. To solve that problem, this letter proposes an evolutionary aeromagnetic compensation method based on the Woodbury equation. With this method, the magnetic interference model parameters can be evolved during the mission flight on basis of the previous interference model and the updated acquired data. To evaluate the performance of the evolutionary method, both simulation and the experiment were conducted. The results indicate that the proposed method can effectively reduce the influence of changing magnetic interference. In the experiment, the standard deviation (STD) of measured data before compensation in the last mission flight is 1.2070 nT, the traditional compensation can reduce the STD to 0.0863 nT, while our evolutionary aeromagnetic compensation method can reduce STD to 0.0450 nT. The results show that increasing changes in magnetic interference will make evolutionary aeromagnetic compensation vital for isolating signals created by geologic features from signals created by the aircraft. [ABSTRACT FROM AUTHOR]

Magnetic interferential compensation plays a vital role in geomagnetic vector measurement applications. Traditional compensation accounts for only the permanent interferences, induced field interferences, and eddy-current interferences. However, nonlinear magnetic interferences are found, which also have a great impact on measurement, and it cannot be fully characterized by a linear compensation model. This paper proposes a new compensation method based on a back propagation neural network, which can reduce the influence of the linear model on compensation accuracy due to its good nonlinear mapping capabilities. The high-quality network training requires representative datasets, yet it is a common problem in the engineering field. To provide adequate data, this paper adopts a 3D Helmholtz coil to restore the magnetic signal of a geomagnetic vector measurement system. A 3D Helmholtz coil is more flexible and practical than the geomagnetic vector measurement system itself when generating abundant data under different postures and applications. Simulations and experiments are both conducted to prove the superiority of the proposed method. According to the experiment, the proposed method can reduce the root mean square errors of north, east, and vertical components and the total intensity from 73.25, 68.54, 70.45, and 101.77 nT to 23.35, 23.58, 27.42, and 29.72 nT, respectively, compared with the traditional method. [ABSTRACT FROM AUTHOR]

Owing to the ferromagnetism interferential field, the component compensation of magnetic field distortion is vital for a geomagnetic field vector measurement system. However, magnetic interferences, such as eddy-current field, have not been considered in the traditional component compensation methods due to the reference component magnetic field, and its variation with time is difficult to obtain. In this article, a new compensation method using a 3-D Helmholtz coil is proposed, in which the eddy-current field related to the reference component magnetic field and its variation with time are integrated into a compensation model. In addition, the rotation equipment is not necessarily needed to construct the equations of an error model, and the 3-D Helmholtz coil is used to generate any required compensation data to estimate the compensation model error parameters, which makes the compensation procedure more simple and convenient. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method contributes to the accuracy improvement of the geomagnetic vector measurement system. After compensation, the rms errors of North, East, Vertical components, and total intensity are reduced from 3648.3, 2986.2, 3496.2, and 3894.1 to 38.72, 29.58, 35.42, and 42.82 nT, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Since animal domestication, breeders have been selecting candidates for breeding based on phenotypic performance. Estimating breeding values through the best linear unbiased prediction method represents a revolutionary shift in animal breeding. On this basis, selection and mating are utilized to improve the production level of animals. The application of genomic selection has once again revolutionized animal breeding methods. However, although this kind of truncated selection based on breeding values can significantly improve genetic gain, the genetic relationship between individuals with a high breeding value is usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the rate of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic gain and inbreeding is required. Genomic mating is the use of candidate individuals' genomic information to implement optimized breeding and mating, which can effectively control the rate of inbreeding in the population and achieve long-term and sustainable genetic gain. It is more suitable for modern animal breeding, especially for conservation and genetic improvement of local domestic animal breeds. Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry. [ABSTRACT FROM AUTHOR]

The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice. [ABSTRACT FROM AUTHOR]

A thermal barrier coating (TBC), which is composed of a top coating (TC) and bond coating (BC), can keep a turbine engine working in high temperature. The TC is an insulated ceramic layer, and the BC is a conductive layer between the TC and engine blade. Owing to poor working conditions, some failures such as sintering, thinning of coating thickness, and oxide layer initiation will occur in the TBC. Once any part of the TBC fails, it will seriously threaten the safety of the aircraft. The quantitative detection of TBC parameters is realized with the electromagnetic/capacitive dual modality sensor in this paper. The measurement grid algorithm is used to inverse the thickness of the TC layer and the conductivity of the BC layer, and an analytical method is proposed to inverse the relative permittivity of the TC layer. According to the experiment, the inversion errors of these parameters are all less than 4%, which can meet the industry needs well. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate group‐ and breed‐specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five‐trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from.01 (for FI in Yorkshire) to.30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from.14 to.20 and were consistent for each breed, and for FI, the estimates varied from.01 to.11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were −.25, −.27, −.18, −.04, −.10, −.02, and.28 for FI‐TNB, FI‐NBA, FI‐LWF, AFF‐TNB, AFF‐NBA, AFF‐LWF and FI‐AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non‐genetic factors such as management and measurement. [ABSTRACT FROM AUTHOR]

The abundance of single nucleotide polymorphisms (SNPs) makes the haplotype-based method instead of single-maker-oriented method the main approach to association studies on QTL mapping. The key problem in haploptype-based method is how to reconstruct haplotypes from genotype data. Directly assaying haplotypes in diploid individuals by experimental methods is too expensive, therefore the in silico haplotyping-determination methods are the major choice at the present. This paper presents a rapid and reliable algorithm for haplotype reconstruction for tightly linked SNPs in general pedigrees. It is based on six rules and consists of three steps. First, the parental origins of alleles in offspring are assigned conditional on genotypes in parent-offspring trios; second, the redundant haplotypes are eliminated based on the six rules; and finally, the most likely haplotype combinations are chosen via maximum likelihood method. Our method was verified and compared with PEDPHASE by simulated data with different pedigree sizes, numbers of loci, and proportions of missing genotypes. The result shows that our algorithm was superior to PEDPHASE in terms of computing time and accuracy of haplotype estimation. The computing time for 100 runs was 10–15 times less and the accuracy was 4%–10% higher than PEDPHASE. The result also indicates that our method was very robust and was hardly affected by pedigree size, number of loci, and proportion of missing genotypes. [ABSTRACT FROM AUTHOR]

Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use transfer learning to improve the feature expression capabilities of the model under conditions of limited data. A function based on metric learning is adopted to increase the distance between different classes. Finally, a dataset with eight types of surface targets is established. Comparative experiments on our self-built dataset show that the proposed method focuses more on discriminative regions, avoiding problems like gradient disappearance, and achieves better classification results than B-CNN, RA-CNN, MAMC, and MA-CNN, DFL-CNN. [ABSTRACT FROM AUTHOR]

Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF% using computer vision technology and meat quality traits. • 6–10 photos per pig to enhance the representation of eye muscle images. • Adding muscle and fat density to the IIMF% formula to improve the representation of IIMF%. • Adding meat quality traits to prediction factor of model to Improve model accuracy. • Comparison of IMF Estimation Models by Stepwise Regression and GBM. [ABSTRACT FROM AUTHOR]