Conventional Integration Methods Research Articles

Powerful numerical method for analysis of electromagnetic scattering from multiple 3D coated targets buried under rough surface

ABSTRACT In this work, an efficient numerical method is proposed for three-dimensional (3D) electromagnetic scattering from multiple coated targets buried under a two-dimensional dielectric rough surface. In the proposed method, the interior region of each coated target is regarded as an independent subregion. The field within each coated target is modeled by 3D vector finite element method (FEM). The multiple scattering interactions between the buried targets and between targets and rough surface are considered by the electric field integral equation and the Poggio–Miller–Chang–Harrington–Wu integral equations, respectively. The fields inside and outside the targets are coupled together via boundary conditions on the boundary of each target. To improve the calculation efficiency and reduce the memory requirement, the fast multipole technique is used in our method. The presented method is numerically validated by comparing it with method of moments and conventional FEM-boundary integral method (BIM). The results indicate that, compared with traditional FEM-BIM, our method has significant advantages in computational efficiency and memory requirements.

Simultaneous measurement of NH3 and NO by mid-infrared tunable diode laser absorption spectroscopy based on machine-learning algorithms

Accurate measurements of NH3 and NO are essential for controlling NOx emissions from coal-fired power plants, reducing ammonia slip, and improving the efficiency of selective catalytic reduction (SCR) operation. We propose a method for measuring NH3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO2 and H2O spectral lines in flue gas. Our approach introduces a novel method for acquiring mixture spectra for the model. Initially, we measure the spectra of individual components under different concentrations and temperatures. Subsequently, mixed spectral samples are generated by combining the measured spectra of the individual components. This approach simplifies the spectral measurement process while preserving accuracy. The particle swarm optimization support vector machine (PSO-SVM) algorithm is leveraged, providing a reliable foundation for the continuous and synchronous measurement of NH3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. The methodology of this paper is expected to play an important role in reducing the influence of interfering components and improving the accuracy of field measurements.

Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.

Calculation of noise field in main transformer room of indoor substation based on thermal-acoustic coupling

In order to calculate the noise field distribution in the main transformer room of an indoor substation accurately, a thermal-acoustic coupling idea was introduced, and a noise field solving the algorithm based on thermal-acoustic coupling was proposed in view of the noise quality loss caused by the effect of ambient temperature changes. Firstly, the fluid-structure coupling model of the main transformer room is established, and the computational fluid dynamics method is used to calculate the flow field distribution and temperature boundary conditions under the action of the heat source; Then, extracting the rheological equation of the environmental boundary conditions in the flow field calculation, and use the difference function to couple the rheological equation to the sound grid to obtain the sound field coupling model under the condition of variable temperature; Finally, the modified parameter acoustic integral equation under the sound field coupling model is calculated based on the conventional Gauss numerical integration method, and the sound field of the main transformer room considering the effect of variable temperature is obtained. The algorithm has been successfully applied in the analysis of the noise field of Xi'an 110 kV Zhenxing Substation, and the error with the measured value is 2.083%.

Design of compact UWB antenna using characteristic mode analysis and its quad-port MIMO realization with novel isolation technique

This paper presents a miniaturized Quad-Port MIMO antenna for UWB application. Employing Characteristic Mode Analysis, the single element UWB antenna is analysed to achieve a compact design. Using the Theory of Characteristic Mode, the significant modes and their current distribution are analysed, which helps to modify the radiator and ground plane to generate more significant modes to yield a wider frequency band. Four circular-shaped radiators with tapered feeds are placed orthogonally on the FR4 substrate to improve isolation, and partial ground planes are located on the opposite surface. Furthermore, a modified Pattee-cross-shaped parasitic element is introduced between the antenna elements and shorted with the ground planes via metallic shorting pins. This novel technique improves isolation compared to the conventional integration method of the partial ground planes through strip lines. The proposed antenna achieves less than −20 dB mutual coupling over most of the frequency band ranging from 2.51–18 GHz.

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).

Conformal Mapping of a Z-Shaped Domain

For the problem of conformal mapping of a half-plane onto a Z-shaped domain with arbitrary geometry, an efficient method is developed for finding parameters of the Schwarz–Christoffel integral, i.e., the preimages of the vertices (prevertices) and the pre-integral multiplier. Special attention is given to the situation of crowding prevertices, in which case conventional integration methods face significant difficulties. For this purpose, the concept of a cluster is introduced, its center is determined, and all integrand binomials with prevertices from this cluster are expanded into a fast-convergent series by applying a unified scheme. Next, the arising integrals are reduced to single or double series in terms of Gauss hypergeometric functions F(a, b, c, q). The fast convergence of the resulting expansions is ensured by applying formulas for analytic continuation of F(a, b, c, q) to a neighborhood of the point q = 1 and using numerically stable recurrence relations. The constructed expansions are also fairly efficient for choosing initial approximations for prevertices in Newton’s iteration method. By using the leading terms of these expansions, the approximations for the prevertices are expressed in explicit form in terms of elementary functions, and the subsequent iterations ensure the fast convergence of the algorithm. After finding the parameters in the integral, the desired mapping is constructed as a combination of power series expansions at prevertices, regular expansions at the preimage of the center of symmetry, a Laurent series in a semi-annulus, and special series near the preimages of the vertical segments. Numerical results demonstrate the high efficiency of the developed method, especially in the case of strong crowding of prevertices.

Neuro‐PINN: A hybrid framework for efficient nonlinear projection equation solutions

AbstractNonlinear projection equations (NPEs) provide a unified framework for solving various constrained nonlinear optimization and engineering problems. This paper presents a deep learning approach for solving NPEs by incorporating neurodynamic optimization and physics‐informed neural networks (PINNs). First, we model the NPE as a system of ordinary differential equations (ODEs) using neurodynamic optimization, and the objective becomes solving this ODE system. Second, we use a modified PINN to serve as the solution for the ODE system. Third, the neural network is trained using a dedicated algorithm to optimize both the ODE system and the NPE. Unlike conventional numerical integration methods, the proposed approach predicts the end state without computing all the intermediate states, resulting in a more efficient solution. The effectiveness of the proposed framework is demonstrated on a collection of classical problems, such as variational inequalities and complementarity problems.

Underwater weak moving target detection method based on wideband Multi-pulse coherent integration

When active sonar detects a weak moving target in a noisy background, coherent integration can be used to improve the signal-to-noise ratio of echo. The active sonar often transmits wideband signals and the target velocity ambiguity often occurs, in this case, the conventional coherent integration method makes the echo energy disperse and coherent integration gain decrease due to non-constant ambiguity factors in the bandwidth. To solve this problem, an underwater weak moving target detection method using multi-pulse coherent integration based on the wideband keystone transform is proposed. To compensate the non-constant ambiguity factors, a phase compensation function is constructed by replacing a single ambiguity factor with a set of ambiguity factors in the wideband keystone transform. After the proposed method, a well-focused image can be obtained in the delay-Doppler domain, and the coherent integration peak is detected to estimate delay and Doppler of the weak moving target. Simulations and lake test results verify the effectiveness of the proposed method, and show that the coherent integration gain with the proposed method is ca. 7–8 dB higher than that with the conventional method. The simulation results of integrating thirty pulses show that when the detection probability is greater than 0.7, the root-mean-square error of delay estimation of the proposed method is about 1/8 of that of the conventional method, and the root-mean-square error of Doppler estimation is about 1/3 of that of the conventional method, indicating that the proposed method has better parameter estimation accuracy.

Dynamic response analysis of a typical time-varying mass system: A moving-interface pipe model, the WKB-recursive solution and experimental validation

This research aims to seek a suitable way for analyzing the time-varying characteristics of some engineering phenomena such as the fuel consumption of flying rockets, the movement process of control rods used to regulate the reaction rate in nuclear reactors, etc. To address this problem, a simulated procedure containing modeling and response solving is proposed. A common variable cross-section pipeline with time-varying mass is designed as a practical time-varying system. A simplified segmented beam model with a moving interface is proposed to simulate the pipe's time-varying behavior. Then the Wentzel-Kramers-Brillouin (WKB)-recursive method is proposed to solve this time-varying problem. A two-segmented beam example is used to verify its computability. The computational efficiency is greatly improved in comparison with the conventional numerical integration methods. To validate this proposed procedure, numerical simulation is carried out and an experiment is specially designed and implemented. In the experiment, many cases of different rates of mass variation and excitation forces are carried out. Overall, the numerical dynamic responses match well with the experimental ones, which indicates that the proposed procedure is suitable for analyzing the system's time-varying characteristics.

A Study on a Fully Integrated Coil Based on the LCCL-S Compensation Topology for Wireless EVs Charging Systems

This study proposes a full integration method for the double capacitances and inductance–series (LCCL-S)-compensated inductive power transfer (IPT) of electric vehicles (EVs). The transmitter and receiver coils adopt the unipolar coil, and the compensation inductor is designed as an extended DD coil. Specifically, the use of an extended DD coil enhances the misalignment tolerance of the EVs. When the IPT system is in the misaligned state, a primary transfer path for magnetic flux is established between the transmitter and receiver coils, and a secondary transfer path is established between the extended DD coil and receiver coil. The distance between the two unipolar coils of the extended DD coil is optimized to maximize the magnetic flux on the secondary transfer path, thereby increasing the total power of the system misaligned state. Simultaneously, the most suitable turns and inner diameter of the extended DD coil are designed by using the finite element method (FEM) simulation tool. In order to verify the performance of the proposed integrated coil method, a 3.3 kW experimental prototype with a 100 mm air gap was constructed and compared with the conventional integration method under the same conditions. The experimental results show that the proposed magnetic coupling structure maintains at least a 63.6% well-aligned value at a door-to-door 150 mm misaligned state, and the output power of the system is 1.05 kW higher than that of the traditional integration method without extra control algorithms.

Joint elimination method of scale effect and range migration/Doppler migration for hypersonic maneuvering target under large time-bandwidth product condition

Under the large time-bandwidth product condition, the detection of hypersonic maneuvering target usually confronts two main issues: the scale effect (SE) and range migration (RM)/Doppler migration (DM). To address these issues, this paper considers the intrapulse and interpulse motions simultaneously to establish the echo model of hypersonic target with acceleration firstly. Then, the matched filtering-scaled modified location rotation transform (MF-SMLRT) method is proposed to jointly eliminate the SE and RM/DM, which are realized by searching target’s rotation angle, initial range cell and acceleration. The proposed method shows better pulse compression (PC), integration and detection performance than the conventional narrow-band long-time coherent integration (CI) methods and existed CI methods applicable to large time-bandwidth products (LTBP) condition. Finally, simulated results are given to certify the validity of the proposed method.

In-situ integration and performance verification of large-scale PZT network for composite aerospace structure

Piezoelectric transducer (PZT) based structural health monitoring (SHM) technology has been proved to be effective in increasing safety and reliability of composite aircraft structures. However, the attachment of PZT network to the host structure is considered as a weak link when facing the long term durability requirement of aerospace SHM, which should be overcome for aerospace SHM. In this paper, a surface-mounted co-curing method is creatively proposed to realize in-situ integration of large-scale PZT network and composite structure. By jointly controlling curing temperature and pressure, the proposed method can realize integration of large-scale PZT network and structural surface with high reliability and performance consistency. Compared with the conventional integration methods, the proposed method does not affect the manufacturing process or reduce the mechanical property of structure, and can be easily implemented whenever the host structure is available. To evaluate the effectiveness of the proposed integration method, a large-scale PZT network with an overall dimension of 1100 mm × 600 mm is integrated with a carbon fiber composite panel of wing box. The integration caused influence on functional integrity is first assessed by performing electro-mechanical impedance based theoretical analysis and experimental investigation. Then guided wave signals of the integrated large-scale PZT network are also acquired and analyzed, which proves the good signal repeatability and consistency of the network. At last, the SHM performance of the network is verified by conducting impact damages on the composite panel and performing damage monitoring, experimental results show that accurate monitoring is realized.

Research on active disturbance rejection control technique for underwater welding robot based on model compensation

PurposeAn active disturbance rejection controller (ADRC) based on model compensation is proposed in this paper. The method should first be taken a nominal model of the robot to compensate. Subsequently, the uncertain external disturbance is estimated and compensated is used an expansion state observer (ESO) in real time, which can reduce the estimating range of observation for ESO. The purpose of this paper is to suggest a novel method to improve the system tracking performance, as well as the dynamic and static performance index.Design/methodology/approachA welding robot is a complicated system with uncertainty, time-varying, strong coupling and a nonlinear system; it is more complex as if it is used in an underwater environment, and it is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the conventional proportional integral derivative method to realize automatic tracking of the seam.FindingsThe simulation experiment is carried out by MATLAB/Simulink, and the application experiment is recorded. The experimental results show that the control method is correct and effective, and the system’s tracking performance is stable, and the robustness and tracking accuracy of the system are also improved.Originality/valueThe seam gets plumper and smoother, with better continuity and no undercut phenomenon.

Subaperture Keystone Transform Matched Filtering Algorithm and Its Application for Air Moving Target Detection in an SBEWR System

Long-time coherent integration is an effective approach to improve detection performance for weak air moving targets (AMTs). The range position variation and azimuth Doppler variation will easily exceed range gate and Doppler resolution in a long observation time, resulting in severe performance degradation, especially for high maneuverability target. Besides, the high detection performance and low computational complexity ability are, most of times, the contradiction requirements by using the existing long-time coherent integration algorithms. To overcome above constraints, a novel subaperture Keystone transform matched filtering (SAKTMF) method is developed in this paper based on the conventional hybrid integration algorithm, which can realize coherent integration both within the subaperture and among subapertures, effectively improving the detection performance of a weak moving target. Furthermore, the proposed SAKTMF is applied for weak AMT detection in space-borne early warning radar (SBEWR), which considers serious extended clutter, range migration, and Doppler migration problems simultaneously. Simulation experiments processing results show that the proposed method can provide improved detection performance compared with the conventional hybrid integration methods.

Accelerating a spatially varying aberration correction of holographic displays with low-rank approximation.

Correction of spatially varying aberrations in holographic displays often requires intractable computational loads. In this Letter, we introduce a low-rank approximation method that decomposes sub-holograms into a small number of modes, thereby reformulating the computer-generated hologram calculation into a summation of a few convolutions. The low-rank approximation is carried out with two different algorithms: the Karhunen-Loeve transform as the optimum solution with respect to the mean-squared error criterion and a novel, to the best of our knowledge, optimization method to provide uniform image quality over the entire field of view. The proposed method is two orders of magnitude faster than the conventional point-wise integration method in our experimental setup, with comparable image quality.

CRISPR/Cas9 - mediated knock-in method can improve the expression and effect of transgene in P1 generation of channel catfish (Ictalurus punctatus)

Transgenesis has a wide range of applications in fish breeding and generation of fish models. Previously, it was common to produce transgenic fish by transferring plasmid DNA into early embryos, resulting in random integration, but more precision, targeted integration is possible with CRISPR/Cas9 technology. Channel catfish (Ictalurus punctatus) is an economically important farmed fish in the United States. To make channel catfish an even richer source of nutrients, we produced P1 fish carrying masu salmon (Oncorhynchus masou) elovl2 (OmElovl2) transgene to increase the content of omega-3 (n-3) fatty acids with CRISPR/Cas9-mediated knock-in targeting non-coding region of chromosome 1, and random integration methods. Mosaicism, transgene expression and fatty acids contents were determined. Integration rates of seven-month-old channel catfish generated by CRISPR/Cas9 and random integration methods were 19% and 27.3%, respectively. However, when we tested five tissues including barbel, fin, muscle, liver and kidney of three channel catfish, 13 out of 15 total observations were verified to carry the transgene from three positive P1 fish produced by CRISPR/Cas9 technology. Only five of 15 tissues carrying transgene were detected in three positive P1 fish produced by random integration. Genomic quantitative real-time PCR (qRT-PCR) also suggested that CRISPR/Cas9 transgenic fish had extremely higher average transgene copy numbers than randomly integrated transgenic fish. Additionally, reverse transcription PCR (RT-PCR) and fatty acids analysis revealed that CRISPR/Cas9 P1 fish had strong OmElovl2 transgene expression in most tissues and 20.7% higher DHA than their controls, while randomly integrated P1 fish did not have detectable OmElovl2 expression in any of five tissues detected. There were no significant differences for any fatty acids between transgenic fish produced by random integration and their non-transgenic controls. CRISPR/Cas9 mediated knock-in technology efficiently reduced mosaicism, improved transgene expression and the biological effects of the foreign gene in P1 generation compared to the conventional random integration method. Therefore, transgenesis based on CRISPR/Cas9 technology would shorten breeding programs and improve applications of gene function studies.

GNSS-RTK Adaptively Integrated with LiDAR/IMU Odometry for Continuously Global Positioning in Urban Canyons

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.

Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction.

Multiomics cancer profiles provide essential signals for predicting cancer survival. It is challenging to reveal the complex patterns from multiple types of data and link them to survival outcomes. We aim to develop a new deep learning-based algorithm to integrate three types of high-dimensional omics data measured on the same individuals to improve cancer survival outcome prediction. We built a three-dimension tensor to integrate multi-omics cancer data and factorized it into two-dimension matrices of latent factors, which were fed into neural networks-based survival networks. The new algorithm and other multi-omics-based algorithms, as well as individual genomic-based survival analysis algorithms, were applied to the breast cancer data colon and rectal cancer data from The Cancer Genome Atlas (TCGA) program. We evaluated the goodness-of-fit using the concordance index (C-index) and Integrated Brier Score (IBS). We demonstrated that the proposed tight integration framework has better survival prediction performance than the models using individual genomic data and other conventional data integration methods. https://github.com/jasperzyzhang/DeepTensorSurvival. Supplementary data are available at Bioinformatics online.

Live imaging of micro and macro wettability variations of carbonate oil reservoirs for enhanced oil recovery and CO2 trapping/storage

Carbonate hydrocarbon reservoirs are considered as potential candidates for chemically enhanced oil recovery and for CO2 geological storage. However, investigation of one main controlling parameter—wettability—is usually performed by conventional integral methods at the core-scale. Moreover, literature reports show that wettability distribution may vary at the micro-scale due to the chemical heterogeneity of the reservoir and residing fluids. These differences may profoundly affect the derivation of other reservoir parameters such as relative permeability and capillary pressure, thus rendering subsequent simulations inaccurate. Here we developed an innovative approach by comparing the wettability distribution on carbonates at micro and macro-scale by combining live-imaging of controlled condensation experiments and X-ray mapping with sessile drop technique. The wettability was quantified by measuring the differences in contact angles before and after aging in palmitic, stearic and naphthenic acids. Furthermore, the influence of organic acids on wettability was examined at micro-scale, which revealed wetting heterogeneity of the surface (i.e., mixed wettability), while corresponding macro-scale measurements indicated hydrophobic wetting properties. The thickness of the adsorbed acid layer was determined, and it was correlated with the wetting properties. These findings bring into question the applicability of macro-scale data in reservoir modeling for enhanced oil recovery and geological storage of greenhouse gases.