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International guidelines and standards for human protection from electromagnetic fields have been revised recently. The epithelial/absorbed power density (APD) has been used as a new physical quantity for local exposure to frequencies > 6 GHz, related to the temperature rise in the superficial layer. The assessment methods of APD for practical exposure scenarios are crucial for realistic exposures. This study investigates averaging methods for APD in nonplanar skin models for local electromagnetic field exposure from 10 to 90 GHz. Eight research groups compared the calculated APDs using distinct numerical approaches and postprocessing techniques. The intercomparison aimed to clarify the primary causes of variations in aspects, such as APD averaging methods, skin model structure, and types of exposure antennas. Statistical analyses reveal that the maximum differences in relative confidence intervals (RCI) due to different average methods and skin models are within 9.4% and 5.1%, respectively. In contrast, when the distance between the antenna and skin is set to 5 mm, the discrepancy attributed to the exposure antenna reaches 59.4% at 10 GHz. This difference does not exceed 9.2% under other computational conditions. Additionally, the spatially averaged APD appears to have a linear relationship with the maximum skin surface temperature elevation, based on regression analysis. The findings indicate that the variances in the spatially averaged APD are largely independent of both the APD averaging methods and skin model structures. However, they slightly depend on the antenna types used as exposure sources.

International organizations have collaborated to revise standards and guidelines for human protection from exposure to electromagnetic fields. In the frequency range of 6–300 GHz, the permissible spatially averaged epithelial/absorbed power density, which is primarily derived from thermal modeling, is considered as the basic restriction. However, for the averaging methods of the epithelial/absorbed power density inside human tissues, only a few groups have presented calculated results obtained using different exposure conditions and numerical methods. Because experimental validation is extremely difficult in this frequency range, this paper presents the first intercomparison study of the calculated epithelial/absorbed power density inside a human body model exposed to different frequency sources ranging from 10–90 GHz. This intercomparison aims to clarify the difference in the calculated results caused by different numerical electromagnetic methods in dosimetry analysis from 11 research groups using planar skin models. To reduce the comparison variances caused by various key parameters, computational conditions (e.g., the antenna type, dimensions, and dielectric properties of the skin models) were unified. The results indicate that the maximum relative standard deviation (RSD) of the peak spatially averaged epithelial/absorbed power densities for one- and three-layer skin models are less than 17.49% and 17.39%, respectively, when using a dipole antenna as the exposure source. For the dipole array antenna, the corresponding maximum RSD increases to 32.49% and 42.55%, respectively. Under the considered exposure scenarios, the RSD in the spatially averaged epithelial/absorbed power densities decrease from 42.55% to 16.7% when the frequency is increased from 10–90 GHz. Furthermore, the deviation from the two equations recommended by the exposure guidelines

Due to climate change, Spodoptera litura has the potential to become an increasingly severe pest because of increased habitat suitability. To support precision agriculture, it is essential to accurately predict the life cycle of Spodoptera litura, and use the information for pest control. This paper proposes BugTalk to predict the life of Spodoptera litura (Common Cutworm). Based on the Internet of Things (IoT) technology, BugTalk is a real-time prediction version of modified Insect Life Cycle Modeling software (ILCYM), an open-source software package that implements the functions used in the four models of the four life stages of Spodoptera litura. In this paper, we significantly improve the ILCYM functions and several models of the previous studies to improve the accuracy of the prediction. We develop the first real-time prediction system that can predict the number of Spodoptera litura for the farm fields in real-time. The results are compared with the measurements during 2014-2020. We extend the temperature-based ILCYM model to accommodate humidity for the larvae emergence stage, which further improves the accuracy of the model. The Mean Arctangent Absolute Percentage Error (MAAPE) between the measurements and the BugTalk prediction is 24.593%. This result has been practically utilized in farm management.

This paper is concerned with the electromagnetic theory of characteristic modes for general homogeneous material bodies using surface integral equations (SIEs), which are computationally more efficient than using volume integral equations (VIEs). However, the generalized eigenvalue equations (GEEs) based on SIEs are prone to generate spurious or extra modes, unlike their counterparts based on VIEs. In this work, several variant SIE-based GEEs are proposed to eliminate the spurious/extra modes and improve the numerical performances. These variant GEEs share a common point that the reactive power operator related to the material domain should be removed from the left-hand sides of GEEs by explicitly enforcing the boundary condition, because it seems sensitive to incur unwanted modes. These variant GEEs are diverse in choosing the active power operator related to the material domain on the right-hand sides to suit different medium parameters, which even can be dropped directly for lossless cases. Numerical results show that these variant GEEs do not generate spurious/extra resonant modes in observed frequency range, and at least one of the variants would have acceptable numerical accuracy.

The electromagnetic-mechanical wedge brake system (EMWB) is a new structure designed for brake-by-wire system. This structure is composed of a DC motor, an electromagnet device, two wedges, two friction plates, a rotating rod and a brake disk. The presented EMWB system contains two brake actuators such as electromagnetic brake and motor brake. The EMWB system provides a mechanical backup brake model driven by the DC motor when the electromagnetic brake fails. In the braking relief phase, the maximum torque of the DC motor should meet the requirement of separating the friction plates from the brake disk, so the brake performance of EMWB system is simulated with Matlab/Simulink and optimized under the multi-objective constraints, and the simulation results demonstrate that the mechanical backup brake model can meet the brake requirements both for the front axle and the rear axle. Through the multi-objective optimization, the EMWB system can reach an acceptable brake performance and the DC motor can meet the requirement of relieving braking.

This paper proposes a new absorbing boundary condition (ABC) computation approach based on the deep learning technique. Benefited from the sequence dependence feature, the Long Short-Term Memory (LSTM) network is employed to replace the conventional perfectly matched layer (PML) ABC for the Finite-Difference Time-Domain (FDTD) solving process. The newly proposed LSTM based PML model is trained by the electromagnetic field data on the interface of the conventional PML. Different from the conventional PML, the newly proposed model only needs one cell layer as the boundary. Hence, the newly proposed method conveniently reduces both the algorithm's complexity and the area of computation domain of FDTD. Additionally, the newly proposed LSTM based PML model can achieve higher accuracy than the conventional artificial neural network (ANN) based PML, thanks to the sequence dependence feature of the LSTM networks. Numerical examples have illustrated the capability and the accuracy of the proposed LSTM model. The results illustrate that the new method can be compatibly embedded into the FDTD solving process with the high accuracy.

Electromagnetic track brake is increasingly applied to improve emergency braking force together with the main wheel-rail brake system, while larger electromagnetic braking force is demanded. The bottom of pole shoe is designed as the same curved surface like track top in order to achieve a good contact between pole shoe and track and the pole shoe chamfering is also designed to improve electromagnetic attractive force. Three main variables that decide the electromagnetic attractive force are selected in the paper: pole shoe contact width, protection block length and pole shoe length. The reliability of electromagnetic simulation is verified through prototype test and then the electromagnetic attractive force in this study is achieved by electromagnetic simulation. First, the effects of the three variables on electromagnetic attractive force are investigated singly. Then, a uniform design method is employed in this study to further optimize electromagnetic attractive force by researching the interactional effects of the three variables. A regression model is established with pole shoe contact width, protection block length and pole shoe length as the independent variables, and electromagnetic attractive force as the dependent variable. Depending on the regression model, the optimum conditions of the three variables are achieved. In addition, finite element simulation of electromagnetic track brake is developed to investigate electromagnetic attractive force and magnetic field distribution by Ansoft Maxwell. Compared to the existing electromagnetic track brake, the electromagnetic braking force and electromagnetic braking deceleration of the optimized electromagnetic track brake are improved by 6.3% under the optimum conditions, which can greatly improve train safety in emergency. The proposed reinforcement optimizations have two outstanding characteristics. The first is keeping the space usage of electromagnetic track brake unaltered, and the other is its simplicity to conduct.

In this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added into the training data to simulate the interference in the real application scenario. The proposed CNN composes of three pairs of convolutional and activation layers with one additional fully connected layer to realize the recognition, i.e., the imaging process. The feasibility of utilizing the trained deep CNN for imaging is validated by numerical benchmarks. Distinguished from the subwavelength imaging methods, the spatial response and Green's function need not be measured and evaluated in the proposed method.

A highly efficient algorithm for analysis of electromagnetic scattering by a large cluster of independently moving objects is proposed in this paper. A double octree structure is introduced, which consists of a main octree stationary to the cluster as a whole, and many sub-octrees moving with individual objects. Based on the new double octree structure, the computation of interactions between two elements is divided into four categories: the two elements are on the same object and are near; the two elements are on the same object but are far; the two elements are on different objects but the two objects are adjacent; the two elements are on different objects and the two objects are non-adjacent. For different interacting categories, different approaches are adopted to maximize the computing efficiency. The new algorithm enables possible fast simulations of scattering by hundreds of independently moving objects. Compared with the conventional Multilevel Fast Multipole Algorithm (MLFMA) and the recently developed Multi-Moving-Object (MMO)-MLFMA, the present scheme has substantial improvement in saving CPU time, providing a viable solution for engineering applications. Numerical validations for accuracy and efficiency are performed.

Data hiding techniques for steganography, which embed secret data in multimedia imperceptibly, are useful for protecting information security. By taking advantage of the popularity of MIDI files on the Internet, a new data hiding method via MIDI files is proposed, which modifies the velocities of musical note sequences to embed secret data. Initially, musical note sequences with monotonic pitches, each consisting of at least three consecutive notes with pitches either entirely non-decreasing or entirely non-increasing, are found from an input MIDI file. Next, for each of such musical note sequences, a reference velocity is generated for each non-end note in the sequence by a linear interpolation scheme. Then, a number of data bits are embedded into each non-end note by adding the decimal value of the bits to or subtracting the value from the corresponding reference velocity value to yield a new velocity for the note. The new velocity value does not differ much from the original one and fits the velocity trend in the musical note sequence, so that the resulting stego-MIDI file does not yield abnormal note strengths and the musical expression is kept. Moreover, a melody humanization scheme is proposed for modifying the velocity values in strength-invariant MIDI channels to create data embeddability without producing unreasonable melodies. The original MIDI file size is also kept unchanged after data embedding, avoiding attracting attentions from hackers. Experimental results show the feasibility of the proposed method. Also, a comparison with five other methods show that the method has the merit of reducing the resulting melody distortion or file-size change while yielding a reasonable secret-bit embedding rate.

Three-dimensional (3D) body imaging has become an important mean for applications based on body measurement, such as apparel customization and obesity assessment. In this article, we present a 3D body imaging system built upon stereovision technology. The system utilizes paired, high-resolution single-lens reflex (SLR) cameras to image the front and back body surfaces of a person, and robust and efficient stereo matching algorithms to reconstruct the 3D surface of the body with high-density data clouds. The system's accuracy and repeatability have been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3D body models, including body circumferences and volumes, were highly repeatable and consistent with the manual and other 3D instrumental measurements (CV 2 > 0.99).

Pulmonary valve diseases in children and adults include different degrees of stenosis, regurgitation, or congenital defects. Valve repair or replacement surgery is used to treat valvular dysfunction and to improve regurgitations flow for pulmonary valve pathologies. Handmade trileaflet valve designs with different ranges of diameters have been used for pulmonary valved conduit reconstruction among children or adult patients with available conditions. In this paper, we propose a multiple regression model as a cascadecorrelation-network-based estimator to determine optimal trileaflet parameters, including width, length, and upper/lower curved structures, for trileaflet valve reconstruction. The diameter of the main pulmonary artery is determined via computed tomography pulmonary angiography, and a trileaflet valve template is rapidly sketched. The actual valve is constructed using an expanded polytetrafluoroethylene material. Using an experimental pulmonary circulation loop system, design parameters and valve efficacy can be validated by the Taguchi method through calculation of signal-to-noise ratios. Experimental results indicate that in contrast to commercial valve stents, the handmade trileaflet valve exhibits good performance and is a valuable option in treatment of severe pulmonary regurgitation.

Percutaneous pulmonary valve implantation is an improved technique that is used to treat narrowed pulmonary valves or leaky pulmonary valves in patients with congenital heart disease. This technique represents a promising strategy to reduce surgical risk and operation. In clinical cases, commercial valve stents are sometimes not available for children or special subjects due to restrictions in stent size. Hence, the handmade pulmonary valved conduit provides a strategy to design stents with customized size for valve replacement. In this paper, we propose a meta-learning-based intelligent model to train an estimator (including two sub-estimators) to determine optimal trileaflet parameters for customized trileaflet valve reconstruction. This estimation model overcomes the problem of empirical parameter determination. The meta-learning model possesses learning-to-optimization capability for training generalized regression neural network by particle swarm optimization algorithm. Through incremental training patterns, this scheme can gradually enhance optimization to provide refined parameters for customized designs that can be applicable to individuals of all age groups. The customized handmade pulmonary valved conduit was validated by assessing the regurgitation fraction and the heart pump efficiency using an experimental cardiopulmonary circulation loop system.

Despite being a critical communication skill, grasping humor is challenging—a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features and provides inline annotations of them on the video script. In particular, to better capture the build-ups, we introduce content repetition as a complement to features introduced in theories of computational humor and visualize them in a context linking graph. To help users locate the punchlines that have the desired features to learn, we summarize the content (with keywords) and humor feature statistics on an augmented time matrix. With case studies on stand-up comedy shows and TED talks, we show that DeHumor is able to highlight various building blocks of humor examples. In addition, expert interviews with communication coaches and humor researchers demonstrate the effectiveness of DeHumor for multimodal humor analysis of speech content and vocal delivery. [ABSTRACT FROM AUTHOR]

Entire-structure-oriented work-energy theorem (WET) framework is used to establish the characteristic mode theory (CMT) for material scatterers. Diagonalizing driving power operator (DPO) method is proposed to calculate characteristic modes (CMs). Solution domain compression (SDC) scheme is introduced to suppress spurious modes. An electromagnetic energy characteristic equation similar to the energy eigen-equation in quantum mechanics is derived for unifying the modal analysis theories of classical electrodynamics and quantum mechanics. Using the concept of driving power in the WET framework, the physical meaning of characteristic values is revealed; the reason leading to the nonorthogonal characteristic far fields of lossy scatterers is provided; it is explained why the conventional CMT fails to analyze some classical transmitting antennas; the physical interpretation for normalizing modal real power to 1 is given; the Parseval’s identity in CMT is derived; the classical concept of quality factor ($Q$ -factor) is generalized to a novel concept of field-current phase-mismatching factor ($\Theta $ -factor). Using the diagonalizing DPO method with the SDC scheme, the spurious modes outputted from the conventional diagonalizing impedance matrix operator method are suppressed. Both the novel SDC and conventional dependent variable elimination schemes confirm the same conclusion that the spurious modes originate from overlooking the dependence relationships among the currents in CM-generating operator. [ABSTRACT FROM AUTHOR]

This article focuses on further improving the characteristic mode theory (CMT) for metal-material composite scattering objects. The improved CMT is alternatively established in a novel work-energy principle (WEP) framework. Under the WEP framework, a novel driving power operator (DPO) is introduced as the generating operator for characteristic modes (CMs). Then, a novel diagonalizing DPO method is proposed to construct the CMs. In addition, a novel solution domain compression (SDC) scheme is developed to suppress spurious modes. Compared with the conventional integral equation (IE) framework, the novel WEP framework more clearly reveals the core physical feature of CMs, and then easily derives the famous Parseval’s identity. Compared with the conventional impedance matrix operator (IMO), the novel DPO is more advantageous in the aspect of distinguishing independent variables from dependent variables, and has a smaller computational burden, and has a more acceptable numerical performance in the aspect of calculating CMs. Compared with the conventional dependent variable elimination (DVE) scheme, the novel SDC scheme is equally effective in the aspect of suppressing spurious mode, and reconfirms the same conclusion that the spurious modes result from ignoring the dependence relationships among the currents contained in DPO. [ABSTRACT FROM AUTHOR]

In this letter, an efficient vector interpolation method based on Lebedev quadrature to accelerate the matrix-vector multiplications in far-field regions for the multilevel fast multipole algorithm (MLFMA) is presented. This approach avoids the singularity on sphere poles in the traditional interpolation method based on Gaussian quadrature and maintains the same accuracy without increasing computational costs. As a consequence, both memory consumption and processing time can be reduced considerably, providing a viable alternative to solve large-scale electromagnetic scattering problems using MLFMA. [ABSTRACT FROM AUTHOR]

Combining fiber Bragg grating (FBG) sensors and finite element simulation analysis, an online monitoring method for non-invasive distributed flow velocity in pipelines has been proposed. A placement strategy has been proposed for the deployment of these FBG sensors to ensure effective sensing of strain. The cross-sensitivity problem of FBG sensors can be solved by differential FBG measurements. The principle of online monitoring of flow velocity based on the flow velocity function and finite element analysis is described and derived. Finite element analysis results show the $\boldsymbol {\varepsilon }_{1}- \boldsymbol {\varepsilon }_{2}$ at 45° of the elbow of the bend is quadratically proportional to the flow velocity. Meanwhile, the feasibility of the method is verified by experiments. The calibration results show a fitted correlation coefficient R2 = 0.9982 in the velocity range of 4.25-10.62m/s, fitted by least squares to the wavelength data. The repeatability error of the flow measurement of the proposed sensor is no more than 5.06%FS. Compared with the existing literature, the method proposed in this paper has a wider range of flow velocity measurement and applications, especially suitable for applications with strong electromagnetic interference capability and harsher environment, showing great potential for engineering applications, especially in the distributed detection of long-distance pipeline networks. [ABSTRACT FROM AUTHOR]