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This paper presents an analytical study and an upgrade of reference metal-strip antenna for laptop applications. In recent years, the laptop industry has seen a trend towards more compact designs. This orientation posed new limitations to both designers and manufacturers. Thus, the volume allocated to the internal antenna must be reduced. The results demonstrated in this proposal were obtained through a comprehensive study of the reference design dimensions.

The pullout resistance of reinforcement is an important parameter in the design of reinforced retaining structures. At incipient failure, the kinematics of failure in a reinforced retaining structure shows that the sliding mass of soil pulls the reinforcement obliquely along the slip surface. The response of reinforcement to oblique pull can be considered to be made up of equivalent axial and transverse components of the oblique pull. Accordingly, axial and transverse pullout tests were conducted on geostrip, and metal strip (both smooth and ribbed) reinforcements embedded in uniform sand. Ribbed metal strip reinforcement registered higher pullout resistance than smooth metal strip and geostrip reinforcements. The modified axial pullout resistance factors accounting for transverse pull ranged from 0.44 to 1.23, 1.4 to 3.5, and 2.0 to 5.2 for geostrip, smooth-metal-strip, and ribbed-metal-strip reinforcements, respectively. While the axial pullout resistance factors ranged from 0.34 to 0.65, 0.75 to 1.1, and 0.94 to 1.3.

To achieve a steep subthreshold slope (SS) and a better I ON / I OFF ratio is a major concern for switching applications in semiconductor devices. To overcome these issues, the tunnel field effect transistor (TFET) is a promising device, as it has low leakage current and a low subthreshold slope at room temperature, making it a highly useful device for ultra-lower circuit applications. However, physical doping leads to random doping fluctuations, which is a serious issue in device technology. For this purpose, we report an electrically doped TFET with a metal strip implanted in the oxide layer between the channel/source junction to improve the performance of the device in terms of steep SS and I ON / I OFF at very small gate voltage. Furthermore, we have considered the appropriate length and work function of the metal strip to maintain the improved SS and I ON / I OFF ratio. The introduction of a metal strip in the oxide layer on a conventional device offers a higher I ON / I OFF ratio on the order of 10 8 , steep subthreshold slope (Point SS = 8.07 mV/decade) and significant change in analog/RF performance. The analog/RF figures of merit are observed in terms of transconductance ( g m ), gate-to-drain capacitance ( C gd ), cutoff frequency ( f T ), and gain bandwidth product. The proposed device would be very useful for ultra-low power and high frequency circuit applications at low gate voltages. All simulated results are carried out using 2-D ATLAS software.

In this paper, a metal-strip integrated filtering waveguide is proposed. The overall structure consists of a traditional rectangular waveguide and a metal-strip surface which is loaded at the bottom wall of the waveguide. The customized surface can be considered as a meta-surface, the working property of which can be transformed between perfect electric conductor (PEC) and perfect magnetic conductor (PMC) depending on its operational frequency. When the surface acts as a PEC, the filtering waveguide works at pass-band and electromagnetic waves can freely travel inside the waveguide like a conventional one. When the surface plays a role as a PMC outside the interested frequency band, a stop-band can be created where the propagation of electromagnetic waves could be effectively prevented. By integrating the band-pass and band-stop functions into the same waveguide, a compact filtering waveguide structure can be obtained. The proposed filtering waveguide operates in Ku-band with pass-band of 12 GHz~15.1 GHz and stopband of 15.8 GHz~17.4 GHz. Experimental results show a favorable consistency with the simulation results and verify the proposed concept. Moreover, the proposed structure also possesses a compact size and characterizes for easy-fabrication, having a promising practicability in advanced satellite communication system applications.

Purpose The transverse flux heating (TFH) concept offers very high electrical efficiency in combination with unique technological flexibility. Numerous advantages make this method beyond competition to be applied in e.g. processing lines. However, all potential advantages of TFH can be realized in practice only by optimal design of the inductor shape using numerical modelling and optimization techniques. This paper aims to describe a hierarchical approach to the optimal design of a one-sided induction coil, which will be used for one-sided TFH of continuous moving thin metal strip to achieve a homogeneous temperature distribution along the strip width. Design/methodology/approach Depending on the design step, 2D or 3D FEM simulations using ANSYS® Mechanical including the electromagnetics package are used. The harmonic electromagnetic solution is coupled to a transient thermal model which takes the strip movement into account. All models use the symmetries of the inductor workpiece arrangement to keep the calculation times as low as possible. Findings Due to the geometry of a TFH coil, the models can image a quarter or half of the arrangement. Preliminary investigations of different inductor head shapes can be carried out quickly and then further improved on more complex models in combination with the use of optimization algorithms. Practical implications Using hierarchical structure for designing a one-sided TFH coil, offers an efficient and quick way to create a coil which is adapted to the application. Originality/value The one-sided inductor design is considered, and the results are generally valid.

Thermomechanical experiments were carried out to reproduce the hot stamping process and to investigate the effects of process parameters on the microstructure and mechanical properties of stamped parts. The process parameters, such as austenitizing temperature, soaking time, initial deformation temperature and cooling rate, are studied. The resulting microstructures of specimens were observed and analyzed. To evaluate the mechanical properties of specimens, tensile and hardness tests were also performed at room temperature. The op-timum parameters to achieve the highest tensile strength and the desired microstructure were acquired by comparing and analyzing the results. It is indicated that hot deformation changes the transformation characteristics of 22MnB5 steel. Austenite deformation promotes the austen-ite-to-ferrite transformation and elevates the critical cooling rate to induce a fully martensitic transformation.

Surface plasmon polaritons (SPPs) are localized surface electromagnetic waves that propagate along the interface between a metal and a dielectric. Owing to their inherent subwavelength confinement SPPs have a strong potential to become building blocks of a type of photonic circuitry built up on 2D metal surfaces; however, SPPs are difficult to control on curved surfaces conformably and flexibly to produce advanced functional devices. Here we propose the concept of conformai surface plasmons (CSPs), surface plasmon waves that can propagate on ultrathin and flexible films to long distances in a wide broadband range from microwave to mid-infrared frequencies. We present the experimental realization of these CSPs in the microwave regime on paper-like dielectric films with a thickness 600-fold smaller than the operating wavelength. The flexible paper-like films can be bent, folded, and even twisted to mold the flow of CSPs.

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

Deep learning techniques, especially Convolutional Neural Networks (CNN), dominate the benchmarks for most computer vision tasks. These state-of-the-art results are typically obtained through supervised learning, for which large annotated datasets are required. However, acquiring such datasets for manufacturing applications remains a challenging proposition due to the time and costs involved in their collection. To overcome this disadvantage, a novel framework is proposed for data augmentation by creating synthetic images using Generative Adversarial Networks (GANs). The generator synthesizes new surface defect images from random noise which is trained over time to get realistic fakes. These synthetic images can be used further for training of classification algorithms. Three GAN architectures are trained, and the entire data augmentation pipeline is implemented for the Northeastern University (China) Classification (NEU-CLS) dataset for hot-rolled steel strips from NEU Surface Defect Database. The classification accuracy of a simple CNN architecture is measured on synthetic augmented data and further it is compared with similar state-of-the-arts. It is observed that the proposed GANs-based augmentation scheme significantly improves the performance of CNN for classification of surface defects. The classically augmented CNN yields sensitivity and specificity of 90.28% and 98.06% respectively. In contrast, the synthetically augmented CNN yields better results, with sensitivity and specificity of 95.33% and 99.16% respectively. Also, the use of GANs is demonstrated to disentangle the representation space and to add additional domain knowledge through synthetic augmentation that can be difficult to replicate through classic augmentation. The proposed framework demonstrates high generalization capability. It may be applied to other supervised surface inspection tasks, and thus facilitate the development of advanced vision-based inspection instruments for manufacturing applications.

A field paper-based heavy metal strip was designed and implemented for simultaneous detection of the heavy metals Zn, Cr, Cu, Pb and Mn in wastewater samples. The colorimetric paper strip was fabricated by drop-casting of chromogenic reagents onto detection zones. When the fabricated paper strip was exposed to Zn, Cr, Cu, Pb and Mn, multiple colors appeared that were then recorded with a smartphone followed by processing in the Color Picker application. After optimizing the analytical parameters, such as the chromogenic concentration, pH and reaction time, the paper strip achieved detection limits of 0.63, 0.07, 0.17, 0.03 and 0.11 mg/L for Zn, Cr, Cu, Pb and Mn, respectively. Five heavy metals analyses were able to be performed within 1 min on one paper strip. This paper strip is accurate with recoveries from 87 to 107%. The results of the proposed paper strip correlated well with those determined by inductively coupled plasma-optical emission spectrometry of wastewater samples. The use of a single paper strip integrated with a smartphone for the detection of five heavy metals in wastewater represents an all-in-one device with on-site detection, leading to cost-effective and rapid assays that show a great application potential for on-site environmental monitoring.