3D Frame Research Articles

Novel amine-functionalized zinc-based metal-organic framework for low-temperature chemiresistive hydrogen sensing

Low-temperature operating chemiresistive gas sensors are attractive for a variety of real-time gas monitoring applications, with benefits such as low power consumption, profitability, and miniaturization of devices. In this regard, we developed a low-temperature operating H2 gas sensor using solvothermal-processed novel amine-functionalized zinc-based metal-organic framework (Zn-BDC-NH2) as a detection material. The Zn-BDC-NH2 structure is consists of the Zn4O secondary building units and 2–aminoterephthalate acidic linker that form the 3D frame structure. Prior to sensing studies, various techniques were employed to confirm -NH2 functionalization and to characterize structure, surface morphology, thermal stability, surface area, and surface chemistry of synthesized Zn-BDC-NH2 materials. Benefitting from the simple synthesis process and larger surface area (880 m2g-1) with adequate porosity (~13 Å), Zn-BDC-NH2 has proven to be an excellent chemiresistive sensor for the effective sensing of low concentrations of H2 at 50 °C. Moreover, the sensor shown significant sensitivity to the detection of lower H2 concentrations of 1–10 ppm, a response value of 2.93–10 ppm H2, and complete recovery characteristics at 50 °C. We discussed the mechanisms for attaining the excellent H2 sensing. The utilized room temperature solvothermal approach opens up a perspective for synthesizing Zn-BDC-NH2 material with suitable functionalities and their use in low temperature H2 sensors.

Seismic reliability analysis of building structures using subset simulation coupled with deep learning-based surrogate model

The reliability of building structures subjected to ground motion is a time-consuming, complex, and iterative computation problem involving multidiscipline theories such as probability theory, reliability theory, and dynamic structural analysis. In order to address this challenge, this study proposes a highly efficient approach based on the subset simulation and a deep learning-based surrogate model. The subset simulation is an efficient sampling strategy that significantly diminishes the number of samples to compute when determining the failure probability, especially for very small ones. On the other hand, the surrogate model based on a deep learning algorithm can deliver equivalently accurate structures’ responses compared to the well-known finite element method with markedly smaller time complexity, given appropriate available training data. The efficiency and effectiveness of the proposed approach are demonstrated in detail through three case studies with increasing complexity: a 1-DoF problem, a 2D frame, and a 3D structure based on experimental data showing a reduction up to two orders of magnitude in time complexity compared to the Monte Carlo simulation using finite element method.

Texture Image Classification Based on Deep Learning and Wireless Sensor Technology

The main purpose of the object detection process is to determine the category of the scene object and use the display 3D and 3D frame size. At present, in the case of 3D object detection, we can extract more accurate features by learning a large number of data, and this deep learning network has good results, but there is a very big problem, including the error of input information, extraction error, and so on. Therefore, solving the above problems has become an important direction to promote the rapid development of 3D target detection technology. This paper mainly studies the deep learning wireless sensor technology and also studies the deep learning infrared and visible image fusion. At the same time, based on the introduction of wireless sensor technology and research status, this paper summarizes the existing algorithms. Texture image classification is a more important visual cue in life. Because it will be affected by light intensity, noise size, image scale, and so on. This makes the classification and feature extraction of image scale and texture image more difficult. To solve these problems has become a hot topic of computer vision research in recent years. The shape of the point cloud is completed by using the 3D target detection method to complete the algorithm research. The radar point cloud is extracted by the 3D target detection method, and the radar point group of the overall shape of the object is obtained. The principal component analysis algorithm is used to extract the principal features of the radar point cloud with the complete shape of the object, and the more accurate 3D target frame is obtained after feature adjustment.

Road-Type Detection Based on Traffic Sign and Lane Data

Establishing the current road type constitutes a significant assistance to car drivers, as, by default, the road type determines the legal speed limit. Although there are GPS- and map-based navigation systems that can retrieve the actual road type and speed limit and some can even access and indicate current traffic volumes, it was our aim to develop and test a software prototype of a road-type detection (RTD) system that relies solely on video and sensor data collected on board. Such a system can still work during GPS signal outages. The study presents a heuristic approach to RTD that is based on type and distance data relating to traffic control devices (TCDs) installed along a road. The road is used by an ego vehicle with an on-board smart camera looking ahead and with a number of vehicular sensors. A complex processing step—not detailed in the study—detects TCDs with reasonable probability and error rate and locates them with respect to a 3D coordinate frame fixed to the ego vehicle. The prototype system takes data describing the detected TCDs as its input. This data are then evaluated in a multiscale manner by computing empirical statistics of occurrences over short, medium, and long patches of road. Such an evaluation is carried out in conjunction with each considered road type, and the resulting values are compared to respective reference values. Heuristics is then used in decision-making to resolve any interscale and interroad-type disaccords. The proposed decision rules take into account the possibility of TCDs having been missed and of faulty detections. Short preprocessed synchronised video and signal sequences recorded in different countries and road environments were used for testing the prototype system. These short sequences were carefully strung together into coherent chains. Distance-based recognition precisions 78.9% and 88.9% were gained for European (continental) and for UK roads, respectively.

Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold.

Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target. Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors. Results: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain–computer interfaces.

An energy-based method to calculate the vulnerability scores of RC buildings for rapid structural assessment methods

In this study, the some of the vulnerability scores commonly used in rapid structural assessments methods are obtained by an energy based method. For this purpose, non-linear time history and non-linear pushover analyses of several 2D frames with different structural and dynamic properties are performed. Both of the material and geometric nonlinearity are considered in the structural models. The non-linear time history analyses are performed with response spectra compatible far fault ground motion records. The analyses results revealed that the vulnerability score of soft story formation may be improved by considering it in a parametric manner instead of using a constant vulnerability score. The presence of brick masonry infill walls reduces the negative effect of soft story formation. The presence of weak story formation (for the considered case) also affects the earthquake performance of structure but this negative effect is not considerable as that of soft story formation. The combined effect of structural deficiencies can be more significant than the values of vulnerability scores calculated by summing the vulnerability score of each structural deficiency.

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density superresolution image. Here, we demonstrate a computational strategy based on deep neural networks (DNNs) to reconstruct high-density superresolution images from far fewer raw image frames. The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy (OR-PAM) and 2D labeled localization photoacoustic computed tomography (PACT). For the former, the required number of raw volumetric frames is reduced from tens to fewer than ten. For the latter, the required number of raw 2D frames is reduced by 12 fold. Therefore, our proposed method has simultaneously improved temporal (via the DNN) and spatial (via the localization method) resolutions in both label-free microscopy and labeled tomography. Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

Multiple-feature-based zero-watermarking for robust and discriminative copyright protection of DIBR 3D videos

Zero-watermarking is a key technique for achieving lossless and flexible copyright protection of depth image-based rendering (DIBR) videos. Existing approaches extract features of both 2D frames and depth maps via a single mechanism to protect them simultaneously. However, it is difficult for these schemes to fully satisfy the copyright protection requirements of the two components, including the remarkable discriminative capability of 3D videos and robustness against various attacks. Hence, in this paper, we propose a novel multiple-feature-based zero-watermarking scheme to protect the copyright of DIBR 3D videos. To the best of our knowledge, this is the first scheme that integrates multiple features to improve both the discriminative capability and robustness against various attacks. Specifically, dual-tree complex wavelet transform and discrete cosine transform features enhance the robustness against DIBR conversion and noise addition, respectively, while ring-partition statistical residual features ensure robustness against geometric attacks and provide sufficient discriminative capacity. In addition, we use a logistic-logistic chaotic system to encrypt these multiple features for enhanced security and design an attention-based fusion approach to offer an optimal copyright protection solution. Extensive experimental results demonstrate that our proposed scheme has stronger robustness and discriminative capacity compared to state-of-the-art zero-watermarking methods.

Cobalt phosphide embedded in a 3D carbon frame as a sulfur carrier for high-performance lithium-sulfur batteries

• A porous three-dimensional carbon frame embedded with CoP nanoparticles (CoP@3DC) is synthesized. • CoP has a catalytic effect and can accelerate the conversion of LiPSs to inhibit the shuttle effect. • CoP@3DC/S delivers a superior discharge capacity of 640.6 mAh g −1 after 600 cycles at 1C. Lithium-sulfur (Li-S) batteries deliver a higher energy density than lithium ion batteries and are therefore regarded as next-generation batteries. However, the shuttle effect of soluble lithium polysulfides (LiPSs) and their slow redox kinetics are still the main setbacks for practical application. In this study, a porous three-dimensional carbon frame embedded with CoP nanoparticles (CoP@3DC) was synthesized by pyrolyzing and phosphating an acrylic cation exchange resin previously subjected to cobalt ion exchange. The abundant pores of CoP@3DC inhibit the diffusion of LiPSs, while its conductive three-dimensional carbon skeleton is conducive to the rapid transmission of electrons. In addition, CoP nanoparticles uniformly dispersed in the porous carbon network not only provide numerous chemical adsorption sites for LiPSs, but also accelerate their conversion to Li 2 S. Constant-current charging and discharging tests revealed that the CoP@3DC/S cathode achieves an initial discharge capacity of 1117.37 mAh∙g −1 at 0.5C and maintains a capacity of up to 740.56 mAh∙g −1 after 600 cycles, with a decay rate per cycle of only 0.056 %. Our work provides a facile supporting strategy for transition metal nanoparticles to significantly improve the performance of Li-S batteries.

Surrogate-based optimum design of 3D reinforced concrete building frames to Eurocodes

The optimum structural design of real-world 3D concrete building frames to modern design standards is a complex and computationally expensive task. Hence, the use of surrogate-based optimization (SBO) methodologies must be investigated to reduce computational cost. The present study applies, for first time, a fully-fledged SBO algorithm to the optimum design of 3D concrete building frames. More particularly, the algorithm is applied to the minimum material cost design of a 4-storey and a 12-storey 3D RC building according to Eurocodes. It is found that the SBO algorithm can converge earlier than other well-established metaheuristic optimization algorithms reducing considerably the required computational effort. Nevertheless, it is likely to get trapped in local optima for large-scale RC frames. To overcome this drawback, a novel hybrid approach is also proposed herein that offers improved computational performance for large-scale concrete building frames.

A reduced order model for nonlinear time history seismic analyzes of elasto‐plastic 3D frame structures

AbstractA reduced order model for the nonlinear dynamic seismic analysis of elasto‐plastic 3D frame structures is presented. It is based on an approximated solution space for the displacement field that is assumed as the sum of two subspaces. The first subspace is generated by the relevant linear elastic modes of the generalized stiffness/mass linear eigenvalue problem in terms of participation factor. The second one is defined by collapse mechanisms associated to appropriate static load distributions. The time history analyzes are carried out within the reduced solution space, while the internal forces are evaluated on the full model to guarantee an accurate description of the constitutive laws. The proposed reduction method was tested for different structural geometries varying frequency content, direction and amplitude of the ground motion. Numerical results show accuracy and robustness also employing a few tens of modes, including elastic modes and plastic collapse mechanisms. In particular, the need for plastic modes in capturing the structural dynamics in case of seismic actions leading to significant plastic excursions is emphasized. This highlights the limits of simplified approaches based on the hypothesis of shape conservation in nonlinear problems, regardless of the building regularity.

Determination of optimal designs for geothermal energy piles in the soil supporting a multi-storey building

The article presents the optimal design of geothermal energy piles supporting a multi-story building at different pile spacing. The optimization model OPTPILE, based on the construction cost of the single pile, was used for this purpose. It was provided with geotechnical and structural design constraints that satisfy the requirements of building codes. The optimal design of a geothermal energy pile was studied for a 10-storey building with different pile spacing. So far, only a single energy pile has been optimized and therefore the spacing between the piles has not yet been considered in the design. However, in this work, pile spacing is taken into account by considering the entire load distribution of the multi-story building. A 3D beam-slab frame was created to determine the pile loads. The recommendation for the optimal design of geothermal energy pile spacing for a 10-storey building was developed. The results show that the optimal pile spacing (square distribution) for a 10-storey building is about 7 m. The construction cost for all thermal pile foundations and concrete structural components for a 10-storey building is estimated to be 101.1 €/m2. The optimal architecturally reasonable spacing of piles with a square distribution for a 10-storey building is 8.5 m. In this case, the cost of the concrete structural elements of the building and the piles increases by 1% to 102.1 €/m2. The cost of installing the heating pipes in the pile is about 1 €/m2.

Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

In order to study the semantic detection accuracy of 3D vehicle accident video, an accident detection method combining 2D image and 3D information was proposed. The 3D semantic bounding box generated by the 3D detection and tracking task of the vehicle is used to extract the motion features of the vehicle, it includes the trajectory of the vehicle and the dimension and orientation of the 3D bounding frame, and the 3D semantic bounding frame is used to establish the evaluation index of the accident detection. The experimental results show that the average loss function of each batch of 1000 images is calculated by the stochastic gradient descent method to update the parameter values. The learning rate was set to 0.001 in the first 30,000 iterations and 0.0001 in the last 10,000 iterations. The MOTA of the CEM algorithm is 78.4%, FP is 1.1%, and FN is 3.5%, and the MOTA of the 3-DCMK algorithm is 88.6%, FP is 0.9%, and FN is 1.9%. The MOTA of this method is 89.3%, FP is 0.9%, and FN is 1.2%. The 3D target semantic detection of vehicle accident video has stability and accuracy.

Behaviour of thin flush end-plate connections in a 3D bare steel frame under fire loading: experimental study

PurposeThe paper aims to report a fire test conducted on a three-dimensional frame in order to investigate the behaviour of bare steel flush end-plate connections with relatively low thickness at elevated temperatures.Design/methodology/approachA half-scale model was fabricated and exposed to modified (scaled) ISO 834 heating curve using a semi-open furnace. The maximum temperature inside the furnace reached 1,026 °C.FindingsThe rotations of connections are reported and compared with those of a previous study on an exactly the same model with thick end-plates. Various modes of failure such as local buckling of the beams flanges and lateral-torsional buckling of beams were observed during the test. Finally, the structure collapsed after 29 min of heating due to the fracture of weld between one of the beams and one of its attached end-plates whilst the other beam had undergone a maximum deflection of 35 cm (≈ 1/6 span length). Other observed failure modes included bolt fracture, bolt thread stripping and large inelastic deformation of the end-plates.Originality/valueAlthough the adoption of thin end-plates increased the rotational capacity of the connections, it did not improve the robustness of the structure under fire conditions.

Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma.

Current in vitro models for osteosarcoma investigation and drug screening, including two-dimensional (2D) cell culture and tumour spheroids (i.e. cancer stem-like cells), lack extracellular matrix (ECM). Therefore, results from traditional models may not reflect real pathological processes in genuine osteosarcoma histological structures. Here, we report a three-dimensional (3D) bioprinted osteosarcoma model (3DBPO) that contains osteosarcoma cells and shrouding ECM analogue in a 3D frame. Photo-crosslinkable bioinks composed of gelatine methacrylamide and hyaluronic acid methacrylate mimicked tumour ECM. We performed multi-omics analysis, including transcriptomics and DNA methylomics, to determine differences between the 3DBPO model and traditional models. Compared with 2D models and tumour spheroids, our 3DBPO model showed significant changes in cell cycle, metabolism, adherens junctions, and other pathways associated with epigenetic regulation. The 3DBPO model was more sensitive to therapies targeted to the autophagy pathway. We showed that simulating ECM yielded different osteosarcoma cell metabolic characteristics and drug sensitivity in the 3DBPO model compared with classical models. We suggest 3D printed osteosarcoma models can be used in osteosarcoma fundamental and translational research, which may contribute to novel therapeutic strategy discovery.

Video-rate high-precision time-frequency multiplexed 3D coherent ranging

Frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) is an emerging 3D ranging technology that offers high sensitivity and ranging precision. Due to the limited bandwidth of digitizers and the speed limitations of beam steering using mechanical scanners, meter-scale FMCW LiDAR systems typically suffer from a low 3D frame rate, which greatly restricts their applications in real-time imaging of dynamic scenes. In this work, we report a high-speed FMCW based 3D imaging system, combining a grating for beam steering with a compressed time-frequency analysis approach for depth retrieval. We thoroughly investigate the localization accuracy and precision of our system both theoretically and experimentally. Finally, we demonstrate 3D imaging results of multiple static and moving objects, including a flexing human hand. The demonstrated technique achieves submillimeter localization accuracy over a tens-of-centimeter imaging range with an overall depth voxel acquisition rate of 7.6 MHz, enabling densely sampled 3D imaging at video rate.

Experimental and numerical investigation on reserve strength of jackets with single diagonal and X brace configurations

ABSTRACT Jacket framing arrangement X, K, V and single bracing systems used in jackets influence the ultimate strength and redundancy in the system. Experimental investigation was conducted on a 2D frame (1:20) with two different bracing patterns (2D-1 and 2D-2) the results are compared with that obtained from nonlinear pushover analysis. Parametric assessment of Reserve Strength Ratio has been carried out on three bracing patterns, namely 3D-A, 3D-B and 3D-C using non-linear pushover analysis including pile-soil interaction. It is observed that RSR increases by 15% for X brace configuration (2D-2) compared to single brace configuration (2D-1). The RSR for 3D-B is found to be higher by be 35 % and 12% compared to bracing patterns 3D-A and 3D-C respectively. The brace pattern 3D-C exhibits similar strength to 3D-B, illustrating the insignificance of the brace at the top bay and this leads to reduction in wave loads on the system.

Gluon gravitational structure of hadrons of different spin

The gravitational form factors (GFFs) of hadrons encode the matrix elements of the energy momentum tensor of QCD. These quantities describe how energy, spin, and various mechanical properties of hadrons are carried by their quark and gluon constituents. We present the gluon GFFs of the pion, nucleon, $\rho$ meson, and $\Delta$ baryon as functions of the squared momentum transfer $t$ in the region $0 \leq -t < 2 \; \text{GeV}^2$, as determined in a lattice QCD study with pion mass $m_{\pi} = 450(5) \; \text{MeV}$. By fitting the extracted GFFs using multipole and z-parameter expansion functional forms, we extract various gluon contributions to the energy, pressure, and shear force distributions of the hadrons in the 3D and 2D Breit frames as well as in the infinite momentum frame. We also obtain estimates for the corresponding gluon mechanical and mass radii, as well as the forward-limit gluon contributions to the momentum fraction and angular momentum of the hadrons.

Optimum performance-based design of unsymmetrical 2D steel moment frame

The most commonly used analysis method in performance-based design (PBD) is nonlinear static analysis (NSA). In unsymmetrical 2D frames, unlike for the symmetrical state, NSA should be performed in two lateral loading directions. This complicates the process of achieving a feasible optimal design in addition to increasing the volume of calculations. In this study, to overcoming this issue, a two-step approach is proposed for the optimal PBD of unsymmetrical 2D steel moment-resisting frames (SMRFs). In this approach, in two independent steps, the structure is analyzed with a lateral loading pattern based on the first mode shape in the positive and negative directions. The implementation of the second step is conditional on the satisfactory completion of the first step. The objective function takes into account the differences between successful and unsuccessful steps. The constraints considered are based on the acceptance criteria for SMRF according to FEMA-356 at each performance level. The effectiveness of the proposed approach has been investigated by employing four meta-heuristic optimization algorithms to determine the optimum design for case studies of SMRF structures having three and nine stories.

Study on 3D multi-performance composite films of Fe3O4 decorated CNTs/graphene oxide

In this work, three-dimensional (3D) Fe3O4 decorated CNTs/graphene oxide (Fe3O4-CNTs/GO) composite film was used to enhance glass fiber reinforced polymer (GFRP) resulting in three excellent performances of interlaminar toughening, resistance to temperature changes, and electromagnetic interference (EMI) shielding were all achieved. The end notch bending (ENF) test results showed that Fe3O4-CNTs/GO film enhanced GFRP was 60.51% and 79.64% higher than the baseline (GFRP without reinforcement film) after RT or temperature cycle treatment, while the GFRP of pure CNTs film was 21.79% and 26.34% higher than the baseline, respectively. The change in interlaminar toughness of Fe3O4-CNTs/GO samples by temperature cycle was also lower than that of the other two samples (Fe3O4-CNTs/GO film: 4.15%, pure CNTs film:11.15%, baseline: 14.35%). The average EMI shielding effectiveness of Fe3O4-CNTs/GO films with a thickness of only 0.052 mm was 25 dB, about 500% higher than the 5 dB of the control group (ordinary CNTs/Fe3O4/GO films, thickness 0.047 mm). Scanning electron microscopy (SEM) further confirmed the existence of the 3D frame structure and interlamellar strengthening interface of Fe3O4-CNTs/GO films.