Real Test Research Articles

Lattice Structures—Mechanical Description with Respect to Additive Manufacturing

Lattice structures, characterized by their repetitive, interlocking patterns, provide an efficient balance of strength, flexibility, and reduced weight, making them essential in fields such as aerospace and automotive engineering. These structures use minimal material while effectively distributing stress, providing high resilience, energy absorption, and impact resistance. Composed of unit cells, lattice structures are highly customizable, from simple 2D honeycomb designs to complex 3D TPMS forms, and they adapt well to additive manufacturing, which minimizes material waste and production costs. In compression tests, lattice structures maintain stiffness even when filled with powder, suggesting minimal effect from the filler material. This paper shows the principles of creating finite element simulations with 3D-printed specimens and with usage of the lattice structure. The comparing of simulation and real testing is also shown in this research. The efficiency in material and energy use underscores the ecological and economic benefits of lattice-based designs, positioning them as a sustainable choice across multiple industries. This research analyzes three selected structures—solid material, pure latices structure, and boxed lattice structure with internal powder. The experimental findings reveal that the simulation error is less than 8% compared to the real measurement. This error is caused by the simplified material model, which is considering the isotropic behavior of the used material PA12GB (not the anisotropic model). The used and analyzed production method was multi jet fusion.

A Case Study on the Risk Assessment for O&M of a 1 MW Tidal Current Energy Converter

Tidal power is a technology that generates electricity by utilizing tidal current energy, and in recent years, with the advancement of technology, it has reached the stage of real sea performance test. However, in the case of Korea, there is a lack of construction experience and equipment, and work in fast flows such as the Uldolmok region involves various risk factors. In order to establish measures to reduce these risk factors, Korea’s Occupational Safety and Health Act recommends performing a risk assessment to identify and manage risk factors that may occur during work in advance. Risk assessment is a process aimed at identifying and evaluating potential risk factors that may arise during work, with the goal of minimizing losses caused by accidents. Therefore, in this study, a risk assessment was conducted to identify risk factors that may occur during offshore O&M work of the 1 MW tidal current energy converter and to ensure the safety of workers and the environment. A total of 60 risk factors were identified, including marine and weather conditions, equipment and personnel, and work environments, and a qualitative risk assessment was conducted three times based on the judgment of several field experts.

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.

A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

ABSTRACTBy increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.

A Virtual Assembly Technology for Virtual–Real Fusion Interaction of Ship Structure Based on Three-Level Collision Detection

With the rapid advancement of new-generation information technology, the virtual–real fusion interaction has increasingly become a crucial technique for structural analysis to determine the strength envelope of hulls. A high-precision assembly of the experimental devices in a virtual environment is vital. A virtual assembly method for a structural virtual–real fusion test based on the oriented bounding box (OBB) algorithm, the Devillers and Guigue algorithm, and differential triangle facets algorithm are proposed in this paper. The experiment of the connector in a typical offshore floating platform is performed as a case, which indicates that the virtual assembly method proposed in this paper enables the assessment of assembly virtually prior to the actual experiment, and the assembly accuracy can reach 0.01 mm. The digitalization and virtual–real fusion interaction for the mechanical experiment of hulls are advanced to ensure efficiency, safety, and economy.

Boron-doped carbon dots with time-resolved room temperature phosphorescence for detection of ciprofloxacin hydrochloride and information encryption applications.

Novel boron-doped carbon dots (BCDs) with extended afterglow characteristics were synthesized via a one-step solvothermal method using acrylamide, sulfosalicylic acid, and sodium tetraborate as protective matrices. The presence of boron from sodium tetraborate introduced an empty orbital, allowing it to form a more extended conjugated system with adjacent oxygen atoms, thereby lowering the energy level of the lowest unoccupied molecular orbital in the system. The phosphorescence emission of these BCDs exhibits a red shift over time from 450 to 500 nm. These BCDs have been effectively utilized to produce anti-counterfeit phosphorescent powder. Additionally, the BCDs display optimal fluorescence excitation at 330 nm and optimal emission at 420 nm. They demonstrate a detection limit for ciprofloxacin hydrochloride of 37 nM in the 1-100 µM concentration range and 26 nM in the 100-210 µM range. This fluorescence detection is governed by an inner filter effect. Real sample testing further confirms that these BCDs serve as excellent sensors for ciprofloxacin hydrochloride.

Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications.

Influence of trains meeting on the ventilation performance of equipment compartment with independent air duct in high-speed train: numerical and experimental study

AbstractDuring the train meeting events, train equipment compartments are exposed to the worst pressure changes, potentially affecting the ventilation performance of equipment, particularly for electrical facilities equipped with independent air ducts. In this paper, a two-step method is used for numerical computation: (1) obtaining the temporal and spatial transient node data of the flow field sections during the train-passing simulation and (2) using the data as the input data for the equipment compartment simulation. In addition, this paper also compares the difference in equipment ventilation between the single-train and train-passing scenarios in real vehicle tests. The results indicate that the primary factors influencing ventilation effectiveness are the aerodynamic compression and deceleration of airflow induced by the other train’s nose, as well as the instability of the external flow field in the wake of the other train. During train crossing, the air is forced into the air duct, with a maximum ratio of the airflow in-duct to the airflow out-duct reaching 3.2. The average mass flow falls below the rated mass flow for the converter. Compared to the rated air volume of converter, the maximum suppression rates obtained from testing and simulation are – 24.5% and – 16.8%, respectively. Compared to the single-train operation, the maximum suppression rates obtained from testing and simulation are – 15% and – 18%, respectively. These findings provide valuable insights into the design and operation of high-speed trains.

Dynamic Inversion-Enhanced U-Control of Quadrotor Trajectory Tracking

This study proposes a universal (U)-control enhanced with dynamic inversion for a class of multiple-input multiple-output (MIMO) quadrotor flight tracking operations. In the technique, a robust compensator is integrated with the U-controller to achieve stabilisation at the equilibrium and setpoint tracking in the presence of any unmodelled uncertainties and external disturbances. A series of bench tests of simulated and real experiments on a Parrot Mambo quadrotor are conducted to show the design framework from the academic formulation to Simulink simulation and real flight tests.

Quantifying marine growth on pipelines with archival remotely operated vehicle footage – a case study

Abstract. Marine growth can substantially affect the hydrodynamic properties of subsea infrastructure. The ability to accurately quantify this growth is essential for designing and maintaining subsea structures. Traditionally, quantifying marine growth has been done by applying simple ratio equations putting in relationship the known distance of an object and its length observed in the image to the length of an unknown object only observed in an image. This method is very sensitive if measurements are not taken in the same plane. Hence, this study aims to establish a method for creating a 3D model of a subsea pipeline segment based on Structure from Motion (SfM). The proof of concept is done using a case study. For this study, Remotely Operated Vehicle (ROV) footage from a 2019 inspection survey is utilised. The scenario in which the procedure is tested represents a difficult, but realistic test case. Despite the lack of metadata, video quality and obstructions (e.g. watermarks), meaningful results were produced. The SfM workflow included masking obstructions (e.g. watermarks), and an iterative camera alignment procedure to generate 3D models, enabling the measurement of marine growth using either the models or ortho images created. While the resulting models were sufficiently accurate to provide meaningful measurements for harder growth types, the method faced challenges in accurately modelling softer growth types, suggesting the need for further refinement. Nevertheless, SfM was proven to be a more reliable method of measuring biofouling than the ratio approach that been traditionally used.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Adaptive sample size strategies for process dispersion using EWMA control chart

AbstractMonitoring the stability of measured dispersion in a process quality parameter is one of the critical steps in statistical process control (SPC) and is a paramount activity carried out by specialists. For this purpose, the current research presents a promising and computationally effective tool where the exponentially weighted moving average (EWMA) statistic utilizes the quadratic sigmoid function by incorporating sample size leading to the Quadratic Sigmoid Exponential weighted Moving Average (QSEWMA) control chart. The adaptive sample size is a strategy that increases or decreases the sample size according to the conditions of the process and accordingly, increases the sensitivity and response of the control chart. The proposed strategy improves the detection of process dispersion changes, making it a more efficient and effective way of quality assurance. Our proposed control chart is superior to the EWMA‐S2 chart, HHW‐EWMA chart CUSUM‐S2chart, and parametric free adaptive EWMA (AEWMA) charts for dispersion through extensive Monte Carlo simulations. The proposed QSEWMA chart has better detection sensitivity and fewer false alarms, and is, in general, more effective comparatively. At last, a real data application test is done to measure the practicability and highest accuracy of the above chart.

Investigating constraint programming and hybrid methods for real world industrial test laboratory scheduling

AbstractIn this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests are performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches.

A Turn‐On Fluorescence Probe Based on a New Salamo‐Co (II) Coordination Polymer for Tyrosine Detection and Its Application in Water Samples

ABSTRACTA novel salamo‐Co (II) coordination polymer probe CP was synthesized firstly, and its crystal structure, [Co3(L)2(PTA)(H2O)2]n·4nDMF, was determined by X‐ray diffraction analysis. The probe has a rare aggregation‐induced luminescence enhancement (AIEE) effect. When applied to amino acid detection, it was found to have a high selective recognition of tyrosine. With the increase of tyrosine, the fluorescent strength of the probe was enhanced in a short period of time. In addition, the binding ratio of probe to Tyr was determined by fluorescence titration experiment, and the detection limit LOD of probe CP for Tyr was calculated to be 2.57 × 10−9 M, and the binding constant Ka was 2.96 × 105 M−1. According to UV absorption spectrum, FT‐IR spectra, ESI mass spectrometry, and DFT calculation, it was speculated that Tyr can bind to the probe after adding Tyr into the probe solution, triggering FRET and ICT effects, resulting in increased fluorescence intensity and blue shift, and finally achieving the specific recognition of tyrosine by probe CP. Finally, the practical application of probe CP was studied. Through strip test and real water sample test, it was found that the probe can be used for qualitative and quantitative analysis of tyrosine.

Integrated Motion Control Strategy Considering Parameter Robustness for Distributed Vehicle by Wire

<div>To address the issues of functional conflicts in execution subsystems and the deterioration of control performance due to model parameter uncertainties in the motion control of distributed vehicle by wire, this article proposes an integrated control strategy considering parameter robustness. This strategy aims to compensate for model mismatch, resolve functional conflicts, and achieve motion coordination. Based on the over-actuation characteristics of distributed vehicle by wire, this article constructs the dynamic model and utilizes the tire cornering properties along with phase portraits to delineate the working regions of the execution subsystems. To deal with model parameter uncertainties and mismatch, tube-based model predictive control (tube-based MPC) is applied to the control strategy design, which compensates for model deviations through state feedback and constructs a robust positively invariant set (RPI) to constrain the system state. Correspondingly, the weights of control inputs are adjusted adaptively, according to the working regions, to optimize the coordination logic of integrated control. In order to verify the effectiveness and feasibility of the strategy, extreme driving condition tests are executed on hardware-in-the-loop (HIL) and real vehicle test platforms. The test results indicate that the strategy proposed in this article is able to reduce the sideslip angle and tracking error of yaw rate, improve driving stability under extreme conditions through integrated control, and especially, it can still maintain precise stability control performance under severe model mismatch, exhibiting strong robustness facing parameter uncertainties.</div>

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.

Constructing boosted charge separation for efficient H2O2 production and pollutant degradation by highly defective ZnIn2S4/ carbon doped boron nitride

Solar-driven photocatalytic production of H2O2 as a promising environmentally-friendly approach has been applied as a chemical reagent and for enhancing photocatalytic remediation. However, this process is still limited by unsatisfactory efficiency and hard separation of photogenerated charge carriers. Herein, we design a dual pathway to improve hydrogen peroxide production by establishing a 2D/2D heterojunction between carbon-doped boron nitride (BCN) and defective zinc indium sulfide (ZIS). The introduction of multiple vacancies and dopants at the interface successfully promoted the interfacial charge transfer and peroxide generation. The Z-scheme heterojunction establishes a built-in electronic field, facilitating continuous electron accumulation and depletion, thereby inducing band bending. As a result, the optimized BCN/ZIS-30 composite (>420 nm wavelength) exhibits 925 μmol/g/h in pure water with no other additions and advances this production to 2006.1 μmol/g/h (2 times as defective ZIS and 12.1 times as BCN) under the existence of isopropanol and aeration. Mechanism investigation indicated that H2O2 is generated via a two-step electron reduction route and water oxidation reaction. The generated H2O2 successfully constructs a self-photo-Fenton system by forming hydroxyl radicals. Accompanied by strong oxygen activation ability, the photo-Fenton system also exhibits rapid degradation of antibiotics with 95 % elimination in 60 minutes. The practical applicability of this photocatalyst was demonstrated through real water remediation tests, exhibiting its effectiveness in removing antibiotics, COD, and total ammonia nitrogen, highlighting its potential for practical water treatment applications. This work provides a novel idea on constructing heterojunction materials for energy saving and water remediation.

Real-Time Embedded Control of Vehicle Dynamics Using ESP32: A Discrete Nonlinear Approach

This article explores the application of the Espressif ESP32 System-On-Chip (SoC) for managing vehicle dynamics through real-time digital proportional–integral (PI-like) control. We present the development of advanced driving assistance algorithms for Active Front Steering (AFS) and Rear Torque Vectoring (RTV) on this cost-effective, commercially available embedded system. Using digital PI-like control algorithms designed for AFS and RTV, the primary ESP32 board receives and processes steering signals, executing a discrete-time control model of the vehicle dynamic to enable dynamic adjustments to steering and torque. To enhance simulation realism, a secondary ESP32 is employed to generate the steering signal, effectively mimicking a steer-by-wire system via its analog output ports. This configuration facilitates the simulation and evaluation of control algorithms in a realistic test environment, ensuring enhanced vehicle dynamic stability and maneuverability under various conditions. Additionally, simulations are conducted using MATLAB 2023a and CarSim 2017.1 to compare the efficacy and benefits of the implementation. Our objective is to establish a platform for evaluating discrete controllers capable of real-time vehicle operation. This methodology accelerates and reduces the cost of improving vehicle system stability and responsiveness, enabling the immediate verification and fine-tuning of control parameters as needed.

Does Embodiment in Virtual Reality Boost Learning Transfer? Testing an Immersion-Interactivity Framework

This study investigates the role of embodiment when learning a technical procedure in immersive virtual reality (VR) by introducing a framework based on immersion and interactivity. The goal is to determine how increasing the levels of immersion and interactivity affect learning experiences and outcomes. In a 2 × 2 factorial design, 177 high school students were assigned to one of four experimental conditions, varying levels of immersion (learning in immersive virtual reality wearing a head-mounted display (VR) vs. learning via a computer screen (PC)) and interactivity (directly manipulating objects using controllers/mouse and keyboard (congruent) vs. indirectly manipulating objects with a laser pointer to select a course of action (incidental)). The main outcome measure was a transfer task in which students were required to perform the task they had learned in the virtual environment using concrete objects in real life. Results demonstrated that students in the VR conditions experienced significantly higher levels of presence, agency, location, body ownership, and embodied learning compared to participants in the PC conditions. Additionally, students’ performance during the virtual lesson predicted their real-life transfer test. However, there were no significant effects of immersion or interactivity on any of the transfer measures. The results suggest that high immersion in VR can increase self-reported measures of presence, agency, location, body ownership, and embodied learning among students. However, increased embodiment—manipulated by adding immersion and congruent manipulation of objects did not improve transfer.

The LIFE SILENT project's approach to sustainable noise mitigation

The main objective of the LIFE SILENT PROJECT is the development of sustainable and eco-friendly solutions to mitigate noise in complex urban environments, where multiple and diverse noise sources, mainly roads and railways, coexist across densely populated areas. Mitigating noise in such environments generally excludes the use of solutions that might interfere with the urban context, such as noise barriers. This is mainly due to the proximity of receivers to the noise source, which reduces the visibility of the surroundings and air circulation, leading to microclimatic modifications and social opposition. This is why noise mitigation measures acting directly on the source are recommended, such as low-noise pavements for roads, and dampers, rail grinding, and silent brakes for railways. However, these solutions suffer from either durability issues or high implementation costs. Thus, an improvement is necessary. In the LIFE SILENT project, innovative and sustainable low-noise and long-lasting pavements, as well as low-height noise barriers will be developed and demonstrated in a real test environment. Additionally, procedures will be established to facilitate their synergistic implementation, providing transport infrastructure owners and managers with solid information to support their widespread adoption. In this paper, a general overview of the LIFE SILENT project is given.