Experimental Verification Tests Research Articles

Analysis and Optimization of Dynamic and Static Characteristics of the Compliant-Amplifying Mechanisms.

Compliant amplifying mechanisms are used widely in high-precision instruments driven by piezoelectric actuators, and the dynamic and static characteristics of these mechanisms are closely related to instrument performance. Although the majority of existing research has focused on analysis of their static characteristics, the dynamic characteristics of the mechanisms affect their response speeds directly. Therefore, this paper proposes a comprehensive theoretical model of compliant-amplifying mechanisms based on the multi-body system transfer matrix method to analyze the dynamic and static characteristics of these mechanisms. The effects of the main amplifying mechanism parameters on the displacement amplification ratio and the resonance frequency are analyzed comprehensively using the control variable method. An iterative optimization algorithm is also used to obtain specific parameters that meet the design requirements. Finally, simulation analyses and experimental verification tests are performed. The results indicate the feasibility of using the proposed theoretical compliant-amplifying mechanism model to describe the mechanism's dynamic and static characteristics, which represents a significant contribution to the design and optimization of compliant-amplifying mechanisms.

Investigation on the operating characteristics of a directly-attached light crystalline silicon PV roof

The building-integrated photovoltaic (PV) technology can alleviate electricity consumption during peak usage time. However, there are heavy and fragile problems in traditional silicon PV modules while low efficiency and poor stability in thin film modules. Hence, in the present study, a novel lightweight crystalline silicon PV module is first proposed. Then an electro-thermal coupling model for the novel lightweight PV roof is established and an experimental verification test platform based on four different substrates was built. Based on the data of typical meteorological days, the characteristics of operating, energy saving, and emission reduction of the novel lightweight PV roof in summer are analyzed. Finally, the influences of substrate thickness and wind speed on the characteristics of operating, energy-saving, and emission reduction of the novel lightweight PV roof are revealed. The results show that compared with the conventional roof, the lightweight PV roof slightly increases the building cooling load, but the electricity benefits guarantee the lightweight PV roof with a good building energy-saving effect in return. Among different substrates, the PV roof based on asphalt substrate achieves the best PV performance, building energy saving and emission reduction. The PV performance and the building energy saving of the lightweight PV roof based on TPO substrate are the most sensitive to thickness and wind speed while the asphalt is the least. In addition, thinning the substrate and increasing the wind speed could promote the net yield and CO2 emission reduction of the novel lightweight PV roof.

Partially Isolated Multiport Converter With Automatic Current Balancing Interleaved PWM Converter and Improved Transformer Utilization for EV Batteries

Multiport converters (MPCs) that can regulate multiple input/output ports have been proposed to reduce the converter count in electric vehicle battery systems consisting of multiple batteries (12-V, 48-V, and high-voltage batteries). Conventional MPCs consist of an interleaved PWM converter and DAB converter, and an active current balancing control is indispensable for the interleaved converter. Furthermore, the transformer utilization unavoidably decreases because switches are driven with a nominal duty cycle of 0.75 for 48-/12-V batteries. This article proposes a novel MPC with an automatic current balancing capability and improved transformer utilization. A flying capacitor (FC) is added to the interleaved PWM converter to automatically balance inductor currents. The added FC also allows operations with a nominal duty cycle of 0.5 for 48-/12-V batteries, improving the transformer utilization. The experimental verification tests using a 1-kW prototype demonstrated the automatic current balancing capability, improved transformer utilization, and flexible power exchange among three batteries.

Development and Performance Evaluation of an IoT-Integrated Breath Analyzer.

Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns.

Design and evaluation of a volatile particle remover combining hot dilution and a thermodenuder

Portable and efficient removal of volatile particles from motor vehicle exhaust is essential for the accurate measurement of involatile solid particle number concentration in on-board emission measurement systems. For this purpose, a volatile particle remover with a combination of hot dilution and a thermodenuder (referred to as VPR-TD) was designed. The flow and temperature fields of the VPR-TD were simulated and analyzed by COMSOL Multiphysics software by which experimental verification and system performance tests were performed. The simulation showed that the effective heating length and residence time of the VPR-TD increased by 6 cm and 25%, respectively, as the dilution ratio increased from 1 to 10 at a total flow of 0.6 L/min at 350 °C. In addition, the experimental results showed that the penetration efficiency of 100 nm sodium chloride (non-volatile particles) in the VPR-TD was higher than 70.4%, and the removal efficiency of 30 nm tetracontane (volatile particles) was higher than 99% at a total flow 0.6 L/min, heating temperature 350 °C, and dilution ratios of 5 and 10. In summary, when the dilution ratio was set to 10, the device met the requirements of the Light Duty Vehicle Emission Limits and Measurement Methods (CHINA 6) for a volatile particle remover. At dilution ratio of 5, VPR-TD may be used as a pre-conditioning unit in portable emissions measurement systems.

Tristate ferroelectric memory effect attained by tailoring the ferroelectric behavior in Bi1/2(Na0.8K0.2)1/2TiO3 with Eu doping.

The ferroelectric behavior of Bi1/2(Na0.8K0.2)1/2TiO3 has been tailored by Eu3+ doping and the intermediate relaxor state is utilized for tristate ferroelectric memory effect. As Eu3+ content increases, the local structural disorder tends to get enhanced and the stability of ferroelectric order gets weakened. The disruption effect of Eu3+ is manifested structurally in XRD and PL spectra, and electrically in the ferroelectric, dielectric and piezoelectric properties. We found that the BNKT:3.0%Eu which owns a relaxor state under electrical cycle would be suitable for tristate ferroelectric memory, where two ferroelectric states and the relaxor state are respectively served as the "±1" and "0" memory states. We designed the verification experiments, and the results show good feasibility and stability. Moreover, it is innovative using PL spectra of Eu3+ to understand the structural changes related to different memory states, owning to its sensitivity to local structural symmetry. It also implies the possibility for non-destructive optical readout.

Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net

Image semantic segmentation has become an essential part of autonomous driving. To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed. Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network. To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test on the Cityscapes semantic segmentation dataset. The validation and comparison results show that the overall segmentation results of the algorithm network can achieve 78.02% MIoU on Cityscapes validation set, which is better than the basic algorithm network and the other latest semantic segmentation algorithms network. Besides meeting the stability and accuracy requirements, it has a particular significance for the development of image semantic segmentation.

THEORETICAL AND EXPERIMENTAL MODELLING OF THE TRIBOLOGICAL BEHAVIOUR ASPECTS OF CONTACT ELEMENTS IN THE PRECESSIONAL GEARING (PG)

This paper deals with the theoretical-experimental model of the tribological behavior of the contact elements in precessional gears (PG) in the functions of conjugate profiles of linear velocities “convex-concave” or “convex-convex” for the corresponding geometric parameters (ρki, r, ρki – r) and the values of the kinematic force parameters (VE1, VE2, Vai) for each concrete position of the ki points. The calculation of the constructive-functional and kinematic parameters of the “roller-shoe” and “roller-roller” elements of the tribocouple were adjusted for the experimental verification tests on the SMT-1 installation and adapted to the specific test conditions of the tribocouples with conformal contact of gear teeth made of plastics, which will be subjected to sliding friction and rolling processes of the concrete tribocouple elements.

Development of AI Algorithm for Weight Training Using Inertial Measurement Units

Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health.

The Ground-Based BIOMEX Experiment Verification Tests for Life Detection on Mars.

The success of an astrobiological search for life campaign on Mars, or other planetary bodies in the Solar System, relies on the detectability of past or present microbial life traces, namely, biosignatures. Spectroscopic methods require little or no sample preparation, can be repeated almost endlessly, and can be performed in contact or even remotely. Such methods are therefore ideally suited to use for the detection of biosignatures, which can be confirmed with supporting instrumentation. Here, we discuss the use of Raman and Fourier Transform Infrared (FT-IR) spectroscopies for the detection and characterization of biosignatures from colonies of the fungus Cryomyces antarcticus, grown on Martian analogues and exposed to increasing doses of UV irradiation under dried conditions. The results report significant UV-induced DNA damage, but the non-exceeding of thresholds for allowing DNA amplification and detection, while the spectral properties of the fungal melanin remained unaltered, and pigment detection and identification was achieved via complementary analytical techniques. Finally, this work found that fungal cell wall compounds, likely chitin, were not degraded, and were still detectable even after high UV irradiation doses. The implications for the preservation and detection of biosignatures in extraterrestrial environments are discussed.

Experimental verification tests of isokinetic equipment used in sport and rehabilitation

AbstraktThe paper deals with experimental verification of selected types of tests on isokinetic, diagnostic IsoForce2 training equipment. The isokinetic device provides accurate and immediate information about the developed strength in the exerciser's movement. The main criterion is its versatility when used in sport or rehabilitation. Therefore, it was necessary to create a portable adjustable structure that allows all the movements to be carried out and must meet other specific requirements. An important information is the amount of data collected, which is collected through the measurement system and then analyzed. The first test was elbow flexion, which was verified in arm-wrestling. The second test was knee flexion, which tested patients in rehabilitation after knee surgery to determine the difference in maximum strength between the lower limbs. The results of measurements are accurate, repeatable and provide athletes, patients, coaches and physiotherapists with immediate information about the level of strength abilities in a given type of test or movement.

AIR FLOW CONTROL VALVE DEVELOPMENT WITH REINFORCED OPERATING PARAMETERS

Recent research has shown that particles in the air and harmful gases in the environment increase the risk of various respiratory diseases and mortality rates. For effective ventilation, particles and harmful gases in the environment must be captured before they are released into the environment. Air flow control valves (AFCVs) play an active role in the capture of particles and harmful gases in the environment with central ventilation system. In the study, an innovative AFCV, which is the most critical part of central ventilation systems, has been developed. Performance criteria in the innovative AFCV design have been the capture rate, the flow rate and turbulence intensity. In this study, six valves are designed as an alternative to the current valve. The designed valves were mounted on the central ventilation system hood and numerical analysis was performed. ANSYS Fluent software was used for the analysis. The best and worst valves were determined as a result of numerical analysis. Prototypes of the determined valves were created and experimental verification tests were carried out. Again, numerical analyses simulating the experimental tests were performed with these two valves. The experimental test results and numerical analysis results were compared. As a result, due to the innovative AFCV design, the air flow and the capture speed have been increased and the turbulence intensity has been reduced.

Endoscopic image recognition method of gastric cancer based on deep learning model

AbstractThe combination of computer algorithms to diagnose clinical images has attracted more and more attention. This research aims to improve the efficiency of gastric cancer (GC) diagnosis, so deep learning (DL) algorithms are tentatively used to assist doctors in the diagnosis of gastric cancer. In the experiment, the collected 3591 gastroscopic images were divided into network training set and experimental verification test set. The lesion samples in the image are all marked by many endoscopists with many years of clinical experience. In order to improve the experimental effect, 5261 endoscopic images were obtained by expanding the training set. Then the obtained training set is input into the convolutional neural network (CNN) for training, and finally get the algorithm model DLU‐Net. By inputting 598 test set samples into the CNN constructed in this paper, five results such as advanced GC, early GC, precancerous lesions, normal and benign lesions can be identified and output, with a total accuracy of 94.1%. It can be concluded that the DL algorithm model constructed in this paper can effectively identify the staging characteristics of cancer as well as gastroscopic images, greatly improve efficiency, and effectively assist physicians in the diagnosis of GC under gastroscopy.

Generation Method of Cutting Tool Paths for High-Speed and High-Quality Machining of Free-Form Surfaces

In general, NC programs for machining free-form surfaces using a computer numerical control (CNC) machine tool are generated using a computer-aided manufacturing (CAM) system. The tool paths (CL data) generated by a CAM system are approximated straight-line segments based on tolerance (allowable error). As a result, the tolerance affects the machining accuracy and time. If the tolerance is set to a small value, the lengths of the segments are shortened, and the machining accuracy is improved. The process in which a CNC machine tool reads and analyzes an NC program and controls the motors requires a minimum processing time of an NC program block (block-processing time). Therefore, if the lengths of the approximated straight-line segments are too small, it will be impossible to reach the indicated feed speed, and the machining time will be longer. In this study, by identifying the block-processing time of a CNC controller and deriving the appropriate length of the approximated straight-line segment based on the block-processing time, a CL data creation method that is capable of high-speed and high-accuracy free-form surface machining is proposed. In addition, experimental verification tests of the method are conducted.

Panel-to-Substring Differential Power Processing Converter With Embedded Electrical Diagnosis Capability for Photovoltaic Panels Under Partial Shading

Various kinds of differential power processing (DPP) converters have been developed to improve the energy yield of partially-shaded photovoltaic (PV) panels by precluding partial shading issues, such as the reduction in power generation and the occurrence of multiple maximum power points. Meanwhile, PV systems require fault diagnosis tools to detect premature deterioration and malfunctions that lead to a reduction in power generation of the system as a whole and, in the worst case, hazardous situations, such as a fire. This article proposes a novel DPP converter as well as an electrical diagnosis technique using the DPP converter. The proposed DPP converter not only addresses the partial shading issues but also provides the electrical diagnosis capability. In the diagnosis mode, the DPP converter injects ac currents to a panel, and an ac impedance of the panel can be calculated from the voltage response to the injected ac currents. The detailed operation analysis and experimental verification tests using a prototype were performed. The results demonstrated the improved power yield from partially shaded PV panels and the electrical diagnosis capability of the proposed DPP converter.

Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance

In this paper, meta-heuristic tuning of LQR weighting matrices using various objective functions on the experimental flexible arm under the effects of disturbance is investigated. The use of flexible and lightweight systems provides certain advantages such as high operating speeds, low electricity consumption, and low initial investment costs. However, such systems are more prone to vibration-related problems than their rigid and heavyweight counterparts. One of the closed-loop control methods used to suppress these vibrations is LQR control, but the success of the controller depends on the choice of the gain and regulator matrices. In this study, the Bees algorithm, a meta-heuristic search algorithm, is used to determine LQR matrices. The system performance is compared with similar studies in the literature. The proposed objective function reveals an improvement of 3.1% in rotor maximum overshoot compared to studies in the literature. Flexible link maximum overshoot has been reduced from 17.8 to 7.1%. In order to validate the applicability and repeatability of the proposed method under the noise and disturbance in real systems, experimental verification tests were conducted using the Quanser Flexible Link system. As a result, the proposed method has been experimentally validated and it is estimated that it may well be a very useful controller design approach in various engineering systems and related control system developments.

Post-Processing of FDM 3D-Printed Polylactic Acid Parts by Laser Beam Cutting.

In this paper, the post-processing of 3D-printed poly lactic acid (PLA) parts is investigated. Workpieces are manufactured by fused deposition modeling (FDM) 3D printing, while they may have defects in some areas such as edges. A post-processing is introduced here for 3D-printed samples by low power CO2 laser. The thickness of the FDM samples are 3.2 mm and printed by optimum conditions. Effects of process parameters such as focal plane position (−3.2–3.2 mm), laser power (20–40 W), and laser cutting speed (1–13 mm/s) are examined based on the design of experiments (DOE). Geometrical features of the kerf; top and bottom kerf; taper; ratio of top to the bottom kerf are considered as output responses. An analysis of the experimental results by statistical software is conducted to survey the effects of process parameters and to obtain regression equations. By optimizing of the laser cutting process; an appropriate kerf quality is obtained and also optimum input parameters are suggested. Experimental verification tests show a good agreement between empirical results and statistical predictions. The best optimum sample with 1.19 mm/s cutting speed, 36.49 W power and 0.53 mm focal plane position shows excellent physical features after the laser cutting process when 276.9 μm top and 261.5 μm bottom kerf width is cut by laser.

Computational Modeling of Prefrontal Cortex for Meta-Cognition of a Humanoid Robot

For robot intelligence and human-robot interaction (HRI), complex decision-making, interpretation, and adaptive planning processes are great challenges. These require recursive task processing and meta-cognitive reasoning mechanism. Naturally, the human brain realizes these cognitive skills by prefrontal cortex which is a part of the neocortex. Previous studies about neurocognitive robotics would not meet these requirements. Thus, it is aimed at developing a brain-inspired robot control architecture that performs spatial-temporal and emotional reasoning. In this study, we present a novel solution that covers a computational model of the prefrontal cortex for humanoid robots. Computational mechanisms are mainly placed on the bio-physical plausible neural structures embodied in different dynamics. The main components of the system are composed of several computational modules including dorsolateral, ventrolateral, anterior, and medial prefrontal regions. Also, it is responsible for organizing the working memory. A reinforcement meta-learning based explainable artificial intelligence (xAI) procedure is applied to the working memory regions of the computational prefrontal cortex model. Experimental evaluation and verification tests are processed by the developed software framework embodied in the humanoid robot platform. The humanoid robots’ perceptual states and cognitive processes including emotion, attention, and intention-based reasoning skills can be observed and controlled via the developed software. Several interaction scenarios are implemented to monitor and evaluate the model’s performance.

LLC Resonant Voltage Multiplier-Based Differential Power Processing Converter Using Voltage Divider with Reduced Voltage Stress for Series-Connected Photovoltaic Panels under Partial Shading

Partial shading on photovoltaic (PV) strings consisting of multiple panels connected in series is known to trigger severe issues, such as reduced energy yield and the occurrence of multiple power point maxima. Various kinds of differential power processing (DPP) converters have been proposed and developed to prevent partial shading issues. Voltage stresses of switches and capacitors in conventional DPP converters, however, are prone to soar with the number of panels connected in series, likely resulting in impaired converter performance and increased circuit volume. This paper proposes a DPP converter using an LLC resonant voltage multiplier (VM) with a voltage divider (VD) to reduce voltage stresses of switches and capacitors. The VD can be arbitrarily extended by adding switches and capacitors, and the voltage stresses can be further reduced by extending the VD. Experimental verification tests for four PV panels connected in series were performed emulating partial shading conditions in a laboratory and outdoor. The results demonstrated the proposed DPP converter successfully precluded the negative impacts of partial shading with mitigating the voltage stress issues.

Hypoid gear integrated wear model and experimental verification design and test

Hypoid gear abrasive wear significantly influences transmission error which may worsen gearing performance. Wear and transmission error are best studied jointly. This paper reports on a proposed integrated hypoid gear wear model which combines Archard's wear equation with a conjugate approach, tooth contact analysis. Transmission error calculation is included in this integrated wear model. The model is also tested and verified by an experimental design in which transmission error measures wear rate. This allows hypoid gear wear prediction simulation to be controlled by transmission error. It can be examined, calibrated, and synchronized with solid hypoid gear wear progress. Identical no-wear and used some-wear vehicle hypoid gears with different wear times are applied experimentally. Statistical analysis standard deviations and geometric means are used to quantify transmission error magnitude and fluctuation. Magnitude controls wear prediction geometry updates. Fluctuation examines integrated wear model accuracy by comparing transmission errors. The test suggests that the accuracy of this integrated hypoid gear wear model is acceptable as there is minimal difference between predicted gear wear and actual gear wear.