Experimental Performance Evaluation Research Articles

Experimental evaluation of performance of geo-mat system on weak to intermediate subgrade

Geo-mat system is considered as an alternative to a flexible or rigid pavement that can be used for fire lanes, parking areas, or roadway shoulders for cars, pickup trucks, utility/delivery trucks, and fire trucks. Also, it can be used to reduce the thickness of the base course in unpaved roads. In this investigation, six cyclic loading tests were conducted to assess the efficacy of the geo-mat system on weak to intermediate subgrade layers, characterized by California Bearing Ratios (CBR) of 2%, 3%, and 4%. Control sections, consisting of a 300-mm thick base course over subgrade layers with CBR values of 2% or 4%, were compared against the test sections comprising a geo-mat system placed atop aggregates of varied thickness (150 or 50 mm) on a geotextile layer over the same subgrade layers. Each test section underwent cyclic loading, progressively increasing from 17.8 to 71.2 kN using a 300-mm diameter steel plate. The results demonstrate that the geo-mat system, particularly when placed over a 150 mm-thick aggregate base, exhibited comparable or superior performance to the control sections with a 300 mm thickness, especially under substantial cyclic loading. This was evidenced by the reduced permanent deformations beneath the loading plate and a wider load distribution. The increase of the subgrade CBR value reduced the permanent deformation significantly. When the intermediate subgrade with a 4% CBR was used, the geo-mat system played a more important role than the aggregate base and the base thickness had a minor effect on the performance.

Comparative Analysis of Machine Learning Algorithms for Diabetic Disease Identification

This article introduces a comparative analysis of different types of machine learning algorithms (MLAs) used for diabetic disease identification. Today machine learning algorithms are a major role in solving and identifying the different type of diseases in the medical sector. In the early prediction of diabetic to easily treat physicians and protect from other diseases in patients seven types of MLAs such as support vector machine (SVM), decision tree (DT), logistic regression (LGR), Gradient boost method (GDBM), k-nearest neighbour (KNN), XG boost (XGBM) and random forest (RF) are used for diabetic identification. PIMA Indian diabetic dataset (PIMAIDD) is used to train and test the MLAs. Confusion matrix, accuracy (ACR), precision (PCN), recall (RCL), f1-score (FSC), receiver operating curve (ROC) and K-fold cross-validation are the metrics used for performance evaluation of MLAs and experiments are implemented by Jupyter notebook and python sci-kit libraries. Six types of test cases were conducted whereas test case 4 (70%-30%) was well performed in which RF reported better results in diabetic identification that differentiates from other machine learning metric scores.

Experimental Evaluation of Mechanical Properties, Thermal Analysis, Morphology, Printability, and Shape Memory Performance of the Novel 3D Printed PETG‐EVA Blends

Experimental Evaluation of Mechanical Properties, Thermal Analysis, Morphology, Printability, and Shape Memory Performance of the Novel 3D Printed PETG‐EVA Blends

Corrosion and wear characterization of Ti6-Al-4 V alloy: Experimental analysis and performance evaluation

The research investigates the corrosion and tribological characteristics of the Ti-6Al-4 V alloy, which is extensively used in biomedical applications because of its lightweight construction, resistance to corrosion, and advantageous tribological qualities. Corrosion assessment included evaluating the electrochemical performance of samples in three distinct electrolytes: NaCl, HBSS, and Ringers. Results showed that NaCl exhibited the lowest corrosion rate, measuring at 0.0372104 mmpy.Tribological analysis using an engine reciprocating tribometer demonstrated varying wear rates, with the lowest recorded at 0.05 m/sec (2.047 × 10−17(m3/ Nm), while highest wear rate of 4.786 × 10−17(m3/ Nm) were revealed at sliding velocity of 0.08 m/sec.These findings contribute to understanding the performance of the alloy under different conditions, crucial for optimizing its use in biomedical application.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Experimental investigation and evaluation of shear performance of hollow slab beams strengthened by cavity grouting method

After 20 years of service, multiple web shear cracks were observed in 20 m-span prestressed concrete hollow slab beams. Shear enhancement is required to improve structural safety. To evaluate the strengthening effect of the new material on the hollow slab beam structure, fiber-reinforced cementitious composites (FRCC) were used to fill the cavities at the beam ends, and a comprehensive field load test was carried out on the shear-resistant performance of the actual bridge. The web principal stresses changed before and after strengthening under various working conditions, which were analyzed and compared with the performance of the traditional channel steel technique. The findings show that the web principal stresses are significantly reduced by more than 50 % after filling with FRCC, notably superior to the channel steel technique. In addition, both methods can reduce mid-span deflections by 10–20 %. The channel steel technique specifically enhances transverse connection capabilities. Furthermore, strain responses in the beam end sections were reduced by about 30–45 %. Finally, finite element simulations of single beam strengthening were also conducted to complement the experimental analysis. The simulation results show that the shear span ratio (λ), FRCC length and strand prestress loss remarkably impact the strengthening effect. The ultimate load capacity of the strengthened specimens decreases with the increase of λ and prestress loss and increases with the increase of FRCC length. In conclusion, the utilization of FRCC presents a superior alternative for shear enhancement in prestressed concrete hollow slab beams, offering substantial reductions in web principal stresses and enhanced structural performance compared to the traditional strengthening method.

Experimental investigation and performance evaluation of an indirect solar dryer: Effect of drying trays

Indirect solar dryers represent a clean and sustainable solution for reducing the moisture content of a variety of food and medical products. This technique enables products to be preserved over long periods, stored in compact volumes, and used when required.In this context, improving the efficiency of these systems is essential. Therefore, an experimental study of an indirect solar dryer operating with natural convection has been carried out in this paper. The experimental tests were performed at the Mechanics and Energy Laboratory at the Technopole research center,Oujda, Morocco, in August 2022. The main objective of this work is to evaluate the drying process of banana slices using three different types of drying trays, two made of wire mesh and one of perforated galvanized sheet. The evaluation of the drying system was mainly based on the variation in the moisture content of banana slices. Accordingly, five samples corresponding to different positions in each tray were examined. The obtained results show that banana slices in A-type mesh trays have a faster reduction in moisture content and a better quality than the other trays.

Devlopment of Foot Powered Laundry Machine

This paper presents the design and performance evaluation of a foot powered laundry machine aimed at providing sustainable laundry solutions for households. The proposed design integrates human- powered mechanical energy to drive the washing process, offering an eco-friendly alternative to conventional electric washing machines. The design process involved the selection of appropriate materials, mechanical components, and ergonomic considerations to ensure user comfort and efficiency. Performance evaluation experiments were conducted to assess the washing machine's effectiveness in removing stains, water consumption, and energy efficiency compared to electric counterparts. The results demonstrate the feasibility and potential environmental benefits of pedal-powered washing machines for promoting sustainable living practices.

Disposable portable buoy for data transmission between seafloor equipment and onshore laboratories

Communications between seafloor observatories and onshore laboratories are desired but difficult to achieve. Herein, we proposed and designed a disposable portable buoy that can be installed on a seafloor observatory to facilitate data transmission from the observatory to an onshore laboratory. The buoy receives data from the observatory through optical communication equipment on the seafloor. Once released, the buoy ascends to the sea surface and transmits data to the onshore laboratory using a satellite. The buoy comprises a carbon fiber cavity, employs an electromagnetic release mechanism, and can be deployed at an underwater depth of 4000 m. During numerical and experimental performance evaluations, the buoy accomplished reliable data transmission, release, and ascent within a high-pressure chamber. Finally, the buoy was integrated into a seafloor observatory in Qingjiang Lake, Hubei Province, China, and field-tested, demonstrating the feasibility of data transmission using the proposed buoy.

Experimental Performance Evaluation of RC Beams Strengthened by TRM with Improved Bond Capacity

Experimental Performance Evaluation of RC Beams Strengthened by TRM with Improved Bond Capacity

Autonomous traffic sign detection for self-driving car system using convolutional neural network algorithm

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data.

Tactical vessel deployment and empty container repositioning considering container turnover times

Global trade imbalances lead to the common occurrence of empty container flows from export-dominated to import-dominated ports in container shipping networks. Efficient strategies for repositioning empty containers are essential for addressing deficits at major ports. Existing studies often assume an arbitrary supply of empty containers at a port before repositioning. However, the actual quantity depends on laden containers shipped and turnover times. Considering this interdependence is crucial for reliable repositioning plans, especially at the tactical planning level.This paper explores an integrated problem of vessel deployment and empty container repositioning, aiming to deploy vessels along a route and periodically reposition empty containers, accounting for turnover times. We present an integer programming model minimizing operational and container shortage costs. A novel branch-and-price algorithm is developed, efficiently solving the master problem at each branching tree node using a novel approach that combines column generation and Lagrangian relaxation. Performance evaluation and computational experiments on instances generated from container port turnover statistics and shipping line operational data demonstrate the algorithm’s efficacy. The study highlights the impact of container turnover times on repositioning decisions, emphasizing the need for a comprehensive understanding of this dynamic in strategic planning.

EXPERIMENTAL EVALUATION OF TRIBOLOGICAL PERFORMANCE OF BORON STEEL PLOUGHSHARE UNDER DRY SLIDING CONTACT

Experimental evaluation of the tribological performance of boron steel ploughshare used for soil tillage operation has been studied under dry sliding contact. A ball-on-disc dry sliding tribological properties test was performed following ASTM standards. Surface morphology examination of the ploughshare sample was performed using optical microscopy. The effect of applied load, sliding speed, and sliding time on the tribological properties of the ploughshare sample was analyzed. Results showed that with increasing applied load from 2 to 10N, friction performance of the ploughshare material operated within the boundaries of 0.232≤µ≤0.325, 0.283≤µ≤0.378, 0.308≤µ≤0.381, 0.338≤µ≤0.405 at constant sliding speed and time of (5cm/s, 360s), (10cm/s, 540s), (15cm/s, 720s) and (20cm/s, 900s) respectively showing that friction increased as sliding speed and time increased with percentage increase of 22%, 8.8%, 9.7% and 16.3%, 0.8%, 6.3% for minimum and maximum friction respectively. Wear performance of the ploughshare material operated within the boundaries of 44.44≤WR≤94.44 x 10-6g/m, 16.67≤WR≤64.81 x 10-6g/m, 12.96≤WR≤53.70 x 10-6g/m, 18.89≤WR≤42.22 x 10-6g/m at constant sliding speed and time of (5cm/s, 360s), (10cm/s, 540s), (15cm/s, 720s) and (20cm/s, 900s) respectively showing that wear rate decreased as sliding speed and time increased with percentage decrease of 31.4%, 17.1%, 21.4% for maximum wear rate.

Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data

High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employing the LeakyReLU function as the final activation layer, the accuracy of decompressed data reconstruction can be significantly improved. To tackle the challenges of fitting the distribution of surface sound speed data caused by significant variations and noise, we propose two loss functions: slice mean square error and elemental mean square error. These loss functions are combined with mean squared error through weighted summation to enhance CAE-HROSSP’s ability to fit the distribution of surface sound speed values and minimize the reconstruction errors of compressed data. Performance evaluation experiments reveal that CAE-HROSSP outperforms two existing methods in compressing HROSSP data, achieving superior performance with smaller data reconstruction errors at higher compression ratios. Furthermore, transfer learning is utilized to enhance the training of CAE-HROSSP, employing HROSSP data from the area where the mesoscale eddy is situated, as well as at the convergence of cold and warm ocean currents. The compression performance of both the training set and the validation set is comparable in the sea, where the structure of the sound speed profile varies greatly. This indicates that CAE-HROSSP can compress highly variable sound speed profile data in more sea areas using transfer learning, and has the potential to be extended globally. The findings and insights obtained from this study provide guidance for future endeavors in utilizing autoencoders to compress HROSSP data.

Experimental study and performance evaluation of an air source heat pump (ASHP) system with a frost-free evaporator

The air source heat pump (ASHP) is operated in a low temperature and high humidity working environment. There is always a lot of frost forming on the outdoor heat exchanger, which hinders the heat transfer rate of the heat exchange surface, leading to the decrease of performance of the ASHP system. This paper presents a new type of frost-free evaporator for ASHP system. The heat transfer zone of the evaporator is composed of three parts: non-packing zone, plain finned-tube falling film zone and horizontal smooth tube bundle falling film zone. The experimental system of the frost-free evaporator is established, and the reliability of the experimental data is verified. The effects of refrigerant flow rate, inlet air temperature, fan frequency and solution spray mode on energy efficiency ratio, heat transfer expansion coefficient, air side pressure drop and exergy loss of the evaporator are studied. The performance of the evaporator is evaluated. The results show that with the increase of inlet air flow and temperature, the energy efficiency ratio of frost-free evaporator increases by about 8%–12%, and the air side pressure drop also increases. As the inlet air temperature increases from −10 to 15 °C, the heat transfer expansion coefficient of evaporator decreases by about 3%–6%, indicating that the increase of inlet air temperature will lead to an increase of sensible heat transfer ratio and a decrease of latent heat transfer ratio. The results also show that compared with no spray mode and continuous spray mode, the intermittent spray mode is more recommended as a feasible operation scheme to realize the continuous, efficient and stable operation of the new evaporator.

Experimental evaluation of combustion performance in the three main anatomical sections of poplar wood

ABSTRACT The interrelationship and organization between the axial and transversal systems gives rise to three main views, namely cross, radial and tangential sections. This study investigated the differences in combustion properties of the three main anatomical sections of poplar wood and explains the reasons for the differences regarding wood microstructure, pore structure, heat conduction, and microstructure after heat treatment. The thermal conductivity of poplar wood in the cross-section (Wood-CS) was 0.1864 W/m. K, exceeding those of the radial (Wood-RS) and tangential sections (Wood-TS) by 47% and 38%, respectively. In the cone calorimeter test (CC), the peak heat-release rate (pHRR) of Wood-CS samples was 92% and 73% higher than those of the Wood-RS and Wood-TS samples, respectively. This is attributed to the differences in microstructures and the occurrence of macropores in different anatomical sections. This study explored the combustion performance and mechanisms of wood samples in the three main sections, providing crucial theoretical insights for flame retardant impregnation levels in treating wood and aiding in the selection of fire-resistant wood-based building materials.

Performance evaluation of Raspberry Pi 4 and STM32 Nucleo boards for security-related operations in IoT environments

At present, Internet of Things devices are revolutionizing and impacting increasingly more areas of our daily lives. However, there remain doubts related to the lack of sufficient security in the IoT ecosystem. As such devices have limitations in terms of resources such as computational power and batteries, adding security mechanisms is often perceived as an unnecessary burden, slowing the further expansion of IoT-based solutions and applications.In this study, an investigation is conducted to verify the possibility of applying security measures such as strong encryption without hindering the performance of IoT devices. This factor is especially important from the perspective of the European project SILVANUS, in which various IoT application scenarios are envisioned. Taking into account the above, in this work, an extensive experimental performance evaluation campaign is performed using the DTLS-based encryption of network traffic for two popular boards: Raspberry Pi 4 and STM32 Nucleo. The obtained results demonstrate that the utilization of strong encryption is feasible (with some limitations) on IoT devices, but the resulting performance or battery life is not a reasonable argument anymore to leave IoT ecosystems completely insecure.

Text-independent speaker identification system using discrete wavelet transform with linear prediction coding

AbstractOne of the key problems of the modern day is the presentation of an identity verification system that can perform sufficient accuracy in identity verification, is resilient to assaults and noises, and can be recorded in the simplest possible method. In this study, a new speaker feature extraction which based on discrete wavelet transform (DWT) and linear prediction coding (LPC) algorithm (WLPCA) are investigated. This paper's primary objective is to evidence the performance of the new method for speaker identification by a Gaussian mixture model (GMM). The proposed method improves the recognition rate over the Mel-frequency cepstral coefficient (MFCC). Experimental evaluation of the process performance is performed on two speech databases; our recorded database and the publicly available TIMIT database. We show that the speech features derived by the newly proposed method are more suitable for GMM (91.53%), in terms of the time-consumed, by requiring less Gaussian mixtures than MFCC (85.77%). For testing the presented method in a noisy environment, Additive white Gaussian noise (AWGN) was added to the TIMIT database, where a slight improvement in the performance of the presented method (60.02%) over the MFCC (59.89%) was observed.

Experimental Performance Evaluation and Optimization of a Weak Gel Deep-Profile Control System for Sandstone Reservoirs.

Globally, most oil fields use excessive water flooding to recover oil. By injection of water between wells, channels are created, which result in lower oil recovery. Water-plugging deep-profile control must be used to control the excessive water production from an oil reservoir. This laboratory study used sulfonated polyacrylamides with a molecular weight of 10.3-13.0 × 106 Da (FPAM) and polyethylenimine (PEI-600) to formulate a weak gel system to control excessive water production from deep formations. Using different FPAM and PEI-600 concentrations, the Sydansk bottle test approach was applied to evaluate the gelation time, strength, and stability of the weak gel. The weak gel concentration of 0.5 wt % FPAM and 0.4 wt % PEI-600 was confirmed for deep-profile control by this approach. The temperature and salt resistance of the selected weak gel system were evaluated using the same bottle test methodology. The gelation time depends on temperature: 5-7 days at <100 °C to 0.5 days >100 °C. Salinity >20,000 mg/L significantly affected the weak gel system's strength. By performing a viscometer test, the viscosity of the weak gel system at different times was evaluated, confirming the gelation time of the selected weak gel. Next, a microfluidic chip flooding test analyzed weak gel performance and plugging ability in porous media. This micromodel provided a visual analysis of the weak gel plug. Finally, a low- to medium-permeability sandstone core-flooding was conducted to determine the plugging rate of weak gel at the core scale, followed by an evaluation of the injection pressure, blocking effect, and oil recovery. According to the study, the selected weak gel has an extended gelation time with a significantly low viscosity, which affects its injectivity and can move from injection wells into deep formations. In the core-flooding test, the weak gel's blocking rate after 7 days of gelation time exceeded 90%.

First experimental evaluation of count-rate performance for micrometre resolution deep silicon detector

Objective. An ultra-fine-pitch deep silicon detector has been developed for clinical photon-counting computed tomography (CT). With a small pixel size of 14 × 650 μm2, it has shown potential to reach micrometre spatial resolution in previous simulation studies. A detector prototype with such geometry has been manufactured, and we report on the first experimental evaluation of its count-rate performance. Approach. The measurement was carried out at MAX IV synchrotron laboratory with 35 keV monochromatic x-rays. By inserting tungsten attenuators of 50, 75, 100, 150, 200, 225, 325 μ m-thicknesses into the beam, the response of the detector to fluence rates from 3.3 × 107 to 1.3 × 1011 mm−2 s−1 was characterized. Main results. The measurement result showed that the detector exhibited count rate linearity up to 6.66 × 108 mm−2 s−1 with 13% count loss and was still functional at count rate up to 2.9 × 1010 mm−2 s−1. A semi-nonparalyzable dead-time model was fitted to the count-rate behaviour of the detector, showing great agreement with the measured data, with an estimated nonparalyzable dead time of 2.9 ns. Significance. This is the first experimental evaluation of the count-rate performance for a deep silicon detector with such small pixel geometry. The results suggest that this type of detector shows the potential to be used at fluence rates encountered in clinical CT with little count loss due to pile-up.