Experimental Performance Evaluation Research Articles

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.

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.

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.

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

Experimental evaluation of performance, drying and thermal parameters of guava slabs dried in a forced convection indirect solar dryer without and with thermal energy storage

The forced convection indirect solar dryer (FISD) provides better quality food products for lower duration than open sun drying (OSD). The drying experiments on guava slices were executed on an FISD without a thermal energy storage (TES) (model-1) and with TES (model-2) to determine the impact of TES on drying. The guava slices attained a final moisture content in 14 and 9 sunshine hours in model-1 and model-2, respectively. The drying models appropriate to describe guava slices in model-1, model-2 and OSD were Page and modified Page models. The drying rate in model-1 and model-2 was found to be 0.3936 and 0.5728 kg/h, while the effective diffusivity was 7.98 × 10−9 and 8.63 × 10−9 m2/s (average), respectively. The average heat and mass transfer coefficients were 4.842 W/m2K and 4.20 × 10−3 m/s in model-1 & 5.394 W/m2K and 4.68 × 10−3 m/s in model-2. The average drying efficiency and specific moisture extraction rate were 6.84 % and 0.597 kg/kWh in model-1 & 9.77 % and 0.972 kg/kWh in model-2. The specific energy consumption and activation energy were decreased by 38.57 % and 5.25 % in model-2 compared to model-1. The performance, drying and thermal parameters were found to be better in model-2.

The key security management scheme of cloud storage based on blockchain and digital twins

As a secure distributed ledger technology, blockchain has attracted widespread attention from academia and industry for its decentralization, immutability, and traceability characteristics. This paper proposes a cloud storage key security management scheme based on blockchain. To resist brute-force attacks launched by adversaries on ciphertexts, the scheme uses an oblivious pseudo-random function (OPRF) to generate randomized convergent keys and improve data confidentiality. Second, the scheme enhances the reliability of concurrent key management through a secret sharing mechanism, where convergent keys are split into key fragments and distributed on blockchain for storage. Even if a certain number of key fragments are lost or damaged, users can still recover complete key information through block transaction records. In addition, the scheme effectively supports file-level and block-level data security deduplication. Security analysis and experimental performance evaluation indicate that this scheme can ensure the security of keys and the confidentiality of data, and it has a low computational overhead for generating file-level encryption keys under this scheme. Even for a 100 MB file, the computational overhead required for generating encryption keys is less than 2 s, which improves computational efficiency.

Temporal-channel cascaded transformer for imagined handwriting character recognition

Neuroelectric signals recorded by micro-electrodes reflect the spontaneous and rhythmic activities of brain neurons. Numerous deep learning frameworks have been designed for various neuroelectric signal decoding tasks, most of which are based on convolutional neural network (CNN) and recurrent neural network (RNN). However, neither CNNs or RNNs can perceive the global dependencies of neural activities in both time and channel dimensions. To address this issue, this paper presents a temporal-channel cascaded transformer network to decode the neural activities of imagined handwriting movements, which can perform imagined handwriting character recognition from spiking activity recorded by two micro-electrode arrays (MEAs). Specifically, we design a temporal-channel cascaded framework and a dense residual transformer encoder structure, which can promote the hierarchical learning and fusion of the temporal and channel features. In addition, a mutual learning strategy of multiple class tokens is proposed to improve classification performance. We conduct performance evaluation experiments on a single-character handwriting-imagination dataset and a sentence handwriting-imagination dataset, which are collected from the public Handwriting BCI dataset. The comparison results demonstrate the superiority of the proposed framework and strategy. Especially in the imagined single-character recognition task, the recognition accuracy of our model can achieve 95.78%, which provides an improvement of +2% over the existing state of the art models.

?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks

Sensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives.

Optimal design of short-period structures equipped with sliding tuned liquid column damper and numerical and experimental control performance evaluation

Optimal design of short-period structures equipped with sliding tuned liquid column damper and numerical and experimental control performance evaluation

DATran: Dual Attention Transformer for Multi-Label Image Classification

Multi-label image classification is a fundamental yet challenging task, which aims to predict the labels associated with a given image. Most of previous methods directly exploit the high-level features from the last layer of convolutional neural network for classification. However, these methods cannot obtain global features due to the limited size of convolutional kernels, and they fail to extract multi-scale features to effectively recognize small-scale objects in the images. Recent studies exploit the graph convolution network to model the label correlations for boosting the classification performance. Despite substantial progress, these methods rely on manually pre-defined graph structures. Besides, they ignore the associations between semantic labels and image regions, and do not fully explore the spatial context of images. To address above issues, we propose a novel Dual Attention Transformer (DATran) model, which adopts a dual-stream architecture that simultaneously learns spatial and channel correlations from multi-label images. Firstly, in order to solve the problem that current methods are difficult to recognize small-size objects, we develop a new multi-scale feature fusion (MSFF) module to generate multi-scale feature representation by jointly integrating both high-level semantics and low-level details. Secondly, we design a prior-enhanced spatial attention (PSA) module to learn the long-range correlation between objects from different spatial positions in images to enhance the model performance. Thirdly, we devise a prior-enhanced channel attention (PCA) module to capture the inter-dependencies between different channel maps, thus effectively improving the correlation between semantic categories. It is worth noting that PSA module and PCA module complement and promote each other to further augment the feature representations. Finally, the outputs of these two attention modules are fused to obtain the final features for classification. Performance evaluation experiments are conducted on MS-COCO 2014, PASCAL VOC 2007 and VG-500 datasets, demonstrating that DATran model achieves better performance than current state-of-the-art models.

Knowledge Graph Based Large Scale Network Security Threat Detection Techniques

Abstract This paper constructs a detection technique for large-scale network security threats based on a knowledge graph, extracts the attack features of network security threats using feature template FT, and combines the CNN layer, BiLSTM layer and CRF layer to establish FT-CNN-BiLSTM-CRF large-scale network security threat detection technique. Network security threat performance evaluation experiments and multi-step attack experiments have verified the detection capability of this paper's method. The recall rate of the method built in this paper in detecting malicious data is about 62.39%, the average F1-Score for normal and malicious traffic detection is 0.7482, and the anomaly score for normal traffic detection is almost 0. The detection performance of this paper's method for multi-step network attacks is superior to that of other methods, and it is capable of detecting malicious attacks quickly. Experiments have proved that the method constructed in this paper can meet the requirements of detection capability and efficiency in large-scale network security threats and has high feasibility and application value.