Comparative Performance Assessment Research Articles

Load frequency controller for an islanded hybrid power system using WCO

This research proposes a new metaheuristic algorithm based on World Cup Optimization (WCO) to determine the optimal values of the Proportional Integral Deviation (PID) controller for the load frequency control problem. An islanded hybrid power system consists of a Wind Turbine Generator (WTG), Diesel Generation (DEG), and an Energy Storage System (ESS), such as a Battery Energy Storage System (BESS), Super Magnetic Energy Storage (SMES), and an Ultra Capacitor (UC) unit. A single-area power system was designed as a model network for the MATLAB-Simulink simulation. Furthermore, a hybrid system was employed with Proportional-Integral (PI) controllers and PI derivative controllers with proportionate gains to the DEG, and an ESS. A comparative performance assessment of the WCP was conducted to tune the PI and PID controllers. The simulation results indicate that a renewable integrated isolated power system with an Energy Storage System (ESS) tuned with a PID controller provides better responses than the PI controller for different loading conditions. Based on time settling, transient, and overshoot analysis, it is proven that a WCO-tuned PID controller is better than a PI controller.

Multi‐level deep learning based lung cancer classifier for classification based on tumour‐node‐metastasis approach

AbstractTreatment of non‐small cell lung cancer depends on detecting the cancer stage. The oncologist decides the cancer stage based on the tumour‐node‐metastasis (TNM) staging suggested by the American Joint Committee on Cancer (AJCC). This study simplifies the complicated problem of classifying computed tomography (CT) images into TNM‐based classes using deep learning algorithms at various levels. In the first level, an optimised conditional generative adversarial network (c‐GAN) network is developed for automatic lung segmentation, including nodules within the lung and juxtapleural nodules. Earlier studies used time‐consuming manual identification of the region of interest patches from the lung CT image before applying the deep learning classification algorithm. At the next level, three different deep learning algorithms, along with three support vector machine classifiers, are used for the classification of Tumour, Node and Metastasis as per the AJCC staging nomenclature. The specially designed c‐GAN network's performance is maximised using the Taguchi approach, which helps automatically preprocess CT images by removing unwanted background noises. Further, three different pre‐trained Resnet50 networks are trained using transfer learning for extracting the deep features for finally applying to three different classifiers, resulting in three different classes. The comparative segmentation performance assessment in the form of the average dice similarity coefficient and Jaccard index indicates that the proposed c‐GAN gives the best segmentation performance of the lung without losing the nodule compared to other segmentation algorithms. The proposed approach gives the classification performance for the Tumour as 91.94%–97.32%, the Nodule as 91.99%–100%, and the Metastasis as 99.25%–100.00%.

Performance assessment of planar and non-planar cryogenic pulsating heat pipe with novel condenser

Pulsating heat pipe (PHP) is gaining popularity as a viable solution for thermal conductive links associated with a cryogenic cooling system. A competitive commercial environment requires PHP with a more compact configuration, high heat load removal capacity, and free of orientation dependency. Making orientation-free PHP, a planar PHP (2D layout), requires a large number of turns, increasing the overall size of the PHP device. A PHP with a non-planar (3D layout) layout is capable of accommodating a large number of turns in a short area to tackle both compactness and orientation-dependent issues. The present paper focuses on a comparative thermal performance assessment of planar and non-planar configurations of cryogenic PHP by developing a novel cylindrical shell-type condenser (CSTC). Liquid nitrogen is used as a working fluid. Two separate test setups are developed with 2D Four-Turn PHP (2D FT-PHP) and 3D Four-Turn PHP (3D FT-PHP) with a novel CSTC. The condenser, adiabatic, and evaporator lengths of PHP are kept as 60 mm, 120 mm, and 50 mm, respectively. The thermal performance is assessed by varying Filling Ratio (FR) (38−95 %) and heat load (1 W–130 W) with an evaporator at the bottom position. It is observed that, for a particular heat load, a 3D FT-PHP performed better than a 2D FT-PHP, irrespective of FR. The anti-dry-out ability of 3D FT-PHP is observed to be superior to 2D FT-PHP at a low FR. The frequency analysis results with Wavelet transform show that the working fluid oscillates with a more dominant frequency in 3D FT-PHP than in 2D FT-PHP configuration.

Reaching the Maximal Unquenched Orbital Angular Momentum L = 3 in Mononuclear Transition-Metal Complexes: Where, When and How?

The conditions for achieving the maximal unquenched orbital angular momentum L = 3 and the highest magnetic anisotropy in mononuclear 3d complexes with axial coordination symmetry are examined in terms of the ligand field theory. It is shown that, apart from the known linear two-coordinate 3d7 complex CoII(C(SiMe2ONaph)3)2 characterized by record magnetic anisotropy and single-molecule magnet (SMM) performance (with the largest known spin-reversal barrier Ueff = 450 cm−1), the maximal orbital angular momentum L = 3 can also be obtained in linear two-coordinate 3d2 complexes (V3+, Cr4+) and in trigonal-prismatic 3d3 (Cr3+, Mn4+) and 3d8 (Co+, Ni2+) complexes. A comparative assessment of the SMM performance of the 3d2, 3d3 and 3d8 complexes indicates that they are unlikely to compete with the record linear complex CoII(C(SiMe2ONaph)3)2, whose magnetic anisotropy is close to the physical limit for a 3d metal.

Performance-based life-cycle assessments of a resilient bridge system equipped with smart bearings

A resilient bridge system with smart bearings, that is, shape memory alloy (SMA)-cable-based bearings, is proposed. Its superior resilience property over the conventional bridge system still lacks a practical assessment approach. This paper aims to conduct a comparative performance assessment of both resilient and conventional bridge systems regarding fragility and life-cycle loss aspects. The quasi-static test on the smart bearing prototype is firstly conducted. The cloud method is subsequently utilized to carry out a fragility assessment on the bridge system with uncertainties (e.g. damping, mass, friction factor, and length of slack cable). The result shows that the damage probabilities of the smart bearing at component level and the resilient bridge at system level are both dramatically alleviated although there is a moderate increase of the reinforced concrete pier at component level. Additionally, the analysis result reveals that the life-cycle economic loss of the resilient bridge system is remarkably reduced only at the cost of a slight increment in construction. This study tries to provide decision-makers with a more comprehensive understanding of the seismic superiority of the proposed resilient bridge system with smart bearings and to promote its application in engineering practice for resilience enhancement.

A Comparative Assessment of Thermodynamic and Exergoeconomic Performances of Three Thermochemical Water-Splitting Cycles of Chlorine Family for Hydrogen Production

Hydrogen production processes utilizing hydrocarbon-based sources as feedstocks have severe environmental implications (when not incorporating carbon capturing and storage technology) in addition to resulting in depletion of non-sustainable naturally occurring fuel sources. In this regard, thermochemical water-splitting is an extremely environmentally benign pathway for obtaining sustainable hydrogen. Thus, this paper studies and comparatively evaluates three different thermochemical cycles of the chlorine family for hydrogen production which include iron-chlorine, copper-chlorine, and magnesium-chlorine cycles. Each cycle is first modeled and simulated in Aspen-plus and then analyzed from thermodynamic and exergoeconomic viewpoints. A thermodynamic and exergoeconomic performance comparison of the three thermochemical cycles is further performed in terms of their overall efficiencies, net heat input and rejection rates, exergy destruction rates, unit costs of hydrogen, hourly levelized cost rates, and cost rates of exergy destruction. All cycles are modeled and simulated by considering the incorporation of waste heat recovery from a steel plant and internal heat recovery within each cycle to improve the process efficiencies by reducing the overall thermal energy consumption. According to the performed analyses, the copper-chlorine cycle has the highest energy (79.7%) and exergy (81.1%) efficiencies followed by the iron-chlorine cycle (energy and exergy efficiencies of 48.8% and 49.6%, respectively) with the magnesium-chlorine cycle exhibiting the lowest overall energy (19.8%) and exergy (20.2%) efficiencies. Moreover, the iron-chlorine cycle demonstrates the lowest unit cost of hydrogen (0.7 $/kg) followed by the copper-chlorine cycle (4 $/kg) with the magnesium-chlorine cycle exhibiting an extremely high unit hydrogen cost (8.9 $/kg). This study also identifies certain key factors that largely impact the cost of hydrogen and comprehensively discusses the influence and extent of each factor on the cost of hydrogen.

Comparative performance assessment of deep learning based image steganography techniques

Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.

Comparative assessment of strain-controlled fatigue performance of SS 316L at room and low temperatures

In the present study, strain-controlled low cycle fatigue (LCF) behaviour of SS 316L at room temperature (RT) and −80 ℃ were investigated. RT LCF specimens show cyclic softening, whereas −80 ℃ specimens show initial cyclic softening followed by hardening due to strain-induced martensitic transformation (SIMT). Fatigue damage evolution mechanism is proposed for specimens tested at −80 °C, where compressive stresses develop due to SIMT and retards the crack growth. This leads to an increase in fatigue life for −80 °C specimens. An increase in back stress component at the initiation of secondary hardening is noticed for −80 °C specimens.

A comparative landfill leachate treatment performance in normal and electrodes integrated hybrid constructed wetlands under unstable pollutant loadings.

This study provides a comparative pollutant removal performance assessment between organic or construction materials-based four hybrid wetland systems that received landfill leachate. The hybrid systems included vertical flow (VF) followed by horizontal flow (HF)-based unplanted and planted systems, and planted electrodes incorporated microbial fuel cell (MFC) integrated hybrid wetlands systems. All the systems were run in free-draining mode. Overall mean chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) removal percentage of the hybrid systems ranged between 81 and 99%, 82 and 96%, 74 and 99%, respectively, under unstable input pollutant loading conditions. Additionally, up to 27% organic and up to 14% nitrogen removal improvement was observed in electrodes integrated free-draining VF wetlands. Free-draining and additional oxygen availability from atmospheric diffusion, rootzone improved the removal performance of MFC-based VF wetlands. Input load increment decreased organic, nutrient removals in second stage HF units due to saturated media. The chemical composition of the employed media supported biotic, abiotic organic, nutrient removal pathways. Nutrient accumulation percentage in plants tissue was very low, i.e., ≤3%. Bioenergy production across the MFC-based VF-HF wetlands decreased with input pollutant load increment. The single anode electrode-based VF wetland achieved maximum power density production, i.e., 294 mW/m2.. The electrodes integrated hybrid systems achieved comparatively stable removal performance despite input pollutant/hydraulic load variation.

Operation control, energy management, and power quality enhancement for a cluster of isolated microgrids

Several isolated microgrids (MGs) may exist in remote areas because of the geographical and/or utility investment limitations. The isolated MG has its challenges depending on its configuration, energy sources, and nature of loads resulting in power quality issues. Clustering these MGs based on their relative locations to form several clusters and interconnecting them may provide additional benefits such as improved reliability, stability, and cost-effective operation. This entails the capability of energy sharing between the energy sources through distributive MGs and the power exchange within MGs through designing an effective energy management system (EMS). In this work, a proposed EMS built on the marine predator algorithm (MPA) is presented, with its design based on a hierarchical decentralized strategy for a cluster of interconnected MGs. The criterion of the proposed EMS design encompasses two key steps. First, developing an MG central controller (MGCC) in each MG to optimally handle the use of power with large-scale hybrid distributed energy resources as well as the resolution of power quality issues. Second, the MG operator (MGO) assists in the power exchange among the cluster MGs to surmount any probable unbalance between generations and demand in the cluster's entire coverage area. To enhance the complete MG’s dynamic performance, an adaptive dynamic voltage restorer (ADVR) with a proportional-integral-derivative (PID) controller based on MPA is employed as a supervisory controller. Different cases are studied using the MATLAB/Simulink environment. Finally, two comparative performance assessment of various optimization strategies and with other controller like fractional order PID (FOPID) are presented in order to achieve the effectiveness and robustness of the presented ADVR.

Comparative Assessment of Diagnostic Performance of Cytochrome Oxidase Multiplex PCR and 18S rRNA Nested PCR.

Malaria elimination and control require prompt and accurate diagnosis for treatment plan. Since microscopy and rapid diagnostic test (RDT) are not sensitive particularly for diagnosing low parasitemia, highly sensitive diagnostic tools are required for accurate treatment. Molecular diagnosis of malaria is commonly carried out by nested polymerase chain reaction (PCR) targeting 18S rRNA gene, while this technique involves long turnaround time and multiple steps leading to false positive results. To overcome these drawbacks, we compared highly sensitive cytochrome oxidase gene-based single-step multiplex reaction with 18S rRNA nested PCR. Cytochrome oxidase (cox) genes of P. falciparum (cox-III) and P. vivax (cox-I) were compared with 18S rRNA gene nested PCR and microscopy. Cox gene multiplex PCR was found to be highly specific and sensitive, enhancing the detection limit of mixed infections. Cox gene multiplex PCR showed a sensitivity of 100% and a specificity of 97%. This approach can be used as an alternative diagnostic method as it offers higher diagnostic performance and is amenable to high throughput scaling up for a larger sample size at low cost.

Comparative performance and ergonomic assessment of different types of weeder for better adaptability of machine

The research was planned to predict the performance and ergonomic assessment of different types (tractor operated weeder, the engine operated inter-row rotary weeder, tractor-drawn high clearance cultivator, and manual hand hoe) of weeder in cotton crop. The study will help the farmers to purchase good quality weeder. The experimental study consists of five treatments. Weeding operations have been done at three stages (pre-square, square, and flowering) of the cotton crop and the performance parameter was recorded. The highest weeding efficiency was found in manual hand hoe (Kasola) with 85.50-89.59% whereas efficiency varied from 74-76% in tractor operated weeders. Crop parameters were also recorded. Plant height and canopy at the flowering stage differ significantly and plant height was found significant at the square stage also. In all treatments, after weeding pulse rate varies from 104-122 beats/min. The engine operated power weeder resulted in more blood pressure (142/90) than the manual weeder(135/88). So resulted obtained from the research, the tractor operated inter-row rotary weeder (M2) was found best and recommended.

A comparative performance assessment of hydrostatic thrust bearings operating with electrorheological lubricant

PurposeAn electrorheological (ER) fluid comprises dielectric particles suspended in an insulating viscous medium. ER lubricants are considered smart lubricants. They have been applied in hydraulic valves, power transmission devices and damping systems. The purpose of this study is to investigate the performance of hydrostatic thrust bearing operating with ER lubricant.Design/methodology/approachReynold’s equation was used to model the flow of the ER lubricant in the bearing. The continuous Bingham model was used to express the viscosity of the ER lubricant as a function of yielding stress, applied electric field and shear strain rate. The Reynolds equation is solved using the finite element method (weighted residual approach) to compute the film pressure as a primary variable and the lubricant flow rate, load-carrying capacity, stiffness and damping parameters as associated performance indices.FindingsThe effects of the pocket shape, compensating elements and ER lubricant on the bearing performance were investigated. The application of ER lubricant significantly enhanced the load-carrying capacity (48.2%), stiffness (49.8%) and damping (4.95%) of the bearings. Circular and triangular pocket bearings with constant-flow valves have been reported to provide better steady-state and rotor-dynamic performances, respectively.Originality/valueThis study presents the effect of an ER lubricant on the rotor-dynamic performance of hydrostatic thrust bearings with different pocket shapes.

A comparative assessment of performance, combustion, and emissions of a gasoline port and direct injection camless engine

AbstractThe lower part‐load efficiency of the spark‐ignition (SI) engine is because of the higher pumping loss during the gas exchange process. In this study, the part‐load efficiency of a port fuel injection (PFI) and gasoline direct injection (GDI) camless engines were investigated. An in‐house developed electro‐pneumatic variable valve actuation (VVA) system controls the camless engine's intake valve events. The effect of un‐throttled operation with early intake valve closings (EIVC) on performance, combustion, and emissions was analyzed. The IVC timing for GDI camless engine was found to be lower (2°–7° crank angle) than the PFI camless engine for all operating conditions due to the in‐cylinder charge cooling. Hence, the pumping mean effective pressure (PMEP) for GDI camless engine was reduced, and brake specific fuel consumption (BSFC) was improved (maximum 2.1%) compared to the PFI camless engine. The THC and CO emissions were higher for GDI camless engine due to the relatively in‐homogeneous mixture formation, whereas the NOx emission was lower for all operating conditions.

Comparative structural performance assessment of electrified road systems

ABSTRACT An intrinsic integration of different new advances into the practical roads, including the electrified road system, could be an important feature of a future smart road. One of the most important tasks in feasibility analysis of implementing electrified road (eRoad) systems into the practice is to quantitatively assess pavement performance after an integration of the functional units and, consequentially, the influences to the sustainability potential of road electrification. In this study, by means of using a simulation tool based on Finite Element (FE) method, some preliminary insights into the structural performance of several promising eRoad systems are obtained. These analyses provide some guidance support for eRoad technological design and deliver the crucial boundary inputs to the life cycle sustainability assessment of using these systems.

A deep convolutional neural network-based approach for detecting burn severity from skin burn images

Burns being one of the leading causes of clinically significant morbidity can lead to a dramatic physiological reaction with prolonged repercussions, metabolic disturbance, severe scarring, catastrophic organ failure, and death if not properly treated. Appropriate burn treatment management is associated with the severity of burn wounds which can be extremely challenging to anticipate at an early stage due to various factors using traditional clinical methods. Therefore, this study proposed a Deep Convolutional Neural Network (DCNN) based approach for detecting the severity of burn injury utilizing real-time images of skin burns. The DCNN architecture leverage the utilization of transfer learning with fine tuning employing three types of pretrained models on top of multiple convolutional layers with hyperparameter tuning for feature extraction from the images and then a fully connected feed forward neural network to classify the images into three categories according to their burn severity : first, second and third degree burns. In order to validate the efficacy of the suggested strategy, the study also applies a traditional solution to mitigate this multi-class categorization problem, incorporating rigorous digital image processing steps with several conventional machine learning classifiers and then conducts a comparative performance assessment. The study’s findings demonstrate that using pretrained models, the recommended DCNN model has gained significantly greater accuracy, with the highest accuracy being obtained using the VGG16 pretrained model for transfer learning with an accuracy of 95.63% . Thus, through the use of intelligent technologies, the proposed DCNN-based technique can aid healthcare practitioners in evaluating the burn damage condition and providing appropriate treatments in the shortest feasible time, remarkably reducing the unfavorable consequences of burns.

Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films

The present study addresses a comparative performance assessment of multivariate regression (MVR) and well-optimized feed-forward, generalized regression and radial basis function neural network models which aimed to predict transmitted light intensity (Itr) of carbon nanotube (CNT)-loaded polymer nanocomposite films by employing a large set of spectroscopic data collected from photon transmission measurements. To assess prediction performance of each developed model, universally accepted statistical error indices, regression, residual and Taylor diagram analyses were performed. As a novel performance evaluation criterion, 2D kernel density mapping was applied to predicted and experimental Itr data to visually map out where the correlations are stronger and which data points can be more precisely estimated using the studied models. Employing MVR analysis, empirical equation of Itr was acquired as a function of only four input elements due to sparseness and repetitive nature of the remaining input variables. Relative importance of each input variable was calculated separately through implementing Garson’s algorithm for the best ANN model and mass fraction of CNT nanofillers was found as the most significant input variable. Using interconnection weights and bias values obtained for feed-forward neural network (FFNN) model, a neural predictive formula was derived to model Itr in terms of all input variables. 2D kernel density maps computed for each ANN model have shown that correlations between measured data and ANN predicted values are stronger for a specific Itr range between 0% and 18%. To measure the stability of the ANN models, as a final analysis, 5-fold cross-validation method was applied to whole measurement data and 5 different iterations were additionally performed on each ANN model for 5 different training and test data splits. Statistical results found from 5-fold cross-validation analysis have reaffirmed that FFNN model exhibited outperformed prediction ability over all other ANN models and all FFNN predicted Itr values agreed well with experimental Itr data. Taken all computational results together, one can adapt our proposed FFNN model and neural predictive formula to predict Itr of polymer nanocomposite films, which can be made from different polymers and nanofillers, by considering specific data range as presented in this study with statistical details.

Comparative Assessment of Academic Performance among Kims Medical Students before and during Lockdown:a Cross Sectional Study

Background: With the advent of Covid[5], it became very difficult to cater to the 1.7 billion[6] students whose educationwas on a technical hold. With the online classes around it became difficult for the students to imbibe certain basic conceptsof knowlede and they had to instead mug-up a lot of portions. All these also led to a deviation from the normal teacherstudentrelationship. As a consequence it affected the grades a student obtained during the time of lockdown.Hence thisstudy with the ojectives to assess the psychological impact of lockdown on students,comparative assement of studentsbefore and during lockdown and the pattern of reading from home and college is of significance.Methodology: A cross sectional study was done for a period of one month on medical students for “ComparativeAssessment of medical students before and during lockdown.”The students were selected by convenience sampling. Apretested semi structured questionnaire was sent through whatsapp and the data collected.It was entered in MicrosoftExcel sheet,analysed using SPSS version 25 and findings were represented in the form of figures and tables.Conclusion: Even though the number of students gaining first class and second class marks have increased during theperiod of lockdown,the number of students securing distinction has considerably fallen.Many felt that their reading is farbetter when at hostel and in the presence of companions in the college campus and with the teacher-student relationshiparound with in room classes.As a matter of fact majority of them wanted to come back to campus as soon as possible andresume normal in-room classes.

SCANM: A Novel Hybrid Metaheuristic Algorithm and Its Comparative Performance Assessment

SCANM: A Novel Hybrid Metaheuristic Algorithm and Its Comparative Performance Assessment

Solar energy harvesting pavements on the road: comparative study and performance assessment

• Performance of various solar energy harvesting pavements are compared. • The pavements’ impact on the urban heat island effect is analyzed. • The electricity and heat yields of the pavements are simulated. • The PIPVT module can achieve the average overall energy efficiency of 37.31%. As a significant infrastructure in modern cities, road pavements occupy a great share of urban surfaces and absorb abundant solar radiation. Thus, pavements have tremendous potential in solar energy utilization and can serve as distributed energy generators in smart and sustainable cities. Some solar energy harvesting pavement modules have emerged in recent years, including pavement-integrated photovoltaic (PIPV) module, pavement-integrated solar thermal (PIST) module, and pavement-integrated photovoltaic thermal (PIPVT) module. In this study, for all the above modules, simulations were conducted with the developed and validated two-dimensional transient models on the weather conditions of four typical days in different seasons in Shanghai. Both the operating performance and influence on the urban heat island (UHI) effect were investigated to evaluate and compare their energy performance and effect on urban microclimatic conditions. The results show that the electricity yield of the PIPVT module is slightly higher than that of the PIPV module, while its heat yield is lower than that of the PIST module on all typical days. When considering both electricity and heat yields, the PIPVT module can achieve an average overall energy efficiency of 37.31%. Compared with the conventional pavement module, all three solar energy harvesting pavement modules have lower maximum asphalt average temperatures that can decelerate the thermal aging of asphalt concrete. The PIPVT module has the most significant effect on the reduction of the maximum asphalt average temperature, decreasing it by 10.57°C on average. Additionally, all three solar energy harvesting pavement modules can mitigate the UHI effect, and the PIPVT and PIST modules have the most and the least influence on UHI effect mitigation, respectively.