Zhangjie Ji, Yiming Shi, Changquan Xia, Haitao Chen, Liwen Cheng
{"title":"A trilayer waveguide grating antenna with offset etching grooves for high directionality and large effective length","authors":"Zhangjie Ji, Yiming Shi, Changquan Xia, Haitao Chen, Liwen Cheng","doi":"10.1088/2631-8695/ad66b0","DOIUrl":"https://doi.org/10.1088/2631-8695/ad66b0","url":null,"abstract":"\u0000 In this study, a trilayer waveguide grating antenna with offset etching grooves is proposed, the trilayer waveguide composed of a diffractive layer (Si3N4) - waveguide layer (Si) - diffractive layer (Si3N4) buried in SiO2 cladding. The inserted Si3N4 diffraction layers effectively reduce the refractive index contrast between the traditional Si waveguide layer and SiO2 cladding, which diminishes the disturbance coefficient of the conventional antenna, resulting in a large effective length. In addition, the offset etching grooves located on the diffraction layers break the vertical symmetry of the antenna, which enhances the directionality markedly. Simulation results demonstrate a directionality exceeding 87% and a beam steering range of 6.9° along the θ axis in a range of wavelength from 1500 nm to 1600 nm. Specifically, at a wavelength of 1550 nm, the peak directivity of the antenna exceeds 96% while the maximum effective length exceeds 4.4 mm.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"73 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm","authors":"Rdv D.V. Prasad, Dr. Arun Vikram Arun Kothapalli, Srinivasa Rao Mss, Lakshmi VK Vennela, Vijaya Krishna Kanth Tammi","doi":"10.1088/2631-8695/ad6663","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6663","url":null,"abstract":"\u0000 Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc. Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm/rev) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 oC) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm/rev and 0.25 mm, while the responses for these optimal conditions are 69.92μm, 39.48oC and 0.137 Kg-CO2 in that order.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"25 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review of memristor based neuromorphic computation: opportunities, challenges and applications","authors":"S. S, Ravi V","doi":"10.1088/2631-8695/ad6662","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6662","url":null,"abstract":"\u0000 The memristor is regarded as one of the promising possibilities for next-generation computing systems due to its small size, easy construction, and low power consumption. Memristor-based novel computing architectures have demonstrated considerable promise for replacing or enhancing traditional computing platforms that encounter difficulties in the big-data era. Additionally, the striking resemblance between the mechanisms governing the programming of memristance and the manipulation of synaptic weight at biological synapses may be used to create unique neuromorphic circuits that function according to biological principles. Nevertheless, getting memristor-based computing into practice presents many technological challenges. This paper reviews the potential for memristor research at the device, circuit, and system levels, mainly using memristors to demonstrate neuromorphic computation. Here, the common issues obstructing the development and widespread use of memristor-based computing systems are also carefully investigated. This study speculates on the prospective applications of memristors, which can potentially transform the field of electronics altogether.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"7 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nan Wang, Jin-Hang Liu, Yang Li, Lan Huang, Zhongyi Wang
{"title":"Complex-valued multi-frequency electrical impedance tomography based on deep neural networks","authors":"Nan Wang, Jin-Hang Liu, Yang Li, Lan Huang, Zhongyi Wang","doi":"10.1088/2631-8695/ad6664","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6664","url":null,"abstract":"\u0000 The utilization of multi-frequency electrical impedance tomography (mfEIT), a non-invasive imaging technique, allows for the visualization of the conductivity distribution in biological tissues across different frequencies. However, the analysis of phase angle information within complex impedance remains a challenge, as most existing deep learning-based mfEIT algorithms are limited to real number processing. To address this limitation, this study proposes a novel approach that integrates deep learning techniques with conventional reconstruction algorithms. The complex-valued conductivity distribution in the measurement region is pre-reconstructed using a sparse Bayesian learning approach. Subsequently, the pre-reconstructed results are refined using an optimized UNet network. The experimental outcomes validate the efficacy of the proposed algorithm in accurately reconstructing the complex-valued conductivity distributions of diverse biological tissues, such as potato and pig kidney, across different frequencies. Furthermore, the algorithm exhibits exceptional performance in mitigating the presence of image artifacts during the reconstruction process.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"10 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimizing binders with maximum using of industrial waste to develop low carbon ultra-high performance concrete","authors":"Zhongmei Lu, Zhide Huang, Xiaotao Feng, Tianlin Qin, Xiaohui Zhu, Aiqin Zhang","doi":"10.1088/2631-8695/ad6665","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6665","url":null,"abstract":"\u0000 Optimizing binders by using industrial waste and then adjusting microstructure has potential to develop low production cost and low carbon emission ultra-high performance concrete (UHPC) to meet different practical engineering requirements. This study first investigates the influence of silica fume, cenosphere and fly ash on paste mixing time, flowability, flexural and compressive strength of UHPC by using three-factor and four-level orthogonal test. Moreover, the effect of fly ash and slag content were further discussed, and the modification mechanisms of binders were revealed by analyzing pore structure, interface and characteristics of hydration products. The results show that silica fume and cenosphere are the most sensitive factors affecting paste mixing time and flowability of UHPC, respectively. The sensitivity of binders on compressive and flexural strength of UHPC can be ranked according to silica fume > fly ash > cenosphere and cenosphere > fly ash > silica fume, respectively. The binders’ composition for low-carbon UHPC with compressive strength grade of 120 MPa is that cement: cenosphere: silica fume: fly ash equals to 1:0.33:0.33:56, and the total amount of fly ash reaches 40%. Meanwhile, the 28d compressive/flexural strength decreases and the flowability increases with the increase of fly ash content, resulting from pore volume with size of 5-50 nm and Ca/Si ratio on the interface between steel fiber and matrix increase. The flowability of UHPC with slag is reduced and the cumulative pore volume with size of 50 nm-5 um pores is increased due to the influence of slag morphology, but the porosity and interfacial Ca/Si ratio is reduced because of the high pozzolanic activity of slag, leading to similar compressive and flexural strength with that of UHPC with fly ash.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"8 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Farhad Ayazi, Maninder Singh, Rajiv Kumar
{"title":"Prediction and Modelling Marshall Stability of Modified Reclaimed Asphalt Pavement with Rejuvenators using latest Machine Learning Techniques","authors":"Mohammad Farhad Ayazi, Maninder Singh, Rajiv Kumar","doi":"10.1088/2631-8695/ad65b7","DOIUrl":"https://doi.org/10.1088/2631-8695/ad65b7","url":null,"abstract":"\u0000 The primary problem with the experimental evaluation of Marshall stability (MS) of reclaimed asphalt pavement (RAP) is the inherent complexity and variability involved in the process. Traditional experimental methods for predicting MS can be time-consuming, labor-intensive, and costly. In the present research, an effort has been made to assess the most appropriate machine learning model for the prediction of MS of RAP. The study addresses the problem of accurately predicting MS by using a variety of input parameters derived from experimental work. The data for models was split in 7:3 for training and testing of models. Bitumen content (BC %), virgin binder percentage (VB %), virgin binder performance grade (VB-PG), RAP percentage (RAP %), RAP binder percentage (RAPB %), RAP binder PG (RAPB-PG), rejuvenator type (Rej type) and rejuvenator percentage (Rej %) were applied as input parameters for MS prediction. Several machine learning models including random tree (RT), M5P, Gaussian process (GP), support vector machine (SVM), and random forest (RF) were utilized for determining the most appropriate prediction model. Seven metrics were used for assessing the performance of these models, such as CC, MAE, RMSE, RA, RRSE, WI, and NSE. Based upon these metrics, the RF model is found to outperform the other applied models with the values of CC=0.9959 and 0.9763, MAE=0.3129 and 0.7847, RMSE=0.3976 and 1.0492, RAE=9.0062 and 21.8247, RRSE=9.3624 and 23.6832, WI=0.998 and 0.984 and NSE=0.991 and 0.944 for training and testing stages, respectively. Also, box plots and sensitivity analysis confirm the superiority of the RF model over other models. Finally, the sensitivity analysis suggests the importance of bitumen content in the prediction of MS of reclaimed asphalt pavement modified with rejuvenators.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":" 838","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An improved clock synchronization model for typical IoT applications","authors":"Divya Upadhyay, Ashwani Kumar Dubey","doi":"10.1088/2631-8695/ad65b6","DOIUrl":"https://doi.org/10.1088/2631-8695/ad65b6","url":null,"abstract":"\u0000 — The Internet of Things (IoT) has transformed the way people live their lives by enabling data exchange between pervasive devices in various applications. However, clock synchronization is essential to ensure seamless transmission and synchronization among IoT entities involved in processing and communication. In this paper, we propose a clock synchronization algorithm based on linear quadratic regression to address synchronization errors in IoT applications. The algorithm uses a linear model of skew and offset to estimate clock parameters, and performance is evaluated in terms of Root Mean Square Error (RMSE) and R-Square Error. Our proposed algorithm outperformed traditional algorithms with an RMSE of 0.379% and an R-Square Error of 0.71%. We also evaluated the stability of the proposed model using the correlation coefficient, which indicated a high correlation between the variables at 86%. These results demonstrate the effectiveness of our proposed algorithm in addressing clock synchronization errors for IoT applications.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":" 877","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ANFIS-Based Control of Resonant Converters for Optimized Charging System of Electric Vehicle (EV) Batteries","authors":"Sapna Verma, Ashok Pandey","doi":"10.1088/2631-8695/ad65b8","DOIUrl":"https://doi.org/10.1088/2631-8695/ad65b8","url":null,"abstract":"\u0000 This paper presents a resonant converter-based Electric Vehicle (EV) battery charging module utilizing Proportional-Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control for an optimized charging system. The EV charging module is integrated with a resonant converter comprising a full bridge, HFTF, and DBR. The module utilizes a Resonant Converter which reduces the switching loss incurred during converter operation at high frequency by offering ZCS or ZVS at the switching time. A standard PI Controller manages the duty ratios of the primary full bridge switches with tuned gains. The CC and CV controllers each have their own PI Controller for current and voltage, respectively. To enhance the performance of the EV System, the standard PI Controllers in both the CC and CV control systems are replaced with ANFIS Controllers which are trained as per the data generated by the CC and CV control using an optimization technique that controls the duty ratio of the switches. The proposed ANFIS-based and PI-based control strategy provides an adaptive and flexible approach to control the battery voltage and current by intelligent adjustment of Constant Current (CC) and Constant Voltage (CV) operation modes and the passive elements switching across specific ranges of State-Of-Charge (SOC) to enhance the performance and safety of the charging system. MATLAB Simulation results demonstrated that the proposed ANFIS-based control reduces current ripple content compared to PI-based control. The ANFIS Controller improves overall battery performance, reliability, and stability, which makes it a better choice for next-generation EV charging systems.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":" 722","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sreedeep Krishnan, Karuppasamypandian M, Ranjeesh R Chandran, D. Devaraj
{"title":"Utilizing deep learning via computer vision for agricultural production quality control: jackfruit growth stage identification","authors":"Sreedeep Krishnan, Karuppasamypandian M, Ranjeesh R Chandran, D. Devaraj","doi":"10.1088/2631-8695/ad6531","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6531","url":null,"abstract":"\u0000 Jackfruit (Artocarpus heterophyllus), a tropical fruit renowned for its diverse culinary uses, necessitates identifying the optimal growth stage to ensure superior flavor and texture. This research investigates employing deep learning techniques, particularly convolutional neural networks (CNNs), for accurately detecting jackfruit growth stages. Despite the challenge posed by the nuanced visual differences among fruits at various maturity stages, a meticulously curated dataset of labeled jackfruit images was developed in collaboration with experts, utilizing the BBCH scale. This dataset facilitated training and evaluation. A modified version of the Places 365 GoogLeNet CNN model was proposed for classifying four distinct growth stages of jackfruit, compared with a state-of-the-art CNN model. The trained models demonstrated varying levels of accuracy in classification. Furthermore, the proposed CNN model was trained and tested using both original and augmented images, achieving an impressive overall validation accuracy of 90%. These results underscore the efficacy of deep learning in automating the detection of growth stages, offering promising implications for quality control and decision-making in jackfruit production and distribution.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finite-time stability control with hardware-in-the-loop testing of a chaotic permanent magnet synchronous motor","authors":"Arif Iqbal, F. I. Bakhsh, G.K. Singh","doi":"10.1088/2631-8695/ad6532","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6532","url":null,"abstract":"\u0000 Stability of Permanent Magnet Synchronous Motor (PMSM) is of prime importance for the successful operation of the drive system, which is found to be dependent on initial operating conditions showing chaotic characteristic. Therefore, the paper presents the stabilization of a chaotic PMSM system through the finite-time stability approach. Form this aspect, three controllers have been developed which are effective for the suppression of machine chaotic behaviour. Proposed controllers are simpler and numerically stable in comparison with previous works. Furthermore, performance comparisons of the proposed controllers are also presented in term of their settling time and peak overshoot. Selection among the proposed controllers depends on the consideration of a particular operating index (settling time or peak overshoot) wherein, performance is also varies on system parameter λ. Performance evaluation of proposed controllers has been presented in MATLAB environment. In order to validate the obtained MATLAB results, a real-time analysis has been carried out using Typhoon Hardware-in-the-loop (HIL) emulator. The comparison shows that both results are follow the same trend and are closely coordinated with each other, which validates the proposed controllers.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}