PurposeFor compactness and ease in assembling, a novel miniature size tubular moving magnet linear oscillating actuator (MT-MMLOA) design for miniature linear compressor application is proposed in this paper.Design/methodology/approachThis MT-MMLOA design possesses a modular C-core stator structure having separation at the middle. Axially magnetized tubular permanent magnets are accommodated on the mover. To improve the output parameters of the linear oscillating actuators (LOA), all the design parameters are optimized using a parametric sweep. Finite element analysis of the proposed design is performed to examine the magnetic flux density as well as thrust force under both static and dynamic analysis within the intended stroke range.FindingsCompared to conventional LOA for miniature compressors, the motor constant of the proposed LOA is 37 N/A that is 85% greater while keeping the same size of LOA. Permanent magnet volume used in the investigated topology of LOA is 26% reduced. Additionally, the overall volume of the machine is 10.3% decreased. Furthermore, the proposed topology is simple, inexpensive and easy to manufacture.Originality/valueElectromagnetic performance comparison with different topologies proposed earlier in literature is carried out to prove the performance superiority of the proposed design.

Purpose The purpose of this work is to make a computer aided detection system for epileptic seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more unprovoked seizures. The common and significant tool for aiding in the identification of epilepsy is Electroencephalography (EEG). The EEG signals contain information about the electrical activity of the brain. Conventionally, clinicians study the EEG waveforms manually to detect epileptic abnormalities, which is very time-consuming and error-prone. Design/methodology/approach The authors have presented a three-layer long short-term memory network for the detection of epileptic seizures. Findings The network classifies between seizure and non-seizure with 99.5% accuracy only in 30 epochs. This makes the proposed methodology useful for real-time seizure detection. Originality/value This research work is original and not plagiarized.

PurposeMaintaining a balanced neutral point, reducing power loss, execution time are important criteria for the controlling of neutral point clamped (NPC) inverter. However, it is tough to meet all the challenges and also supplying the load current within the harmonic limit. This paper aims to maintain load current quality within the Institute of Electrical and Electronics Engineers 519 standard and meet the above-mentioned challenges.Design/methodology/approachThe output load current of a three-level simplified neutral point clamped (3 L-SNPC) inverter is controlled in this paper using model predictive control (MPC). The 3 L-SNPC inverters is considered because fewer semiconductor devices are used in this topology; this will enhance the reliability of the system. MPC is used as a controller because it can handle the direct current-link capacitors’ voltage balancing problem in a very intuitive way. The proposed 3 L-SNPC yields similar current total harmonic distortion (THD), transient and steady-state responses, voltage stress and over current protection capability as the conventional NPC inverter. To reduce the computational burden of the proposed SNPC system, two simplified MPC strategies are proposed, namely, single voltage vector prediction-based MPC and selective voltage vector prediction-based MPC.FindingsThe system shows a current THD of 2.33% at 8.96 kHz. The overall loss of the system is reduced significantly to be useful in medium power applications. The required execution times for the simplified MPC strategies are tested on the hardware dSPACE 1104 platform. It is found that the single voltage vector prediction-based MPC and the selective voltage vector prediction-based MPC are computationally efficient by 8.28% and 62.9%, respectively, in comparison with the conventional MPC-based conventional NPC system.Originality/valueMultiple system constraints are considered throughout the paper and also compare the SNPC to the conventional NPC inverter. Proper current tracking, over-current protection, overall power loss reduction especially switching loss and maintaining capacitor voltages balance at a neutral point are achieved. The improvement of execution time has also been verified and calculated using hardware-in-loop of the dSPACE DS1104 platform.

PurposeThis paper aims to measure peak temperatures and cooling rates for distinct locations of thermocouples in the butt weld joint of mild steel plates. For experimental measurement of peak temperatures, K-type thermocouples coupled with a data acquisition system were used at predetermined locations. Thereafter, Rosenthal’s analytical models for thin two-dimensional (2D) and thick three-dimensional (3D) plates were adopted to predict peak temperatures for different thermocouple positions. A finite element model (FEM) based on an advanced prescribed temperature approach was adopted to predict time-temperature history for predetermined locations of thermocouples.Design/methodology/approachComparing experimental and Rosenthal analytical models (2D and 3D) findings show that predicted and measured peak temperatures are in close agreement, while cooling rates predicted by analytical models (2D, 3D) show significant variation from measured values. On the other hand, 3D FEM simulation predicted peak temperatures and cooling rates for different thermocouple positions are close to experimental findings.FindingsThe inclusion of filler metal during simulation of welding rightly replicates the real welding situation and improves outcomes of the analysis.Originality/valueThe present study is an original contribution to the field of welding technology.

PurposeThis paper aims to study the effect of mineral additions on the mortars’ physical, mechanical and durability properties. Two local mineral additions, considered inert, are chosen: limestone fillers from North-East of Algeria and natural dune sand from Algerian desert areas.Design/methodology/approachTwo local additions are finely crushed to a fineness greater or equal to that of the used cement and incorporated into the mortars with predetermined rates; (0, 10%, 15% and 20%) compared to the cement weight to examine their effects on the mortars’ properties at different ages. Two conservation environments are chosen: freshwater as a neutral area and rising water table as an aggressive area to appreciate the effect of the two additions on physical and mechanical properties and durability.FindingsThe results showed the beneficial effect of these additions on compactness, mechanical resistance and durability toward the rising water table. The results have also allowed us to make an experimental comparison between the limestone addition which is commonly used in the Algerian cement industry and the dune sand, which is not yet well explored and exploited.Originality/valueThe added value of this study is the use of crushed dune sand which is a local addition of southern Algeria for improving the resistance of mortars and concrete toward the aggressiveness of rising water table which presents a major problem for the infrastructure of civil and public construction.

Purpose The purpose of this paper is to investigate the effect of snow on the radio link performance of wireless sensor nodes in Indian Himalayan conditions and to propose empirical path loss models for radio wave propagation. Design/methodology/approach At the remote test site, one source and three listening wireless sensor nodes were deployed at frequency of 433 MHz. The path loss models are derived from experimental data collected during the period of snowfall and clear weather conditions. Linear, exponential, second and third-order polynomials path loss models have been investigated along with experimental data. Findings With the help of curve fitting and goodness-of-fit tests, it is found that path loss can be modelled through third-order polynomial equation during the snowfall period. However, if sensor is buried, the acceptable path loss model is exponential. Similarly, for unified modelling requirement, exponential path loss model over linear can be a preferred choice. Originality/value Results show that path loss can be estimated priori for deciding optimum transmission energy in wireless sensor network. Presented work is usable in extending the lifetime of health monitoring devices buried in snowy environment.

PurposeThe purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.Design/methodology/approachThis research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.FindingsLSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.Originality/valueThe fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.

Purpose The purpose of this study is to bring out the machining characteristics of abrasive jet machining on carbon fibre reinforced thermoplastic composites utilized in aerospace and biomedical applications. Biocompatibility materials such as carbon fibres and polyether thermoplastics, like polyether ether ketone (PEEK) are widely used in trauma and orthopaedic surgery. Due to the heterogeneity, layered construction of reinforcing phase bonds with a resin matrix and abrasiveness of the reinforcing fibre, traditional drilling of carbon fibre-reinforced composites (CFRPs) are always challenging task. Design/methodology/approach An investigation is carried out using abrasive jet machine for drilling PEEK filled with 30 Wt.% carbon fibre (CF 30) using threaded and unthreaded nozzle to study the effect of abrasive jet process variables on surface roughness (Ra) and delamination factor (DF). Pressure (P) and stand-off distance (SOD) as important technological abrasive jet factors were evaluated. It is found that higher abrasive jet pressure and minimum SOD maybe selected to achieve minimum delamination. Findings The study further reported that the threaded nozzle minimized the surface roughness by 43% and delamination factor up to 12%. Originality/value This study of experimenting and observing the machining characteristics of CF30 by using a threaded nozzle is being tried for the first time and the results are deliberated.

Purpose This paper aims to study the influence of significant design parameters of elephant trunk soft pneumatic actuator and presents maximum optimized geometric structure of the actuator using finite element method. Design/methodology/approach Analysis of variance (ANOVA) is used to examine the influence of significant parameters such as wall thickness, bottom layer thickness and gap between adjacent chambers on the performance of the soft actuator. The most influencing parameter is found to be the wall thickness compared to the gap between adjacent chambers and bottom layer thickness. Findings The optimization of bending moment recommends a wall thickness of 1.5 mm, a gap between the adjacent channels of 1.5 mm and bottom layer thickness of 4 mm for the actuator. The theoretical expression of mechanical parameters is described. Originality/value The design optimization of elephant trunk shaped soft actuator with respect to bending angle and force analysis has not been investigated.

Purpose COVID-19 would have a far-reaching impact on the international health-care industry and the patients. For COVID-19, there is a need for unique screening tests to reliably and rapidly determine who is infected. Medical COVID images protection is critical when data pertaining to computer images are being transmitted through public networks in health information systems. Design/methodology/approach Medical images such as computed tomography (CT) play key role in the diagnosis of COVID-19 patients. Neural networks-based methods are designed to detect COVID patients using chest CT scan images. And CT images are transmitted securely in health information systems. Findings The authors hereby examine neural networks-based COVID diagnosis methods using chest CT scan images and secure transmission of CT images for health information systems. For screening patients infected with COVID-19, a new approach using convolutional neural networks is proposed, and its output is simulated. Originality/value The required patient’s chest CT scan images have been taken from online databases such as GitHub. The experiments show that neural networks-based methods are effective in the diagnosis of COVID-19 patients using chest CT scan images.