Showing papers in "The Journal of Engineering in 2018"
TL;DR: In this paper, a convolutional sequence to sequence nonintrusive load monitoring model is proposed to extract information from the sequences of aggregate electricity consumption and residual blocks are also introduced to refine the output of the neural network.
Abstract: A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this study. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. The authors apply the proposed model to the reference energy disaggregation data set dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.
56 citations
TL;DR: The effect of applying optimization methods on the collection process of solid waste, with particular interest in mathematical programming and geographic information system approaches in developing countries, is reviewed.
Abstract: This paper reviews the effect of applying optimization methods on the collection process of solid waste, with particular interest in mathematical programming and geographic information system approaches in developing countries. Mathematical programming approaches maximize or minimize an objective function for improvement in procedure, to ensure operational efficiency and also serve as decision support tools. They however provide partial solutions when implemented in reality and cannot fully handle road network constraints. Geographic information system approaches allow processing of additional considerations, often ignored in other methods, such as the street network modeling. Incorporating environmental pollution consideration is very challenging in this approach, the vehicle routing problem solver encountering limits for large data. For enhanced efficiency of the vehicle routing systems, studies should further focus on incorporating all network constraints, environmental pollution considerations, and impact of land use changes on routing.
54 citations
TL;DR: The proposed modified hill climbing algorithm only needs to embed several lines of additional programs in the conventional hill climbing maximum power point tracking (MPPT) control program and does not require additional hardware components, which reduces the cost of PV power generation.
Abstract: To ensure the photovoltaic (PV) system can still output maximum power under changing environmental conditions, a modified hill climbing algorithm is proposed. The algorithm uses a variable step-size strategy to reduce the steady-state oscillations and prevent operating point from diverging away from the maximum power point by introducing boundary conditions. To verify its effectiveness, the proposed algorithm is compared with the conventional and adaptive hill climbing method under the environmental condition of irradiance step change and gradual change. The simulation results show that the proposed algorithm can increase the dynamic response speed of the PV system by 75% under varying irradiance, and can achieve a steady-state tracking accuracy of 99.8%. Besides, the proposed algorithm only needs to embed several lines of additional programs in the conventional hill climbing maximum power point tracking (MPPT) control program and does not require additional hardware components, which reduces the cost of PV power generation.
53 citations
TL;DR: A traditional classification algorithm based on digital image processing was attempted, and a defect classification algorithms based on convolutional neural network was proposed, which can achieve a fairly high classification accuracy (95.7%), which is much higher than the traditional method, and the new method has stronger stability than thetraditional one.
Abstract: Printed circuit board (PCB) inspection is an essential part of PCB production process. Traditional PCB bare board defect detection methods have their own defects. However, the PCB bare board defect detection method based on automatic optic inspection is a feasible and effective method, and it is having more and more application in industry. Based on the idea of the reference comparison method, this study aims at studying the classification of defects. First of all, the method of extracting defect areas using morphology is studied; meanwhile, a data set containing 1818 images with 6 different detailed defect area image parts are produced. Then, in order to classify defects accurately, a traditional classification algorithm based on digital image processing was attempted, and a defect classification algorithm based on convolutional neural network was proposed. After experimental demonstration, in the actual results, the defect classification algorithm based on convolutional neural network can achieve a fairly high classification accuracy (95.7%), which is much higher than the traditional method, and the new method has stronger stability than the traditional one.
49 citations
TL;DR: An improved bare PCB defect detection approach is proposed by learning deep discriminative features, which also greatly reduced the high requirement of a large dataset for the deep learning method.
Abstract: Robust and precise defect detection is of great significance in the production of the high-quality printed circuit board (PCB). However, due to the complexity of PCB production environments, most previous works still utilise traditional image processing and matching algorithms to detect PCB defects. In this work, an improved bare PCB defect detection approach is proposed by learning deep discriminative features, which also greatly reduced the high requirement of a large dataset for the deep learning method. First, the authors extend an existing PCB defect dataset with some artificial defect data and affine transformations to increase the quantity and diversity of defect data. Then, a deep pre-trained convolutional neural network is employed to learn high-level discriminative features of defects. They fine-tune the base model on the extended dataset by freezing all the convolutional layers and training the top layers. Finally, the sliding window approach is adopted to further localise the defects. Extensive comparisons with three traditional shallow feature-based methods demonstrate that the proposed approach is more feasible and effective in PCB defect detection area.
45 citations
TL;DR: The presented MOPSO has the ability to minimise the operation cost of the MG with respect to the renewable penetration, the fluctuation in the generated power, uncertainty in power demand, and continuous change of the utility market price.
Abstract: This study introduces an efficient energy management system (EMS) for a wind–photovoltaic (PV)–fuel cell (FC)–battery energy scheme with an effective control strategy to integrate with the utility grid. The suggested technique utilises the multi-objective particle swarm optimisation (MOPSO) to minimise the operation cost of the microgrid (MG) and maximise the generated power by each source. The presented MOPSO has the ability to minimise the operation cost of the MG with respect to the renewable penetration, the fluctuation in the generated power, uncertainty in power demand, and continuous change of the utility market price. The proposed optimisation strategy makes the exact choice of sources in right planning and chooses the power that must be created by each source and the power required by the utility network. The price fluctuation data of power and the variance of the renewable energy produced by each unit will be sent to the EMS by a communication network using global positioning system. The dynamic performance and the operation cost of the MG have been tested under different weather conditions and the variance of the power demand through a whole simulation period of 24 h. The stability of the MG, power quality, and voltage regulation is verified by Matlab simulation and experimental results.
42 citations
TL;DR: The authors present a new method for predicting the pedestrian's trajectory, which is called Social-Grid LSTM based on RNN architecture, which combines the human–human interaction model called social pooling and the Grid L STM network model.
Abstract: In the design of intelligent driving systems, reliable and accurate trajectory prediction of pedestrians is necessary. With the prediction of pedestrians’ trajectory, the possible collisions can be avoided or warned as early as possible by changing the behaviour of intelligent vehicles. The trajectory prediction problem can be considered as a sequence learning problem, in which one of the recurrent neural network (RNN) models called long short term memory (LSTM) has been regarded as a promising method. The authors present a new method for predicting the pedestrian's trajectory, which is called Social-Grid LSTM based on RNN architecture. The proposed method combines the human–human interaction model called social pooling and the Grid LSTM network model. The performance of the proposed method is demonstrated on two available public datasets, and compared with two baseline methods (LSTM and Social LSTM). The experimental results indicate that the authors’ proposed method outperforms previous prediction approaches.
33 citations
TL;DR: In this article, the use of Pongamia pinnata (karanja), a non-edible feedstock from the state of Sarawak, Malaysia, to produce biodiesel to be known as crude karanja oil (CKO).
Abstract: The application of nonedible feedstock for the production of biodiesel has become an area of research interest among clean energy experts in the past few years. This research is aimed at the utilization of Pongamia pinnata (karanja), a nonedible feedstock from the state of Sarawak, Malaysia, to produce biodiesel to be known as crude karanja oil (CKO). A one-step transesterification process utilizing 7 : 1–10 : 1 wt% methanol ( ) and 0.5–1.2 wt% sodium hydroxide (NaOH) at 65°C for 1.5 hrs has been used for the biodiesel production yielding 84% conversion. The physiochemical properties of the CKO produced revealed that it conforms with EN14214 standards for brake power (BP), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE) as they are all noted be optimal at B40.
33 citations
TL;DR: An artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram and electrooculogram recordings is proposed.
Abstract: We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.
31 citations
TL;DR: The quantitative comparison with five traditional techniques for electrical load and wind speed forecasting reveals that the GA-NARX-NN method is more accurate and reliable.
Abstract: Electrical load and wind power forecasting are a demanding task for modern electrical power systems because both are closely linked with the weather parameters, such as temperature, humidity, and air pressure. The conventional methods of electrical load and wind power forecasting are useful to handle dynamic and uncertainties in un-regulated energy markets. However, there is still need of relative improvement by incorporating weather parameter dependencies. Considering above, a genetic algorithm-based non-linear auto-regressive neural network (GA-NARX-NN) model for short- and medium-term electrical load forecasting is presented with relative degree of accuracy. Causality, a new modelling technique, is employed for monthly and yearly wind speed patterns predictions and long-term wind speed forecasting. Real-time historical electrical load and weather parametric data are used to critically observe the performance of the proposed models compared to various state-of-the-art forecasting schemes. Numerical simulations are conducted that validates the proposed models based on various error calculation methods, such as mean absolute percentage error, root mean-square error, and variance ( σ 2 ). The quantitative comparison with five traditional techniques for electrical load and wind speed forecasting reveals that the GA-NARX-NN method is more accurate and reliable.
31 citations
TL;DR: The results prove that BSO can do better in dealing with optimisation problems of constrained multi-dimensional functions.
Abstract: A portfolio model is established after analysing the investment environment of the artificial intelligence concept stocks in China. To reduce the risk of investment, the beetle swarm optimisation (BSO) is proposed. BSO, based on the beetle antennae search (BAS) and the standard particle swarm optimisation (PSO), is derived from the standard PSO but the update rules of each particle originate from BAS. In global searching, BSO, making the model get a lower value at risk, is more capable than standard PSO, which is easily trapped in local optimal defects. This study tries to solve portfolio model by using BSO algorithm. The results prove that BSO can do better in dealing with optimisation problems of constrained multi-dimensional functions.
TL;DR: The proposed scheme for fault location in distribution systems using network topology information and circuit breaker reclosure-generating travelling waves can be effective in fault location for distributed feeders, regardless of grounding system type.
Abstract: Fault location in distribution systems is difficult for multiple discontinuities, such as branch and junction points on feeders. This paper proposes a scheme for fault location in distribution systems using network topology information and circuit breaker reclosure-generating travelling waves. Based on the topology information, the circuit breaker closure-generating travelling waves can be got through analysis. When a permanent fault occurs, the circuit breaker reclosure-generating travelling waves contain the information on fault position. In this paper, the difference between the pre-fault travelling waves and post-fault travelling waves induced by the breaker reclosing in a feeder, defined as reclosing superimposed travelling waves, is calculated. For the reclosing superimposed travelling waves, the initial travelling wave reflects the fault distance. Subsequently, wavelet transform is applied to extract the travelling wave reflected from the fault point. Finally, the time difference between the reclosing instant and the arrival instant of the reflected travelling wave is employed to calculate the fault distance. In order to verify the effectiveness of the proposed scheme, several simulations were carried out using ATP-EMTP software. The simulation results indicated that the proposed scheme can be effective in fault location for distributed feeders, regardless of grounding system type.
TL;DR: A multi-task CNN model is proposed to handle the multi-label learning problem by defining each label learning as a binary classification task by customising the loss function.
Abstract: Due to the rapid development of printed circuit board (PCB) design technology, inspection of PCB surface defects has become an increasingly critical issue. The classification of PCB defects facilitates the root causes of detects’ identification. As PCB defects may be intensive, the actual PCB classification should not be considered as a binary or multi-category problem. This type of problem is called multi-label classification problem. Recently, as one of the deep learning frameworks, a convolutional neural network (CNN) has a major breakthrough in many areas of image processing, especially in the image classification. This study proposes a multi-task CNN model to handle the multi-label learning problem by defining each label learning as a binary classification task. In this study, the multi-label learning is transformed into multiple binary classification tasks by customising the loss function. Extensive experiments demonstrate that the proposed method achieves great performance on the dataset of defects.
TL;DR: An RGB-D static gesture recognition method based on fine-tuning Inception V3 is proposed, which can eliminate the steps of gesture segmentation and feature extraction in traditional algorithms.
Abstract: In the area of human–computer interaction (HCI) and computer vision, gesture recognition has always been a research hotspot. With the appearance of depth camera, gesture recognition using RGB-D camera has gradually become mainstream in this field. However, how to effectively use depth information to construct a robust gesture recognition system is still a problem. In this paper, an RGB-D static gesture recognition method based on fine-tuning Inception V3 is proposed, which can eliminate the steps of gesture segmentation and feature extraction in traditional algorithms. Compared with general CNN algorithms, the authors adopt a two-stage training strategy to fine-tune the model. This method sets a feature concatenate layer of RGB and depth images in the CNN structure, using depth information to promote the performance of gesture recognition. Finally, on the American Sign Language (ASL) Recognition dataset, the authors compared their method with other traditional machine learning methods, CNN algorithms, and the RGB input only method. Among three groups of comparative experiments, the authors’ method reached the highest accuracy of 91.35%, reaching the state-of-the-art currently on ASL dataset.
TL;DR: In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed and is found to be better as compared with other state-of-the-art methods.
Abstract: The electroencephalogram (EEG) signal is an electrical representation of brain's working that reflects various physiological and pathological activities such as alcoholism. Alcohol can affect whole parts of the body but, it particularly affects the brain, heart, liver, and the immune system; its effects on the brain are called brain disorders. Nowadays, automatic identification of alcoholic subjects based on EEG signals has become one of the challenging problems in biomedical research. In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed. The uniqueness of EMD method is to decompose non-stationary and non-linear signals into a set of stationary intrinsic mode functions (IMFs) that are band limited signals. These IMFs are transformed into analytic representations by applying the Hilbert transform. From these analytic IMFs, various features namely mean, kurtosis, skewness, entropy, and negentropy are extracted; these features are used as input to least squares support vector machines (LS-SVMs) classifier with radial basis function (RBF) kernel and polynomial kernel. The accuracy results achieved for LS-SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92%, respectively, which are found to be better as compared with other state-of-the-art methods.
TL;DR: A new fault-tolerant control strategy with real-time fault diagnosis for power transistor faults in SRM drives is proposed and an online fault diagnostic algorithm based on high-frequency (HF) signal injection is proposed.
Abstract: Switched reluctance motors (SRMs) are considered a competitive technology for more electric aircraft and automotive applications due to their excellent fault-tolerant capabilities and robust configuration. In such applications, system reliability is a crucial feature. Therefore, here, a new fault-tolerant control strategy with real-time fault diagnosis for power transistor faults in SRM drives is proposed. The developed fault-tolerant topology is composed of four additional power switches and a relay network based on the conventional asymmetric converter. Moreover, an online fault diagnostic algorithm based on high-frequency (HF) signal injection is proposed. In contrast to the existing strategies that use additional sensors, this diagnostic algorithm extracts the fault signatures from the fundamental current by injecting an HF voltage signal into the upper switches of the asymmetric converter. Open- and short-circuit faults of the power switches are analysed by monitoring the variation in frequency and amplitude of the resulting HF current along with the amplitude variation in fundamental current with the occurrence of fault. Simulations performed on a three-phase 12/8 SRM drive and results are presented to validate the effectiveness of the proposed strategy.
TL;DR: In this paper, the potential of improved efficiency of liquid piston based OCAES with use of various heat transfer enhancement techniques is investigated using an experimental liquid piston compressor, which showed that adiabatic and isothermal OCAEs showed improved efficiency over diabatic OCAE by storing thermal exergy in thermal energy storage.
Abstract: Optimal utilization of renewable energy resources needs energy storage capability in integration with the electric grid. Ocean compressed air energy storage (OCAES) can provide promising large-scale energy storage. In OCAES, energy is stored in the form of compressed air under the ocean. Underwater energy storage results in a constant-pressure storage system which has potential to show high efficiency compared to constant-volume energy storage. Various OCAES concepts, namely, diabatic, adiabatic, and isothermal OCAES, are possible based on the handling of heat in the system. These OCAES concepts are assessed using energy and exergy analysis in this paper. Roundtrip efficiency of liquid piston based OCAES is also investigated using an experimental liquid piston compressor. Further, the potential of improved efficiency of liquid piston based OCAES with use of various heat transfer enhancement techniques is investigated. Results show that adiabatic OCAES shows improved efficiency over diabatic OCAES by storing thermal exergy in thermal energy storage and isothermal OCAES shows significantly higher efficiency over adiabatic and diabatic OCAES. Liquid piston based OCAES is estimated to show roundtrip efficiency of about 45% and use of heat transfer enhancement in liquid piston has potential to improve roundtrip efficiency of liquid piston based OCAES up to 62%.
TL;DR: The authors put forward the problems of power coupling and strong non-linearity in traditional three-port triple active bridge (TAB) converters, and propose a decoupled TAB topology, which has the feature of decoupling power flow.
Abstract: It is an important trend to develop the more electric aircraft (MEA) ±270 V high-voltage direct current (HVDC) power system because of its better reliability, power quality and power density. However, there also exists the low-voltage 28 V DC system in HVDC power system, for the aviation battery and some equipment still rely on it. In traditional MEA power systems, battery is only used as a backup power supply, hardly participating in the adjustment of the power system. On the other hand, the function of turbine engine is unidirectional. Hence, it's necessary to add an energy storage port. In this research, the authors put forward the problems of power coupling and strong non-linearity in traditional three-port triple active bridge (TAB) converters, and propose a decoupled TAB topology, which has the feature of decoupled power flow. The topology is compatible with ±270 V DC, 28 V DC and energy storage system with high-power density and efficiency. In addition, the authors unify the efficiency optimisation problem of dual active bridge topology mathematically and provide an optimal parameter design approach for inductance and turns ratio. Simulation results show the power flow control strategy is appropriate for the topology.
TL;DR: In this paper, two methods of magnetic nanoparticles (MNPs) of iron oxide (Fe3O4) were conducted with different parameters, such as temperature (25 and 80 C), adding a base to the reactants and the opposite process, and using nitrogen as an inert gas.
Abstract: Magnetic nanoparticles (MNPs) of iron oxide (Fe3O4) represent the most promising materials in many applications. MNPs have been synthesized by co-precipitation of ferric and ferrous ions in alkaline solution. Two methods of synthesis were conducted with different parameters, such as temperature (25 and 80 C), adding a base to the reactants and the opposite process, and using nitrogen as an inert gas. The product of the first method (MNPs-1) and the second method (MNPs-2) were characterized by x-ray diffractometer (XRD), Zeta Potential, atomic force microscope (AFM) and scanning electron microscope (SEM). AFM results showed convergent particle size of (MNPs-1) and (MNPs-2) with (86.01) and (74.14) nm respectively. Also, the zeta potential values of (MNPs-1) and (MNPs-2) were (2.77) and (-12.48) mV, respectively, which indicates more stability of (MNP-2).
TL;DR: This framework has achieved good results in speed and accuracy and has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features.
Abstract: With the continuous development of the electronics industry, the number of printed circuit board (PCB) has grown at a rapid rate, and the requirements for the detection systems of PCB have also continuously increased. In the traditional PCB detection, the main reference is the comparison method. However, in a real scene, there are a series of problems such as non-uniform illumination, tilting of the camera angle, and the like, resulting in a less satisfactory effect of the reference comparison method. So, the authors proposed a non-reference comparison framework of PCB defects detection. This framework has achieved good results in speed and accuracy. The authors extract the histogram of oriented gradients and local binary pattern features for each PCB image, respectively, put into the support vector machine to get two independent models. Then, according to Bayes fusion theory, the authors fuse two models for defects classification. The authors have established a PCB data set that includes both defective and defect-free. It has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features. The authors also illustrate the effectiveness of Bayes feature fusion in terms of speed.
TL;DR: A robust optimization model of virtual power plant bidding strategy in the electricity market, which considers the cost of charge and discharge of energy storage power station and transmission congestion and is solved by CPLEX.
Abstract: For the virtual power plants containing energy storage power stations and photovoltaic and wind power, the output of PV and wind power is uncertain and virtual power plants must consider this uncertainty when they participate in the auction in the electricity market. In this context, this paper studies the bidding strategy of the virtual power plant with photovoltaic and wind power. Assuming that the upper and lower limits of the combined output of photovoltaic and wind power are stochastically variable, the fluctuation range of the day-ahead energy market and capacity price is stochastically variable. If the capacity of the storage station is large enough to stabilize the fluctuation of the output of the wind and photovoltaic power, virtual power plants can participate in the electricity market bidding. This paper constructs a robust optimization model of virtual power plant bidding strategy in the electricity market, which considers the cost of charge and discharge of energy storage power station and transmission congestion. The model proposed in this paper is solved by CPLEX; the example results show that the model is reasonable and the method is valid.
TL;DR: In this article, a mathematical model of the dual three-phase permanent magnet synchronous motor (PMSM) with one open phase is set up by the vector space decomposition modeling method, and two optimal current control modes of minimum stator loss and maximum torque output are achieved by vector control strategy.
Abstract: The high fault-tolerance ability is one important application characteristic of multiphase motors. At present, researches on fault-tolerant control of multiphase motors are focused on open-phase faults. When an open-phase fault occurs for the dual three-phase machine, the α
–
β subspace and z
1–
z
2 subspace currents are no longer decoupled, so the mathematical model with open phases should be set up. In this study, the mathematical model of the dual three-phase permanent-magnet synchronous motor (PMSM) with one open phase is set up by the vector space decomposition modelling method, and two optimal current control modes of minimum stator loss and maximum torque output are achieved by vector control strategy. The experimental results demonstrate the effectiveness and feasibility of the proposed strategy.
TL;DR: In this article, a survey has been conducted in 10 primary schools in Khulna, Bangladesh and the results highlighted that desktop height and seat height were too high and seat width was too small for all of the students.
Abstract: Students spend a large portion of their time in school In this broadened time of sitting, poor fitting furniture can cause various types of musculoskeletal disorders and discomforts Thus, it is crucial to use anthropometric data to coordinate the arrangement of school furniture To fulfill this perception, a survey has been conducted in 10 primary schools in Khulna, Bangladesh Anthropometric measurements were accumulated from 300 students of these primary schools Seven furniture dimensions were measured and fifteen anthropometric measurements were taken and they were compared to identify potential mismatch A significant degree of mismatch was found between furniture and student anthropometric measurements The results highlighted that desktop height and seat height were found too high and seat width was too small for all of the students The paper also proposes furniture dimensions, which reduce mismatch percentage of students ranging from 90% to 10%
TL;DR: An active flux-balancing control method is proposed to eliminate steady-state dc biases and it uses separate proportional integral controls and adjusts duty ratios of both sides independently to achieve flux balance.
Abstract: Due to non-ideal behaviours and switching characteristics, dc bias might be generated in the dual active bridge converters. It could be more serious in the high-frequency transformers because of small loop resistances. It could also restrict the range of operating flux density due to the threat of saturation. This study analyses the steady-state dc bias and divides it into two categories. Then an active flux-balancing control method is proposed to eliminate these dc biases. It uses separate proportional integral controls and adjusts duty ratios of both sides independently to achieve flux balance. A generalised average model is established to verify its dynamic performance and stability. Simulation and experimental verification on a laboratory prototype confirm the performance of this approach.
TL;DR: In this paper, two ultra wideband (UWB) filters having the fractional bandwidth >120% are designed, analysed and fabricated, and a good agreement between the measured and predicted results is achieved, which validate the filter designs.
Abstract: In this study, two ultra-wideband (UWB) filters having the fractional bandwidth >120% are designed, analysed and fabricated. The first filter is designed with three quarter-wavelength short-circuited stubs and second by using exponential tapered impedance line stub loaded microstrip resonator. The first filter consists of five transmission poles within the passband. The second filter with tapered inductive loading on quarter-wavelength high impedance line exhibits a sharp notch stopband around 5.5 GHz, to suppress the interference from IEEE 302.11a WLAN band signals with an attenuation level >30 dB. A good agreement between the measured and predicted results is achieved, which validate the authors’ filter designs.
TL;DR: To improve the protection scheme for long-distance remote internal fault, a second element utilising the concavity of the forward travelling wave power is proposed, which can be easily implemented since it will require fewer hardware resources.
Abstract: This study presents a novel time-domain protection technique for application to DC grids. The technique utilises the power developed by the forward and backward travelling waves produced by a fault to distinguish between internal and external faults. For an internal fault, the calculated travelling wave power must exceed a predetermined setting; otherwise the fault is external. The ratio between the forward travelling wave power and the backward travelling wave power provides a directional comparison. For a forward directional fault, this ratio is less than unity, whereas the ratio is greater than unity for reverse directional faults. To improve the sensitivity of the protection scheme for long-distance remote internal fault, a second element utilising the concavity of the forward travelling wave power is proposed. The proposed technique is time domain based and does not require complex mathematical burden; moreover, as such can be easily implemented since it will require fewer hardware resources. Simulations were carried out in power systems computer-aided design/electromagnetic transient simulations, and the results presented considering wider cases of fault scenarios including 500 Ω remote internal fault shows the suitability of the proposed scheme as all fault scenarios indicated were identified within 500 µs following the application of the fault.
TL;DR: In this article, the dimensional properties of knitted fabrics are investigated and it is found that the loop length, wpc, cpc, stitch density, tightness factor, loop shape factor and take-up rate of fabrics made from 100% cotton and cotton/elastane yarns are significantly influenced by the presence of an elastane fiber.
Abstract: The dimensional characteristics such as loop length (l), wales per centimeter (wpc), courses per centimeter (cpc), stitch density (s), tightness factor (K), loop shape factor (R) and take-up rate (T) of single jersey, 1x1rib, 1x1 interlock, single pique, and two-thread fleece knitted fabrics made from 100% cotton and cotton/elastane yarns (5% elastane yarn content) are investigated in this research. Dimensional properties of knitted fabrics are an important property and determine the materials consumption during production, productions parameter, and applications of different knitted structures. The sample fabrics have been conditioned for 24 hours at 20±1°C temperature and 65±2% relative humidity. The specimens used for sampling are determined as per the test standards described in the paper for each yarn type, property, and structure. As observed in the result, the properties are related to each other. It is found that the loop length, wpc, cpc, stitch density, tightness factor, loop shape factor and take-up rate of single jersey, 1x1rib, 1x1interlock, single pique, and two-thread fleece knitted fabrics made from 100% cotton and cotton/elastane yarns are significantly influenced by the presence of an elastane yarn. The loop length of single jersey, 1x1rib, and interlock knitted fabrics made from elastane yarns reduced while the single pique and fleece increased. Similarly, other dimensional properties are significantly influenced by the yarn types used during knitting.
TL;DR: In this article, the effect of the surface roughness on the statistical and fractal parameters of the laser speckle pattern has been investigated, and the results show that some of the proposed statistical parameters have definite relationships with the surfaces roughness.
Abstract: The surface roughness of a machined metal surface is crucial to its appearance and performance. There are hardly any in-situ methods for surface roughness measurement of moving surfaces. A study of the digital speckle patterns generated by rough surfaces illuminated by a laser is performed experimentally. Laser speckle phenomenon can be used to monitor the surface roughness in a non-contact way. By investigating the effect of the surface roughness on the statistical and fractal parameters of the laser speckle pattern, an assessment method for evaluating surface roughness is discussed. The results show that some of the proposed statistical parameters have definite relationships with the surface roughness and can be explored to evaluate the surface roughness. Furthermore, the fractal parameters of the speckle pattern are sensitive to the type of machining process and therefore they can be used to classify the machined surface. The method can be a practical tool to achieve in-situ surface roughness measurement of moving surfaces.
TL;DR: An electromagnetic transient model of permanent magnet synchronous generator (PMSG) was proposed and the expression of three-phase fault current was obtained and its components, frequency, and attenuation speed of every component and other characteristics were analysed comprehensively.
Abstract: Fault characteristics analysis is precondition of relay protection. Theoretical derivation of permanent magnet synchronous generator (PMSG) fault current is necessary. In accordance with the theory that grid-side converter of PMSG is a power balance system under control action, an electromagnetic transient model of PMSG was proposed. Based on it, the active and reactive current was deduced and then the expression of three-phase fault current was obtained and its components, frequency, and attenuation speed of every component and other characteristics were analysed comprehensively. Simulation on PSCAD and field fault recording data as practical analysis verifies that the expressions and the characteristics are correct. The results of this paper are not only important to relay protection, but also are references to transient calculation and transient modelling.
TL;DR: A virtual reality (VR)-based test platform for autonomous-driving vehicles was built combined with the AirSim system and the UE4 engine by establishing a model library which contains the vehicle dynamics model, sensor models and traffic environment model.
Abstract: In order to mitigate risks from road tests for autonomous-driving vehicles, reduce costs and accelerate development, a virtual reality (VR)-based test platform for autonomous-driving vehicles was built combined with the AirSim system and the UE4 engine by establishing a model library which contains the vehicle dynamics model, sensor models and traffic environment model. The controller-in-the-loop simulation method was implemented to complete the simulation test for autonomous vehicles under different driving conditions and the simulation results were used to optimise the autonomous-driving control system. The actual autonomous driving road test can now be done in an immersive VR simulation environment where autonomous-driving road-testing is done safely and cost-effectively. This plays a significant role in the future development of autonomous-driving vehicles.