Acceleration Coefficients Research Articles

Seismic passive earth pressure resistance for caisson in layered soil using modified pseudo‐dynamic approach

AbstractThe determination of seismic passive earth pressure resistance is of utmost importance during the analysis of geotechnical structures. In the present study, seismic passive earth pressure coefficient for caisson embedded in n‐layered soil is computed using modified pseudo‐dynamic method. Limit equilibrium method of analysis considering poly‐planar failure wedge has been adopted in the study and parametric variation of geometry of failure wedge has also been studied. The current study proposes formulation for variation of horizontal and vertical seismic acceleration profile with depth through various layers and studies the effect of number of layers, relative depths of various layers and relative soil friction angle in those layers, soil‐wall friction angle, horizontal and vertical seismic acceleration coefficients and normalized width of caisson. The study has been extended for submerged soil and the effect of excess pore pressure has been investigated. The results of the present study have been compared with previous theoretical studies and found to be in good agreement. It is observed that the extent of failure wedge formed in front of caisson increases with increasing soil friction angle, soil‐wall friction angle, horizontal seismic acceleration coefficient and width of caisson.

Wave Propagation Characteristics and Compaction Status of Subgrade during Vibratory Compaction.

Vibratory compaction status has a significant influence on the construction quality of subgrade engineering. This study carried out field experiments to study the propagation characteristics of the vertical vibration wave in the soil field along the traveling direction of the vibratory roller. The propagation coefficients of the peak acceleration at different positions and compacting rounds are compared in both the time and frequency domains. The compaction status is estimated in the form of dynamic modulus of deformation (Evd) obtained by plate load tests. The experiment results show that the propagation coefficient of peak acceleration is affected by the traveling speed, excitation amplitude, and frequency of the vibratory roller, as well as the compacting rounds. An exponential relationship between the wave amplitudes of the fundamental mode and higher-order modes is revealed. The amplitude of the fundamental wave is maximum at the speed of 3 km/h, whereas the amplitudes of higher-order waves have a maximum of 1.5 km/h. The influences of compaction rounds on the average value of Evd are also investigated to provide a practical reference for engineering construction.

Development of a Method for Evaluating Elderly Mobility Functions Using Acceleration Sensors in Smartphones

Locomotive syndrome (LS) is a condition in which a person's mobility is impaired due to musculoskeletal disorders caused by aging or lack of exercise, signaling an imminent need for primary nursing care. This study aims to develop a smartphone application that can distinguish the LS stage in a safe, simple, and quantifiable manner. Using a six‐axes accelerometer built into the smartphone, acceleration and angular velocity data were collected from elderly subjects performing alternating one‐legged stances at intervals of 2.66 s. Frequency analysis was then performed with a fast Fourier transform (FFT) and an autocorrelation coefficient to compare the data characteristics of subjects with and without LS. Due to huge individual variances, significant differences were not observed between LS and non‐LS, and discriminating the LS stage using frequency analysis was considered difficult in the present study. In contrast, the autocorrelation coefficient showed significant differences between LS and non‐LS in acceleration and angular velocity in specific directions. Using the autocorrelation coefficient of acceleration and angular velocity data to detect the presence of LS was concluded to be effective. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Study on Lifespan Prediction of Oil–Paper Bushing Considering the Influence of Moisture-Accelerated Aging

The moisture-dependent parameter in the aging kinetic model is inaccessible due to the fluctuation of moisture during the aging period of bushing insulation. This significantly restricts the adoption of aging kinetics-based approaches for predicting transformer bushing insulation lifespan. Given this issue, an improved aging kinetic model is proposed in this article, where the influence of moisture fluctuation is interpreted by the moisture acceleration coefficient. To obtain the involved parameters in the improved model, the humidity of bushing insulation is determined by recognizing the moisture-related feature, which can be extracted from the logarithmic-derivative method derived from frequency domain spectroscopy (FDS). Finally, the improved aging kinetic model and logarithmic-derivative method are then used to provide a method for predicting the lifespan of bushing insulation. According to verification experiments carried out with this model, the relative errors of the re-created laboratory samples are 8.12%, 8.89%, and 25.15%, respectively. In that regard, this article is likely to serve as a model for predicting the lifespan of oil–paper insulation on genuine condenser bushings.

Simulation optimization for parcel hub scheduling problem in closed-loop sortation system with shortcuts

With the growth of e-commerce, the quantity of delivery orders and parcels is steadily increasing. Reliable and fast parcel delivery services are of utmost importance to customers. To achieve this target, the central parcel consolidation terminals (CPCT) known as parcel sorting hubs are dedicated to improving the operational efficiency especially in the optimal scheduling of inbound trucks. This study aims to determine the optimal schedule and assignment of inbound trucks to the inbound docks, known as the parcel hub scheduling problem with shortcuts (PHSPwS), for minimizing the expected makespan and expected waiting time of inbound trucks in the closed-loop automated sorting conveyor (ASC) system with shortcuts of the parcel sorting hubs. We first construct a mathematical formulation to present the PHSPwS problem based on the traffic control model. However, due to the congestion behavior on the conveyors and stochastic and dynamic nature of the ASC system, the expected objective functions are not analytically available from the mathematical model. Then, a simulation optimization (SO) approach combined with an adaptive genetic-based discrete particle swarm optimization (AGDPSO) algorithm is proposed to tackle the PHSPwS problem in a stochastic and dynamic ASC environment. The closed-loop ASC simulation model is first established to describe the detail operation of the closed-loop ASC sortation system with shortcut. Based on performance evaluation of this simulation model, a novelty algorithm AGDPSO, which hybridized genetic algorithm and particle swarm optimization algorithm with self-tuning parameters is developed to search and find the optimal inbound truck schedule in the large-scale solution space. In particular, the time-varying acceleration coefficients embedded in the AGDPSO are adopted to amplify the effectiveness of the global and local best solutions to obtain a better balance between exploration and exploitation of the searching space. The experimental results reveal that AGDPSO outperforms other algorithms and provides a better and more stable solution. Moreover, the impact of the strategic and operational factors of the closed-loop ASC system for the expected makespan and truck queueing time are also investigated, while the automatic truck unloading mode is confirmed to significantly improve the system efficiency.

Research and application of load spectrum technology of reliability running test method for vehicle

In order to maximize the use of load spectrum data in vehicle reliability driving test, as much as possible to solve practical problems, this paper preprocesses, edits, and compiles the original data obtained based on the multi-channel data acquisition system firstly. Then the effective and simplified application data of load spectrum of vehicle reliability driving test are obtained. Secondly, based on the measured load spectrum, the test and use equivalent relationship and acceleration coefficient of different components are calculated. The road simulation bench research is carried out to verify the reliability of the bench test and provide the basis for the indoor simulation reliability test. The reliability driving specification is studied, and the test speed of each road is determined. Meanwhile, the stress amplitude analysis method is used to analyze the fault, study the fault mechanism and improve it through optimization design. Finally, the influence mechanism of road surface on vehicle is studied by spectrum and amplitude analysis. This study which is based on solving practical problems expands the application field and direction of the measured test load spectrum from the test practice, and provides ideas and feasible methods for the application of reliable driving load spectrum.

Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors

In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the root mean square of vibration velocity, as well as resonant demodulated spectrum analysis of vibration acceleration, have been widely used for bearing fault diagnosis. These are integrated in almost any commercially available device for diagnosing bearing faults. However, the unsuitable use of these devices results in many false diagnoses. In light of this, they were selected as research objects and were investigated experimentally. In three induction motors, faults of different severity in the bearing outer race and cage were modeled for tests, and the corresponding results were used to evaluate the performance of the selected diagnosis methods. Some vague information in engineering was clarified, and some instructive rules were outlined to improve the bearing fault diagnosis performance. Taking the kurtosis coefficient of vibration acceleration (Ku) as an example, in engineering, Ku = 4 is generally taken as the diagnostic threshold of bearing faults. This means the following rule applies: if Ku ≤ 4, the bearing is healthy; otherwise, the bearing is faulty. However, the test results in this paper show that even if Ku ≤ 4, the bearing might be faulty; if Ku > 4, the bearing is indeed faulty. Therefore, the diagnostic rule should be improved as follows: if Ku > 4, the bearing is faulty (which can be assured), and if Ku ≤ 4, the status of the bearing is still undetermined. Thus, this paper can be helpful for researchers to gain an experimental understanding of the selected diagnosis methods and provides some improved rules on their use for reducing false diagnoses.

Updated Particle Swarm Optimization for Mechanical Engineering Design Optimization

In the optimization area, particle swarm optimization is an excellent optimization algorithm. It influenced thru position and velocity update of the particles. Particles velocity update significantly depend on its factor inertia weight (with previous velocity) and acceleration coefficients (with cognitive and social components). Also, PSO has various advantages like simplicity and fast convergence. However, PSO easily trapped into local optima due to simple search procedure. To improve the presentation of PSO, this article advises an updated-PSO (U-PSO) for solving mechanical engineering design optimization problems. In U-PSO, novel parameters (inertia weight, acceleration coefficients and lively coefficient) for velocity update and position update equation are employed to retain exploitation and exploration. To verify the efficiency and robustness, proposed U-PSO applied to solve two complex mechanical engineering designs optimization problems. The experimental outcomes validate that the suggested U-PSO has significant advantages over the compared algorithms.

Optimal path finding in 3D environment: Application for the exhibition space through 3D visualization and BIM

The application of 3D visualization technology in building construction has also increased. The study used Revit software to construct a 3D building information model (BIM) for the exhibition space of Chuzhou Higher Education City Development Collaborative Innovation Center to achieve a 3D visualization display; based on the 3D visualization, a particle swarm optimization (PSO) algorithm was used to find the optimal path for the exhibition space, so as to achieve the layout design of the exhibition space. The PSO algorithm was optimized in terms of inertia weight, acceleration coefficient, and initial population to obtain the improved PSO (IPSO) algorithm. The experimental results showed that the optimal path found by the IPSO algorithm was 78.56 meters in distance, 98.2 seconds in time consumption, and 50.11% in smoothness, which were better than the other two algorithms. Meanwhile, the IPSO algorithm had a lower value of particle fitness function, indicating that the IPSO algorithm had the highest performance and the strongest path finding ability among the three algorithms. It is confirmed that it is feasible to use the IPSO algorithm for optimal visit path finding in 3D environment. It is effective to visualize the exhibition space in 3D by constructing a BIM.

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying. It is impractical to identify a model that can restore the model of real papermaking process. Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process. In this study, a strange nonchaotic particle swarm optimization (SNPSO) algorithm is proposed to identify the models of real papermaking processes, and this identification ability is significantly enhanced compared with particle swarm optimization (PSO). First, random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions. Furthermore, the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO. The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization. To illustrate the effectiveness of SNPSO, step response data are used to identify the models of real industrial processes. Compared with classical PSO, PSO with time-varying acceleration coefficients (PSO-TVAC) and modified particle swarm optimization (MPSO), the simulation results demonstrate that SNPSO has stronger identification ability, faster convergence speed, and better robustness.

Selection of PSO parameters based on Taguchi design-ANOVA- ANN methodology for missile gliding trajectory optimization

The proposed research deals with selection of particle swarm optimization (PSO) algorithm parameters for missile gliding trajectory optimization relying on Taguchi design of experiments, analysis of variance (ANOVA) and artificial neural networks (ANN). Population size, inertial weight and acceleration coefficients of PSO were chosen for the present study. The experiments have been designed as per Taguchi's design of experiments using L25 orthogonal array for selection of better PSO parameters. Missile gliding trajectory is optimized by discretizing angle of attack as control parameter, consequent conversion of optimal control problem to nonlinear programming problem (NLP) and finally solving the problem using PSO with optimized parameters to obtain optimum angle of attack and realization of maximum gliding range. Simulation results portrayed that the gliding range is maximized and missile glide distance is enhanced compared to earlier experiments. The efficiency of proposed approach was verified via different test scenarios.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA), Differential evolution (DE), and, finally, Flower Pollination (FP) algorithms. In phase II, the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT (BA) and Multi-Swarm BAT algorithms. In phase III, the proposed PLTVACIW-PSO is employed to augment the feature selection problem for medical datasets. This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms. Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

Renewable energy sources (RESs) are becoming promising nowadays. Deploying distributed energy resources (DERs) is thus increasing accordingly, but the intermittent nature associated with RES remains a significant challenge. Also, the distribution grid faces technological difficulties with the large-scale integration of PV generations and EV charging loads. This paper suggests a strategy for a novel optimal voltage regulator (VR) placement model associated with the optimal placement of electric vehicle chargers (EVCs) and photovoltaic (PV) cells. Also, VRs’ reference voltages are determined optimally to enhance the hosting capacity (HC) of three-phase unbalanced distribution feeders for large-scale residential/public EVCs and PVs. Two scenarios are defined to study the impact of VRs on the network’s HC for DERs, namely with and without VRs in the real three-phase unbalanced feeder. The developed model determines the optimal placement of VRs and their reference voltages linked with EVCs and PVs penetration. Time-Varying Acceleration Coefficients Iteration Particle Swarm Optimization (TVAC-IPSO) algorithm has been implemented to improve the real three-phase unbalanced distribution feeder’s HC for DERs. The efficiency of the proposed model and the solution method is validated on the modified IEEE 37-node medium voltage (MV) radial distribution network connected to 35 low voltage (LV) distribution feeders. The obtained results represent that installing a proper number of VRs will increase the network’s HC for DERs.

Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Diagnosis of brake friction faults in high-speed trains based on 1DCNN and GraphSAGE under data imbalance

A braking friction fault diagnosis method based on one-dimensional convolutional neural network (1DCNN) and GraphSAGE network is proposed to solve the problem of fault imbalance samples in actual high-speed train braking friction operation, taking into account the correlation between different fault features. To begin, the original sample is created using the friction interface state characterisation parameters such as vibration noise, vibration acceleration and friction coefficient. Second, the graph is built using the sample’s characteristics as well as the Jensen-Shannon divergence between each sample. The 1DCNN is then used to extract and compress the graph node features; Next, the GraphSAGE is used to aggregate the information of each node in the graph, compensating for the neural network’s inability to learn the features of small samples and ensuring that all kinds of fault information are fully extracted. Finally, GraphSAGE outputs the braking friction fault state category to realise braking friction fault diagnosis with imbalanced data. The proposed network was tested using various imbalanced data sets and it was discovered that even with fewer fault samples and more normal samples, the network can still achieve at least 93.83% effective diagnostic accuracy. The effectiveness of the proposed network for each braking fault identification is further verified using precision, recall, F1 score and t-distribution stochastic neighbour embedding (t-SNE) visualisation. The superiority of the proposed network is validated when compared to the imbalanced data processing method and other state-of-the-art networks, indicating that the proposed network can achieve more effective fault diagnosis under imbalanced data without data expansion and large changes to the network, providing a new feasible method for research in this direction.

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques

Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.

Techno-economic analysis of robust gas-to-power distributed generation planning for grid stability and environmental sustainability in Nigeria

With an average generating capacity of around 4000 MW of electricity for a population of over 200 million people, less than 50% of Nigerians have access to electricity. Nevertheless, Nigeria has one of the largest deposits of Natural gas, often disposed of by indiscriminate flaring. Natural gas has been classified as a clean-burning alternative energy source with enormous potential for meeting the immediate quest for sustainable energy transition worldwide. Thus, in this study, a techno-economic analysis to show the potential of investing in modern gas-to-power distributed generators (GtP-DGs) to enhance the electricity infrastructure and promote environmental sustainability in Nigeria was investigated. The grid stability limits and loss sensitivities are used for optimal locations for GtP-DGs and reactive power compensators along the existing grid. Particle swarm optimization with a time-varying acceleration coefficients algorithm is used to solve the formulated optimal gas and electricity power flow for optimal capacity sizing. Credible technical parameters of the grid’s operation in steady-state such as power loss, voltage magnitude, stability, and grid security, are analyzed using MATLAB®. Based on the obtained results, further investigation was done for economic and environmental impact assessments. The optimized amount of gas required for one hour of electricity was calculated to be about 9.060 × 106 CF with a financial saving of approximately USD 36,240 $. The percentage emission reduction expressed as a fraction of recovered gas fuel instead direct flaring is calculated as 14.12% for CO2, 0.34% for CH4 and 1.84% for N2O. The results show considerable technical and economic benefits and significant environmental sustainability paybacks for investment in robust natural gas technologies for immediate sustainable electrical energy production.

Dynamic optimal power flow for isolated microgrid incorporating renewable energy sources

Enhanced investiture to distribution generation (DG) had moved for alteration of existing networks for developing microgrid which can function autonomously or together with grid. Dynamic optimal power flow (DOPF) for isolated microgrid incorporating renewable energy sources has been studied here. The DOPF problem has been solved by using elephant clan optimization (ECO) algorithm, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). The IEEE 33-bus system, IEEE 69-bus system, IEEE 118-bus system and real existing isolated microgrid of 15-bus system have been used for validation. In all the four test systems, the cost obtained from ECO is the lowest among all the three techniques.

The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations

Traditional economic dispatch methods, which are used to minimize fuel costs, have become inadequate because they do not consider the environmental impact of emissions in the optimization process. By taking into account the horizon year load and carbon taxes, this paper examines the operation and dispatch of power units in a power system. The objective function, including the cost of fuels and the cost of carbon taxes, is solved by the modified particle swarm optimization with time-varying acceleration coefficient (MPSO-TVAC) method under operational constraints. Based on different load scenarios, the influences of various carbon taxes for the dispatch of units are simulated and analyzed. The efficiency and ability of the proposed MPSO-TVAC method are demonstrated using a real 345KV system. Simulation results indicate that the average annual CO2 emissions are 0.36 kg/kwh, 0.41 kg/kwh, and 0.44 kg/kwh in 2012, 2017 and 2022, respectively. As the capacity of gas-fired plants was increased in 2017 and 2022, the average cost in 2017 and 2022 doubled or tripled compared with the average cost in 2012. Reasonable solutions provide a practical and flexible framework for power sectors to perform feasibility assessments of power system dispatch. They can also be used to assist decision-makers in reaching minimal operation cost goals under the policies for desired emissions.