Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights

Modelling Photosynthetic Active Radiation (PAR) through meteorological indices under all sky conditions

In this study, ten-minute meteorological data-sets recorded at Burgos, Spain, are used to develop models of Photosynthetic Active Radiation () following two different procedures: multilinear regression and Artificial Neural Networks. Ten Meteorological Indices (MIs) are chosen as inputs to the models: clearness index (), diffuse fraction (), direct fraction (), Perez's clear sky index (ɛ), brightness index (), cloud cover (), air temperature (), pressure (), solar azimuth cosine (), and horizontal global irradiation (). The experimental data are clustered according to the sky conditions, following the CIE standard sky classification. A previous feature selection procedure established the most adequate MIs for modelling in clear, partial and overcast sky conditions. was the common MI used by all models and for all sky conditions. Additional variables were also included: the geometrical parameter, , and three variables related to the sky conditions, , and Both modelling methods, multilinear regression and ANN, yielded very high determination coefficients () with very close results in the models for each of the different sky conditions. Slight improvements can be observed in the ANN models. The results underline the equivalence of multilinear regression models and ANN models of PAR following previous feature selection procedures.Regional Government of Castilla y León, under projects BU021G19 and INVESTUN/19/BU/0004 and the Spanish Ministry of Science & Innovation under the I+D +i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00). Diego Granados López expresses his thanks to the Junta de Castilla y León for economic support (PIRTU Program, ORDEN EDU/556/2019)

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approach

In today's modern electric vehicles, enhancing the safety-critical cyber-physical system CPS 's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. A deep neural network DNN is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.Published versio

Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data

Alzheimer’s disease (AD) is the most prevalent form of dementia. The accurate diagnosis of AD, especially in the early phases is very important for timely intervention. It has been suggested that brain atrophy, as measured with structural magnetic resonance imaging (sMRI), can be an efficacy marker of neurodegeneration. While classification methods have been successful in diagnosis of AD, the performance of such methods have been very poor in diagnosis of those in early stages of mild cognitive impairment (EMCI). Therefore, in this study we investigated whether optimisation based on evolutionary algorithms (EA) can be an effective tool in diagnosis of EMCI as compared to cognitively normal participants (CNs). Structural MRI data for patients with EMCI (n = 54) and CN participants (n = 56) was extracted from Alzheimer’s disease Neuroimaging Initiative (ADNI). Using three automatic brain segmentation methods, we extracted volumetric parameters as input to the optimisation algorithms. Our method achieved classification accuracy of greater than 93%. This accuracy level is higher than the previously suggested methods of classification of CN and EMCI using a single- or multiple modalities of imaging data. Our results show that with an effective optimisation method, a single modality of biomarkers can be enough to achieve a high classification accuracy

Monitoring and assessment of weld penetration condition during pulse mode laser welding using air-borne acoustic signal

Real-time monitoring system is one of the essential criteria in the era of the fourth industrial revolution (Industry 4.0). Among the monitoring systems in laser welding applications, acoustic methods have recently caught the attention of researchers due to their benefits in promoting simple, low-cost, and non-contact systems. However, applying this method in PW mode laser was challenging due to the different characteristic of signal and noise acquired from this process as compared to CW process. Therefore, this particular work aims to investigate the characteristics of acoustic sound signal from PW Fiber laser, develop an appropriate signal processing algorithm to suppress the effect of noise on the extracted sound features, and develop an empirical model for weld depth estimation. To achieve the objectives, a 1.8 mm thick 22MnB5 boron steel plate was welded with varied laser peak power (PP) and pulse duration (PD) levels. Simultaneously, the sound signal was acquired between the frequency of 20 Hz to 12.8 kHz throughout the process. Signal features, such as mean absolute deviation (MAD), standard deviation (SD), kurtosis (K), L-scale (LS), L-kurtosis (LK), bandpower (BP), and sum of synchrosqueezed wavelet coefficient (CSqWCsum) were extracted from the acquired sound. To develop the signal processing algorithm, multi-lag phase space (MLPS) method was adopted in which some modifications on its original algorithm were made by introducing the localized crest factor (CF) thresholding method to reduce the influence of noise. Results showed that the acquired sound recorded transient behaviors with a slight change in its overall amplitudes with respect to the change in the level of weld parameters. Meanwhile, the dominant frequency was found to be fluctuated between 5760 Hz and 7000 Hz without a clear pattern in the case of different levels of weld parameters involved in this study. The results from feature selection analysis show that the combination of SD, L-kurtosis, and modified-MLPS recorded the most significant relation with weld penetration. Furthermore, the combination of these features with the laser peak power and pulse duration recorded a better regression trend with an adjusted R-squared of 0.937. Two empirical models for weld depth estimation were developed from the combination of these sound features and weld parameters using the multiple linear regression (MLR) and artificial neural network (ANN) methods. Through MLR method, the obtained model was DOP = 0.634SD - 0.814LK + 0.0014MLPS + 116.44PD + 0.0014PP - 0.7781. Results from the model validation analysis showed that both models could significantly estimate weld penetration during the PW laser welding process with an estimation error less than 8%. However, the ANN model recorded a more accurate and precise estimation with the lowest estimation error, i.e., 3.3%. The results of the analysis suggest that the acoustic methods can be used to monitor weld penetration on a real-time basis during PW mode laser welding process. Moreover, the methods can also be used to provide a quantitative assessment on weld penetration during the process. This finding gives alternative solution to the development of a real-time process monitoring system in PW mode laser welding, which aligns with the criteria needed in the new era of manufacturing system

Characterization of driver neuromuscular dynamics for human-automation collaboration design of automated vehicles

In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles

Nonlinear model predictive control-based optimal energy management for hybrid electric aircraft considering aerodynamics-propulsion coupling effects

Hybrid electric propulsion systems have been identified as the feasible solutions for regional jets and narrow-body aircrafts to reduce block fuel burn, emissions, and operating cost. In this paper, a Nonlinear Model Predictive Control based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. Firstly, the Artificial Neural Network (ANN) model is adopted to predict turbofan engine performance, meanwhile gas turbine-electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear model predictive control with Cross-Entropy Method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is re-formulated as a state-unconstrained problem with penalty function to reduce the computational load. Finally, the proposed MPC-EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global Genetic Algorithm (GA) based EMS and Equivalent Consumption Minimization Strategy (ECMS). The simulation results indicate that the proposed MPC-EMS can effectively reduce the computational time compared with Global GA-based EMS while achieving global optimization performance with only a minor difference of 1.71% of block fuel burn and emissions reductions

Levenberg-marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system

As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios

Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract

Co-culture fermentation is a fermentation process involving two defined microorganisms growing together in the same culture. A co-culture of lactic acid-producing bacteria (LAB) and propionic acid-producing bacteria (PAB) is beneficial in producing direct-fed microbial (DFM) products. The synergistic activity between LAB and PAB in co-culture fermentation can improve the survival of LAB and the growth of PAB. On this basis, the objectives of this study are two-fold. Firstly, the optimisation of co-culture fermentation involving Lactobacillus casei and Propionibacterium jensenii in the agricultural waste extract. Secondly, the development of an artificial neural network (ANN) predictive model for predicting the cell biomass concentration and the co-culture-specific growth rate. In the preliminary phase, two different substrates, namely rice bran and banana peel, were used in this study. This step was conducted to select the suitable carbon source for L. casei to grow and produce lactic acid for P. jensenii consumption. From the observation, rice bran was found more suitable as a carbon source and fermentation medium. Next, the co-culture optimisation of L. casei and P. jensenii fermentation was conducted using the one-factor-at-a-time approach. The fermentations were optimised for rice bran at concentration of 5% to 25% w/v; incubation temperature (30? to 42?); inoculation ratio (1:1 to 1:10 % v/v) and the initial pH (5.0 to 7.0). The optimum fermentation condition was obtained at 20% w/v rice bran concentration, incubated at 35? with an inoculation ratio of 1:4 % v/v and initial pH of 6.5. The optimum growth (2.74 g dry cell weight/L) was recorded after 96 hours of incubation. The highest viable cell counts for L. casei and P. jensenii were 9.10 log CFU/mL and 9.42 log CFU/mL, respectively. The optimum specific growth rate, µ obtained, was 0.41 h-1. The growth of L. casei and P. jensenii was compared to its monoculture fermentation, and it was found that the co-culture did not affect the growth of L. casei but helped maintain its survival. Moreover, P. jensenii gained benefits in the co-culture system, as its growth improved compared to during its monoculture. The ANN predictive model was developed using the multilayer perceptron and trained using the Levenberg-Marquardt training algorithm. Five input parameters, incubation time (h), the concentration of total reducing sugar (g/L), pH culture, incubation temperature (?) and inoculation ratio (% v/v), were used to train the network for the prediction of cell biomass concentration (g/L) and the co-culture specific growth rate, µ (h-1). The model has a low mean square error and high regression coefficient (R2) for the training and testing set, indicating the model is fit to predict the cell biomass produced and its specific growth rate during the co-culture of L. casei and P. jensenii. The structure obtained for ANN predictive model consist of five inputs, eight hidden nodes and two outputs, 5-8-2. The optimum predicted cell biomass concentration and the specific growth rate, µ, were 2.24 g dry cell weight/L and 0.51 h-1, respectively. In conclusion, this work provides a strategy to produce multispecies DFM through co-culture fermentation using rice bran and presented the first predictive ANN model to predict the cell biomass concentration and the co-culture-specific growth rate of L. casei and P. jensenii