The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Sin...
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The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR.
Traditional methods for evaluating adsorption mechanisms rely on material characterization and its linear relationship with adsorption capacity. However, this approach has limitations, as it assumes a linear correlati...
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Traditional methods for evaluating adsorption mechanisms rely on material characterization and its linear relationship with adsorption capacity. However, this approach has limitations, as it assumes a linear correlation, and when this fails, it is often speculated that multiple mechanisms are involved without detailing their contributions. This study overcomes these challenges by using artificial intelligence to analyze the adsorption of Cu (II) onto alternative adsorbents. An artificialneuralnetwork (ANN) combined with 3D porous texture simulations, based on mercury intrusion porosimetry, established non-linear correlations among 13 textural and chemical characteristics and adsorption capacity. The material with the highest adsorption capacity (107 mg g(-1)) featured an accessible porous texture rich in -COOH groups. The ANN quantified the contributions of two governing mechanisms: diffusion through the porous texture (67.07 %) and interaction with -COOH sites (32.93 %). Chemometric analysis revealed that the greatest weight in the ANN model was attributed to the average pore diameter (17.11 %), which was consistent with the characterization of the saturated material by SEM-EDX, showing that adsorption occurs primarily in the exposed cavities of the material. The adsorption mechanism proposed by the ANN study explains the atypical points observed in the different materials, showing that the adsorption process is governed by a combination of two mechanisms: one associated with the porous texture and the other with surface chemistry. The findings provide a deeper understanding of the key variables influencing adsorption and offer guidance for optimizing material synthesis.
The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in veg...
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The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of artificialneuralnetworks (ANN) so-called bayesian regularized artificial neural network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m(2)/m(2) for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a bayesian regularized artificial neural network (BRANN) capable of predicting adsorption capacity of the...
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Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a bayesian regularized artificial neural network (BRANN) capable of predicting adsorption capacity of the FACCs to synthesize tailor-made materials with potential application as dialysis membranes. Characterization studies showed that FACCs have a high surface area (1354-2073 m2 g- 1) associated with increased micropo-rosity (W0, average: 0.57 cm3 g- 1). Materials are carbonaceous, with a carbon content between 69 and 92%. Chemical treatments modify the pHpzc of materials between 4.1 and 7.8 due to incorporating functional groups on the surface (C=O, -COOH, -OH, -NH, -NH2). Uremic toxins tests showed a high elimination rate of p-cresol (73 mg g- 1) and creatinine (90 mg g- 1) which is not affected by the matrix (aqueous solution and simulated serum). However, in the case of uric acid, adsorption capacity decreased from 143 mg g- 1 to 71 mg g- 1, respectively. When comparing the kinetic constants of the adsorption studies in simulated serum versus the studies in aqueous solution, it can be seen that this does not undergo significant changes (0.02 min-1), evidencing the versatility of the material to work in different matrices. The previous studies, in combination with characterization of the materials, allowed to establish the adsorption mechanism. Thus, it permitted to train the BRANN to obtain mathematical models capable to predict the kinetic adsorption of the toxins studied. It is concluded that the predominant adsorption mechanism is due to 7c-7c interactions between the adsorbate unsaturations with the material's pseudo-graphitic planes. Results show that FACCs are promising materials for hemodialysis membranes. Finally, taking into consideration the adsorption capacities and rates, as well as the semiquantitative analysis of the environmental impact associated with the preparation of the adsorbents, the best adsorbent (CC, Eco-Scale = 91.5) was selec
This work presents an examination of the wire electric discharge machining (WEDM) in processing Inconel 625. The major WEDM variables (pulse-on time, pulse-off time, servo voltage, wire feed rate) were experimentally ...
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This work presents an examination of the wire electric discharge machining (WEDM) in processing Inconel 625. The major WEDM variables (pulse-on time, pulse-off time, servo voltage, wire feed rate) were experimentally investigated to address multiple aspects of this process, namely to reduce the gap current and surface roughness and increase the cutting speed. After testing the statistical significance of the major WEDM parameters for the three responses, an advanced statistical procedure was utilized for tackling correlations among outputs and their integration into the WEDM output performance in a fully objective manner. The bayesianregularizedneuralnetwork established a highly accurate process model that was engaged as the objective function for the evolutionary algorithms to identify the optimal machining conditions. The following algorithms were employed: particle swarm optimization, teaching-learning based optimization, grey wolf optimization and Jaya algorithm. Their results were thoroughly analyzed in terms of accuracy, i.e., repeatability of the resulting solutions, convergence speed and computational time, including assessment of the hyperparameter effects. The obtained optimal solution was highly convincing and it was successfully validated in a confirmation run. Therefore, the benefits of these findings are twofold, offering: (i) a thorough analysis of the four metaheuristics effectiveness in dealing with a real industrial problem, (ii) useful insights for controlling WEDM variables to enhance the technological, environmental and economic aspects at the same time.
In this paper, a method to detect ambulance siren in a traffic using the smart phone in real time is discussed. Ambulance uses siren sound which alerts other road users which makes them to move efficiently through tra...
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ISBN:
(纸本)9781538692790
In this paper, a method to detect ambulance siren in a traffic using the smart phone in real time is discussed. Ambulance uses siren sound which alerts other road users which makes them to move efficiently through traffic. It may be possible that the siren sound of the ambulance is missed due to soundproofing or audio system inside the vehicles. To overcome such situations, it is suggested that the driver's mobile phone will have an app installed that will rely on the phone's micro-phone to automatically detect the siren and alert the user. The proposed method first divides the recorded audio signal into windows and then extracts features in both time and frequency domain. Then features are used to train a bayesian regularized artificial neural network (BRANN). A new model that relies on two feature sets at a time thereby improving accuracy and decreasing possible delay is proposed and implemented. It is observed that the proposed method provides an accuracy of greater that 99 percent in simulated conditions using sound data from prerecorded audio. Further, the contribution of the ambulance sound with the other noise is also estimated.
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