Intelligent feature extraction and classification of anuran vocalizations

An intelligent identification system for mixed anuran vocalizations is developed in this work to provide the public to easily consult online. The raw mixed anuran vocalization samples are first filtered by noise removal, high frequency compensation, and discrete wavelet transform techniques in order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Six features, including spectral centroid, signal bandwidth, spectral roll-off, threshold-crossing rate, spectral flatness, and average energy, are extracted and served as the input parameters of the classifier. Meanwhile, a decision tree is constructed based on several parameters obtained during sample collection in order to narrow the scope of identification targets. Then fast learning neural-networks are employed to classify the anuran species based on feature set chosen by wrapper feature selection method. A series of experiments were conducted to measure the outcome performance of the proposed work. Experimental results exhibit that the recognition rate of the proposed identification system can achieve up to 93.4%. The effectiveness of the proposed identification system for anuran vocalizations is thus verified.     

Numerical simulation of non-adiabatic capillary tubes. Special emphasis on the near-saturation zone

The aim of this article is to present a distributed numerical model that simulates the thermal and fluid-dynamic phenomena inside non-adiabatic capillary tubes. The resolution approach is based on a two-phase flow model where the fluid domain is discretized in a one-dimensional way, and the governing equations (continuity, momentum, and energy) are solved by means of a step-by-step algorithm. The model explained herein consists of an improved and extended version of previous works (Escanes et al., 1995; García-Valladares et al., 2002a,b; Ablanque et al., 2010) including two additional features. On the one hand, it allows the simulation of the two typical geometric arrangements found in capillary-tube/suction-line heat exchangers (i.e. concentric and lateral). On the other hand, it has an enhanced capability to address the convergence difficulties found in distributed models at the near-saturation zone. This document presents the major numerical adaptations done to the model, a comprehensive validation of the two geometric configurations, the model performance when tackling the aforementioned numerical difficulties and finally, some numerical studies.     

Usage derived recommendations for a video digital library

We describe a minimalist methodology to develop usage-based recommender systems for multimedia digital libraries. A prototype recommender system based on this strategy was implemented for the Open Video Project, a digital library of videos that are freely available for download. Sequential patterns of video retrievals are extracted from the project's web download logs and analyzed to generate a network of video relationships. A spreading activation algorithm locates video recommendations by searching for associative paths connecting query-related videos. We evaluate the performance of the resulting system relative to an item-based collaborative filtering technique operating on user profiles extracted from the same log data.     

Effect of loaded silver nanoparticles on TiO2 for photocatalytic degradation of Acid Red 88

Nanometer sized Ag–TiO₂ nanoparticles were prepared (by photoreduction of Ag⁺ ions) in order to assess its photocatalytic degradation ability of target pollutant (textile dye; Acid red 88) upon visible light irradiation. Furthermore, oxidative reagents such as peroxomonosulfate (PMS), peroxodisulfate (PDS) and hydrogen peroxide (H₂O₂) were added to the photocatalytic system, which may act as an alternative electron acceptor and result in a notably enhanced degradation rate (seven-fold increase with PMS as oxidant) of pollutant destruction. Mineralization of target pollutant was also performed by total organic carbon (TOC) analysis and from the results, it was confirmed that 65% mineralization was achieved in 7 h using PMS as electron acceptor. Overall, this system is relatively inexpensive, reproducible, extremely stable and efficient in complete degradation of dye in aqueous solution. In order to obtain maximum information about the performance of Ag–TiO₂ photocatalyst, we did experiments under different operating conditions, i.e., variation of amount of catalyst, concentration of dye and electron acceptors. In addition to the above, a comparative study on the photocatalytic activities of TiO₂ was also made.     

Single crystal synthesis of higher-order sulfohalogenides in the temperature gradient: Bi19S27Br3, Bi2Cu3S4Br,Pb1−xBi2+xCu4−xS5I2

The present paper reports on the single crystal synthesis of three sulfohalogenides (ternary, quaternary, and quinary) in the temperature gradient and focuses primarily on the thermochemical aspects of the synthesis.     

Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk,fuel cost and CO2 emission

Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO₂ emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.     

An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances

This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for classifying power system disturbances using particle swarm optimization (PSO). Learning time is an important factor while designing any computational intelligent algorithms for classifications. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are chosen randomly and the output weights are calculated analytically. However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. One of the advantages of ELM over other methods is that the parameter that the user must properly adjust is the number of hidden nodes only. But the optimal selection of its parameter can improve its performance. In this paper, a hybrid optimization mechanism is proposed which combines the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the number of hidden nodes to enhance the performance of ELM. The experimental results showed the proposed algorithm is faster and more accurate in discriminating power system disturbances.     

Enhancing multimodal disaster tweet classification using state-of-the-art deep learning networks

During disasters, multimedia content on social media sites offers vital information. Reports of injured or deceased people, infrastructure destruction, and missing or found people are among the types of information exchanged. While several studies have demonstrated the importance of both text and picture content for disaster response, previous research has primarily concentrated on the text modality and not so much success with multi-modality. Latest research in multi-modal classification in disaster related tweets uses comparatively primitive models such as KIMCNN and VGG16. In this research work we have taken this further and utilized state-of-the-art models in both text and image classification to try and improve multi-modal classification of disaster related tweets. The research was conducted on two different classification tasks, first to detect if a tweet is informative or not, second to understand the response needed. The process of multimodal analysis is broken down by incorporating different methods of feature extraction from the textual data corpus and pre-processing the corresponding image corpus, then we use several classification models to train and predict the output and compare their performances while tweaking the parameters to improve the results. Models such as XLNet, BERT and RoBERTa in text classification and ResNet, ResNeXt and DenseNet in image classification were trained and analyzed. Results show that the proposed multimodal architecture outperforms models trained using a single modality (text or image alone). Also, it proves that the newer state-of-the-art models outperform the baseline models by a reasonable margin for both the classification tasks.