Abstract

The rapid expansion of the development of hydropower in geologically and seismically active areas has numerous technological risks involved. Tunnels in rock masses have different stress and deformation responses than other engineering materials due to the existence of weak planes. This paper describes the use of numerical modeling approaches to assess tunnel deformation and tunnel stability under the earthquake loading for an 8.8 m horseshoe-shaped power tunnel excavated in Vishnugarh Pipalkoti, Uttaranchal, India. In this analysis, two different approaches are used to perform pseudo-static analyzes to understand the impacts of seismicity on tunnels. The rock mass is modeled as continuum in the first approach, and the tunnel is treated as a continuum equivalent with the inclusion of critical joint sets in the

Abstract

In this work, NiMg@OH/reduced graphene oxide (rGO) nanocomposites are synthesised through microwave-assisted synthetic methodology using various reducing agents. The structural features and surface morphology of synthesised materials are fully examined with different spectroscopy and microscopy techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-visible spectroscopy (UV-Vis), Raman and Fourier Transform Infrared (FT-IR) spectroscopy. Further, this material was subjected to cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques for the systematic study of the properties and impact of reducing agents at various percentages of rGO on the specific capacitance of synthesised materials. The presence of rGO gradually improves the charge transfer process at the electrode/electrolyte interface. Among these nanocomposite materials, maximum specific capacitance of 650.31 F/g was observed for NiMg@OH-1.5 wt% rGO synthesised using NH4OH reducing agent in 6 M KOH electrolyte. Hence, the result signifies these composite materials are a promising

Abstract

Automatic identification of emotions from a given music track would be of significant importance in the area of Music Information Retrieval. Several studies have found lyrics an important feature in the task of emotion classification [1]. Using lyrics, till date, a transformer-model which is a Natural-Language-Processing (NLP) based Deep-Learning model has achieved state-of-the-art results in identifying music emotion as represented in the Valence-Arousal (VA) space [2]. While transformer models rely on parsing the syntactic structure of sequential input data, we assume that semantic relations could be efficiently learned from these representations using novel capsule networks [3]. The “capsules” are akin to neuronal populations breaking down information into parts that mimics human processing of information by chunking for efficient encoding and evaluation of the stimulus. We extend the same logic further to say that capsule networks should be able to look at the semantic representation of each word in the lyrics of a song and should be able to learn the relationship between these words and the emotion that they represent as a group

Abstract

Personality is known to influence emotional experiences including those induced by music listening. Previous research has largely relied on self-report and models with limited terms, which may not adequately capture naturalistic behavior and experience. Data from the platform Last.fm allows for the analysis of listening behaviors using both musical features and semantic tags provided by listeners, for a more naturalistic exploration of personality, music and emotion.

Abstract

Large pretrained models have seen enormous success in extractive summarization tasks. In this work, we investigate the influence of pretraining on a BERT-based extractive summarization system for scientific documents. We derive significant performance improvements using an intermediate pretraining step that leverages existing summarization datasets and report state-of-the-art results on a recently released scientific summarization dataset, SciTLDR. We systematically analyze the intermediate pretraining step by varying the size and domain of the pretraining corpus, changing the length of the input sequence in the target task and varying target tasks. We also investigate how intermediate pretraining interacts with contextualized word embeddings trained on different domains.

Abstract

ECG analysis comprises the following steps: preprocessing, segmentation, feature extraction, and classification of heart-beat instances to detect cardiac arrhythmias. This work focuses on the first three steps in cardiac arrhythmia analysis. Since no publicly available feature sets are available for the ECG arrhythmia detection problem, researchers have to explicitly extract the feature sets from the raw signals available in PhysioBank repository. In this work, the complete feature extraction pipeline has been developed using Matlab environment. Both the Matlab code and resulting feature sets for all records have been deposited and made publicly available in the Github repository. This is one of the major

Abstract

This research was carried out along the Bhadra River, which is one of the tributaries of the TungaBhadra and originates near Gangamula in Karnataka’s the Western Ghats. The study stretch is around 27 km divided into three reaches with elements of 1 km as 3, 4, and 20 for each reach, respectively. The objectives of the study are to assess the effects of wastewater discharges on the water quality of the Bhadra River stretch, to simulate the Dissolved oxygen (DO) by varying the Biochemical oxygen demand (BOD) loads coming from different pollutant sources within the study stretch using the QUAL2K river water quality model. The study period considered is about 24 months (April 2014-March 2016) for the simulation of temperature, pH, DO, and Nitrate parameters with the help of observed data at three monitoring stations. The investigation period was separated into two parts: the model was calibrated using monthly average data from April 2014 to March 2015 (12 months), and the model was validated using monthly average data from April 2015 to March 2016 (12 months). The R-square (R2 ) value for temperature and pH ranges from 0.87-0.56 and 0.64-0.62, respectively. The Root Mean Square Error (RMSE) for Nitrate and DO ranges from 0.06-0.02 and 0.47-0.34, respectively. The results revealed that the Bhadra river stretch was highly polluted due to effluents coming from the industries present in the study stretch. It was also discovered that river flow rate, point source discharge, fast Carbonaceous Biochemical oxygen demand (CBOD) oxidation rate, and nitrification rate as the highly sensitive quality parameters defining the Bhadra river water quality. There must be a reduction of 25% of BOD effluent to reach the minimum standards set by the Central Pollution Control Board (CPCB). It is noted that a 75% reduction of BOD effluent from point sources will lead to an increase of 15% average DO throughout the study stretch. Keyword Qual2k model, Water quality, Bhadra River, Dissolved oxygen, BOD.

Abstract

We motivate the need for a deep relational interpreter for a miniKanren language, and then present metaKanren, a 𝜇Kanren-like language for which we have implemented a deep relational interpreter in miniKanren. We demonstrate this relational interpreter running “backwards” to synthesize metaKanren code and then provide a commented tour of our relational interpreter for metaKanren. Finally, we show how metaKanren can serve as a useful starting point towards a metacircular interpreter for a miniKanren language, and miniKanren program synthesis. Additional Key Words and Phrases: metaKanren, miniKanren, metacircularity, relational programming, interpreters, program synthesis, relational interpreters

Abstract

This article proposes an adaptive-robust maneuvering control framework for a planar snake robot under the influence of parameter uncertainties. The entire control objective of maneuvering control can be viewed as the simultaneous establishments of two goals: one to maintain a time-varying body shape of the snake robot for consistent motion (called the outer layer) and the other dealing with the velocity and head-angle tracking of the same (called the inner layer). Unknown variations in the ground friction coefficients have been considered to be the primary source of time-varying uncertainties which affects the control performance in both the layers. Accordingly, an artificial time delay-based adaptive-robust control (ARC) framework, dual adaptive-robust time-delayed control (ARTDC), is proposed. The term dual signifies simultaneous application of ARTDC for the outer as well as the inner layer. ARTDC comprises of two segments: an artificial time delay-based time-delayed estimation (TDE) part and an ARC part. While TDE approximates the completely unknown friction forces, the ARC tackles the approximation error arising from the TDE. More importantly, compared with the existing ARC methodologies, the proposed ARTDC neither presumes the overall uncertainty to be upper bounded by a constant nor requires any prior knowledge of the bound of uncertainty to implement the controller. A Lyapunov function-based method has been adopted for analyzing the stability of the closed-loop system. Simulation studies affirm the improved performance of the ARTDC in contrast to the classical artificial delay-based methodology.

Abstract

Position control for heavy-lift construction vessels is crucial for safe operation during offshore construction. During the various phases of a typical offshore construction assignment, considerable changes in the dynamics of the crane-vessel system occur. Operational hazard was reported if such interchanging dynamics are not properly handled. However, to date and the best of our knowledge, no systematic control solution is reported considering multiphase offshore construction scenarios. This article proposes a switched dynamical framework to model the interchanging phases and to formulate a comprehensive position control solution for heavy-lift vessels. Stability and robustness against modeling imperfections and environmental disturbances are analytically assessed. The effectiveness of the solution is verified on a realistic heavy-lift vessel simulation platform; it is shown that the proposed switched framework sensibly improves accuracy and reduces hazard compared with a nonswitched solution designed for only one phase of the construction scenario. Index Terms— Dynamic positioning (DP) system, heavy-lift construction vessel, observer-based control, switched systems.