•Classification of three types of ECG beats are proposed.•Normal, CAD and MI are three classes considered in this work.•DCT, DWT and EMD decomposition methods are employed on ECG beat.•Decomposed ...signals are fed to Locality Preserving Projections method.•KNN classifier yielded 98.5% accuracy with seven features for DCT method.
Cardiovascular diseases (CVDs) are the main cause of cardiac death worldwide. The Coronary Artery Disease (CAD) is one of the leading causes of these CVD deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of heart muscle death called Myocardial Infarction (MI). Normally, the presence of these cardiac conditions is primarily reflected on the electrocardiogram (ECG) signal. However, it is challenging and requires rich experience to manually interpret the visual subtle changes occurring in the ECG waveforms. Thus, many automated diagnostic systems are developed to overcome these limitations. In this study, the performance of an automated diagnostic system developed for detection of CAD and MI using three methods such as Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Discrete Cosine Transform (DCT) are compared. In this study, ECG signals are subjected to DCT, DWT and EMD to obtain respective coefficients. These coefficients are reduced using Locality Preserving Projection (LPP) data reduction method. Then, the LPP features are ranked using F-value. Finally, the highly ranked coefficients are fed into the K-Nearest Neighbor (KNN) classifier to achieve the best classification performance. Our proposed system yielded highest classification results of 98.5% accuracy, 99.7% sensitivity and 98.5% specificity using only seven features obtained using DCT technique. The screening system can help the cardiologists in detecting the CAD and hence presents any possible MI by prescribing suitable medications. It can be employed in routine community screening, old age homes, polyclinics and hospitals.
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Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of ...this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy (Eax), signal energy (Ωx), fuzzy entropy (Efx), Kolmogorov–Sinai entropy (Eksx), permutation entropy (Epx), Renyi entropy (Erx), Shannon entropy (Eshx), Tsallis entropy (Etsx), wavelet entropy (Ewx), fractal dimension (FDx), Kolmogorov complexity (Ckx), and largest Lyapunov exponent (ELLEx) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.
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The future space mission LISA will observe a wealth of gravitational-wave sources at millihertz frequencies. Of these, the extreme-mass-ratio inspirals of compact objects into massive black holes are ...the only sources that combine the challenges of strong-field complexity with that of long-lived signals. Such signals are found and characterized by comparing them against a large number of accurate waveform templates during data analysis, but the rapid generation of templates is hindered by computing the ∼10^{3}-10^{5} harmonic modes in a fully relativistic waveform. We use order-reduction and deep-learning techniques to derive a global fit for the ≈4000 modes in the special case of an eccentric Schwarzschild orbit, and implement the fit in a complete waveform framework with hardware acceleration. Our high-fidelity waveforms can be generated in under 1 s, and achieve a mismatch of ≲5×10^{-4} against reference waveforms that take ≳10^{4} times longer. This marks the first time that analysis-length waveforms with full harmonic content can be produced on timescales useful for direct implementation in LISA analysis algorithms.
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We seek to achieve the holy grail of Bayesian inference for gravitational-wave astronomy: using deep-learning techniques to instantly produce the posterior p(θ|D) for the source parameters θ, given ...the detector data D. To do so, we train a deep neural network to take as input a signal + noise dataset (drawn from the astrophysical source-parameter prior and the sampling distribution of detector noise), and to output a parametrized approximation of the corresponding posterior. We rely on a compact representation of the data based on reduced-order modeling, which we generate efficiently using a separate neural-network waveform interpolant A. J. K. Chua, C. R. Galley, and M. Vallisneri, Phys. Rev. Lett. 122, 211101 (2019)PRLTAO0031-900710.1103/PhysRevLett.122.211101. Our scheme has broad relevance to gravitational-wave applications such as low-latency parameter estimation and characterizing the science returns of future experiments.
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We present the fastemriwaveforms (FEW) package, a collection of tools to build and analyze extreme mass ratio inspiral (EMRI) waveforms. Here, we expand on A. J. K. Chua et al., Phys. Rev. Lett. 126, ...051102 (2021). that introduced the first fast and accurate fully-relativistic EMRI waveform template model. We discuss the construction of the overall framework; constituent modules; and the general methods used to accelerate EMRI waveforms. Because the fully relativistic FEW model waveforms are for now limited to eccentric orbits in the Schwarzschild spacetime, we also introduce an improved augmented analytic kludge (AAK) model that describes generic Kerr inspirals. Both waveform models can be accelerated using graphics processing unit (GPU) hardware. With the GPU-accelerated waveforms in hand, a variety of studies are performed including an analysis of EMRI mode content, template mismatch, and fully Bayesian Markov Chain Monte Carlo-based EMRI parameter estimation. We find relativistic EMRI waveform templates can be generated with fewer harmonic modes ( ∼ 10 – 100 ) without biasing signal extraction. However, we show for the first time that extraction of a relativistic injection with semirelativistic amplitudes can lead to strong bias and anomalous structure in the posterior distribution for certain regions of parameter space.
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Air conditioning is essential for maintaining thermal comfort in indoor environments, particularly for hot and humid climates. Today, air conditioning, comprising cooling and dehumidification, has ...become a necessity in commercial and residential buildings and industrial processes. It accounts for a major share of the energy consumption of a building or facility. In tropical climates, the energy consumed by heating, ventilation and air-conditioning (HVAC) can exceed 50% of the total energy consumption of a building. This significant figure is primarily due to the heavy duty placed on cooling technologies to remove both sensible and latent heat loads. Therefore, there is tremendous potential to improve the overall efficiency of the air-conditioning systems in buildings.
Based on today’s practical technology for cooling, the major components of a chiller plant are (1) compressors, (2) cooling towers, (3) pumps (chilled and cooling water) and (4) fans in air handling units. They all consume mainly electricity to operate. When specifying the kW/Rton of a plant, there are two levels of monitoring cooling efficiency: (1) at the efficiency of the chiller machines or the compressors which consume a major amount of electricity; and (2) at the overall efficiency of cooling plants which include the cooling towers, pumps for moving coolant (chilled and cooling water) to all air-handling units. Pragmatically, a holistic approach is necessary towards achieving a low energy input per cooling achieved such as 0.6kW/Rton cooling or lower by considering all aspects of the cooling plant.
In this paper, we present a review of recent innovative cooling technology and strategies that could potentially lower the kW/Rton of cooling systems – from the existing mean of 0.9kW/Rton towards 0.6kW/Rton or lower. The paper, broadly divided into three key sections (see Fig. 2), begins with a review of the recent novel devices that enhances the energy efficiency of cooling systems at the component level. This is followed by a review of innovative cooling systems designs that reduces energy use for air conditioning. Lastly, the paper presents recent developments in intelligent air-control strategies and smart chiller sequencing methodologies that reduce the primary energy utilization for cooling.
The energy efficient cooling technology, innovative systems designs, and intelligent control strategies described in the paper have been recently researched or are on-going studies. Several have been implemented on a larger scale and, therefore, are examples of practical solutions that can be readily applied to suit specific needs.
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A new type of biofuel, pine oil, is introduced in this work for the purpose of fueling diesel engine. The viscosity, boiling point and flash point of the reported oil are lower, when compared to that ...of diesel. Also, the calorific value of pine oil biofuel is comparable to diesel. As a result, it can be directly used in diesel engine without trans-esterifying it. Pine oil biofuel and their blends of 25%, 50%, and 75% with diesel were tested in a single cylinder, four-stroke, direct injection diesel engine and the combustion, emissions and performance results were compared with diesel. The results show that at full load condition, 100% pine oil reduces CO (carbon monoxide), HC (hydrocarbon) and smoke emissions by 65%, 30% and 70%, respectively. The brake thermal efficiency and maximum heat release rate increase by 5% and 27%, respectively. However, the NOx (oxides of nitrogen) emission is higher than that of diesel fuel at full load condition. The experimental work reveals that 100% pine oil can be directly used in diesel engine and potential benefits of pine oil biofuel have been reaped.
•The paper brings in to light a new type of biofuel, pine oil, for diesel engine.•The Pine oil possesses low viscosity and the calorific value of it is comparable to diesel.•The reported biofuel is used directly in diesel engine without trans-esterification.•The brake thermal efficiency and maximum heat release rate increase by up to 5% and 27%.•The HC, CO and smoke emissions are considerably reduced for pine oil at most of the loads.
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Heat pump systems offer economical alternatives of recovering heat from different sources for use in various industrial, commercial and residential applications. As the cost of energy continues to ...rise, it becomes imperative to save energy and improve overall energy efficiency. In this light, the heat pump becomes a key component in an energy recovery system with great potential for energy saving. Improving heat pump performance, reliability, and its environmental impact has been an ongoing concern. Recent progresses in heat pump systems have centred upon advanced cycle designs for both heat- and work-actuated systems, improved cycle components (including choice of working fluid), and exploiting utilisation in a wider range of applications. For the heat pump to be an economical proposition, continuous efforts need to be devoted to improving its performance and reliability while discovering novel applications. Some recent research efforts have markedly improved the energy efficiency of heat pump. For example, the incorporation of a heat-driven ejector to the heat pump has improved system efficiency by more than 20%. Additionally, the development of better compressor technology has the potential to reduce energy consumption of heat pump systems by as much as 80%. The evolution of new hybrid systems has also enabled the heat pump to perform efficiently with wider applications. For example, incorporating a desiccant to a heat pump cycle allowed better humidity and temperature controls with achievable COP as high as 6. This review paper provides an update on recent developments in heat pump systems, and is intended to be a “one-stop” archive of known practical heat pump solutions. The paper, broadly divided into three main sections, begins with a review of the various methods of enhancing the performance of heat pumps. This is followed by a review of the major hybrid heat pump systems suitable for application with various heat sources. Lastly, the paper presents novel applications of heat pump systems used in select industries.
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We compute adiabatic waveforms for extreme mass-ratio inspirals (EMRIs) by "stitching" together a long inspiral waveform from a sequence of waveform snapshots, each of which corresponds to a ...particular geodesic orbit. We show that the complicated total waveform can be regarded as a sum of "voices." Each voice evolves in a simple way on long timescales, a property which can be exploited to efficiently produce waveform models that faithfully encode the properties of EMRI systems. We look at examples for a range of different orbital geometries: spherical orbits, equatorial eccentric orbits, and one example of generic (inclined and eccentric) orbits. To our knowledge, this is the first calculation of a generic EMRI waveform that uses strong-field radiation reaction. We examine waveforms in both the time and frequency domains. Although EMRIs evolve slowly enough that the stationary phase approximation (SPA) to the Fourier transform is valid, the SPA calculation must be done to higher order for some voices, since their instantaneous frequency can change from chirping forward ( ˙ f > 0 ) to chirping backward ( ˙ f < 0 ). The approach we develop can eventually be extended to more complete EMRI waveform models-for example, to include effects neglected by the adiabatic approximation, such as the conservative self-force and spin-curvature coupling.
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