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With the increasing usage, scale, and complexity of Deep Learning (dl) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of dl systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at fine granularity (e.g., at the api level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained dl energy consumption measurement. FECoM enables researchers and developers to profile dlapis from energy perspective. FECoM addresses the challenges of fine-grained energy measurement using static instrumentation while considering factors such as computational load and temperature stability. We assess FECoM's capability for fine-grained energy measurement for one of the most popular open-source dl frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlowapis' energy profiles. Furthermore, we elaborate on the considerations and challenges while designing and implementing a fine-grained energy measurement tool. This work will facilitate further advances in dl energy measurement and the development of energy-aware practices for dl systems. [ABSTRACT FROM AUTHOR]

The growing popularity of mobile and cloud computing raises new challenges related to energy efficiency. This work evaluates four various SQL and NoSQL database solutions in terms of energy efficiency. Namely, Cassandra, MongoDB, Redis, and MySQL are taken into consideration. This study measures energy efficiency of the chosen data storage solutions on a selected set of physical and virtual computing nodes by leveraging Intel RAPL (Running Average Power Limit) technology. Various database usage scenarios are considered in this evaluation including both local usage and remote offloading. Different workloads are benchmarked through the use of YCSB (Yahoo! Cloud Serving Benchmark) tool. Extensive experimental results show that (i) Redis and MongoDB are more efficient in energy consumption under most usage scenarios, (ii) remote offloading saves energy if the network latency is low and destination CPU is significantly more powerful, and (iii) computationally weaker CPUs may sometimes demonstrate higher energy efficiency in terms of J/ops. An energy efficiency measurement framework is proposed in order to evaluate and compare different database solutions based on the obtained experimental results. [ABSTRACT FROM AUTHOR]

Objective Analyzing the applicability of pipeline simulation technology in energy measurement, and examine the deviation between simulation calculation results and actual pipeline measurement values. Methods Using a closed natural gas pipeline network with multiple gas sources will be as an example, a simulation model with functions for natural gas heat generation, pressure, and flow calculation was established. This model is based on the theoretical foundation of pipeline network simulation for energy measurement. Results The deviation between the simulation results of heat generation and the calculated values of the on-site chromatographic analyzer is within 0.3%, which meets the requirements of the maximum allowable error of assigned heat generation in GB/T 18603—2023 Technical requirement of measuring systems for natural gas. The deviation between the pressure simulation calculation result and the recorded value of the on-site pressure transmitter is within 1.0%, and the deviation between the flow simulation calculation result and the measured value of the on-site flow meter is within 2.0%. Conclusions The energy measurement technology based on pipeline simulation is a feasible method for assigning heat generation values to complex multi-gas source pipelines. This method can also be used for real-time tracking or verification of pipeline pressure, flow rate, and natural gas pipeline inventory. [ABSTRACT FROM AUTHOR]

Solar energy measurements are often charted. These charts keep track of the conditions, events, and inspections done on each solar panel. This study describes a paradigm for measuring and monitoring solar energy via the Internet of Things. This service provides monitoring and technical help during corrective inspections within the quality reliability depth services range. These features and monitoring services can provide highly competent, higher-optimization, and excellent services that are safe, fast, and smooth. Compliance with local and international legal regulations, reliability of work, reduction of time and costs spent on corrective inspections, reduction of downtime and production losses, and management of monitoring and testing processes under expert coordination will all benefit from technical support services provided by approved companies for corrective inspections in the company. [ABSTRACT FROM AUTHOR]

A trend reported by both academics and practitioners is that advertising on TV has become increasingly energetic. This study investigates the association between the energy level in ad content and consumers' ad-tuning tendency. Using a data set of over 27,000 TV commercials delivered to U.S. homes between 2015 and 2018, the authors first present a framework to algorithmically measure the energy level in ad content from the video of ads. This algorithm-based measure is then compared with human-perceived energy levels showing that the measure is related to the level of arousal stimulated by ad content. By relating the energy levels in ad content with the ad-tuning tendency using two empirical procedures, the authors document the following: overall, more energetic commercials are more likely to be tuned in or less likely to be avoided by viewers. The positive association between energy levels in ad content and ad tuning is statistically significant after controlling for placement and other aspects of commercials. However, the association varies across product categories and program genres. The main implication of this study is that advertisers should pay attention to components of ad content other than loudness, which has been regulated by law. [ABSTRACT FROM AUTHOR]

In the electrical installations of buildings, there is an increasing number of non-linear energy receivers, which introduce strong distortions of the load current of electronic energy meters. Since the readings of these meters are the basis for financial settlements of electricity consumers, it is very important to determine how much the distortion of the receiver current affects the correct operation of commonly used electronic energy meters. The article will present exemplary results and analyses of research work on the impact of individual current harmonics on the readings and errors of selected energy meters. [ABSTRACT FROM AUTHOR]

We report on a methodology for measuring the energy dissipated per AC high voltage cycle in a cold atmospheric pressure plasma jet (CAPJet). This method is adapted from research by Nisol et al. on plasma polymerization of hexamethyldisiloxane (HMDSO) organosilicon vapor in a large area planar dielectric barrier discharge (DBD) reactor. Here too, we measured ΔEg, the energy difference with and without small HMDSO vapor concentrations in the argon carrier gas flow. From ΔEg we then derived Em, the energy per molecule, and compared values with those of Nisol. Good agreements were found, including in film structures determined from attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectra, thus suggesting that realistic Em values can be successfully obtained also for the CAPJet case. [ABSTRACT FROM AUTHOR]

Abstract With the exploration, development, and research of deep‐sea resources, there is an urgent need for long‐term and continuous observation data of the deep‐sea seabed boundary layer. The traditional method of deep‐sea seabed survey and sampling based on scientific research vessels has the discontinuity of observation data in space and time scales. There are some problems in the seabed in situ observation method based on the seabed observation network for low mobility and high operation and maintenance costs, restricting the in‐depth understanding of the dynamic change process of the deep‐sea floor. To solve the above problems, an open and modular data acquisition control system was designed based on an embedded system and signal processing technology. In terms of the physical, chemical, geological, and ecosystem characteristics of the seafloor or near the seafloor boundary layer, various functional sensors and instrumentation were matched to form an independent underwater integrated measurement or experimental device, eventually realizing in situ multiparameter and long‐time series observations of the seafloor. The system data acquisition and control test were completed through laboratory experiments, which verified the feasibility of the system design. The research showed important theoretical and technical reference significance for the exploration and development of resources in the submarine boundary layer and the promotion of deep‐sea scientific research.

Electricity load forecasting is important to planning the decision-making regarding the use of energy resources, in which the power system must be operated to guarantee the supply of electricity in the future at the lowest possible price. With the rise of the ability of forecasting based on deep learning, these approaches are promising in this context. Considering the attention mechanism promising to capture long-range dependencies, it is highly recommended for sequential data processing, where time series-related tasks stand out. Considering a sequence-to-sequence (Seq2Seq) time series data of the electricity load in Brazil, this paper proposes the use of the long short-term memory (LSTM) with the attention mechanism to perform the time series forecasting. The proposed Seq2Seq-LSTM with attention mechanism outperforms other well-established models. Having a mean absolute error equal to 0.3027 the proposed method is shown to be promising for field applications. The proposed method contributes to time series forecasting by implementing an attention mechanism to Seq2Seq data, therefore, more than one correlated signal can be used to perform the prediction enhancing its capacity when more data is available.

This paper delves into the subject of outlier detection techniques tailored for unique datasets related to residential energy consumption. Building upon the current state of research we introduce the Grubbs and Z-score methods and investigate a range of outlier detection strategies encompassing statistical, probabilistic, and machine learning algorithms. The findings underscore the importance of outlier detection in the Romanian residential energy sector.

With the exploration, development, and research of deep‐sea resources, there is an urgent need for long‐term and continuous observation data of the deep‐sea seabed boundary layer. The traditional method of deep‐sea seabed survey and sampling based on scientific research vessels has the discontinuity of observation data in space and time scales. There are some problems in the seabed in situ observation method based on the seabed observation network for low mobility and high operation and maintenance costs, restricting the in‐depth understanding of the dynamic change process of the deep‐sea floor. To solve the above problems, an open and modular data acquisition control system was designed based on an embedded system and signal processing technology. In terms of the physical, chemical, geological, and ecosystem characteristics of the seafloor or near the seafloor boundary layer, various functional sensors and instrumentation were matched to form an independent underwater integrated measurement or experimental device, eventually realizing in situ multiparameter and long‐time series observations of the seafloor. The system data acquisition and control test were completed through laboratory experiments, which verified the feasibility of the system design. The research showed important theoretical and technical reference significance for the exploration and development of resources in the submarine boundary layer and the promotion of deep‐sea scientific research. [ABSTRACT FROM AUTHOR]

The linear accelerator system of the PBP-CMU Electron Linac Laboratory has been designed with the aim of generating free-electron lasers (FELs) in the mid-infrared (MIR) and terahertz (THz) regions. The quality of the radiation is strongly dependent on the properties of the electron beam. Among the important beam parameters, the electron beam energy and energy spread are particularly important. To accurately measure the electron beam energy, the first dipole magnet in the bunch compressor system and the downstream screen station are employed as an energy spectrometer. The A Space Charge Tracking Algorithm (ASTRA) software is used for the design and optimization of this system. Simulation results demonstrate that the developed spectrometer is capable of accurately measuring the energy within the 5–25 MeV range. The screen station system is designed and constructed to have the ability to capture a beam size with a resolution of 0.1 mm per pixel. This resolution is achieved with a screen-to-camera distance of 1.2 m, which proves sufficient for precise energy measurement. The systematic error in energy measurement is found to be less than 10%, with a minimum energy spread of 0.4% achievable when the horizontal beam size remains below 3 mm.

Background: The beam energy is one of the most significant parameters in particle therapy since it is directly correlated to the particles' penetration depth inside the patient. Nowadays, the range accuracy is guaranteed by offline routine quality control checks mainly performed with water phantoms, 2D detectors with PMMA wedges, or multi‐layer ionization chambers. The latter feature low sensitivity, slow collection time, and response dependent on external parameters, which represent limiting factors for the quality controls of beams delivered with fast energy switching modalities, as foreseen in future treatments. In this context, a device based on solid‐state detectors technology, able to perform a direct and absolute beam energy measurement, is proposed as a viable alternative for quality assurance measurements and beam commissioning, paving the way for online range monitoring and treatment verification. Purpose: This work follows the proof of concept of an energy monitoring system for clinical proton beams, based on Ultra Fast Silicon Detectors (featuring tenths of ps time resolution in 50 μm active thickness, and single particle detection capability) and time‐of‐flight techniques. An upgrade of such a system is presented here, together with the description of a dedicated self‐calibration method, proving that this second prototype is able to assess the mean particles energy of a monoenergetic beam without any constraint on the beam temporal structure, neither any a priori knowledge of the beam energy for the calibration of the system. Methods: A new detector geometry, consisting of sensors segmented in strips, has been designed and implemented in order to enhance the statistics of coincident protons, thus improving the accuracy of the measured time differences. The prototype was tested on the cyclotron proton beam of the Trento Protontherapy Center (TPC). In addition, a dedicated self‐calibration method, exploiting the measurement of monoenergetic beams crossing the two telescope sensors for different flight distances, was introduced to remove the systematic uncertainties independently from any external reference. Results: The novel calibration strategy was applied to the experimental data collected at TPC (Trento) and CNAO (Pavia). Deviations between measured and reference beam energies in the order of a few hundreds of keV with a maximum uncertainty of 0.5 MeV were found, in compliance with the clinically required water range accuracy of 1 mm. Conclusions: The presented version of the telescope system, minimally perturbative of the beam, relies on a few seconds of acquisition time to achieve the required clinical accuracy and therefore represents a feasible solution for beam commission, quality assurance checks, and online beam energy monitoring. [ABSTRACT FROM AUTHOR]

The linear accelerator system of the PBP-CMU Electron Linac Laboratory has been designed with the aim of generating free-electron lasers (FELs) in the mid-infrared (MIR) and terahertz (THz) regions. The quality of the radiation is strongly dependent on the properties of the electron beam. Among the important beam parameters, the electron beam energy and energy spread are particularly important. To accurately measure the electron beam energy, the first dipole magnet in the bunch compressor system and the downstream screen station are employed as an energy spectrometer. The A Space Charge Tracking Algorithm (ASTRA) software is used for the design and optimization of this system. Simulation results demonstrate that the developed spectrometer is capable of accurately measuring the energy within the 5–25 MeV range. The screen station system is designed and constructed to have the ability to capture a beam size with a resolution of 0.1 mm per pixel. This resolution is achieved with a screen-to-camera distance of 1.2 m, which proves sufficient for precise energy measurement. The systematic error in energy measurement is found to be less than 10%, with a minimum energy spread of 0.4% achievable when the horizontal beam size remains below 3 mm. [ABSTRACT FROM AUTHOR]

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download. [ABSTRACT FROM AUTHOR]

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reduce the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g., liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating vehicles’ energy efficiency. We propose in this article an ensemble learning approach based on deep neural networks that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance, and they allowed us to output a measure of predictive uncertainty. [ABSTRACT FROM AUTHOR]

As a new form of test validation, fuel cell and Internet-based distributed test platform has gained increasing attention during the period of dual carbon strategy in China. The reliability of the test platform is an important research direction for the distributed test platform. Under the condition of a large international delay during vehicle powertrain system validation and test, how to predict and optimize the system and how to evaluate the optimization effect becomes a research hotspot. In this paper, on the Internet-based distributed test platform for the fuel cell electric vehicle powertrain system, theoretical and simulation analysis of data transmission and dynamic and economic performance is carried out using observer with large time delay. The analysis found that the observer had an impact on vehicle velocity, fuel cell output power, battery output power and electric motor output torque over the entire test system. In addition, the observer can improve system transparency, which measures the subjective feelings of remote distributed system operators, in the above four indicators and almost has no impact on hydrogen consumption. The results of this study provide a powerful theoretical basis for the optimal design of Internet-based distributed test platforms.

The seismic coherence attribute is one of the most typically used seismic discontinuity feature detection technologies, which is widely used in fault detection, channel boundary characterization, and other occasions. The eigen-structure-based coherence algorithm possesses the property of stability by using the eigenvalues of seismic data’s covariance matrix, but in the algorithm, only the eigenvalues are used to calculate the final coherence value, and the information contained in eigenvectors is ignored. Through analysis, it is found that the elements in the eigenvectors represent the energy differences of seismic traces. By directly measuring the difference of different elements in an eigenvector, the energy differences between different seismic records can be effectively measured. Since the seismic data on both sides of some discontinuous boundaries, such as channel boundaries, principally show amplitude differences, the coherence properties based on eigenvectors can better characterize them. Through theoretical analysis, model testing, and case application, this letter illustrates the reliability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Rotational spectra of thioacetic acid (CH3COSH) have been observed by pulsed-nozzle Fourier transform microwave spectroscopy. Spectroscopic constants are reported for both the syn and anti conformers of the parent species, as well as the 34S and 13C carbonyl isotopologues. Transitions arising from the lowest A and E internal rotor states of the methyl group have been observed and analyzed. Experimental values of the three-fold internal rotation barrier, V3, for the syn and anti conformers of the parent isotopologue are 76.300(12) and 358.056(51) cm−1, respectively, indicating a large effect of the S−H orientation on the CH3 internal rotation potential. M06-2X/6-311+G(d,p) calculations are in good agreement with these results. The block localized energy decomposition method has been applied to understand the origins of this strong dependence of V3 on conformation. The results indicate that π conjugation from the SH to the carbonyl group and steric repulsion between the SH and the methyl group in the anti form are main contributors to the difference. [ABSTRACT FROM AUTHOR]

Objective With continuous progress of energy measurement, some new technologies and equipments for online determination of natural gas calorific value and relative density have emerged in natural gas industry. The physical property method for calculation of natural gas Z-factor specified in GB/T 17747.3-2011 Natural gas-Calculation of compression factor-Part 3: Calculation using physical properties is needed for the application of the before mentioned new measurement method. However, physical property method is seldom used since it was published, evaluating its applicability and accuracy is important for the improvement and effective application of new technologies and equipments in the energy measurement of natural gas. Methods In view of this, this paper assumes that the Z-factor calculated from detailed composition method in GB/T 17747.2-2011 Natural gas-Calculation of compression factor-Part 2: Calculation using molar-composition analysis gives the reference value, then theoretically compared the two algorithms based on 149 natural gases composition data from seven different gas sources and some simulated natural gases to exam the applicability of the physical property calculation method. Results The calculation bias of Z-factors between detailed composition method and physical property method are within ±0.10% for most of natural gases and the accuracy of gross calorific value and relative density shall be within ±0.50% at least to meet the 0.30% accurate requirements of calculated Z-factor by GB/T 18603-2014 Technical requirements of measuring system for natural gas. The study also discovers that bias of the calculated Z-factors between two algorithms can reach to surprising 0.50% when the content of heavy hydrocarbons is relatively high to reach to the upper limit of GB/T 17747.3-2011. Conclusions For most of the different sorts of commercial natural gases, the physical property calculation method is applicable. However, in some particular cases such as natural gas with equivalent C2+ content higher than 10%, the bias of the calculated Z-factors between two algorithms can reach to 0.50%. It is suggested to theoretically analyze the distribution of Z-factor deviations calculated by two methods before the final decision on energy measurement method is made. [ABSTRACT FROM AUTHOR]

The Timepix3 is a hybrid pixellated radiation detector consisting of a 256 px × 256 px radiation-sensitive matrix. Research has shown that it is susceptible to energy spectrum distortion due to temperature variations. This can lead to a relative measurement error of up to 35% in the tested temperature range of 10 °C to 70 °C. To overcome this issue, this study proposes a complex compensation method to reduce the error to less than 1%. The compensation method was tested with different radiation sources, focusing on energy peaks up to 100 keV. The results of the study showed that a general model for temperature distortion compensation could be established, where the error in the X-ray fluorescence spectrum of Lead ( 74.97  keV) was reduced from 22% to less than 2% for 60 °C after the correction was applied. The validity of the model was also verified at temperatures below 0 °C, where the relative measurement error for the Tin peak ( 25.27  keV) was reduced from 11.4% to  2.1 %  at  − 40  °C. The results of this study demonstrate the effectiveness of the proposed compensation method and models in significantly improving the accuracy of energy measurements. This has implications for various fields of research and industry that require accurate radiation energy measurements and cannot afford to use power for cooling or temperature stabilisation of the detector. [ABSTRACT FROM AUTHOR]

Fault signals measured from a damaged rolling bearing usually exhibit amplitude modulation (AM)-frequency modulation (FM) properties. For this kind of signal, the demodulation technique is an effective and common method. In the demodulation techniques, a variety of energy operator (EO) tools have been available due to high computational efficiency and self-adaptability to transient signals. Hence, an alternative energy measure is proposed in this work. The new demodulation technique is achieved by combining a multiresolution higher-order symmetric difference sequence with the envelope-derivative operator (EDO). The multiresolution higher-order symmetric envelope-derivative operator (MHOS-EDO) can extract the weak bearing fault characteristics in a harsh environment without any pre-filtering. Especially in the presence of vibration interferences, its unique energy transformation can eliminate the influence of vibration interferences, so that the vibration interferences will no longer appear in the energy spectrum. Through simulation and practical experiments, the superiority of MHOS-EDO in dealing with noise and interfering components is demonstrated. Meanwhile, the comparison results reveal that the MHOS-EDO outperforms some previous EO and some other bearing fault identification methods. [ABSTRACT FROM AUTHOR]

The Timepix3 readout ASIC chip is a hybrid pixelated radiation detector, designed at CERN, which contains a 256 px × 256 px matrix. Each of the 65,536 radiation-sensitive pixels can record an incoming particle, its energy deposition or time of arrival and measure them simultaneously. Since the detector is suitable for a wide range of applications from particle physics, national security and medicine to space science, it can be used in a wide range of temperatures. Until now, it has to be calibrated every time to the operating point of the application. This paper studies the possibility of energy measurement with Timepix3 equipped with a 500  m thick silicon sensor and MiniPIX readout interface in the temperatures between 10  ∘ C and 70  ∘ C with only one calibration. The detector has been irradiated by X-ray fluorescence photons in the energy range from 8 keV to 57 keV, and 31 keV to 81 keV photons from the 133Ba radioactive source. A deviation of 5% in apparent energy value may occur for a 10  ∘ C change in temperature from the reference point, but, with the next temperature change, it can reach up to −30%. Moreover, Barium photons with an energy of 81 keV appear as deposited energy of only 55 keV at a detector temperature of 70  ∘ C . An original compensation method that reduces the relative measurement error from −30% to less than 1% is presented in this paper. [ABSTRACT FROM AUTHOR]

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity—industrial or commercial—and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted. [ABSTRACT FROM AUTHOR]

This paper delves into the subject of outlier detection techniques tailored for unique datasets related to residential energy consumption. Building upon the current state of research [1] we introduce the Grubbs and Z-score methods and investigate a range of outlier detection strategies encompassing statistical, probabilistic, and machine learning algorithms. The findings underscore the importance of outlier detection in the Romanian residential energy sector. [ABSTRACT FROM AUTHOR]

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures in the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancies and self-interstitial defects sufficiently accurately and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K). [ABSTRACT FROM AUTHOR]

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm – termed relax-then-truncate – and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy policy and a weighted AoI policy, and also depict that it performs close to the optimal solution even for moderate numbers of sensors. [ABSTRACT FROM AUTHOR]

Reverberation chambers (RCs) are now a reliable alternative to anechoic chambers (ACs) to perform antenna radiation efficiency measurements. Most methods, including the two-antenna one used in this article, are based on the evaluation of the stirred components of the scattering parameters. They are obtained by subtracting the average scattering parameters from the measured ones. This averaging is performed because of mechanical stirring, that is, sometimes supplemented by additional stirring such as antenna stirring (which includes platform stirring and source stirring). In this article, we present an extensive study on the role of antenna stirring in the retrieved radiation efficiency. Four measurement campaigns were successively performed: without antenna stirring, with platform stirring only, with source stirring only, and with a combined stirring of both antennas. Performing only source stirring or platform stirring leads to a biased estimation of the radiation efficiency. Therefore, we showed that the stirring of both antennas is mandatory to obtain an unbiased estimation of the radiation efficiency. [ABSTRACT FROM AUTHOR]

Objective The object of natural gas energy measurement is the total heat quantity generated after complete combustion. The energy value of natural gas is usually determined by multiplying the measured value of volume by the volume calorific value, and the volume calorific value depends on the composition of natural gas, that is, the mole fraction of each component in natural gas. The composition of natural gas in multi-gas source pipeline network usually changes with space position and time, thus each metering station of the pipeline network needs to determine the volume calorific value of natured gas input or output from the station at different times. Class A metering stations of long-distance pipelines in China are equipped with on-line gas chromatograph to analysis the calorific value of natural gas, while class B and class C metering stations can use simulation method to assign value to the calorific value of natural gas, so as to reduce the investment, operation and maintenance cost of configuring gas chromatograph. Methods Based on the theoretical basis of pipeline network simulation for energy measurement, aiming at a multi-gas source annular pipeline network, this paper est,1blishes the operation simuh1tion model with the functions of natural gas component tracking and calorific value calculation by using TGNET and SPS simulation software. In order to improve the accuracy of simulation results, the gas transmission efficiency of pipe section, a key parameter in the simulation model, is calibrated based on historical operation dat a. Results TGNET and SPS models are respectively used to dynamically simulate the historical operation process of the pipeline network for 48 hours. The deviation between the calorific value simulation result of TGNET model and the historical value is within 0. 3%. Conclusions The energy measurement of multi-gas source annular pipeline network based on operation simulation is a feasible method. The research results provide an operable method for the implementation and popularization of natural g,1s energy measurement in China. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

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In medical dosimetry, the radiation quantity of interest is frequently the dose to tissue. To make direct dosimeters that show the dose absorbed in soft tissue, tissue-equivalent material is preferred. Plastic scintillators (PLSs) are made of materials with a density similar to soft tissue and relatively have energy independent response. Non-proportionality must be taken into account when reproducing the light output spectrum for photons below 200 keV, as it reduces light output significantly. In comparison to a standard PLS, both low and high-energy photons’ pulse height distribution spectra as a result of lead doping were measured and calculated. The effect of a 0.5% lead doping scintillator is sufficient to make up for the loss or difference between soft tissues and PLSs in response to low-energy photons, particularly below 200 keV. [ABSTRACT FROM AUTHOR]

The $P_{st}$ indicator is one of the parameters used in the power quality evaluation. The International Electrotechnical Commission (IEC) flickermeter with a complex signal chain structure is used for $P_{st}$ measurements. The occurrence of $P_{st}$ values greater than the limit value indicates the occurrence of flicker severity caused by voltage fluctuation. This article presents the analysis of the influence of the sampling rate on $P_{st}$ measurement results. For analysis purpose, the analytical model of the IEC flickermeter signal chain has been prepared, considering the sampling rate of input voltage. Based on the prepared model, the possible effects of incorrect sampling rate selection in $P_{st}$ measurements have been presented. The model test results have been compared with laboratory measurement results for two advanced metering infrastructure energy meters with built-in IEC flickermeter. The obtained laboratory measurement results indicate the occurrence of aliasing for tested energy meters, which causes $P_{st}$ measurement disturbance. [ABSTRACT FROM AUTHOR]

This article examines the trade space between the resilience and cost of an island microgrid. The article presents two models for the resilience and the cost of the microgrid. The resilience model considers the invulnerability and recoverability of the microgrid and represents the power balance of the microgrid, energy storage, and maintenance policies. The cost model adapts the levelized cost of energy measure to the context of island microgrids. We conduct experiments to investigate two microgrid architecture decisions of how much excess generation capacity and redundancy to provide. The experiments show that redundancy of generative sources provides greater resilience for similar cost, and resilience improves quickly as excess power capacity is added while the cost grows more slowly. Case studies for island microgrids show how a redesign of the microgrid can improve resilience without increasing costs. The article contributes to the literature a model for decisions makers to evaluate the tradeoffs between resilience and cost for island microgrids that must depend on their own distributed energy resources. [ABSTRACT FROM AUTHOR]

Abstract Measuring the consumption of electronic devices is a difficult and sensitive task. Data acquisition (DAQ) systems are often used to determine such consumption. In theory, measuring energy consumption is straight forward, just by acquiring current and voltage signals we can determine the consumption. However, a number of issues arise when a fine analysis is required. The main problem is that sampling frequencies have to be high enough to detect variations in the assessed signals over time. In that regard, some popular DAQ systems are based on RISC ARM processors for microcontrollers combined with analog-to-digital converters to meet high-frequency acquisition requirements. The efficient use of direct memory access (DMA) modules combined with pipelined processing in a microcontroller allows to improve the sample rate overcoming the processing time and the internal communication protocol limitations. This paper presents a novel approach for high-frequency energy measurement composed of a DMA rate improvement (data acquisition logic), a data processing logic and a low-cost hardware. The contribution of the paper is the combination of a double-buffered signal acquisition mechanism and an algorithm that computes the device’s energy consumption using parallel data processing. The combination of these elements enables a high-frequency (continuous) energy consumption measurement of an electronic device, improving the accuracy and reducing the cost of existing systems. We have validated our approach by measuring the energy consumed by elemental circuits and wireless sensors networks (WSNs) motes. The results indicate that the energy measurement error is less than 5% and that the proposed method is suitable to measure WSN motes even during sleep cycles, enabling a better characterization of their consumption profile.

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

The application of a CAEN DT5730 digitizer unit for fast neutron metrology is explored. Measurements were made at the Accelerator for Metrology and Neutron Applications in External Dosimetry (AMANDE) monoenergetic neutron facility of the IRSN, France, over a neutron energy range from 1.2 to 7.0 MeV using a BC501A scintillator. The DT5730 digitizer and an analog system based on NIM electronics and an MPA-3 multichannel analyzer acquisition unit were used, under identical beam conditions, for data acquisition. Several observables were compared between the two systems including the quality of pulse shape discrimination (PSD), neutron energy distribution derived from unfolding analyses, and deadtime effects over a range of beam currents from 0.1 to $5.1~\mu \text{A}$. The suitability of the digital system for fast neutron metrology measurements both in the laboratory and in workplace fields is discussed. [ABSTRACT FROM AUTHOR]