Speech Endpoint Detection in Noisy Environments Using EMD and Teager Energy Operator

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

HYBRID WAVELET PACKET-TEAGER ENERGY OPERATOR ANALYSIS AND ITS APPLICATION FOR GEARBOX FAULT DIAGNOSIS

Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed sigaaal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.

NEW DEMODULATION TECHNOLOGY OF OPTIMIZING ENERGY OPERATOR AND APPLICATION TO GEAR FAULT DIAGNOSIS

Although many methods have been applied to diagnose the gear thult currently, the sensitivity of them is not very good. In order to make the diagnosis methods have more excellent integrated ability in such aspects as precision, sensitivity, reliability and compact algorithm, and so on, and enlightened by the energy operator separation algorithm （EOSA）, a new demodulation method which is optimizing energy operator separation algorithm （OEOSA） is presented. In the algorithm, the non-linear differential operator is utilized to its differential equation： Choosing the unit impulse response length of filter and fixing the weighting coefficient for inportant points. The method has been applied in diagnosing tooth broden and fatiguing crack of gear faults successfully. It provides demodulation analysis of machine signal with a new approach.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Energy Storage OperationModes in Typical ElectricityMarket and Their Implications for China

As the Chinese government proposes ambitious plans to promote low-carbon transition,energy storage will play a pivotal role in China’s future power system.However,due to the lack of a mature electricity market environment and corresponding mechanisms,current energy storage in China faces problems such as unclear operational models,insufficient cost recovery mechanisms,and a single investment entity,making it difficult to support the rapid development of the energy storage industry.In contrast,European and American countries have already embarked on certain practices in energy storage operation models.Through exploration of key issues such as investment entities,market participation forms,and cost recovery channels in both front and back markets,a wealth of mature experiences has been accumulated.Therefore,this paper first summarizes the existing practices of energy storage operation models in North America,Europe,and Australia’s electricity markets separately from front and back markets,finding that perfect market mechanisms and reasonable subsidy policies are among the main drivers for promoting the rapid development of energy storage markets.Subsequently,combined with the actual development of China’s electricity market,it explores three key issues affecting the construction of costsharing mechanisms for energy storage under market conditions:Market participation forms,investment and operation modes,and cost recovery mechanisms.Finally,in line with the development expectations of China’s future electricitymarket,suggestions are proposed fromfour aspects:Market environment construction,electricity price formation mechanism,cost sharing path,and policy subsidy mechanism,to promote the healthy and rapid development of China’s energy storage industry.

Time and Energy Operators in Quantum Mechanics

Starting from a time operator, the form of the so called energy operator that is conjugate to the time operator is derived in time representation by analyzing the properties of time translation. This analysis also establishes the commutator between the time and the energy operators. It is seen from the analysis that the energy operator has nothing to do conceptually with the Hamiltonian operator of a system, so that the time operator is not conjugate to the Hamiltonian. Furthermore, it is shown that the Hermiticity of the energy operator requires introducing time integrate inner product. The time energy commutator and the time integral inner product put the time energy uncertainty relation on the same footing as the position momentum uncertainty relation.

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.

Effects of Rotor Solidity on the Performance of Impulse Turbine for OWC Wave Energy Converter

Impulse turbine, working as a typical self-rectifying turbine, is recently utilized for the oscillating water column（OWC） wave energy converters, which can rotate in the same direction under the bi-directional air flows. A numerical model established in Fluent is validated by the corresponding experimental results. The flow fields, pressure distribution and dimensionless evaluating coefficients can be calculated and analyzed. Effects of the rotor solidity varying with the change of blade number are investigated and the suitable solidity value is recommended for different flow coefficients.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Nonlinear Time-Frequency Distributions of Spectrum Energy Operator in Large Vocabulary Mandarin Speaker Independent Speech Recognition System

This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.

A New Class of Exactly Solvable Models within the Schrodinger Equation with Position Dependent Mass

The study of physical systems endowed with a position-dependent mass (PDM) remains a fundamental issue of quantum mechanics. In this paper we use a new approach, recently developed by us for building the quantum kinetic energy operator (KEO) within the Schrodinger equation, in order to construct a new class of exactly solvable models with a position varying mass, presenting a harmonic-oscillator-like spectrum. To do so we utilize the formalism of supersymmetric quantum mechanics (SUSY QM) along with the shape invariance condition. Recent outcomes of non-Hermitian quantum mechanics are also taken into account.

Automatic spikes detection in seismogram

Data processing for seismic network is very complex and fussy,because a lot of data is recorded in seismicnetwork every day,which make it impossible to process these data all by manual work.Therefore,seismic datashould be processed automatically to produce a initial results about events detection and location.Afterwards,these results are reviewed and modified by analyst.In automatic processing data quality checking is important.There are three main problem data that exist in real seismic records,which include:spike,repeated data and

Novel Automated Paced Fractionation Detection Algorithm for Ablating Ventricular Tachycardia

Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually, and are therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present a fully automated fractionation detection algorithm for patients with VT which expands on previously defined fractionation features and which substantially decreases associated study times. Paced electrogram signals were collected from six patients during electrophysiologic study according to a modified paced electrogram fractionation analysis protocol. Data were exported and analyzed offline using custom written software. Electrograms from right ventricular pacing catheter were used as reference. Surface electrograms, along with ventricular geometry and relative catheter locations, were used to identify physiological interference and physiologically irrelevant features. A total of 264 electrograms, collected from a roving catheter, were manually and automatically annotated for fractionation as defined by three features: conduction time (CT), electrogram duration (ED), and number of deflections (ND). Of these, 60 were selected manually to have no discernable features and were successfully discarded by our algorithm;yielding a specificity of 100%. Of the remaining 204, 16 were erroneously discarded by our algorithm;yielding a sensitivity of 92.16%. A comparison between annotations showed correlations of 0.98, 0.97, and 0.94 for AL, ED, and ND respectively.

Optimal Operation of Energy Hub Based Micro-energy Network with Integration of Renewables and Energy Storages

This study proposes an optimized model of a micro-energy network(MEN)that includes electricity and natural gas with integrated solar,wind,and energy storage systems(ESSs).The proposed model is based on energy hubs(EHs)and it aims to minimize operation costs and greenhouse emissions.The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects.A general algebraic modeling system(GAMS)is used to solve the optimal operation problem in the MEN.The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons.It also yields significant reductions in operation costs and emissions.The proposed model can contribute to future research by providing a more efficient network model(as compared with the traditional electricity supply system)to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

Performance evaluation of operational energy use in refurbishment, reuse, and conservation of heritage buildings for optimum sustainability

The operational phase of a building project has increasingly gained importance with their energy performance becoming valuable and determining their operational excellence. In most heritage building projects （HBPs）, the operational energy use aspects are less considered, and a systematic way of analyzing their energy performance following project delivery is often lacking, The aim of this study is to evaluate the operational performance of refurbishment and reuse of UK listed church projects. The objective is to assess the operational energy use with a view to optimizing their sustafnabie performance. The methodology includes eight selected case study buildings refurbished and converted for multipurpose use. The case study approach provided qualitative insights into how the study contributes to a more structured requirements for energy management in HBPs with specific attention to energy-efficient building operations. The findings show the need to focus on fundamental areas of operational management （i.e. by developing and implementing more focused policy on operational energy performance of heritage buildings） to minimize the energy required to operate them. The challenges of implementing changes in operational energy performance improvement of heritage buildings are addressed in the form of recommendations that could Lead to real results. The study concludes that leveraging these areas requires commitment from all heritage building stakeholders because they all have substantial roles in harmonizing the requirement for the project＇s sustainability and not just the building operators. Meanwhile, baseline project planning, periodic updating, monitoring, and managing the energy use pattern are suggested as measures that could greatly facilitate better energy performance to optimizing their sustainable reuse compared with the traditional approach of trying to improve their thermal performance.

A Hierarchical Transactive Energy Management Framework for Optimizing the Reserve Profile in District Energy Systems

District energy systems(DESs)have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings.For DFISs,the operational flexibility could be maintained by energy conversion and storage facilities.This paper proposes a hierarchical optimization framework for leveraging and aggregating the DES flexibility to provide contingency reserves.To characterize and quantify the flexibility in individual DESs,the concept of available reserve profile,which is measured by a set of indices,is established.A two-stage robust optimization(RO)model is developed for calculating the indices,which considers the uncertainties associated with wind power and ambient temperature.The lower stage of the two-stage model is managed by district energy system operators(DESOs)which submit reserve profiles to the district energy system coordinator(DESC)at the upper stage,which is responsible for the coordination process.Correspondingly,information privacy is preserved using a coordinated data-sharing strategy.Using reserve profiles submitted by multiple DESOs,the DESC applies the proposed coordination model to provide a certain reserve capacity schedule to DESs,which satisfies the stated objectives.The coordination model is formulated and solved based on the special ordered set(SOS)technique and particle swarm optimization(PSO)algorithm.A test system is developed to illustrate the technical viability and economic feasibility of the proposed technique.

What leads to variations in the results of life-cycle energy assessment?An evidence-based framework for residential buildings

Residential buildings are one of the major contributors to climate change due to their significant impacts on global energy consumption.Hence,most countries have introduced regulations to minimize energy use in residential buildings.To date,the focus of these regulations has mainly been on operational energy while excluding embodied energy.In recent years,extensive studies have highlighted the necessity of minimizing both embodied energy and operational energy by applying the life-cycle energy assessment(LCEA)approach.However,the absence of a standardized framework and calculation methodology for the analysis of embodied energy has reportedly led to variations in the LCEA results.Retrospective research endeavoured to explore the causes of variations,with a limited focus on calculating embodied impacts.Despite the undertaken attempts,there is still a need to investigate the key parameters causing variations in LCEA results by examining methodological approaches of the current studies toward quantifications of embodied and operational energies.This paper aims to address three primary questions:‘what is the current trend of methodological approach for applying LCEA in residential buildings?’;‘what are the key parameters causing variations in LCEA results?’;and‘how can the continued variations in the application of LCEA in residential buildings be overcome?’.To this end,40 LCEA studies representing 157 cases of residential buildings across 16 countries have been critically reviewed.The findings reveal four principal categories of parameters that potentially contribute to the varying results of LCEAs:system boundary definition,calculation methods,geographical context,and interpretation of results.This paper also proposes a conceptual framework to minimize variations in LCEA studies by standardizing the process of conducting LCEAs.

Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform

This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combined cooling,heating,and power system(CCHP)and photovoltaic(PV)systems are not currently allowed.However,with the Chinese supply-side power grid reform,the permissions for connections between DESs and utilities are gradually evolving.By performing building simulation and using mixed integer linear programming(MILP),a real-time updated operation strategy of a DES is established.Then,considering the DES from Tianjin Eco-city as a case study,a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors:economic efficiency,energy consumption,and CO2 emission.The results show that the updated strategy can reduce 29.12%of electricity time-of-use cost,10.11%of total fuel consumption,and 18.40%of CO2 emission during the cooling season.Besides,a method of“rolling load forecasting”for DES by using Support vector regression machine(SVR)is proposed and discussed.The testing shows that the Mean Absolute Percentage Error(MAPE)is below 7.5%.And when the training sample is large,the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.

Guest editorial:special section on energy storage systems and operational flexibility in power systems

The energy storage system(ESS) is becoming an important component in power systems to mitigate the adverse impact of intermittent renewable energy resources and improve power grid reliability and efficiency.However,storage devices driven by different technologies can have specific grid impacts.This special section is dedicated to reflecting the

Iterative HOEO fusion strategy:a promising tool for enhancing bearing fault feature

As parameter independent yet simple techniques,the energy operator(EO)and its variants have received considerable attention in the field of bearing fault feature detection.However,the performances of these improved EO techniques are subjected to the limited number of EOs,and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction.As a result,the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises.To address these issues,this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively.Specifically,the proposed strategy is conducted through the following three steps.First,a multi-dimensional information matrix(MDIM)is constructed by performing the higher order energy operator(HOEO)on the analysis signal iteratively.MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region.Second,an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses.Third,the intrinsic manifolds are weighted to recover the fault-related transients.Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods,including HOEOs,the weighting HOEO fusion,the fast Kurtogram,and the empirical mode decomposition.