Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
Overseas three-month intra-university training program is rear in Yunnan,China.Using adult learning and social con-structivism as theoretical basis,the author,introduces the background of the program and the course de...Overseas three-month intra-university training program is rear in Yunnan,China.Using adult learning and social con-structivism as theoretical basis,the author,introduces the background of the program and the course design,highlighting the inten-tion for the program and similar programs.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
We investigated the effect of fire disturbance on short-term soil respiration in birch （Betula platyphylla Suk.） and larch （Larix gmelinii Rupr.） forests in Greater Xing’an range, northeastern China for further u...We investigated the effect of fire disturbance on short-term soil respiration in birch （Betula platyphylla Suk.） and larch （Larix gmelinii Rupr.） forests in Greater Xing’an range, northeastern China for further understanding of its effect on the carbon cycle in ecosystems. Our study show that post-fire soil respiration rates in B. platyphylla and L. gmelinii forests were reduced by 14%and 10%, respectively. In contrast, the soil heterotrophic respiration rates in the two types of forest were similar in post-fire and control plots. After fire, the contribution of root respiration to total soil respiration was dramatically reduced. Variation in soil respiration rates was explained by soil moisture （W） and soil tem-perature （T） at a depth of 5 cm. Exponential regression fitted T and W models explained Rs rates in B. platyphylla control and post-fire plots （83.1% and 86.2%） and L. gmelinii control and post-fire plots （83.7%and 88.7%）. In addition, the short-term temperature coefficients in B.展开更多
Cemented paste backfill(CPB) is largely used in underground mines worldwide.A key issue associated with application of CPB is to estimate the stresses in backfilled stopes and on barricades.Recent numerical and experi...Cemented paste backfill(CPB) is largely used in underground mines worldwide.A key issue associated with application of CPB is to estimate the stresses in backfilled stopes and on barricades.Recent numerical and experimental results show that arching effect is absent shortly after the placement of CPB in stopes.However,stress decreases in barricade drift with increasing distance between the measurement points and drawpoint have also been observed,demonstrating arching effect shortly after the pouring of CPB.To explain these paradoxes,CPB is considered as Bingham fluid having a yield shear stress.Three dimensional analytical solutions are proposed to evaluate the short-term total stresses in backfilled stopes and on barricades,accounting for the CPB's yield shear stress-induced arching effect.Stress diminution due to such arching effect in the backfilled stopes and on barricades is indeed obtained.But the reduction becomes insignificant using typical yield shear stress and stope geometry.More analyses indicate that the typical yield shear stress values do not fully correspond to field conditions where the yield shear stress would increase exponentially due to apparent consolidation(loss of water by drainage,a phenomenon similar to the desiccation of overly saturated fine-grained materials).展开更多
Accuracy in predictions leads to better planning with a minimum of opportunity lost. In open pit mining,the complexity of operations, coupled with a highly uncertain and dynamic production environment,limit the accura...Accuracy in predictions leads to better planning with a minimum of opportunity lost. In open pit mining,the complexity of operations, coupled with a highly uncertain and dynamic production environment,limit the accuracy of predictions and force a reactive planning approach to mitigate deviations from original plans. A simulation optimization framework/tool is presented in this paper to account for uncertainties in mining operations for robust short-term production planning and proactive decision making. This framework/tool uses a discrete event simulation model of mine operations, which interacts with a goalprogramming based mine operational optimization tool to develop an uncertainty based short-term schedule. Using scenario analysis, this framework allows the planner to make proactive decisions to achieve the mine's operational and long-term objectives. This paper details the development of simulation and optimization models and presents the implementation of the framework on an iron ore mine case study for verification through scenario analysis.展开更多
Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.展开更多
With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service respons...With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.展开更多
Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult.This study proposes a meth...Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult.This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing.By examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is obtained.The classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification.Finally,relative dip angle is calculated by nonlinear least square method.Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.展开更多
A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways,...A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.展开更多
Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the m...Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.展开更多
To improve the signal to noise ratio(SNR)and the short-term stability of cesium atomic fountain clocks,the work of two-laser optical pumping is presented theoretically and experimentally.The short-term stability of th...To improve the signal to noise ratio(SNR)and the short-term stability of cesium atomic fountain clocks,the work of two-laser optical pumping is presented theoretically and experimentally.The short-term stability of the NIM6 fountain clock has been improved by preparing more cold atoms in the|F=4,m_(F)=0>clock state with a shortened cycle time.Two π-polarized laser beams overlapped in the horizontal plane have been applied after launching,one is resonant with|F=4>→|F′=4>transition and the other is resonant with|F=3>→|F′=4>transition.With optical pumping,the population accumulated in the|m_(F)=0>clock state is improved from 11%to 63%,and the detection signal is increased by a factor of 4.2,the SNR of the clock transition probability and the short-term stability are also improved accordingly.展开更多
This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute ac...This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.展开更多
Listening comprehension is an important part in most of the English examinations because of the commonly using of listening in communication. It is a common phenomena that most of the students have problems in this pa...Listening comprehension is an important part in most of the English examinations because of the commonly using of listening in communication. It is a common phenomena that most of the students have problems in this part because of the limit?ed capacity of short-term memory. In this essay, we will talk about the relationship between the short-term memory and listen?ing comprehension, and try to find the way to train the short-term memory to improve this part.展开更多
Short-term memory plays an essential role in successful listening comprehension.The information extracted from short-term memory in listening comprehension is influenced by many factors.This paper explain some ways of...Short-term memory plays an essential role in successful listening comprehension.The information extracted from short-term memory in listening comprehension is influenced by many factors.This paper explain some ways of improving listening ability while examine,which emphatically point out how to extend short-term memory.展开更多
It was found that the DNA-damaging agents N-methyl-N’-nitro-N-nitrosoguanidine（MNNG）,methyl-methanesulphonate（MMS）and 4-nitroquinoline-N-oxide（4NQO）could stimulate ADP-ribosyl transferase（ADPRT）activity and r...It was found that the DNA-damaging agents N-methyl-N’-nitro-N-nitrosoguanidine（MNNG）,methyl-methanesulphonate（MMS）and 4-nitroquinoline-N-oxide（4NQO）could stimulate ADP-ribosyl transferase（ADPRT）activity and reduce the cellular NAD content in a dose-dependent way.The reduction of NAD after DNA damage could be partially or completelyprevented by ADPRT inhibitors,3-aminobenzamide or nicotinamide,which showed noinfluence on reduction of NAD induced by metabolic blocking agents.Therefore,a simpleand specific method to detect DNA-damaging mutagens by measuring ADPRT-mediateddecrease of cellular NAD content was explored.Using β-naphthofiavone,a mixed functionoxygenase inducer,together with induced or uninduced human amnion FL cells,it was foundthat aflatoxin B<sub>1</sub>,benzo（a）pyrene,2-acetylaminofluorene,9,10-dimethylanthracene andethylcarbamate could induce the ADPRT-mediated decrease of cellular NAD content,while4-acetylaminofluorene,anthracene,isopropyl-N-（3-chlorophenyl）-carbamate,β-propiolactone,γ-butyrolactone,cyclophosphamide and safrol could not.The results indicate that this isa cheap and specific method to detect DNA damage caused by chemical carcinogens/mutagenswith a spccificity approaching that of the unscheduled DNA synthesis assay.展开更多
With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere ...With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere has been derived. The intensity in the absence of turbulence and the long-term average intensity in turbulence can both also be expressed in this formula. As special cases, comparisons among short-term average intensity, long-term average intensity, and the intensity in the absence of turbulence for flat topped beam and annular beam are carried out. The effects of the order of MGB, propagation distance and aperture radius on beam spreading are analysed and discussed in detail.展开更多
This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model...This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.展开更多
Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated dev...Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.展开更多
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
文摘Overseas three-month intra-university training program is rear in Yunnan,China.Using adult learning and social con-structivism as theoretical basis,the author,introduces the background of the program and the course design,highlighting the inten-tion for the program and similar programs.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
基金supported by the National Basic Research Program of China(973 Program)(No.2011CB403203)the National Natural Science Foundation(No.31070544)+3 种基金the Fundamental Research Funds for the Central Universities(No:DL12CA07)the Huoyingdong Education Foundation(No.131029)Postdoctoral Science-Research Foundation(LBH-Q12174)the CFERN&GENE Award Funds for Ecological Papers
文摘We investigated the effect of fire disturbance on short-term soil respiration in birch （Betula platyphylla Suk.） and larch （Larix gmelinii Rupr.） forests in Greater Xing’an range, northeastern China for further understanding of its effect on the carbon cycle in ecosystems. Our study show that post-fire soil respiration rates in B. platyphylla and L. gmelinii forests were reduced by 14%and 10%, respectively. In contrast, the soil heterotrophic respiration rates in the two types of forest were similar in post-fire and control plots. After fire, the contribution of root respiration to total soil respiration was dramatically reduced. Variation in soil respiration rates was explained by soil moisture （W） and soil tem-perature （T） at a depth of 5 cm. Exponential regression fitted T and W models explained Rs rates in B. platyphylla control and post-fire plots （83.1% and 86.2%） and L. gmelinii control and post-fire plots （83.7%and 88.7%）. In addition, the short-term temperature coefficients in B.
基金the Natural Sciences and Engineering Research Council of Canada(NSERC)Institut de recherche Robert-Sauve en sante et en securite du travail(IRSST)industrial partners of the Research Institute on Mines and the Environment(RIME UQAT-Polytechnique)
文摘Cemented paste backfill(CPB) is largely used in underground mines worldwide.A key issue associated with application of CPB is to estimate the stresses in backfilled stopes and on barricades.Recent numerical and experimental results show that arching effect is absent shortly after the placement of CPB in stopes.However,stress decreases in barricade drift with increasing distance between the measurement points and drawpoint have also been observed,demonstrating arching effect shortly after the pouring of CPB.To explain these paradoxes,CPB is considered as Bingham fluid having a yield shear stress.Three dimensional analytical solutions are proposed to evaluate the short-term total stresses in backfilled stopes and on barricades,accounting for the CPB's yield shear stress-induced arching effect.Stress diminution due to such arching effect in the backfilled stopes and on barricades is indeed obtained.But the reduction becomes insignificant using typical yield shear stress and stope geometry.More analyses indicate that the typical yield shear stress values do not fully correspond to field conditions where the yield shear stress would increase exponentially due to apparent consolidation(loss of water by drainage,a phenomenon similar to the desiccation of overly saturated fine-grained materials).
基金part of a PhD research, which was supported by Mine Optimization Laboratory, University of Alberta-Canada
文摘Accuracy in predictions leads to better planning with a minimum of opportunity lost. In open pit mining,the complexity of operations, coupled with a highly uncertain and dynamic production environment,limit the accuracy of predictions and force a reactive planning approach to mitigate deviations from original plans. A simulation optimization framework/tool is presented in this paper to account for uncertainties in mining operations for robust short-term production planning and proactive decision making. This framework/tool uses a discrete event simulation model of mine operations, which interacts with a goalprogramming based mine operational optimization tool to develop an uncertainty based short-term schedule. Using scenario analysis, this framework allows the planner to make proactive decisions to achieve the mine's operational and long-term objectives. This paper details the development of simulation and optimization models and presents the implementation of the framework on an iron ore mine case study for verification through scenario analysis.
文摘Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
基金supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data)
文摘With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
基金Supported by the PetroChina Major Scientific and Technological Project(ZD2019-183-006)Fundamental Scientific Research Fund of Central Universities(20CX05017A)China National Science and Technology Major Project(2016ZX05021-001)。
文摘Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult.This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing.By examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is obtained.The classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification.Finally,relative dip angle is calculated by nonlinear least square method.Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.
基金The Project of Research on Technologyand Devices for Traffic Guidance (Vehicle Navigation)System of Beijing Municipal Commission of Science and Technology(No H030630340320)the Project of Research on theIntelligence Traffic Information Platform of Beijing Education Committee
文摘A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
文摘Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.
基金the National Natural Science Foundation of China(Grant No.11873044).
文摘To improve the signal to noise ratio(SNR)and the short-term stability of cesium atomic fountain clocks,the work of two-laser optical pumping is presented theoretically and experimentally.The short-term stability of the NIM6 fountain clock has been improved by preparing more cold atoms in the|F=4,m_(F)=0>clock state with a shortened cycle time.Two π-polarized laser beams overlapped in the horizontal plane have been applied after launching,one is resonant with|F=4>→|F′=4>transition and the other is resonant with|F=3>→|F′=4>transition.With optical pumping,the population accumulated in the|m_(F)=0>clock state is improved from 11%to 63%,and the detection signal is increased by a factor of 4.2,the SNR of the clock transition probability and the short-term stability are also improved accordingly.
基金supported by the China National Natural Science Foundation(52177082)China National Key R&D Program(2020YFC0827001)Science and Technology Project of Jilin Electric Power Co.,Ltd(2020JBGS-03).
文摘This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.
文摘Listening comprehension is an important part in most of the English examinations because of the commonly using of listening in communication. It is a common phenomena that most of the students have problems in this part because of the limit?ed capacity of short-term memory. In this essay, we will talk about the relationship between the short-term memory and listen?ing comprehension, and try to find the way to train the short-term memory to improve this part.
文摘Short-term memory plays an essential role in successful listening comprehension.The information extracted from short-term memory in listening comprehension is influenced by many factors.This paper explain some ways of improving listening ability while examine,which emphatically point out how to extend short-term memory.
文摘It was found that the DNA-damaging agents N-methyl-N’-nitro-N-nitrosoguanidine（MNNG）,methyl-methanesulphonate（MMS）and 4-nitroquinoline-N-oxide（4NQO）could stimulate ADP-ribosyl transferase（ADPRT）activity and reduce the cellular NAD content in a dose-dependent way.The reduction of NAD after DNA damage could be partially or completelyprevented by ADPRT inhibitors,3-aminobenzamide or nicotinamide,which showed noinfluence on reduction of NAD induced by metabolic blocking agents.Therefore,a simpleand specific method to detect DNA-damaging mutagens by measuring ADPRT-mediateddecrease of cellular NAD content was explored.Using β-naphthofiavone,a mixed functionoxygenase inducer,together with induced or uninduced human amnion FL cells,it was foundthat aflatoxin B<sub>1</sub>,benzo（a）pyrene,2-acetylaminofluorene,9,10-dimethylanthracene andethylcarbamate could induce the ADPRT-mediated decrease of cellular NAD content,while4-acetylaminofluorene,anthracene,isopropyl-N-（3-chlorophenyl）-carbamate,β-propiolactone,γ-butyrolactone,cyclophosphamide and safrol could not.The results indicate that this isa cheap and specific method to detect DNA damage caused by chemical carcinogens/mutagenswith a spccificity approaching that of the unscheduled DNA synthesis assay.
文摘With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere has been derived. The intensity in the absence of turbulence and the long-term average intensity in turbulence can both also be expressed in this formula. As special cases, comparisons among short-term average intensity, long-term average intensity, and the intensity in the absence of turbulence for flat topped beam and annular beam are carried out. The effects of the order of MGB, propagation distance and aperture radius on beam spreading are analysed and discussed in detail.
基金the National Sciences and Engineering Research Council of Canada(NSERC)under CDR Grant CRDPJ 500414-16NSERC Discovery Grant 239019the COSMO mining industry consortium(AngloGold Ashanti,BHP,De Beers,AngloAmerican,IAMGOLD,Kinross Gold,Newmont Mining,and Vale).
文摘This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.
基金the Key Research and Development Projects of Sichuan Science and Technology Department under Grant No.2018GZ0464the UESTC-ZHIXIAOJING Joint Research Center of Smart Home under Grant No.H04W210180.
文摘Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.
