Unconventional photon blockade in the two-photon Jaynes–Cummings model with two-frequency cavity drivings and atom driving

In a two-frequency cavity driving and atom driving atom-cavity system,we find the photon blockade effect.In a truncated eigenstates space,we calculate the zero-delay second-order correlation function of the cavity mode analytically and obtain an optimal condition for the photon blockade.By including three transition pathways,we find that higher excitations of the cavity mode can be further suppressed and the zero-delay second-order correlation function can be reduced additionally.Based on the master equation,we simulate the system evolution and find that the analytical solutions match well with the numerical results.Our scheme is robust with small fluctuations of parameters and may be used as a new type of single photon source.

Polar codes based OFDM-PLC systems in the presence of impulsive noise

Power line communication(PLC)has the potential to become the preferred technique for providing broadband communication to homes and offices with advantage of eliminating the need for new wiring infrastructure and reducing the cost.But it suffers from the impulsive noise because it introduces significant time variance into the power line channel.In this paper,a polar codes based orthogonal frequency division multiplexing(OFDM)PLC system is proposed to deal with the impulsive noise and thereby improve the transmission performance.Firstly,the impulsive noise is modelled with a multi-damped sine function by analyzing the time behavior of impulse events.Then the polar codes are used to combat the impulsive noise of PLC channel,and a low complexity bit-flipping decoding method based on CRC-aided successive cancellation list(CA-SCL)decoding algorithm is proposed.Simulations evaluate the proposed decoding algorithm and the results validate the suggested polar codes based OFDM-PLC scheme which can improve the BER performance of PLC with impulsive interference.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

Simplified Technique for Predicting Offshore Pipeline Expansion

In this study,we propose a method for estimating the amount of expansion that occurs in subsea pipelines,which could be applied in the design of robust structures that transport oil and gas from offshore wells.We begin with a literature review and general discussion of existing estimation methods and terminologies with respect to subsea pipelines.Due to the effects of high pressure and high temperature,the production of fluid from offshore wells is typically caused by physical deformation of subsea structures,e.g.,expansion and contraction during the transportation process.In severe cases,vertical and lateral buckling occurs,which causes a significant negative impact on structural safety,and which is related to on-bottom stability,free-span,structural collapse,and many other factors.In addition,these factors may affect the production rate with respect to flow assurance,wax,and hydration,to name a few.In this study,we developed a simple and efficient method for generating a reliable pipe expansion design in the early stage,which can lead to savings in both cost and computation time.As such,in this paper,we propose an applicable diagram,which we call the standard dimensionless ratio(SDR)versus virtual anchor length(LA)diagram,that utilizes an efficient procedure for estimating subsea pipeline expansion based on applied reliable scenarios.With this user guideline,offshore pipeline structural designers can reliably determine the amount of subsea pipeline expansion and the obtained results will also be useful for the installation,design,and maintenance of the subsea pipeline.

Change of Mineral Elements and Amino Acids in <i>Malus hupehensis var. pingyiensis</i>Leaves under Cd Treatment

The objective of this paper is to research the effects of CdCl2 treatment on mineral elements and amino acids in leaves of Malus hupehensis var. pingyiensis. The seedlings of Malus hupehensis var. pingyiensis with 6 leaf were cultured in 1/2 Hoagland nutrient solutions of different CdCl2 treatments (0, 0.5, 5 and 10 mg·L-1), respectively. The mineral elements and amino acids of the leaves in Malus hupehensis var. pingyiensis were measured in the day 30. Compared with the control (0 mg·L-1 CdCl2), the treatments significantly decreased the contents of Mg, Fe and Zn in the tested leaves and obviously increased the contents of Cd in the experimental leaves. As to Ca and Mn, low concentration Cd treatment (0.5 mg·L-1 CdCl2) promoted their absorption, however, high concentration Cd treatments (5 and 10 mg·L-1 CdCl2) inhibited their absorption. The metabolism pathway and content of amino acids in the Malus hupehensis var. pingyiensis leaves under Cd treatment were modified, the content of amino acids in the glycolate pathway became larger than that in control, the content of amino acids in the pyruvic acid synthesis pathway and tyrosine and phenylalanine became smaller than that in control, the content of other amino acids also had made a certain degree change. The results provided the important basis for safety production and quality evaluation of leaves in Malus hupehensis var. pingyiensis.

Optimum active and inert ratios for different metamorphic grade coals

In order to quantitatively describe the difference of optimum active and inert ratio of various metamorphic grade coking coals, the rule of coke micro-strength index （MSI）, determinated by adding different proportions of inert content to ten kinds of single coal, changing with active and inert ratio has been investigated. Three kinds of change rule of the MSI of ten kinds of single coal changing with active and inert ratio have been obtained in the research. It has been demonstrated that Gauss curve model is the optimal model to describe the optimum active and inert ratio of different metamorphic grade coals. On this basis, the optimum active and inert ratio of different metamorphic grade coals can be given.

Kalman-Filtering-Based Frequency Control Strategy Considering Electrolytic Aluminum Load

Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)accounts for large amount of the local electric loads in some areas.The participation of EAL in local frequency control has huge application prospects.However,the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption.Focusing on this problem,this paper proposes a control strategy for EAL to participate in the frequency control.For the controller design of the EAL system,the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL.For the problem of load-side frequency measurement error,the frequency estimation method based on Kalman-filtering is designed.To improve the performance of EAL in the frequency control,a fuzzy EAL controller is designed.The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency,and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.

Focus on the Chicken Manure Power Generation in China: How to Promote the Biomass Waste Industry?

With the vigorous growth of animal husbandry, animal feces in the agriculture sector gradually deteriorate the environment. The chicken manure power generation is becoming viable and useful for energy conversion to comply with the context of environmental protection in China. Based on resource endowments and technical conditions, this paper studies the current situation of chicken manure power generation in China. Combined with the policy environment, the research conducts a PEST-SWOT matrix analysis to thoroughly look into the strengths and weaknesses, the opportunities and challenges. Then, the paper analyzes the distribution of chicken manure and gives some solutions from respects of government regulatory behavior, industrial-organizational behavior and corporation strategic behavior. Finally, it is concluded that: 1) the government should strengthen policy support by actively improving the subsidy mechanism and lowering the threshold of financing and credit;2) enterprises should focus on improving power generation technology and boiler treatment technology.

Assessment of Electricity Productivity in China:Regional Differences and Convergence

Electricity productivity is regarded as a major assessment indicator in the design of energy saving policies,given that China has entered a“New Normal”of economic development.In fact,enhancing electricity productivity in an all-round way,as is one of the binding indicators for energy and environmental issues,means that non-growth target of total electric energy consumption in the economic development is feasible.The Gini coefficient,Theil index,and Mean log deviation are utilized to measure regional differences in China’s electricity productivity from 1997 to 2016 in five regions,and conditionalβconvergence is empirically analyzed with the spatial Durbin model.The results show that:(1)China’s electricity productivity is improving,while the overall feature is that the eastern area has a higher efficiency than the western area.(2)The difference in electricity productivity is the smallest in the northeast and the largest in the northwest.Interregional difference plays an important role and is the main cause for the differences.(3)The electricity productivity in China exhibitsβconvergence,except for the northwest.The positive driving factor is urbanization level(0.0485%),and the negative driving factor is FDI(–0.0104%).Moreover,the urbanization rate(0.0669%),foreign direct investment(0.0960%),and the industrial structure(–0.0769%)have a spatial spillover effect on improving regional electricity productivity.Based on this conclusion,the study provides some recommendations for saving energy policy design in China’s power industry.

The operation of the Norwegian NOX Fund and its inspiration

Norway has a well-established legal system and advanced environmental science and technology in environmental protection.In 2007,the country introduced a tax on the emissions of NOx(nitrogen oxide)in order to control the emission,and it has achieved remarkable result in reducing NOx emission afterwards with the support of NOx Fund and realized the emission reduction target for 2020 under Gothenburg Protocol in 2016 in advance.The NOx Fund has achieved a balance between emission reduction and the development of new technology,which is worth learning from.

Will Daily Chemical and Washing Industry Embrace Reform in the Wake of COVID-19?

The outbreak of COVID-19 during the Spring Festival in 2020 caught the whole China and its people off guard.Stay-at-home,quarantine and examination,people’s lives were changing unprecedentedly.Disinfectant water,sanitizer,antibacterial soap,has become the first choice for people to face the virus.The memory of SARS was triggered.History about SARS On March 12th,2003,the WHO sounded the global alarm on SARS.It was a war without gun shots.Someone passed away while others survived.However,the deepest impression was the image when people were“fighting against SARS with concerted efforts”.According to experts,we needed to pay attention to health by washing our hands frequently and conducting regular disinfection.At that time,liquid soap and sterilized water were out of stock.The discussion on the bad habits of hygiene turned into a hot topic.

Petrogenesis and Tectonic Implications of the Paleoproterozoic A-Type Granites in the Xiong’ershan Area along the Southern Margin of the North China Craton

Paleoproterozoic A-type granites are widely distributed in the southern margin of the North China Craton(SNCC),providing important information for understanding the Paleoproterozoic tectonic regimes in this area.This paper reports newly obtained whole-rock compositions and zircon U-Pb ages for the Tieluping syenogranite porphyry(TLP)and Huoshenmiao alkali granite porphyry(HSM)in the SNCC.Zircons from the TLP and HSM have U-Pb ages of 1805±12 and 1792±14 Ma,respectively.These ages are taken to date the emplacement of these intrusions.They had high total alkali contents(K_(2)O+Na_(2)O>7.13 wt.%),with high 10000×Ga/Al ratios(3.06–3.41)and Zr+Y+Nb+Ce values(709 ppm–910 ppm)as well as high zircon saturation temperatures(864–970℃),indicative of A-type granite affinities.High Y/Nb(1.75–3.32),Ce/Nb(7.72–9.72),and Yb/Ta(2.89–5.60)ratios suggested that TLP and HSM belonged to the A2-type granite.The negative whole rockε_(Nd)(t)values(−8.4 to−6.6)and negative zirconε_(Hf)(t)values(−15.9 to−6.3)confirmed that TLP and HSM were likely generated by the partial melting of an ancient continental crust.TheεHf(t)(−7.4 to+4.0)values of inherited zircons in the TLP suggested that they were derived from the partial melting of Archean basement rocks.Considering the geochemical similarity of the 1.80 Ga A-type granitoids in the SNCC,we propose that the TLP and HSM were formed in a post-collisional regime that was likely associated with the break-off of the Paleoproterozoic subducted slab.Upwelling of the asthenosphere provided huge heat to generate the regional 1.80 Ga A-type granite in the SNCC.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

Leveraging Continuous Prompt for Few-Shot Named Entity Recognition in Electric Power Domain with Meta-Learning

Conventional named entity recognition methods usually assume that the model can be trained with sufficient annotated data to obtain good recognition results.However,in Chinese named entity recognition in the electric power domain,existing methods still face the challenges of lack of annotated data and new entities of unseen types.To address these challenges,this paper proposes a meta-learning-based continuous cue adjustment method.A generative pre-trained language model is used so that it does not change its own model structure when dealing with new entity types.To guide the pre-trained model to make full use of its own latent knowledge,a vector of learnable parameters is set as a cue to compensate for the lack of training data.In order to further improve the model's few-shot learning capability,a meta-learning strategy is used to train the model.Experimental results show that the proposed approach achieves the best results in a few-shot electric Chinese power named entity recognition dataset compared to several traditional named entity approaches.

Exploring best‑matched embedding model and classifier for charging‑pile fault diagnosis

The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment.It is crucial to guarantee normal operation of charging piles,resulting in the importance of diagnosing charging-pile faults.The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams.However,there are other types of fault data,which cannot be used for diagnosis by these existing approaches.This paper aims to fill this gap and consider 8 types of fault data for diagnosing,at least including physical installation error fault,charging-pile mechanical fault,charging-pile program fault,user personal fault,signal fault(offline),pile compatibility fault,charging platform fault,and other faults.We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset.4 word embedding models are investigated for feature extraction of fault data,including N-gram,GloVe,Word2vec,and BERT.Moreover,we classify the word embedding results using 10 machine learning classifiers,including Random Forest(RF),Support Vector Machine,K-Nearest Neighbor,Multilayer Perceptron,Recurrent Neural Network,AdaBoost,Gradient Boosted Decision Tree,Decision Tree,Extra Tree,and VOTE.Compared with original fault record dataset,we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%.Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time.In addition,we discuss the interpretability of RF and GloVe.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

An internal power angle control strategy for high-speed sensorless brushless DC motors

High-speed Brushless DC Motors(BLDCMs)usually adopt a sensorless control strategy and operate in three-phase six-state drive mode.However,the sampling errors of the rotor position and the driving method increase the Internal Power Angle(IPA),resulting in a decrease in the efficiency of the system.Conventional IPA reduction strategies are either sensitive to motor parameters,or ignore diode freewheeling during the commutation process,or require additional current sensors.In this paper,a new strategy to reduce the IPA is proposed.Firstly,a Zero-Crossing Point(ZCP)detection method for the back-EMF without filter is proposed to reduce the sampling errors of the rotor position.Secondly,the relationship between the non-energized terminal voltage and the ZCP of the corresponding back-EMF is analyzed.The non-energized terminal voltage that has completed the diode freewheeling is divided into two triangles by half of the bus voltage.When the IPA is suppressed,the areas of the two triangles are equal.Thirdly,an advanced angle for reducing the IPA is obtained through a PI regulator which can eliminate the deviation between the two areas.Finally,both a simulation model and an experimental circuit are built to verify the proposed control strategy.

Generative Adversarial Network Based Status Generation Simulation Approach

The generative adversarial network based methods have beenapplied in many fields for simulation data generation. For power equipment,due to the combined influences of multiple factors, how to generatereasonable simulation data that meets specific requirements has becomea challenge. This paper proposes a power equipment status generationapproach based on generative confrontation network. This approach considersthe changing factors of power equipment and takes it as the conditionaldistribution of simulation data during training. The proposedapproach is applied to the status generation of power equipment, andthe rationality and effectiveness of the approach are verified throughexperiments.