Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum

Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.

Curved Space-Time at the Planck Scale

This paper presents a physically plausible and somewhat illuminating first step in extending the fundamental principles of mechanical stress and strain to space-time. Here the geometry of space-time, encoded in the metric tensor, is considered to be made up of a dynamic lattice of extremely small, localized fields that form a perfectly elastic Lorentz symmetric space-time at the global (macroscopic) scale. This theoretical model of space-time at the Planck scale leads to a somewhat surprising result in which matter waves in curved space-time radiate thermal gravitational energy, as well as an equally intriguing relationship for the anomalous dispersion of light in a gravitational field.

REAL PALEY-WIENER THEOREMS FOR THE SPACE-TIME FOURIER TRANSFORM

This paper presents an extension of certain forms of the real Paley-Wiener theorems to the Minkowski space-time algebra. Our emphasis is dedicated to determining the space-time valued functions whose space-time Fourier transforms(SFT) have compact support using the partial derivatives operator and the Dirac operator of higher order.

Space-time correlations of passive scalar in Kraichnan model

We consider the two-point,two-time(space-time)correlation of passive scalar R(r,τ)in the Kraichnan model under the assumption of homogeneity and isotropy.Using the fine-gird PDF method,we find that R(r,τ)satisfies a diffusion equation with constant diffusion coefficient determined by velocity variance and molecular diffusion.Itssolution can be expressed in terms of the two-point,one time correlation of passive scalar,i.e.,R(r,0).Moreover,the decorrelation o R(k,τ),which is the Fourier transform of R(r,τ),is determined byR(k,0)and a diffusion kernal.

Coulomb Force, Charge, and Electric Properties under Collision Space-Time

We have recently published a series of papers on a theory we call collision space-time, that seems to unify gravity and quantum mechanics. In this theory, mass and energy are redefined. We have not so far demonstrated how to make it compatible with electric properties such as charge and the Coulomb force. The aim of this paper is to show how electric properties can be reformulated to make it consistent with collision space-time. It is shown that we need to incorporate the Planck scale into the electric constants to do so. This is also fully possible from a practical point of view, as it has recently been shown how to measure the Planck length independent of other constants and without the need for dimensional analysis.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

Fuzzy Inventory Model under Selling Price Dependent Demand and Variable Deterioration with Fully Backlogged Shortages

The objective is to develop a model considering demand dependent on selling price and deterioration occurs after a certain period of time, which follows two-parameter Weibull distribution. Shortages are allowed and fully backlogged. Fuzzy optimal solution is obtained by considering hexagonal fuzzy numbers and for defuzzification Graded Mean Integration Representation Method. A numerical example is provided for the illustration of crisp and fuzzy, both models. To observe the effect of changes in parameters, sensitivity analysis is carried out.

Survey on the Demand for Elderly Care Services of Community Residents in Beijing and Analysis of Influencing Factors

Objective: To investigate the current situation of the demand for geriatric care services of community residents in Beijing and analyze the influencing factors to provide a reference basis for meeting the demand for diversified and professional geriatric care services. Methods: A self-made questionnaire was used to randomly survey 1558 elderly individuals at community health service centers in 8 urban districts where elderly care centers were planned to be built. The influencing factors of the different characteristics of elderly care service needs from three aspects were analyzed using a dichotomous logistic regression model: predisposing, enabling, and, need factors. Results: 69.7% of the elderly required home care services, 22.8% wanted to get care services at elderly care centers, 15.9% wanted to get care services at nursing homes, 12.3% required community care services, and 7.4% didn’t know where to access care services. 68.5% of the elderly required care services for disabilities/semi-disabilities, 58.0% for dementia, 54.7% for common diseases, 34.9% for rehabilitation training, 33.0% for plumbing care, and 7.5% for hospice care. At the same time, there were urban- rural differences in the demand for elderly care services, with suburban elderly having a higher demand for care services than those living in urban areas (P < 0.05). The elderly’s demand for care services was mainly related to age, place of residence, and gender in the causative factors, mode of residence and physical condition among able factors, and mode of care services and care needs among need factors (P < 0.05). Conclusion: The demand for elderly care services was differentiated by factors including place of residence, age, and gender. It is crucial to accurately match the demand for elderly care services, innovate the mode of elderly care services, and improve the service quality to improve the elderly health service system.

Research on Adaptability Analysis of Environmental Space Demand and Design Optimization of Daycare Centers: Taking Chongqing’s Community F as an Example

Currently,there is a lack of research on the detailed environmental spatial design of community daycare centers at the micro level.This study focuses on Community F in Chongqing,using the elderly’s“willingness to demand”as a central aspect.It examines indoor and outdoor environmental space needs at a micro level,considering both functional requirements and spiritual needs based on existing research.The analysis incorporates three adaptive elements:current construction,surrounding environment,and operational management.It explores the feasibility of restructuring spatial layouts,utilizing local resources,and integrating Bayu cultural characteristics.Finally,through design optimization practices,the study proposes three strategies for aging optimization:functional integration and interaction,user-friendly facilities,and emotional connections to place.

Study on the Alignment Between Talent Training Directions and Employment Market Demands in the Landscape Architecture Industry

Currently,talent training in Chinese universities for landscape architecture is mainly divided into three directions:“landscape planning and design,”“landscape construction management,”and“landscape plant planting and maintenance.”However,with the background of the slowing urbanization process and the widespread demand for composite talents in society,it remains to be verified whether the traditional three major talent training directions in landscape architecture align with the job demands in the current construction market.Based on a survey and analysis of over 300 industry practitioners,this study found a clear trend of merging the three major employment directions into“landscape design and construction”and“landscape plant planting and maintenance.”This presents new requirements and directions for the skill training of landscape architecture majors in universities and provides insights into the alignment between talent training and employment demands in other industries.

基于space-time的二叉树表示动态环境中的路径规划

研究具有空间和时间的三维动态环境下的机器人路径规划分析了四叉树表示二维空间的搜索算法,在此基础上,提出采space-time,用二叉树表示二维空间的方法时间信息中增加加速度利用二叉树遍历方法和算法设计一个在动态障碍物环境下进行路径规划的新算法并,,A*,,在足球机器人系统中进行仿真,实现了较好的路径规划。

ON MARKOV CHAINS IN SPACE-TIME RANDOM ENVIRONMENTS

In Section 1, the authors establish the models of two kinds of Markov chains in space-time random environments (MCSTRE and MCSTRE(+)) with abstract state space. In Section 2, the authors construct a MCSTRE and a MCSTRE(+) by an initial distribution Φ and a random Markov kernel (RMK) p(γ). In Section 3, the authors es-tablish several equivalence theorems on MCSTRE and MCSTRE(+). Finally, the authors give two very important examples of MCMSTRE, the random walk in spce-time random environment and the Markov br...

Space-time evolution rules of acoustic emission location of unloaded coal sample at different loading rates

By using MTS815 rock mechanics test system,a series of acoustic emission(AE) location experiments were performed under unloading confining pressure,increasing the axial stress.The AE space-time evolution regularities and energy releasing characteristics during deformation and failure process of coal of different loading rates are compared,the influence mechanism of loading rates on the microscopic crack evolution were studied,combining the AE characteristics and the macroscopic failure modes of the specimens,and the precursory characteristics of coal failure were also analyzed quantitatively.The results indicate that as the loading rate is higher,the AE activity and the main fracture will begin earlier.The destruction of coal body is mainly the function of shear strain at lower loading rate and tension strain at higher rate,and will transform from brittleness to ductility at critical velocities.When the deformation of the coal is mainly plasticity,the amplitude of the AE ringing counting rate increases largely and the AE energy curves appear an obvious ''step'',which can be defined as the first failure precursor point.Statics of AE information shows that the strongest AE activity begins when the axial stress level was 92-98%,which can be defined as the other failure precursor point.As the loading rate is smaller,the coal more easily reaches the latter precursor point after the first one,so attention should be aroused to prevent dynamic disaster in coal mining when the AE activity reaches the first precursor point.

On analyzing space-time distribution of evaporation duct height over the global ocean

The statistical features of the evaporation duct over the global ocean were comprehensively investigated with reanalysis data sets from the National Centers for Environmental Prediction. These data sets have time and spatial resolutions of 1 h and 0.313°x0.312°, respectively. The efficiency of the analysis was evaluated by processing weather buoy data from the Pacific Ocean and measuring propagation loss in the Yellow Sea of China. The distribution features of evaporation duct height （EDH） and the related meteorological factors for different seas were analyzed. The global EDH is generally high and demonstrates a latitudinal distribution for oceans at low latitudes. The average EDH is approximately 11 m over oceans beside the equator with a latitude of less than 20°. The reasons for the formation of the global EDH features were also analyzed for different sea areas.