Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

Study on Decision Method of Neutral Point Grounding Mode for Medium-Voltage Distribution Network

The neutral grounding mode of medium-voltage distribution network decides the reliability, overvoltage, relay protection and electrical safety. Therefore, a comprehensive consideration of the reliability, safety and economy is particularly important for the decision of neutral grounding mode. This paper proposes a new decision method of neutral point grounding mode for mediumvoltage distribution network. The objective function is constructed for the decision according the life cycle cost. The reliability of the neutral point grounding mode is taken into account through treating the outage cost as an operating cost. The safety condition of the neutral point grounding mode is preserved as the constraint condition of decision models, so the decision method can generate the most economical and reliable scheme of neutral point grounding mode within a safe limit. The example is used to verify the feasibility and effectiveness of the decision method.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

Fast Analysis of Power/Ground Networks via Circuit Reduction

This paper presents an efficient algorithm for reducing RLC power/ground network complexities by exploitation of the regularities in the power/ground networks. The new method first builds the equivalent models for many series RLC-current chains based on their Norton＇s form companion models in the original networks,and then the precondition conjugate gradient based iterative method is used to solve the reduced networks,which are symmetric positive definite. The solutions of the original networks are then back solved from those of the reduced networks.Experimental results show that the complexities of reduced networks are typically significantly smaller than those of the original circuits, which makes the new algorithm extremely fast. For instance, power/ground networks with more than one million branches can be solved in a few minutes on modern Sun workstations.

Three-dimensional Fusion of Spaceborne and Ground Radar Reflectivity Data Using a Neural Network–Based Approach

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties.

An Integrated Tool for Power/Ground Network Design, Optimization,and Verification for Cell Based VLSIs

A CAD tool based on a group of efficient algorithms to verify,design,and optimize power/ground networks for standard cell model is presented.Nonlinear programming techniques,branch and bound algorithms and incomplete Cholesky decomposition conjugate gradient method (ICCG) are the three main parts of our work.Users can choose nonlinear programming method or branch and bound algorithm to satisfy their different requirements of precision and speed.The experimental results prove that the algorithms can run very fast with lower wiring resources consumption.As a result,the CAD tool based on these algorithms is able to cope with large-scale circuits.

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.

Cause Analysis of Consumer‑Grade UAV Accidents Based on Grounded Theory‑Bayesian Network

In order to reduce the accident rate of consumer-grade unmanned aerial vehicles(UAVs)in daily use scenarios,the accident causes are analyzed based on the accident cases of consumer-grade UAVs.By extracting accident causing factors based on the Grounded theory,the relationship between these factors is analyzed.The Bayesian network for consumer-grade UAV accidents is constructed.With the Grounded theory-Bayesian network,the probability of four types of accidents is inferred:fall,air collision,disappearance,and personal injury.With the posterior probability of each factor being reversely reasoned,the causal chain with the maximum probability of each accident is obtained.After the sensitivity of each factor is analyzed,the key nodes in the network accordingly are inferred.Then the causing factors of consumer-grade UAV accidents are analyzed.The results show that the probability of fall accident is the highest,the fall accident is associated with the probabilistic maximum causal chain of personal injury,and the sensitivity analysis results of each type of accident as the result node are inconsistent.

Identification of pulse-like ground motions using artificial neural network

For more than 20 years,the concept of near-fault pulse-like ground motion has been a topic of great interest due to its distinct characteristics,particularly due to directivity or fling effects,which are hugely influenced by the rupture mechanism.These unexpected characteristics,along with their effective frequency,energy rate,and damage indices,create a near-fault,pulse-like ground motion capable of causing severe damage to structures.One of the most common approaches for identifying these ground motions is done by conducting wavelet decomposition of the ground motion time history to extract a pulse signal and eventually categorize an earthquake by comparing the original signal to the residual one.However,to overcome the intensive calculations required in this approach,this study proposes using artificial neural networks to identify pulse-like ground motions through classification to predict their pulse period by means of regression analysis.Furthermore,the study is intended to evaluate the reliability and accuracy of various artificial neural networks in identifying pulse-like ground motions and predicting their pulse periods.In general,the results of the study have shown that the artificial neural network can identify pulse-like earthquakes and reliably predict their pulse period.

Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities,since it is very closely related to the structural health and reliability with the rapid development of road traffic.Ground penetrating radar(GPR)is widely used to detect road and underground diseases.However,it is still a challenging task due to data access anywhere,transmission security and data processing on cloud.Cloud computing can provide scalable and powerful technologies for large-scale storage,processing and dissemination of GPR data.Combined with cloud computing and radar detection technology,it is possible to locate the underground disease quickly and accurately.This paper deploys the framework of a ground disease detection system based on cloud computing and proposes an attention region convolution neural network for object detection in the GPR images.Experimental results of the precision and recall metrics show that the proposed approach is more efficient than traditional objection detection method in ground disease detection of cloud based system.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

Small-current grounding fault location method based on transient main resonance frequency analysis

The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.

Observations on the application of artificial neural network to predicting ground motion measures

Application of the artificial neural network （ANN） to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure.

Deep convolutional neural network for meteorology target detection in airborne weather radar images

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.

Predicting Ground Effects of Omnidirectional Antennas in Wireless Sensor Networks

Omnidirectional antennas are often used for radio frequency (RF) communication in wireless sensor networks (WSNs). Outside noise, electromagnetic interference (EMI), overloaded network traffic, large obstacles (vegetation and buildings), terrain and atmospheric composition, along with climate patterns can degrade signal quality in the form of data packet loss or reduced RF communication range. This paper explores the RF range reduction properties of a particular WSN designed to operate in agricultural crop fields to collect aggregate data composed of subsurface soil moisture and soil temperature. Our study, using simulation, anechoic and field measurements shows that the effect of antenna placement close to the ground (within 10 cm) signi?cantly changes the omnidirectional transmission pattern. We then develop and propose a prediction method that is more precise than current practices of using the Friis and Fresnel equations. Our prediction method takes into account environmental properties for RF communication range based on the height of nodes and gateways.

Walking Stability Control Method for Biped Robot on Uneven Ground Based on Deep Q-Network

A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.

Quantum communication for satellite-to-ground networks with partially entangled states

To realize practical wide-area quantum communication,a satellite-to-ground network with partially entangled states is developed in this paper.For efficiency and security reasons,the existing method of quantum communication in distributed wireless quantum networks with partially entangled states cannot be applied directly to the proposed quantum network.Based on this point,an efficient and secure quantum communication scheme with partially entangled states is presented.In our scheme,the source node performs teleportation only after an end-to-end entangled state has been established by entanglement swapping with partially entangled states.Thus,the security of quantum communication is guaranteed.The destination node recovers the transmitted quantum bit with the help of an auxiliary quantum bit and specially defined unitary matrices.Detailed calculations and simulation analyses show that the probability of successfully transferring a quantum bit in the presented scheme is high.In addition,the auxiliary quantum bit provides a heralded mechanism for successful communication.Based on the critical components that are presented in this article an efficient,secure,and practical wide-area quantum communication can be achieved.

Defected Ground Structure Multiple Input-Output Antenna For Wireless Applications

In this paper,the investigation of a novel compact 2×2,2×1,and 1×1 Ultra-Wide Band(UWB)based Multiple-Input Multiple-Output(MIMO)antenna with Defected Ground Structure(DGS)is employed.The proposed Electromagnetic Radiation Structures(ERS)is composed of multiple radiating elements.These MIMO antennas are designed and analyzed with and without DGS.The feeding is introduced by a microstrip-fed line to significantly moderate the radiating structure’s overall size,which is 60×40×1 mm.The high directivity and divergence characteristics are attained by introducing the microstripfed lines perpendicular to each other.And the projected MIMO antenna structures are compared with others by using parameters like Return Loss(RL),Voltage Standing Wave Ratio(VSWR),Radiation Pattern(RP),radiation efficiency,and directivity.The same MIMO set-up is redesigned with DGS,and the resultant parameters are compared.Finally,the Multiple Input and Multiple Output Radiating Structures with and without DGS are compared for result considerations like RL,VSWR,RP,radiation efficiency,and directivity.This projected antenna displays an omnidirectional RP with moderate gain,which is highly recommended for human healthcare applications.By introducing the defected ground structure in bottom layer the lower cut-off frequencies of 2.3,4.5 and 6.0 GHz are achieved with few biological effects on radio propagation in human body communications.The proposed design covers numerous well-known wireless standards,along with dual-function DGS slots,and it can be easily integrated into Wireless Body Area Networks(WBAN)in medical applications.This WBAN links the autonomous nodes that may be situated either in the clothes,on-body or beneath the skin of a person.This system typically advances the complete human body and the inter-connected nodes through a wireless communication channel.