Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Evaluation of the dynamic sealing performance of cap rocks of underground gas storage under multi-cycle alternating loads

The static sealing of underground gas storage(UGS),including the integrity of cap rocks and the stability of faults,is analyzed from a macro perspective using a comprehensive geological evaluation method.Changes in pore structure,permeability,and mechanical strength of cap rocks under cyclic loads may impact the rock sealing integrity during the injection and recovery phases of UGS.In this work,the mechanical deformation and failure tests of rocks,as well as rock damage tests under alternating loads,are conducted to analyze the changes in the strength and permeability of rocks under multiple-cycle intense injection and recovery of UGS.Additionally,this study proposes an evaluation method for the dynamic sealing performance of UGS cap rocks under multi-cycle alternating loads.The findings suggest that the failure strength(70%)can be used as the critical value for rock failure,thus providing theoretical support for determining the upper limit of operating pressure and the number of injection-recovery cycles for the safe operation of a UGS system.

Experimental study of hollow rectangular bridge column performance under vertical and cyclically bilateral loads

To investigate the seismic performance of hollow reinforced concrete （RC） bridge columns of rectangular cross section under constant axial load and cyclically biaxial bending, five specimens were tested. A parametric study is carried out for different axial load ratios, longitudinal reinforcement ratios and lateral reinforcement ratios. The experimental results showed that all tested specimens failed in the flexural failure mode and their ultimate performance was dominated by flexural capacity, which is represented by the rupture/buckling of tensile longitudinal rebars at the bottom of the bridge columns. Biaxial force and displacement hysteresis loops showed significant stiffness and strength degradations, and the pinching effect and coupling interaction effect of both directions severely decrease the structural seismic resistance. However, the measured ductility coefficient varying from 3.5 to 5.7 and the equivalent viscous damping ratio varying from 0.19 and 0.26 can meet the requirements of the seismic design. The hollow RC rectangular bridge columns with configurations of lateral reinforcement in this study have excellent performance under bidirectional earthquake excitations, and may be considered as a substitute for current hollow RC rectangular section configurations described in the Guideline for Seismic Design of Highway Bridges （JTG/T B02-01-2008）. The length of the plastic hinge region was found to approach one sixth of the hollow RC rectangular bridge column height for all specimen columns, and it was much less than those specified in the current JTG/T. Thus, the length of the plastic hinge region is more concentrated for RC rectangular hollow bridge columns.

Bending performance of TRC-strengthened RC beams with secondary load under chloride erosion

Textile reinforced concrete(TRC)has good bearing capacity,crack resistance and corrosion resistance and it is suitable for repairing and reinforcing concrete structures in harsh marine environments.The four-point bending method was used to analyze the influence of the salt concentration,the damage degree and the coupled effect of the environment and load on the bending performance of TRC-strengthened beams with a secondary load.The results showed that as the salt concentration increased,the crack width and mid-span deflection of the beam quickly increased,and its bearing capacity decreased.As the damage degree increased,the early-stage crack development and mid-span deflection of the beam were less affected and the ultimate bearing capacity significantly decreased.In addition,the coupled effect of the environment and load on the beams with a secondary load was significant.As the sustained load increased,the ultimate bearing capacity of the strengthened beam decreased,and cracks developed faster in the later stage.In addition,the mid-span deflection of the beam decreased at the same load level because of the influence of the initial deflection due to the sustained load corrosion.

Improving MapReduce Performance by Balancing Skewed Loads

MapReduce has emerged as a popular computing model used in datacenters to process large amount of datasets.In the map phase,hash partitioning is employed to distribute data that sharing the same key across data center-scale cluster nodes.However,we observe that this approach can lead to uneven data distribution,which can result in skewed loads among reduce tasks,thus hamper performance of MapReduce systems.Moreover,worker nodes in MapReduce systems may differ in computing capability due to(1) multiple generations of hardware in non-virtualized data centers,or(2) co-location of virtual machines in virtualized data centers.The heterogeneity among cluster nodes exacerbates the negative effects of uneven data distribution.To improve MapReduce performance in heterogeneous clusters,we propose a novel load balancing approach in the reduce phase.This approach consists of two components:(1) performance prediction for reducers that run on heterogeneous nodes based on support vector machines models,and(2) heterogeneity-aware partitioning(HAP),which balances skewed data for reduce tasks.We implement this approach as a plug-in in current MapReduce system.Experimental results demonstrate that our proposed approach distributes work evenly among reduce tasks,and improves MapReduce performance with little overhead.

Transformer-based correction scheme for short-term bus load prediction in holidays

To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.

Explosion resistance performance of reinforced concrete box girder coated with polyurea:Model test and numerical simulation

To study the anti-explosion protection effect of polyurea coating on reinforced concrete box girder,two segmental girder specimens were made at a scale of 1:3,numbered as G(without polyurea coating)and PCG(with polyurea coating).The failure characteristics and dynamic responses of the specimens were compared through conducting explosion tests.The reliability of the numerical simulation using LS-DYNA software was verified by the test results.The effects of different scaled distances,reinforcement ratios,concrete strengths,coating thicknesses and ranges of polyurea were studied.The results show that the polyurea coating can effectively enhance the anti-explosion performance of the girder.The top plate of middle chamber in specimen G forms an elliptical penetrating hole,while that in specimen PCG only shows a very slight local dent.The peak vertical displacement and residual displacement of PCG decrease by 74.8% and 73.7%,respectively,compared with those of specimen G.For the TNT explosion with small equivalent,the polyurea coating has a more significant protective effect on reducing the size of fracture.With the increase of TNT equivalent,the protective effect of polyurea on reducing girder displacement becomes more significant.The optimal reinforcement ratio,concrete strength,thickness and range of polyurea coating were also drawn.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

Main properties of ultra-high performance fiber reinforced cement composites under couple effect of load and environment

This study aims to reveal the mechanism that how the content of steel fibers and strength grades affect the macro performance of the ultra-high performance fiber reinforced cementitious composite （UHPFRCC） and to study the UHPFRCC durability under the combined effect of loads and environments. Three types of high and ultra-high performance fiber reinforced cement composites with different strength grades （100, 150, 200 MPa） and different steel fiber volume fractions （0%, 1%, 2%, 3%） are prepared. The main properties of mechanical performance and short-term durability are studied. A preloading frame is designed to apply a four- point load external flexural stress with a stress selection ratio of 0.5 for UHPFRCC150 specimens. The results show that the growth in strength grade with a proper content of steel fiber greatly increases the strength and toughness of the HPFRCC and the UHPFRCC while decreasing the dry-shrinkage ratio. For the loaded specimens, the existence of steel fiber can reduce the negative influence of tensile stress on the Cl- penetration resistance of the UHPFRCC in addition to improving its ability to resist the freeze-thaw damage.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

Load Dependent Series-Parallel Systems with Common Bus Performance Sharing Mechanism

A series-parallel system was proposed with common bus performance sharing in which the performance and failure rate of the element depended on the load it was carrying. In such a system,the surplus performance of a sub-system can be transmitted to other deficient sub-systems. The transmission capacity of the common bus performance sharing mechanism is a random variable. Effects of load on element performance and failure rate were considered in this paper. A reliability evaluation algorithm based on the universal generating function technique was suggested. Numerical experiments were conducted to illustrate the algorithm.

Enhanced Loading of ^(40)K from Natural Abundance Potassium Source with a High Performance 2D^+ MOT

40K is one of the most important atomic species for ultra-cold atomic physics. Due to the extremely low con- centration （0.012%） of 40K in natural abundance of potassium, most experiments use 4-10% enriched potassium source, which have greatly suffered from the extremely low annual production and significant price hikes in recent years. Using naturally abundant potassium source, we capture 5.4 × 10 6 cold 40K atoms with the help of a high performance of two-dimensional magneto-optical trap （2D＋ MOT）, which is almost three orders of magnitude greater than previous results without the 2D＋ MOT. The number of the 40K atoms is sufficient for most ultra-cold 40K experiments, and our approach provides an ideal alternative for the field.

Correlation Study of Operational Data and System Performance of District Cooling System with Ice Storage

The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R2) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .

LOW-COST HIGH PERFORMANCE CLUSTER OF WORK-STATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL DEPTH-FIRST SEARCH(DFS)

This paper presented an idea to replace the traditionally expensive parallel machines by heterogeneous cluster of workstations. To emphasise the usability of cluster of workstations platform for parallel and distributed computing, also the paper presented the status report on the effort and experiences for the implementation of a dynamic load balancing for parallel tree computation depth first search(DFS) on the cluster of a workstations project. It compared the speedup performance obtained from our platform with that obtained from the traditional one. The speedup results show that cluster of workstations can be a serious alternative to the expensive parallel machines.

Performance Evaluation and Verification on Load Relief for LM-8 Launch Vehicle

LM-8 inherited mature modules from other launch vehicles,adapting the overall design through the combination of engine throttling,wind compensation and load relief control,so as to reduce the aerodynamic load during flight.This paper proposes a rapid evaluation method for load relief performance,which takes launch vehicle’s characteristics,wind field,control parameters,aerodynamic deviation and structure deviation into consideration,and provides a quick estimation method for load relief performance.The load relief performance of the LM-8 launch vehicle estimated by this method achieved 10%-20%in pitch and 20%-40%in yaw.The specific results are related to parameter deviation,the altitude of wind shear and the relationship between wind direction and trajectory angle.Simulation results for a typical case with six degrees of dynamic freedom of flight proves that the comprehensive load relief performance would be between 14%to 34%,which is in line with the aforementioned method.In addition,simulation results indicate that the more severe the wind shear is,the better performance of load relief can be achieved.

Performance Analysis of Unbalance Radial Feeder with Time Varying Composite Load

The focus of this paper is to present performance indices for unbalance radial feeder having different characteristic and composition of time varying static ZIP load models. These provide a framework for benchmarking of distribution automation projects. 15 minutes characteristics time interval for load flow and load modeling are considered to meet smart grid implementation criterion. A forward-backward sweep method is employed for load flow solution. Developed performance indices were illustrated on modified IEEE 37 node test feeder. Performance indices are useful for analysis, operational, planning and integration of stochastic renewable sources.

Dynamic-Distributed Load Balancing for Highly-Performance and Responsiveness Distributed-GIS (D-GIS)

In recent years, several results have been introduced to enhance distributed GIS performance. While much more efforts have focused on tile map and simple symbologies on dynamic map, load balancing GIS servers have not been addressed by the GIS community so far. This paper, therefore, proposed dynamic distributed load balancing for D-GIS in order to quickly render information to client interface by involving a set of GIS servers which process clients’ requests depending of an algorithm. In the model, several concepts were introduced and defined: Virtual Server within physical machine which constitutes a setup environment for a single GIS server, Load Hash Table which contains information about virtual server’s capacity, real-time load and other mandatory elements, Request Split Table which splits requests depending of the input area’s Quantity of Information and stores request tasks composition for later reconstitution. At last we have Distributed Failover Callback Function Table level one (respectively level two) which determines whether or not the request had been successfully processed by the chosen virtual server (respectively physical machine). This table allows sending back the same request to another virtual server (respectively physical node). Two load handlers (primary and secondary) are defined in case of failure. Our Model achieves efficient load balancing by: providing efficient node selection;optimizing request routing;managing node failover;involving client’s request partitioning and introducing method type decomposition. A simulation of the algorithm shows a low response time when performing GIS operations.

Off-Design Performance of Gas Turbine Power Units with Alternative Load-Control Strategies

Gas turbine power units,as an effective way to cope with the severe challenge of renewable energy accommodation in power grids,arouse the interest of power enterprises in the deep peak-load regulation performance.Two common alternative load-control strategies including constant turbine inlet temperature(TIT)and constant turbine exhaust temperature(TET)regulations were taken into consideration.To comparatively investigate the part-load performance under these strategies,both mathematical and physical models were set up successively to serve as a validation and complementary to each other.For the mathematical model of compressor with inlet guide vane(IGV),combustor and turbine,stage-stacking method based on blade average geometric parameter,energy conservation and turbine stage model were adopted respectively.For the physical model,design and off-design analysis were carried out based on GT PRO and THERMOFLEX respectively.The simulation result of mathematical model validated the reliability of the physical model.Based on this,the influence of ambient temperature and different load-regulating strategies on the off-design performance of gas turbine power units was studied in THERMOFLEX.The results in the case of a PG 9351FA gas turbine show that the ambient temperature has a great impact on system performance,i.e.,every 5℃ increase in the ambient temperature produces a reduction of 3.7%in the relative full-load output and 1.1%in the relative efficiency respectively;when the gas turbine operates under constant TIT strategy,TET starts to rise till it reaches the maximum allowable value with the load rate decreasing,and IGV keeps at the minimum angle with both TIT and TET decreasing when the load rate is lower than 65%;when the gas turbine operates under constant TET strategy,TIT drops slightly at load rate of above 60%while both TIT and TET evidently decrease below 60%load rate operating along the constant corrected speed line at the minimum allowable IGV opening;gas turbine effi-ciency is greatly affected by load rate and the performance degradation is more obvious especially in lower load rate regions;constant TET strategy is superior in the operating efficiency to constant TIT strategy under part-load conditions.

Probabilistic Load Flow Algorithm with the Power Performance of Double-Fed Induction Generators

Probabilistic load flow(PLF)algorithm has been regained attention,because the large-scale wind power integration into the grid has increased the uncertainty of the stable and safe operation of the power system.The PLF algorithm is improved with introducing the power performance of double-fed induction generators(DFIGs)for wind turbines(WTs)under the constant power factor control and the constant voltage control in this paper.Firstly,the conventional Jacobian matrix of the alternating current(AC)load flow model is modified,and the probability distributions of the active and reactive powers of the DFIGs are derived by combining the power performance of the DFIGs and the Weibull distribution of wind speed.Then,the cumulants of the state variables in power grid are obtained by improved PLF model and more accurate power probability distributions.In order to generate the probability density function(PDF)of the nodal voltage,Gram-Charlier,Edgeworth and Cornish-Fisher expansions based on the cumulants are applied.Finally,the effectiveness and accuracy of the improved PLF algorithm is demonstrated in the IEEE 14-RTS system with wind power integration,compared with the results of Monte Carlo(MC)simulation using deterministic load flow calculation.

Investigation of Excavator Performance Factors in an Open-Pit Mine Using Loading Cycle Time

This study presents the effect of excavator model, loading operation location, shift availability and truck-shovel combination on loading cycle time and productivity of an open-pit mine. The loading cycle time was used to assess the material loading system performance which is one of the key components of the total cycle time for material transportation in an open-pit mine. Loading is among the components of cycle time during which material is being handled. The data analyzed?was?collected from a computerized dispatch system at GGM from which 62,000 loading dispatches per month involving several shifts, 14 excavators and 49 trucks were loaded. About 4465 dispatches per excavator and 1276 dispatches per truck were assessed using loading cycle time data for each dispatch for a period of four months (between August and December). Under fixed tonnage loaded and waste type (33 t of non-acid forming waste rock),?it was observed that loading cycle time depends on excavator model, location and truck being loaded. Average cycle times, PDFS?and CDFS of loading cycle time series were used to identify differences in performance under different situations. It was concluded that shift availability for excavators, loading location, excavator model and truck-shovel combinations strongly affect the productivity during loading process in an open-pit mine.