Load Information Research Articles

Effects of Controlling Assist Robots to Follow Lumbar Load on Muscle Fatigue

ABSTRACTWe developed an assist robot that changes its assist force in real time according to the lumbar load estimated from the load information on the hand measured using a hand sensor device and the posture information. Furthermore, using the developed assist robot, the effect of the load‐following control on muscle fatigue was verified. The load was set at 6 and 1 kgf, and the amount of muscle activity in the lumbar region was measured using a muscle potential sensor during continuous flexion–extension exercises. The central frequency of the power spectrum was calculated as muscle fatigue, and its time trend was obtained. For comparison, similar experiments were also conducted without an assistive robot and with an existing assistive robot. Consequently, when load‐following control was used, muscle fatigue was reduced compared with existing assist robots in which the assist force was excessive in relation to the load. This shows the importance of load‐following control that is necessary to control the assist force according to the load when the assist robot is worn during work in which the load changes in a complex manner.

Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

Multiscale Spatio-Temporal Feature Fusion Based Non-Intrusive Appliance Load Monitoring for Multiple Industrial Industries

The appliance types and power consumption patterns vary greatly across different industries. This can lead to unstable identification results of traditional appliance load monitoring methods in different industries. A non-intrusive appliance load monitoring (NIALM) method for multiple industries based on multiscale spatio-temporal feature fusion has been proposed. Firstly, the ConvNeXt Block with efficient channel attention has strong feature extraction capability. Spatial features of appliance state changes and micro-variations generated during operation can be extracted from mixed industrial load information by it. Meanwhile, the bidirectional gated recurrent neural network is used to learn the bidirectional dependencies of the load data, obtaining temporal features. Then, the multi-scale feature extraction module is used to extract temporal and spatial features from different depths of the network layers. And the extracted multi-scale temporal and spatial features are fully integrated. Finally, the proposed model is optimized using the Stochastic Weight Averaging method. During the training process, a certain number of model weights are randomly averaged, which can improve the model's generalization ability and identification accuracy. The experiment was conducted on six different industries. The evaluation indexes such as accuracy, F1 score, and Wasserstein distance are also used to verify the effectiveness and superiority of the method.

An energy management method for combined load forecasting of fuel cell ships

Abstract The article proposes an energy management method (EMM) for fuel cell ships, which consists of two parts: load forecasting (LF) and real-time optimized scheduling (RTOS). LF is composed of Long Short-Term Memory neural networks, which are capable of being trained to predict future loads based on historical ship load information. RTOS consists of model predictive control and can allocate energy in real time. Initially, shiploads and battery status are collected in real time, and then LF passes the predicted load sequence to RTOS. RTOS optimizes real-time energy allocation based on the predicted load and the current operating status of two sets of batteries, while meeting system constraints and maintaining the battery SOC at a healthy level, to minimize operational cost consumption. The simulations demonstrate that the EMM can consistently maintain SOC at a healthy level and reduce fuel cell ship operating costs to some extent, thereby improving economic efficiency.

A short-term load forecasting method based on QRLSTM considering multiple factors analysis

Abstract Complex characteristics of power loads terribly affect the safety of the power grid. Thus, accurate short-term load forecasting is regarded as a solution to mitigate the effect of load complications. In this regard, this work is putting forward a Long Short-Term Memory Network Quantile Regression (LSTMQR) for power load, obtaining the probability density function (PDF). Firstly, the quantile of future power load was obtained using the LSTMQR model. The conditional quantile obtained by the LSTMQR model is used as the input of the Kernel Density Estimation model (KDE) to predict the future probability density distribution of short-term power load at different prediction times. Furthermore, an assessment method is proposed to quantify the effect of different factors on power load to improve prediction accuracy. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness of the proposed forecasting algorithm, indicating the proposed forecasting algorithm can effectively evaluate the relationship between different factors and power load, and probability density prediction can accurately quantify the uncertainty of load information. The proposed algorithm results in a reduction of 37.6% in forecast error compared to the traditional forecasting algorithm.

Ability to Select AAC Graphic Symbols by Severity of Alzheimer’s Disease and Number of Symbols

Purpose: This study aimed to examine the graphic-symbol selection ability of patients with dementia of Alzheimer's type (DAT) based on the severity of dementia by conducting symbol identification and symbol discrimination tasks with increasing numbers of symbols. These tasks were performed with both normal elderly individuals and DAT patients to explore how cognitive load affects them differently.Methods: A total of 21 normal elderly individuals, 17 mild DAT patients, and 20 moderate to severe DAT patients were assessed. They performed symbol identification and symbol discrimination tasks using the Augmentative and Alternative Communication Symbol Assessment Tool. The number of symbols increased in six steps, from 4 to 72. Accuracy and response time were measured for statistical analysis.Results: In the symbol identification task, there was no significant difference in accuracy among the three groups. However, normal group showed significantly faster responses compared to moderate to severe DAT group. In the symbol discrimination task, normal group showed higher accuracy and significantly faster responses than moderate to severe DAT group. As the number of symbols increased, response times increased across all groups, with more time required for the symbol discrimination task.Conclusion: It was confirmed that the greater the severity of DAT, the more cognitive load and impaired information processing were reflected in symbol selection. Findings suggest that response time is a more sensitive indicator of cognitive decline in DAT patients and highlight the need to adjust the number of symbols and presentation methods according to the severity of the patient’s condition in dementia interventions.

Development and Validation of Asphalt Pavement Rutting Prediction Model with Transient Temperature Field Considered

This study developed a transient rutting analysis program based on the principles of heat transfer and the viscoelastic–viscoplastic theory of asphalt mixtures. Using the finite element model (FEM) model corresponding to the multi-layer pavement structure and the subprogram interface of ABAQUS software, along with Python programming, the program was designed to estimate rutting under transient temperature field conditions with climate conditions, traffic flow, and structure layer constitutive parameters of pavement considered. Subsequently, the program was used to validate the efficiency of the solution and analyze the influencing factors of internal temperature field and rutting depth within the asphalt pavement structure. Results showed that, based on meteorological data and recommended values of material thermal properties, the FEM of transient temperature field can accurately simulate the temperature field distribution of pavement structure. Based on loading information and dynamic modulus test, the transient temperature variable rutting with FEM proposed in this paper can simulate and calculate the rutting of pavement structure layer with some errors for different pavement conditions from 5% to 40%. Through parametric analysis of the influence factors of temperature field and rutting, the modified transient temperature field rutting prediction program can effectively simulate rutting of pavement structure, and the minor errors were less than 20%.

Dynamic strain distribution monitoring and fatigue damage analysis on a 100 kW wind turbine blade based on field aerodynamic measurements

Abstract Operation and maintenance (O&M) costs can account for 10%–20% of the total costs of electricity for a wind project. Structural health monitoring method is a form of preventive maintenance consisting of regular monitoring of the wind turbine components to detect potential faults. The strain measurements on the blades are typically used for load monitoring and damage detection, but only obtain a small amount of concentrated load information. In this work, a strain distribution monitoring method was proposed and validated based on aerodynamic measurements in the field. Then the contribution and evolution of the strain distribution from the aerodynamic load, gravity load, inertial load, and centrifugal load in the complex start-up process of the wind turbine were analyzed. The results shows that the mean value of the synthetic strain is mainly determined by the aerodynamic load and the centrifugal load, while the fluctuation amplitude is mainly determined by the gravity and the aerodynamic load. Furthermore, the fatigue damage of the blade root was evaluated based on strain extrapolation, rainflow cycle-counting algorithm, Goodman diagram and Miner’s linear superposition principle. It is found that the fatigue damage is generally greatest near the pressure side and suction side, and least near the leading edge and trailing edge. The strain distribution monitoring method can capture the location of maximum stress and the most severe fatigue damage on the blade, which are helpful for damage detection and load control, and further reduces O&M costs.

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

A distributed load balancing architecture based on in‐band network telemetry

AbstractA data center (DC) is supposed to efficiently distribute the bandwidth of the network to provide high‐quality traffic transmission. However, the load imbalance issue can easily occur due to the complex topology and traffic features. Equal‐Cost Multi‐Path(ECMP) distributes traffic on different paths but doesn't consider network congestion. Although HULA solved some of ECMP's problems, it can easily congest the best path. RPS randomly distributes packets across multiple paths, potentially causing packet reordering in certain scenarios. This paper presents DHLB, a distributed hop‐by‐hop load balancing architecture based on in‐band network telemetry. With active In‐band network telemetry, DHLB gathers essential load information and systematically records it in a load information table. DHLB distributes traffic proportionally on different paths based on their load degree. We build a fat tree topology on mininet to verify the performance of our design. Experimental results indicate that DHLB outperforms other schemes regarding average Flow Completion Time (FCT). It also performs better on additional overhead than another probe‐based scheme.

A novel semi-convex function for simultaneous identification of moving vehicle loads and bridge damage

For the simultaneous identification (SI) problem on the moving vehicle loads and bridge damage, the existing methods fail to appropriately consider the sparse characteristics of structural damage and ignore the prior information of vehicle loads, which make them suffering from challenges such as prolonged computation cost, poor noise robustness, and limited identification accuracy. To address this issue, a novel semi-convex function is proposed to construct a theoretical framework for the SI problem using the bridge dynamic responses subjected to moving vehicles in this study. Firstly, the bridge damage-induced virtual forces are conceptualized as a new form of moving residual forces characterized by block sparsity properties, and the damage location is then determined by the frequency characteristics with the largest fundamental frequency amplitude of moving residual forces. Secondly, a semi-convex function model encompassing moving forces and moving residual forces is defined. Combined with sparse regularization strategy, an improved alternating direction method of multipliers algorithm is proposed and applied to simultaneously solve for both moving vehicle loads and damage degrees. To assess the effectiveness and feasibility of the proposed method, extensive numerical validations are conducted in various bridge damage scenarios. Additionally, a series of truss bridge experiments are performed in laboratory under multiple damage and vehicle axle-weight conditions. The results show that compared with the existing outstanding methods, the proposed semi-convex function method can significantly reduce the identification cost, enhance robustness to noise, and demonstrate notable improvements in accuracy for the SI problem, which provides an innovative and more efficient solution to the SI problem in bridge engineering.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

Identifying key factors influencing import container dwell time using eXplainable Artificial Intelligence

In a container terminal, the length of time that containers remain in the yard, known as Container Dwell Time (CDT), is considered one of the significant operational indicators due to its direct correlation with terminal productivity and efficiency. However, due to complex processing procedure and the involvement of various logistics stakeholders, CDT is subject to high uncertainty, making it more difficult for the terminal to manage. To address this issue, this paper presents a comprehensive framework to identify the Key Factors (KFs) influencing prolongation of CDT for import containers. In order to elucidate abnormal cases from dataset which contains yard loading information, the Process Mining (PM) method is utilized. Subsequently, XAI has been utilized to identify the KFs of import CDT. To reflect reality as closely as possible, we collected event data from a container terminal in Busan, Korea. Based on experiments, the KFs thus identified were: 1) Temperature, 2) Weight of container, 3) Voyage number of container 4) Block, 5) Shipping company, and 6) Month of discharging. To conclude, we formulated domain knowledge-based interpretations of the six most influential KFs.

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Improve operational flexibility of distribution systems using transportable resources

With the continuous progress of carbon neutrality, the accelerating integration of distributed renewable energy sources (RES) poses tremendous pressure on distribution systems' safe and stable operation. The distribution systems that cannot be upgraded in time, e.g., those in border areas, are hard to adapt to the large-scale integration of RES and the growing energy demand, facing a severe lack of operational flexibility. In light of this, the integrated mobile energy station (IMES), which consists of a battery system and photovoltaic cells, is used to improve operational flexibility and alleviate the need for upgrades. An adaptive robust allocation and dispatch model is introduced to coordinate the operation of the distribution system and IMES under multiple uncertainties. The first stage of the proposed model optimizes the locations of IMES using the forecast information of RES and loads, while the second stage optimizes the dispatch plan that is adaptive to the realization of uncertainties. Since the uncertain photovoltaic power in the IMES depends on its deployment location, the proposed model is an adaptive robust optimization with decision-dependent uncertainty, bringing great challenges to the solution. To solve it efficiently, a warmstart-enhanced column-and-constraint generation algorithm is introduced, in which the bi-level subproblem is solved by the dual method and outer approximation algorithm. Finally, case studies demonstrate the effectiveness of the proposed method. The participation of six IMESs can save 5.23 % on the system's total cost and avoid the load shedding caused by transmission congestion, indicating IMES's potential for economical and flexible operation of the system.

A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder

Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide secure operation in complex and changing network environments. To avoid information loss caused when handling traffic data while improving the detection performance of traffic feature information, this paper proposes a multi-information fusion model based on a convolutional neural network and AutoEncoder. The model uses a convolutional neural network to extract features directly from the raw traffic data, and a AutoEncoder to encode the statistical features extracted from the raw traffic data, which are used to supplement the information loss due to cropping. These two features are combined to form a new integrated feature for network traffic, which has the load information from the original traffic data and the global information of the original traffic data obtained from the statistical features, thus providing a complete representation of the information contained in the network traffic and improving the detection performance of the model. The experiments show that the classification accuracy of network traffic anomaly detection using this model outperforms that of classical machine learning methods.

Monitoring of the preload in bolted connections with piezoelectric wafer active sensors

Piezoelectric wafer active sensors are very sensitive to changes of loads in structures, which makes them useful for the load monitoring. Unfortunately, these transducers are also sensitive to environmental effects such as a change of the ambient temperature. These effects can mask the information about the load in the recorded measurement data of the piezoelectric wafer active sensors. Therefore, the effect of the load needs to be distinguished from these masking effects of the environmental influences in order to avoid misleading information about the occurring loads of the monitoring system. In this work, two monitoring procedures, which are applicable with piezoelectric wafer active sensors, shall be applied in order to have two different sources of information and be able to distinguish between the load information and the temperature influence. One approach is based on electro-mechanical impedance measurements and the other one is based on acousto-ultrasonics. The strategy of combining these two information shall be used to monitor the preload in bolted joints. In an experimental setup, piezoelectric wafer active sensors have been attached to the bolt’s head and the face side of its shaft. Then, different preload stages were set. At every load stage, acousto-ultrasonics measurements and also electro-mechanical impedance measurements were carried out. First experiments show very promising results.

A contact sensor-free framework for ground reaction force observation in heavy-legged robots considering unknown loads

As a direct response to robot movements and load distributions, the ground reaction force (GRF) is pivotal for heavy-legged robot (HLR) applications. This study presents a technical framework for GRF monitoring in an electric cylinder-driven HLR, eliminating the need for information on body motion, load distributions, and measured servo outputs. Traditional joint space-based dynamics are extended to include servo currents, compensating for the influence of unknown servo outputs. An essential contribution is incorporating the impact of floating bases and unknown loads into a virtual spatial force (VSF) applied to the HLR hip joint. The VSF is obtained through the nonlinear disturbance observer when the HLR is in a stable contact phase. Subsequently, a high-order GRF observer (HOGO), compensated with VSF, enables GRF observations without the pre-acquired body movement and load distribution data. In contrast to conventional GRF observations, the proposed framework could determine virtual payloads acting on the hip joint while ensuring precise GRF monitoring without requiring supplementary sensors. The GRF observations of the proposed framework are experimentally superior to those of the conventional methods with unknown HLR body motion and load information.

Research on Load Estimation for Commercial Trucks Based on Air Suspension State Parameters

For commercial trucks, accurate vehicle load information is crucial to preventing overloading and uneven load distribution, thereby improving transportation efficiency and reducing transportation costs. Existing research on vehicle load estimation primarily focuses on tires, axles, frames, and leaf springs, with limited studies on load estimation for vehicles equipped with air suspension. This paper presents the design of a vehicle onboard weighing system for civil commercial trucks based on the state parameters of air springs and proposes a matching method for vehicle load estimation. The system utilizes laser distance- and gas pressure sensors to collect the state parameters of the air springs. It estimates the vehicle load and displays the load information in real time on the onboard display. The vehicle load estimation method was explored through prototype testing and theoretical derivation. A fitting model was constructed using surface fitting techniques to establish the relationship between the effective contact area and the height variation and capsule pressure of the air spring. Subsequently, a load prediction model was built based on the force equilibrium equation of the air springs. Finally, the construction and accuracy of the load prediction model for a specific type of diaphragm air spring were verified using finite element analysis. The results demonstrated that the average relative error of the load prediction model could be controlled within 1%, meeting the high accuracy requirements of the vehicle weighing system.

Studying the Structure of the Road Pavement and Specifying the Repair Method

Studying the structural condition of road pavement is essential for making effective decisions about the type of maintenance or rehabilitation that will be carried out on a pavement section. Reliable quantitative assessment of the volume of necessary repairs will ensure the performance characteristics and durability throughout the entire service life at minimal cost. This article presents detailed results from Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) studies. In addition, an analysis of these results and recommendations for strengthening the road pavement for the Sh-176 “Sighnaghi-Saint Nino Monastery” road to develop rehabilitation and maintenance scenarios. FWD results combined with road pavement layer thickness and traffic load information to determine residual service life and necessary treatment type. Thus, using the nonlinear theory of layered halfspace elasticity, pavement structure analyzed in the same way as other civil engineering structures.