Dynamic characteristics and optimal design of the manipulator for automatic tool changer

In order to improve the reliability of changing tool for ATC (automatic tool changer), a horizontal tool changer of machining center is chosen as the example to study the dynamic characteristics in the condition of changing a heavy tool. This paper analyzes the structure and properties of the tool changer by simulation and experiment, and the space trajectory equations of the manipulator and tool are derived. The maximum force is calculated in the processing of changing tool. A virtual platform for the automatic tool changer is built to simulate and verify the dynamic performance of the tool changer; the simulation results show an obvious vibration in the process of changing tool, which increases the probability of failure for changing tool. Moreover, in order to find out the device's vibration reasons, a professional experiment platform is built to test the dynamic characteristics. Based on the testing results for a horizontal tool changer, it is known that the unstable vibration is mainly caused by the collision of the tool. Finally, an optimization method for the manipulator is proposed to reduce this vibration and improve the reliability of the tool changer. The final simulation and experiment results show that the optimized manipulator can grasp the heavy tool stably, and the vibration amplitude is significantly reduced in the process of changing tool

An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs.

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multimodel SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control

A linear recursive state of power estimation for fusion model component analysis with constant sampling time.

The state of power of lithium-ion batteries, as the main product of choice for electric and hybrid electric vehicle energy storage systems, is one of the precise feedback control parameters for the battery management system. The proposed research establishes a method for the analysis of charging and discharging constitutive factors under the sampling time, realizes the online identification of parameters by building an adaptive forgetting factor recursive least-squares method based on the Thevenin model, and uses the online parameters to achieve an effective characterization of the power state under voltage and current limitations. The results demonstrate that the accuracy error of online parameter identification is less than 0.03 V. Combining the analysis of charging and discharging constitutive factors under-sampling time with the fusion model of voltage and current limitation makes the power state estimation more reliable and accurate. The results demonstrate that the power state estimation error in the discharging state is less than 8%

Text-oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences

Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from text, visual, and acoustic modalities. Previous works have focused on representation learning and feature fusion strategies. However, most of these efforts ignored the disparity in the semantic richness of different modalities and treated each modality in the same manner. That may lead to strong modalities being neglected and weak modalities being overvalued. Motivated by these observations, we propose a Text-oriented Modality Reinforcement Network (TMRN), which focuses on the dominance of the text modality in MSA. More specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to make full interaction for text/acoustic and text/visual pairs, and a Text-Gated Self-Attention (TGSA) module to guide the self-reinforcement of the other two modalities. Furthermore, we present an adaptive fusion mechanism to decide the proportion of different modalities involved in the fusion process. Finally, we combine the feature matrices into vectors to get the final representation for the downstream tasks. Experimental results show that our TMRN outperforms the state-of-the-art methods on two MSA benchmarks.Comment: Accepted by CICAI 2023 (Finalist of Best Student Paper Award

Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries.

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system

A novel battery state of charge estimation based on the joint unscented kalman filter and support vector machine algorithms.

With the development of new energy sources becoming the mainstream of energy development strategies, the role of electric vehicle-powered lithium-ion batteries in the field of automobile transportation is becoming more and more obvious. An efficient the Battery Management System is necessary for the real-time usage monitor of each battery cell, which analyzes the battery status to ensure its safe operation. A complex equivalent circuit model is proposed and established. The Improved Equivalent Circuit Model is used to realize the precise mathematical expression of the power lithiumion battery packs under special conditions. The State of Charge estimation method which is based on Unscented Kalman Filter has a good filtering effect on the nonlinear systems. Based on the State of Charge estimation of Support Vector Machine, the samples in the nonlinear space of lithium-ion battery are mapped to the linear space. It can be seen from the experimental analysis that a joint Unscented Kalman Filter and Support Vector Machine algorithms for State of Charge estimation has higher accuracy. The experimental results show that the tracking error is less than 1.00%

Hurricane-induced destratification and restratification in a partially-mixed estuary

Hurricane Isabel made landfall at the Outer Banks of North Carolina and moved past Chesapeake Bay on 18 –19 September 2003. The baroclinic response of this partially-mixed estuary to the passage of Isabel is investigated using the output from a regional atmosphere-ocean model. The hurricane-forced winds caused gradual deepening of the surface mixed layer, followed by rapid destratification in the water-column. The mixed-layer deepening appears to be driven by velocity shear and can be interpreted by a gradient Richardson number. Although strong winds caused complete mixing locally, a large longitudinal salinity gradient of about 10-4 psu m-1 persisted between the estuary\u27s head and mouth. After passage of the storm, the horizontal baroclinic pressure gradient drove restratification and a two-layer circulation in the estuary. The averaged buoyancy frequency increased linearly with time during an initial stage, and reached about 0.03 s-1 one day after the destratification. The model results are in good agreement with the theoretical prediction based on gravitational adjustment. Subsequently, turbulent diffusion works against the longitudinal advection to produce quasi-steady salinity distribution

A novel adaptive particle swarm optimization algorithm based high precision parameter identification and state estimation of lithium-ion battery.

Lithium-ion batteries are widely used in new energy vehicles, energy storage systems, aerospace and other fields because of their high energy density, long cycle life and high-cost performance. Accurate equivalent modeling, adaptive internal state characterization and accurate state of charge estimation are the cornerstones of expanding the application market of lithium-ion batteries. According to the highly nonlinear operating characteristics of lithium-ion batteries, the Thevenin equivalent model is used to characterize the operating characteristics of lithium-ion batteries, particle swarm optimization algorithm is used to process the measured data, and adaptive optimization strategy is added to improve the global search ability of particles, and the parameters of the model are identified innovatively. Combined with extended Kalman algorithm and Sage-Husa filtering algorithm, the state-of-charge estimation model of lithium ion battery is constructed. Aiming at the influence of fixed and inaccurate noise initial value in traditional Kalman filtering algorithm on SOC estimation results, Sage-Husa algorithm is used to adaptively correct system noise. The experimental results under HPPC condition show that the maximum error of the model is less than 1.5%. Simulation results of SOC estimation algorithm under two different operating conditions show that the maximum estimation error of adaptive extended Kalman algorithm is less than 0.05, which realizes high-precision lithium battery model parameter identification and high-precision state-of-charge estimation