The emergence of energy storage system (ESS) enables the service provider to profit from the price difference between purchasing electric energy from utility companies and selling it to customers through battery operations while more frequent charging/discharging behaviors cause battery degradation. However, the accurate estimation of the ESS degradation cost is one of the main obstacles for ESS participating in energy management. This paper considers a smart grid scenario, consisting of an accurate lithium-ion battery degradation (ALBD) model, renewable energy generators and bilateral energy flow from/to the utility grid. We develop an online energy-scheduling approach aiming to maximize the operating profit of the system from energy trading and guarantee the long-term battery usage. We formulate the trade-off between energy trading profit and battery degradation cost as a joint optimization problem, and further transform it into a Markov Decision Process (MDP) problem. To solve the uncertainty of electricity price and demand, we apply a gated recurrent unit to forecast the next day’s price and demand. Then, we develop a deep Q-network to learn an optimized energy-scheduling strategy. The numerical simulations show that our proposed approach can improve the operating profit from 6.04% to 20.54% compared with the three baselines.

Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird’s-in-eye view. Such indexing is instance-level and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4% in navigation accuracy. Code and demo are released at https://ivlmap.github.io/.

Deep learning based virtual try-on system has achieved some encouraging progress recently, but there still remain several big challenges that need to be solved, such as trying on arbitrary clothes of all types, trying on the clothes from one category to another and generating image-realistic results with few artifacts. To handle this issue, we in this paper first collect a new dataset with all types of clothes, i.e., tops, bottoms, and whole clothes, each one has multiple categories with rich information of clothing characteristics such as patterns, logos, and other details. Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach includes three modules: 1) Limbs Prediction Module, which is utilized for predicting the human body parts by preserving the characteristics of the reference person. This is especially good for handling cross-category try-on task (e.g., long sleeves ↔ short sleeves or long pants ↔ skirts, etc.), where the exposed arms or legs with the skin colors and details can be reasonably predicted; 2) Improved Geometric Matching Module, which is designed to warp clothes according to the geometry of the target person. We improve the TPS based warping method with a compactly supported radial function (Wendland’s Ψ-function); 3) Trade-Off Fusion Module, which is to trade off the characteristics of the warped clothes and the reference person. This module is to make the generated try-on images look more natural and realistic based on a fine-tune symmetry of the network structure. Extensive simulations are conducted and our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.

The operational performance of Flexible Hinge Piezoelectric Stages (FHPSs), essential components in precision engineering, is fundamentally constrained by the inherent hysteresis of the piezoelectric actuator (PEA). A significant deficiency in prevailing characterization methods is their failure to consider the dynamic nature of the mechanical preload exerted by the flexible hinge. This position-dependent preload induces substantial deviations in the PEA’s response characteristics, thereby compromising the predictive accuracy of conventional design frameworks. To address this limitation, this paper proposes a Force-Dependent Prandtl–Ishlinskii (FPI) model that explicitly formulates the PEA’s hysteretic behavior as a function of variable preload conditions. The FPI model is subsequently integrated into a comprehensive FPI-FFEM characterization framework. Within this framework, a Force-analyzed Finite Element Method (FFEM) is utilized to compute the dynamic preload throughout the actuator’s operational stroke. This information, notably neglected in conventional FEM analysis, is essential to the fidelity of the proposed FPI model. Experimental validation demonstrates the superior fidelity of the FPI model in comparison to the traditional PI model for tracking preload-induced nonlinearities. Furthermore, the complete FPI-FFEM framework exhibits substantially enhanced prediction accuracy relative to both conventional PI-FEM and advanced LDPI-FEM methodologies, as demonstrated by a significant reduction in the Mean Absolute Error (MAE).

The Coronavirus Disease 2019 (COVID-19) pneumonia poses a critical threat to public health because of its powerful infectiousness, which has spurred the consumer electronics industry to innovate rapidly, leading to the emergence of advanced diagnostic and monitoring devices. Automated detection of COVID-19 based on multi-source generated data of CXR images is therefore essential for prevention. In the clinic, chest X-ray (CXR) plays a pivotal role in diagnosing COVID-19 pneumonia. However, it is challenging to utilize the dataset for diagnosis attributed to the significant imaging similarities observed among various types of pneumonia. To address the aforementioned issue, we present a pioneering classification model, denoted as PDMLP-Bi-LSTM, leveraging multi-source generated data. Its objective is to discriminate Normal, COVID-19, and Other pneumonia cases. Through the fusion of parallel deformable multi-layer perceptrons (MLPs) and Bi-directional Long Short-Term Memory (Bi-LSTM) modules, this model extracts multi-level abstract features and investigates potential correlations between parallel output features, capitalizing on the wealth of generated information. Initially, the chest region of the CXR image is localized and cropped using a pre-trained YOLO-V4 network, through which 13-dimensional transformed images and 16-dimensional depth feature maps are extracted using traditional image filters and convolutional neural network to form the 30-dimensional generated data for training the proposed classification model. The data is then fed spatially and channel-wise into deformable MLP modules, and the relationships of features on parallel channels are analyzed using Bi-LSTM modules. Finally, the classifier formed by fully connected layers and SoftMax function is employed to diagnose COVID-19 pneumonia. Extensive simulations based on 4099 CXR images were conducted to validate the performance of the proposed method. The results indicated that the proposed method exhibits excellent performance with accuracy, specificity, precision, recall, and F1-score by approximately 98% or above, which demonstrates the significant potential of the proposed method for clinically aiding in the diagnosis of patients with COVID-19 pneumonia.