An active balancer with rapid bidirectional charge shuttling and adaptive equalization current control for lithium-ion battery strings

This article proposes an active balancer, which features bidirectional charge shuttling and adaptive equalization current control, to fast counterbalance the state of charge (SOC) of cells in a lithium-ion battery (LIB) string. The power circuit consists of certain bidirectional buck-boost converters to transfer energy among the different cells back and forth. Owing to the characterization of the open-circuit voltage (OCV) vs SOC in LIB being relatively smooth near the SOC middle range, the SOC-inspected balance strategy can achieve more precise and efficient equilibrium than the voltage-based control. Accordingly, a compensated OCV-based SOC estimation is put forward to take into account the discrepancy of SOC estimation. Besides, the varied-duty-cycle (VDC) and curve-fitting modulation (CFM) methods are devised herein to tackle the problems of slow equalization rate and low balance efficacy, which arise from the diminution in balancing current as the SOC difference between the cells decreases in the later duration of equalization especially. The proposed strategies have taken the battery nonlinear characteristic and circuit parameter nonideality into account and can adaptively modulate the duty cycle with the SOC difference to keep balancing current constant throughout the balancing cycle. Simulated and experimental results are given to demonstrate the feasibility and effectiveness of the same prototype constructed. Compared with the fixed duty cycle and the VDC methods, the proposed CFM has the best balancing efficiency of 81.4%, and the balance time is shortened by 27.1% and 18.6%, respectively.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Biomimetic Lamellar Chitosan Scaffold for Soft Gingival Tissue Regeneration

Mucogingival surgery has become a common procedure for soft gingival tissue reparation in dental clinical practice, which mainly relies on autograft or commercial collagen membranes (CM). However, the autograft faces grand challenges in source availability and long-term post-surgery pain management, and the CM is restricted by its poor mechanical properties in an aqueous environment. Here, it is reported that a bio-inspired lamellar chitosan scaffold (LCS) with long range ordered porous structure, manufactured through a bidirectional freezing method, can serve as a promising gingival tissue engineering material. The LCS not only exhibits excellent mechanical properties in the hydrated state but also accelerates vessel formation and soft tissue regeneration in vivo. Most interestingly, the LCS is found to be capable of inducing macrophage differentiation to M2 macrophages, which is thought to play an important role in tissue regeneration. These advantages combined with its easy and low-cost preparation process make the LCS a promising candidate for dental clinical applications.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

基于深度卷积神经网络的视觉里程计研究

视觉里程计利用视频信息来估计相机运动的位姿参数，实现对智能体的定位。传统视觉里程计方法需要特征提取、特征匹配/跟踪、外点剔除、运动估计、优化等流程，解算非常复杂，因此，提出了基于卷积神经网络的方法来实现端对端的单目视觉里程计。借助卷积神经网络对彩色图片自动学习提取图像帧间变化的全局特征，将用于分类的卷积神经网络转化为帧间时序特征网络，通过三层全连接层输出相机的帧间相对位姿参数。在KITTI数据集上的实验结果表明，提出的Deep-CNN-VO模型可以较准确地估计车辆的运动轨迹，证明了方法的可行性。在简化了复杂模型的基础上，与传统的视觉里程计系统相比，该模型的精度也有所提高。     

基于机器视觉的机车齿轮毂全自动双轴拧紧机的设计和应用

设计了基于机器视觉的机车齿轮毂全自动双轴拧紧机,根据拧紧工艺和扭矩控制原理实现对螺栓拧紧力矩的控制;通过丝杠变径机构,实现不同规格齿轮毂螺栓拧紧;利用机器视觉实现螺栓位置的识别以及初始位置的确定;讨论了自动拧紧机控制系统的硬件组成和软件系统配置,并介绍了防错、防漏功能的设计流程。螺栓自动拧紧机可提高装配精度和操作效率,降低工人的劳动强度。     

主/次显示器视距设计对头部关节舒适度的影响

以视平线可与显示器中心重合为准,分别探讨显示器中心在视平线上方、下方时不同视距对头部关节舒适度的影响. 实验自变量为视距(眼睛与显示器中心的水平距离),因变量为头部关节舒适度. 实验结果显示:1)显示器中心在人视平线上方时,头部舒适性状态始终处于不舒适状态,此时视距与人的头部舒适性关系成正比,即视距越大,头部舒适性越好;2)显示器中心在人视平线下方时,头部关节舒适状态始终处于正常范围,舒适性程度随视距增大而减小.     

Multi-modal approach to evaluate adaptive visual stimuli of remote operation system using gaze behavior

A multi-modal approach is proposed to evaluate the usability of Adaptive Visual Stimuli for User Interface (AVS4UI) of remote operation systems. This study focuses on the evaluation of AVS4UI for forklift work because the operation complexity includes driving and cargo handling, which typically requires multiple salient attention. Presenting this amount of information simultaneously on a User Interface (UI) tends to cause confusions to operators and reduces operation efficiency. AVS4UI can therefore be one of the promising solutions where the optimal visual stimuli are autonomously presented for different work conditions. However, evaluation of AVS4UI is challenging because operators may be disoriented by adaptive information and worked without safety considerations. Therefore, novel gaze metrics are proposed to evaluate responses of forklift operators to AVS4UI so that undesired behavior can be evaluated. The proposed metrics implicitly represent gaze pattern in terms of transition and distribution between UI elements, operation safety, and familiarity with the adaptive system. The ideal AVS4UI is expected to minimize the proposed gaze metrics and outperform the non-adaptive UI. More importantly, the results of these metrics are consistent with those of perceived workload defined by NASA-Task Load Index. We also propose a correlation model using stepwise linear regression that provides reasonable estimation of perceived workload. Such novel metrics and correlation model enable objective and online evaluation to minimize biases of subjective response. Therefore, online work support system can be developed to support workers.