Blockchain, the underlying technology of digital currency, such as Bitcoin, has the characteristics of decentralization, stability, security, anonymity, and nontampering. Blockchain is being applied in an increasing number of fields. Supply chain finance is a financing model in which banks connect core enterprises with upstream and downstream enterprises to provide flexible financial products and services. In the traditional supply chain finance, the core enterprises with strong competitiveness and a large scale play an irreplaceable role in managing the supply chain information flow, logistics, and capital flow, which results in inequality and information asymmetry. Fraud in supply chain finance is also very serious. To solve the problems of the traditional supply chain finance, in this article, we build a new type of supply chain financial platform that uses blockchain technology to manage the whole process. This supply chain financial platform solves the problem of nontrust among the participants in the supply chain, improves the efficiency of the capital flow and information flow, reduces costs, and provides better financial services to the relevant parties in the supply chain. To protect users' privacy, we propose a new method of using homomorphic encryption in the blockchain to meet the needs of sensitive data privacy protection in supply chain financial scenarios.

In this paper, the problem of steering control is investigated for vehicle path tracking in presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the change of passenger number or payload, while the vehicle velocity also changes during normal cruising, which significantly influence the vehicle dynamics. Moreover, the vehicle dynamics is strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T-S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under fuzzy Lyapunov function framework. By employing some matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal{H}_{\infty}$ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of T-S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

Medical information is private, and medical data are valuable for medical research. Thus, medical information sharing is challenging because the data might be manipulated improperly and revealed during the operational process. The accuracy and integrity of medical information should be guaranteed throughout the sharing process. Medical institutions require shared information for scientific research and development; however, the issue of privacy inhibits the sharing process. In this article, we propose a new business process and a blockchain-based platform for medical information sharing. Our method exploits the advantages of blockchain in medical information recording and sharing. Information can be stored, shared, and credibly verified among parties in the distributed network. In addition, we propose a new consensus algorithm and a universal anonymous sharing model. These methods improve the efficiency and security of medical information sharing between users. In this way, both the information and the traces of the transaction can be stored in a distributed manner to prevent manipulation and fraud. Consequently, the value of medical information can be fully utilized.

Alternating current (AC) impedance spectra facilitate lithium-ion battery management. Realizing a low-cost and low-complexity on-board impedance measuring system is a vital issue for the management based on the AC impedance. In the paper, a novel impedance measuring system combined with a high-power dual active bridge (DAB) converter and distributed sampling units is proposed and verified. The DAB converter is designed to generate the AC disturbance to ensure quasi-steady measurement of the battery impedance. The distributed signal sampling units simultaneously measure the voltage and current of all the series-connected battery cells in a module to measure their impedance. The measured impedance in a frequency range of 0.1 Hz - 500 Hz shows the feasibility of the system. The root-mean-square errors of the measured impedance phase from 0.2 Hz to 200 Hz and the magnitude from 0.1 Hz to 500 Hz are less than 6.9% and 4.0%, respectively. The errors in some frequency ranges are slightly larger, which are analyzed. The novelty is reflected in that the system is easily integrated into a bidirectional on-board charger and compatible with the battery management system, thus reducing costs and complexity. It provides a basis for the on-board application of the battery impedance.

The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at mias.group/RoadFormer .

In this paper, the problem of steering control based on T-S fuzzy vehicle lateral dynamics is investigated for autonomous driving with nonlinearities, system uncertainties, and actuator constraints. During normal vehicle cruising, the vehicle velocity always changes due to the different road conditions and/or steering wheel maneuvers, and moreover, the vehicle dynamics is also significantly influenced by the tire/road forces under different road surface conditions, which brings many difficulties in steering controller design. By adopting fuzzy modeling techniques and varying look-ahead control strategy, an approach to the T-S fuzzy anti-windup output feedback controller design is proposed for the steering control in path tracking within T-S fuzzy-model-based analysis framework, where the actuator amplitude saturation and rate limit are simultaneously taken into consideration. Finally, valuation results with Carsim/Matlab joint simulation are shown to demonstrate the effectiveness of the developed methods, and some comparison results in path tracking performance with fixed look-ahead distance control rule and the driver model controller embedded in Carsim are provided, which illustrate the advantages of the developed controller design method.