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Accurately identifying the source and controlling the total amount of pollutants are the basis for achieving regulation of pollution sources, which is critical for the prevention and control of surface water pollution. For this purpose, this study used the Xinjian River in Jinyun County, Lishui City, Zhejiang Province, China, as a case study to explore whether and how the tributary inflow impacts the downstream water quality. The main pollution sources in the upstream, midstream, and downstream of the Xinjian River were apportioned using the Positive Matrix Factorization (PMF) model based on the water quality data from four sample stations from January 2018 to September 2022. According to the unmatched factor in different sections, it is plausible to infer that the TN and TP are mainly caused by the tributaries. To enhance the reliability of pollution source apportionment based on the receptor model, a series of remote sensing images with high resolution were used to derive the water quality concentrations to present the spatial distribution and reveal the long-term trend of the local water environment. It is anticipated that the apportionment results could be of great assistance to local authorities for the control and management of pollution, as well as the protection of riverine water quality. [ABSTRACT FROM AUTHOR]

Many numerical and measuring approaches are widely used in predicting and analysing the acoustic performances of laminated materials for noise control applications. However, numerical methods generally require a set of non-acoustic parameters, and the measuring methods are not available for exceedingly thick materials. The main aim of this article is to address a hybrid approach of evaluating the normal incidence sound transmission loss and absorption coefficients of multi-layered materials. This method is performed with some special parameters that contain the sound transmission and reflection coefficients of each sub-layer of a multi-layer specimen and can be measured by a standing wave tube system. The accuracy and feasibility of the present method are validated by the experimental and numerical comparisons between different methods and samples. Moreover, this present approach can be applied as a numerical tool of estimating the acoustical behaviours of multi-layered structures in noise control treatments, such as automotive, building and aerospace industries. [ABSTRACT FROM AUTHOR]

Many pipelined adaptive signal processing systems are subject to a tradeoff between throughput and signal processing performance incurred by the pipelined adaptation feedback loops. In the conventional synchronous design regime, such throughput/performance tradeoff is typically fixed since the pipeline depth is usually determined in the design phase and remains unchanged in the run time. Nevertheless, in many real-life scenarios, the overall system performance can be potentially improved if we can run-time dynamically configure this tradeoff. With this motivation, we propose to apply self-timed pipeline, an alternative to synchronous pipeline, to implement the pipelined adaptive signal processing systems, in which the pipeline depth can be dynamically changed to realize run-time configurable throughput/performance tradeoffs. Based on a well-known high speed serf-timed pipeline style, we developed architecture and circuit level design techniques to implement the serf-timed pipelined adaptation feedback loop with configurable pipeline depth. We demonstrate the proposed design approach using a delayed least mean square (DLMS) adaptive equalizer for magnetic recording read channel. The data transfer rate in hard disk varies as the read head moves among tracks with different distance from the center of the disk platter. By adjusting the pipeline depth on-the-fly, the DLMS equalizer can dynamically track the best equalization performance allowed by the varying data transfer rates. Simulation result shows a significant performance improvement compared with its synchronous counterpart. Index Terms--Adaptive signal processing, delayed least mean square (DLMS), self-timed, pipelining.

The main aim of this study is to introduce an improved method for determining the sound properties of acoustic materials which is more precise than the common wavefield decomposition method and simpler than the common transfer matrix method. In the first part of the article, a group of formulae for calculating sound transmission loss is represented by combining the wavefield decomposition and transfer matrix methods. Subsequently, a formula for calculating sound absorption coefficients is derived from these formulae by definition. Furthermore, the present formulae are validated by comparing the experimental results achieved with the present formulae and those results obtained by other methods recorded in published articles. Eventually, it is demonstrated that the method can accurately measure the sound insulation performance of materials and the sound absorption properties of limp and lightweight materials. [ABSTRACT FROM AUTHOR]

A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle. [ABSTRACT FROM AUTHOR]

The new type of transportation based on intelligent driverless vehicles will bring great changes to people's travel modes and put forward higher requirements for the ride comfortability of vehicles. This paper presents a new observing algorithm to estimate the suspension states in real time and cooperate with sliding mode controller to improve the ride comfortability. First, the nonlinear model of an air suspension system equipped with a continuously controllable damper is described in detail. Then, this nonlinear suspension model is linearized precisely based on the differential geometry theory; a linear Kalman filter observer is implemented for this linearized model; through the coordinate reverse transformation, the designed linear observer is transformed into a nonlinear one, which will be suitable for the original nonlinear system, so that, the proposed state observer can estimate all the states of the nonlinear quarter car suspension system. Then, in this nonlinear suspension system, a model reference sliding mode controller is designed to continuously control the damping force to improve the ride comfortability. Finally, the effectiveness and advantage of the proposed feedback linearization Kalman observer is illustrated by comparing with a traditional extended Kalman filter observer. The simulation research shows that the proposed feedback linearization observer enjoys a better estimation accuracy, higher operation efficiency, and greater control performance while cooperating with the sliding mode controller in ride comfortability control. [ABSTRACT FROM AUTHOR]