AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

The motion control or trajectory tracking is a fundamental and benchmark problem while controlling the manipulator robots to move along a predefined trajectory. Extensive research in the literature has addressed the motion control problem for robotic manipulators. To address this challenge effectively, it becomes imperative to design suitable controllers and make adequate choices regarding the appropriate feedback gain values. In this study, we consider and investigate two PD-based nonlinear control laws for controlling the motion of manipulator robots, namely the PD-plus-compensation controller and the augmented one. We establish conditions for determining the feedback gains of these two controllers, ensuring tracking error stability. Moreover, conditions guaranteeing the uniqueness of the zero error of the closed-loop robotic system are developed. Finally, we consider a two-degree-of-freedom manipulator robot to reveal the effectiveness of the two proposed controllers in tracking a desired trajectory and hence evaluating their performance in addressing the motion control problem.

Efficient signal modulation schemes are crucial for optimizing communication performance in low Earth orbit (LEO) satellite systems. Traditionally, uniform modulation methods are applied across the satellite constellation, regardless of variations in satellite distances from ground stations. However, this approach leads to inefficient signal power utilization, particularly for satellites at higher altitudes. This paper proposes a novel solution to this issue by implementing dynamic modulation strategies tailored to specific altitude ranges of LEO satellites. Through dynamic modulation, we utilize binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) for satellites positioned farther from earth, while higher-order constellations are used for satellites in closer proximity. This altitude-dependent modulation scheme aims to improve power efficiency throughout the LEO satellite network. Our results demonstrate a significant enhancement in power efficiency across the LEO satellite system compared to the conventional uniform modulation scheme. The adaptability of this dynamic modulation approach to satellite altitude variations allows for more judicious utilization of signal power, thereby maximizing communication quality and network reliability. We validate the effectiveness of the proposed method through extensive simulations.

To address the issues of small target leakage, misdetection, and poor detection effect in helmet-wearing target detection, a small target helmet-wearing detection based on YOLOv7 is designed. Firstly, the space-to-depth (SPD) block is introduced to replace the convolution of the first down-sampling, and all subsequent Max Pooling (MP) structures are replaced with SPD structures, which can better capture the information of different depths in the graphs, and improve the performance of detecting small target helmets. Secondly, the use of ContentAware ReAssembly of Features (CARAFE) for the up-sampling section increases the receptive field, adapts to different types of feature maps, and improves the feature fusion capability; Then, SPPFCSPC is obtained by changing the connection method of SPPCSPC, and the spatial pyramid pooling module GhostSPPFCSPC is built by fusing a Ghost module and an SPPFCSPC module, in which the SPPCSPC module consists of two submodules, the spatial pyramid pooling (SPP) module and the connected spatial pyramid convolution (CSPC) module, which decreases the number of parameters and boosts the network performance in the meantime; Thirdly, the design uses SIoU to optimize the bounding box return loss function, which achieves faster convergence in the training phase, better performance in inference, and improved model accuracy and robustness. To demonstrate that the improved algorithm has better results than the original model with mAP boosted by 5.8%, accuracy boosted by 0.8%, parameter count decreased by 7.08%, and FPS up to 65 f/s.

Data fusion can effectively process multisource information so as to obtain more accurate and reliable results. The data of water quality in a fishpond comes from various sensors, therefore the data must be fused. In this study, K-nearest interpolation, Grubbs criterion, a fuzzy comprehensive evaluation method, as well as an improved fruit fly optimization algorithm (IFOA) to find optimal parameters γ and σ of least squares support vector regression (LSSVR), were combined to provide accurate data for multisource information fusion modeling. The K-nearest interpolation method and grubbs criterion were employed to process abnormal data gross errors. Besides, a batch estimation adaptive weighted fusion algorithm was employed to, respectively, integrate the data from dissolved oxygen, water temperature, PH, and ammonia nitrogen concentration. A fuzzy comprehensive evaluation method, as well as analytic hierarchy process (AHP) were employed to obtain the true value of water quality grade. In addition, an IFOA-LSSVR model was proposed to predict the future water quality, which can better fit the nonlinear relationship between complex environmental factors and water quality. Experimental results show that the presented method can improve the data accuracy and provide decision results and scientific basis for the precision control of water quality environment.

The federated Kalman filter has been an optimal solution when working with distributed systems providing a global estimation without affecting local filters. Several problems including nonlinearities and high-amplitude noise levels have been tackled to improve the performance of global estimations. In this work, we propose a robust federated Kalman filter composed of a set of discrete generalized-proportional-integral (GPI) observers. We demonstrate how this algorithm yields high-precision estimations by using sensor fusion and active disturbance rejection (ADR) features. The proposed method was compared with other state-of-the-art algorithms where ours had the best performance.

Small and medium sized enterprises (SMEs) form backbone of a nation’s economy. Implementation of technologies like Internet of Things (IoT), however, is a challenge for majority of them as the conventional solution requires a lot of investment. Thus, financially restricted SMEs, especially in developing nations, remain aloof from leveraging the benefits of the technology. Resorting to affordable devices such as low-cost sensors, actuators, processors, servers, and network technologies etc., pose challenges like low memory, low computation power, less transmission power, low data transfer rate, and limited network bandwidth. Consequently, there arises a need to develop IoT based solutions that cater to these challenges so that low budget SMEs are also able to benefit from IoT’s umpteen advantages. This paper proposes an affordable IoT based framework for health status monitoring of machines in SMEs keeping the limitations imposed by low cost IoT devices as centre of the solution. The scope of the present research is limited to monitoring the health status of the electromechanical rotating machines only. Four types of commonly occurring faults in the machines at different rotating speeds are investigated using acoustic signals generated within the machines. Mahalanobis distance and Gaussian mixture model (GMM) have been employed for the analysis of the acoustic signals for estimating the unique fault dependent signatures. GMM works satisfactorily with smaller datasets and requires lesser amount of computational power in comparison to machine learning based algorithms. The investigations have showed that GMM may be effectively used in resource constrained SMEs deploying affordable IoT devices for predictive maintenance.

Traditional linear blending skinning (LBS) algorithms can act virtual avatars, but greatly rely on hand-designed body templates and 3D scan data. On the other hand, neural radiation fields can synthesis novel views from sparse views. This paper tackles the issue of obtaining accurate human performance from sparse multiviews without the use of body templates. Combining traditional LBS techniques and multilayer perceptron (MLP)-based neural representations to represent avatars proved to be very powerful in representing visual details, but the quality of the synthesized images still leaves room for worthwhile improvement. Previous research on the creation of template-free animatable volumetric actors based on neural representations has focused on the combination of the two techniques and has not suggested improvements to the internal structure of the MLP. In this paper, we present the application of self-attention to neural representations by amalgamating the benefits of transformers and neural radiation fields to establish gMLP-based neural representations.

In predictive analysis for industrial parts, numerous studies have demonstrated the effectiveness of utilizing raw material data for quality prediction in monitoring systems. By analyzing and defining the composition, properties, and characteristics of raw materials, predictive models can optimize system performance and decision-making processes. In the context of small and medium enterprises (SMEs) like SCODER, limited resources constrain data collection for decision-making due to factors such as limited data availability, smaller production volumes, and less advanced traceability, leading to variability in data quality and structure. This research explores the impact of material characteristics on industrial part conformity with scarce and sparse data. Material impurities significantly affect the quality and stability of industrial machined parts. Some machine learning and AI techniques address the complexity of analyzing material datasets to identify patterns that lead to deficiencies in machined parts. We propose a smart long short-term memory (LSTM) model for predicting part conformity in industrial machined parts. This work focuses on analyzing raw material data to study machining stability and understand the dependencies between material data and part quality holistically. Our approach serves as a tool for ensuring quality production and industrial processing stability, leveraging data collected from machining parts at SCODER, a French SME specializing in ultraprecise stamping for automotive applications.

Source-location privacy (SLP) is an important concern in wireless sensor networks (WSNs) as it can threaten WSN security. This paper first analyzed the SLP problem and then designed a node energy and distance-based SLP (NEDSLP) scheme from the point of selecting phantom nodes. The selection of phantom nodes occurred beyond the visible range of the source node, and the next hop routing was selected by considering both node energy and distance. This paper first briefly analyzed the SLP problem and then designed a node energy and distance-based SLP (NEDSLP) security scheme from the selection of phantom nodes, which selected phantom nodes outside the visual zone of the source node and selected the next hop routes by considering the node energy and distance together. Simulations on OMNet++ found that, compared with a protocol using source-based restricted flooding (PUSBRF) and a source location privacy protection scheme based on ring-loop routing (SLPRR), the NEDSLP scheme had a safety time of 886 when the number of hops was 50, which was 23% higher than the SLPRR. The energy consumption of the NEDSLP scheme was 72 hops, which was slightly higher than the PUSBRF but slightly lower than the SLPRR. The transmission delay was 46 hops, reduced by 25% compared to the PUSBRF and reduced by 18% compared to the SLPRR. The results demonstrate that the scheme proposed in this paper can effectively improve the security of SLPs for the purpose of protecting monitoring targets and can be further applied in WSNs.