A chiller plant is an essential part of a heating, ventilation, and air conditioning system. Chiller plant operation planning is to determine the throughput of active chillers, pumps, and fans in a chiller plant to meet cooling load demands with minimized power consumption. Existing planning methods are limited to chiller plant operation with homogeneous devices subject to constraints for the conservation of energy or with heterogeneous devices without considering the conservation of energy. In this article, a mixed-integer optimization problem is formulated for chiller plant operation planning with heterogeneous devices to minimize power consumption subject to various constraints, including the constraints for the conservation of energy. The formulated problem is reformulated as a global optimization problem and solved via collaborative neurodynamic optimization with multiple projection neural networks. Experimental results based on equipment manufacturers' specifications are elaborated to demonstrate the significantly higher performance of the proposed approach than four mainstream methods in terms of power consumption wattage.
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This paper is to design time-varying delay feedback controllers for outer synchronization between two complex dynamical networks with nonidentical coupling structures. By constructing the Lur'e-Postnikov Lyapunov functional, a new delay-dependent synchronization criterion is derived and formulated in the form of linear matrix inequalities (LMIs). Then, sufficient conditions on the existence of a time-varying delay feedback controller are obtained by using this newly-obtained synchronization criterion. The controller gains can be derived. A numerical example is given to illustrate the effectiveness of the synchronization criteria and the design method.
In this paper, the observer-based stabilization problem is investigated for a class of discrete-time nonlinear stochastic networked control systems (NCSs) with exogenous disturbances. The signal transmission from the sensors to the observer is implemented via a shared digital network, in which both uniform quantization effect and stochastic communication protocol (SCP) are taken into account to reflect several network-induced constraints. The notion of input-to-state stability in probability is introduced to describe the dynamical behaviors of the closed-loop stochastic NCS that is effectively characterized by a general nonlinear stochastic difference equation with Markovian jumping parameters. A theoretical framework is first established to felicitate the dynamics analysis of the closed-loop system in virtue of the switched Lyapunov function method and the stochastic analysis techniques. By making full use of the quantized measurement output under the scheduling of the SCP, the existence conditions for an observer-based controller are established under which the closed-loop system is input-to-state stable in probability. Then, the explicit expression of the gain matrices of the desired controller is given by resorting to a set of feasible solutions of certain matrix inequalities. The effectiveness of the theoretical results is demonstrated by a numerical simulation example.
This paper is concerned with the distributed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -consensus fault detection (FD) filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with faults, switching network topology, channel fading and different communication channels-induced packet dropouts with different missing rates. The purpose of the addressed problem is to design a distributed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -consensus FD filter to guarantee the sensitivity of the residual signal to the faults and the robustness of the residual system to effects of both switching network topology, channel fading and different communication channels-induced packet dropouts with different lossing rates. On the basis of the T-S fuzzy approach and the Lyapunov functional, distributed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -consensus FD filter design criterion is derived such that the residual system is exponentially stable in the mean square, and the optimal H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering performance index is derived. A simulation example is conducted to verify the usefulness of the proposed FD filter design approach.
The positive effects of adding polyphenols on humification have been widely explored during composting. However, the precise impact of polyphenols on the breakdown of lignocellulose throughout the composting process has not yet been elucidated. Two experimental treatments were designed to explore the mechanism by which adding natural organic phenols affected interactive relationship between lignocellulose hydrolysis and humus synthesis: a control group and a gallic acid (GA)-amended group. The results indicated that GA effectively promoted the transformation of polyphenols. Degradation rates of lignin and cellulose increased by 42.3 % and 20.6 %, respectively. Meanwhile, the synthesis of highly humified component 3 of humic acid increased by 22.1 % in GA group. GA dramatically enriched core bacteria associated with humic acid components. Structural equation model showed that GA directly stimulated microbial decomposition of lignocellulose and indirectly accelerated its degradation by enhancing microbial utilization of polyphenols. These enhancements in polyphenol transformation and lignin degradation collectively promoted compost humification. The addition of GA presents a highly promising approach to accelerate the degradation of recalcitrant lignocellulose and promote compost humification, while simultaneously addressing the waste generated during the production process of polyphenols.
This paper deals with the problem of odor source localization based on a multi-robot system. A cooperative control solution is proposed to coordinate multiple mobile robots to locate the source of odor. The proposed solution is independently executed by each robot and consists of four levels. In the first level, a Kalman filter is used to estimate the position of the odor source in terms of wind information and detection events. In the second level, the idea of particle swarm optimization is introduced to improve the precision of the estimated position. In the third level, a movement trajectory is planned according to the resulting position in order that the robot can more probably detect the odor during the movement. In the fourth level, a control algorithm is designed to enable the robot to move along the movement trajectory. Finally, the performance capabilities of the proposed cooperative control solution are illustrated for odor source localization.
The real circuit model, such as a partial element equivalent circuit (PEEC), can be represented as a delay differential equation (DDE) of neutral type. The study of asymptotic stability of this kind of systems is of much importance due to the fragility of DDE solvers. Based on a descriptor system approach, new delay-dependent stability results are derived by introducing some free-weighting matrices. As an application of the results, the delay-dependent stability problem of a PEEC model is investigated. The comparison of the results with the existing ones is finally given by using the PEEC model and another numerical example.
This paper is concerned with network-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for an offshore steel jacket platform subject to nonlinear wave-induced force and external disturbance. A network-based dynamic model of the offshore platform with an active tuned mass damper mechanism is presented, and a network-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control scheme is presented to attenuate the vibration of the offshore platform. The effects of the network-induced delays on the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for the offshore platform is investigated. Simulation results show that the proposed network-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control scheme can improve the control performance of the offshore platform significantly. In addition, the control force and the oscillation amplitudes of the offshore platform under the network-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller with proper network-induced delays are smaller than the ones under the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller without network settings, which means that the proper network-induced delays are of the positive effects on the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control of the offshore platform.
This paper is concerned with the problem of static output feedback control for networked systems with a logic zero-order-hold (ZOH). First, the networked closed-loop system is modeled as a discrete-time linear system with a time-varying delay, whose upper bound can...
Accurate 3D semantic occupancy perception is essential for autonomous driving in complex environments with diverse and irregular objects. While vision-centric methods suffer from geometric inaccuracies, LiDAR-based approaches often lack rich semantic information. To address these limitations, MS-Occ, a novel multi-stage LiDAR-camera fusion framework which includes middle-stage fusion and late-stage fusion, is proposed, integrating LiDAR's geometric fidelity with camera based semantic richness via hierarchical cross-modal fusion. The framework introduces innovations at two critical stages: (1) In the middle-stage feature fusion, the Gaussian-Geo module leverages Gaussian kernel rendering on sparse LiDAR depth maps to enhance 2D image features with dense geometric priors, and the Semantic-Aware module enriches LiDAR voxels with semantic context via deformable cross-attention; (2) In the late-stage voxel fusion, the Adaptive Fusion (AF) module dynamically balances voxel features across modalities, while the High Classification Confidence Voxel Fusion (HCCVF) module resolves semantic in consistencies using self-attention-based refinement. Experiments on two large-scale benchmarks demonstrate state-of-the-art per formance. On nuScenes-OpenOccupancy, MS-Occ achieves an Intersection over Union (IoU) of 32.1% and a mean IoU (mIoU) of 25.3%, surpassing the state-of-the-art by +0.7% IoU and +2.4% mIoU. Furthermore, on the SemanticKITTI benchmark, our method achieves a new state-of-the-art mIoU of 24.08%, robustly validating its generalization capabilities. Ablation studies further confirm the effectiveness of each individual module, highlighting substantial improvements in the perception of small objects and reinforcing the practical value of MS-Occ for safety critical autonomous driving scenarios.
The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models’ capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions.
