Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras.

The construction sector is responsible for almost 30% of the world's total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people's thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people's heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems.

GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis.

Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.

Global Predefined-Time Adaptive Neural Network Control for Disturbed Pure-Feedback Nonlinear Systems With Zero Tracking Error.

This article presents a global adaptive neural-network-based control algorithm for disturbed pure-feedback nonlinear systems to achieve zero tracking error in a predefined time. Different from the traditional works that only solve the semiglobal bounded tracking problem for pure-feedback systems, this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the user specification. In order to get the desired predefined-time controller, first, a mild semibound assumption for nonaffine functions is skillfully proposed so that the design difficulty caused by the structure of pure feedback can be easily solved. Then, we apply the property of radial basis function (RBF) neural networks (NNs) and Young's inequality to derive the upper bound of the term that contains the unknown nonlinear function and external disturbances, and the designed adaptive parameters decide the derived upper and robust control gain. Finally, the predefined-time virtual control inputs are presented whose derivatives are further estimated by utilizing finite-time differentiators. It is strictly proved that the proposed novel predefined-time controller can guarantee that the tracking error globally converges to zero within predefined time and a practical example is shown to verify the effectiveness and practicability of the proposed predefined-time control method.

Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method.

In this work, a neural-networks (NNs)-based adaptive asymptotic tracking control scheme is presented for a class of uncertain nonstrict feedback nonlinear systems with time-varying full-state constraints. First, we construct a novel exponentially decaying nonlinear mapping to map the constrained system states to new system states without constraints. Instead of the traditional barrier Lyapunov function methods, the feasible conditions which require the virtual control signals satisfying the constraint requirements are removed. By employing the Nussbaum design method to eliminate the effect of unknown control gains, the general assumption about the signs of the unknown control gains is relaxed. Then, the nonstrict feedback form of the system can be pulled back to the strict feedback form through the basic properties of radial basis function NNs. Simultaneously, the intermediate control signals and the desired controller are constructed by the backstepping process and the Nussbaum design method. The designed controller can ensure that all signals in the whole closed-loop system are bounded without the violation of the constraints and hold the asymptotic tracking performance. In the end, a practical example about a brush dc motor driving a one-link robot manipulator is given to illustrate the effectiveness of the proposed design scheme.

H∞ Bipartite Synchronization Control of Markov Jump Cooperation-Competition Networks With Reaction-Diffusions.

This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative-competitive interactions and reaction-diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed H∞ bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

Event-triggered delayed impulsive control for nonlinear systems with application to complex neural networks.

This paper studies the Lyapunov stability of nonlinear systems and the synchronization of complex neural networks in the framework of event-triggered delayed impulsive control (ETDIC), where the effect of time delays in impulses is fully considered. Based on the Lyapunov-based event-triggered mechanism (ETM), some sufficient conditions are presented to avoid Zeno behavior and achieve globally asymptotical stability of the addressed system. In the framework of event-triggered impulse control (ETIC), control input is only generated at state-dependent triggered instants and there is no any control input during two consecutive triggered impulse instants, which can greatly reduce resource consumption and control waste. The contributions of this paper can be summarized as follows: Firstly, compared with the classical ETIC, our results not only provide the well-designed ETM to determine the impulse time sequence, but also fully extract the information of time delays in impulses and integrate it into the dynamic analysis of the system. Secondly, it is shown that the time delays in impulses in our results exhibit positive effects, that is, it may contribute to stabilizing a system and achieve better performance. Thirdly, as an application of ETDIC strategies, we apply the proposed theoretical results to synchronization problem of complex neural networks. Some sufficient conditions to ensure the synchronization of complex neural networks are presented, where the information of time delays in impulses is fully fetched in these conditions. Finally, two numerical examples are provided to show the effectiveness and validity of the theoretical results.

Adaptive fixed-time tracking control for a class of nonlinear pure-feedback systems: A relative threshold event-triggered strategy.

In this paper, a fixed-time controller under the mechanism of event-trigger is designed for a class of nonlinear pure-feedback systems with non-differentiable non-affine functions. By properly modeling non-affine terms, the limitation of the partial derivatives of non-affine functions is eliminated. In our design process, we first develop a fixed-time adaptive controller using decoupling method. Then, a relative threshold event-trigger mechanism (ETM) is introduced in Section 3.1. The proposed controller can not only stabilize the system within a fixed-time, but also save communication resources more effectively. Lastly, the feasibility of the proposed control scheme is verified by two simulation examples.

Synchronization of complex networks with time-varying delay of unknown bound via delayed impulsive control.

The synchronization problem for complex networks with time-varying delays of unknown bound is investigated in this paper. From the impulsive control point of view, a novel delayed impulsive differential inequality is proposed, where the bounds of time-varying delays in continuous dynamic and discrete dynamic are both unknown. Based on the inequality, a class of delayed impulsive controllers is designed to achieve the synchronization of complex networks, where the restriction between impulses interval and time-varying delays is dropped. A numerical example is presented to illustrate the effectiveness of the obtained results.

Multiple Lyapunov Functions for Adaptive Neural Tracking Control of Switched Nonlinear Nonlower-Triangular Systems.

In this paper, the problem of adaptive neural tracking control for a type of uncertain switched nonlinear nonlower-triangular system is considered. The innovations of this paper are summarized as follows: 1) input to state stability of unmodeled dynamics is removed, which is an indispensable assumption for the design of nonswitched unmodeled dynamic systems; 2) the design difficulties caused by the nonlower-triangular structure is handled by applying the universal approximation ability of radial basis function neural networks and the inherent properties of Gaussian functions, which avoids the restriction that the monotonously increasing bounding functions of the nonlower-triangular system functions must exist; and 3) multiple Lyapunov functions are utilized to develop a backstepping-like recursive design procedure such that the solvability of the adaptive neural tracking control issue of all subsystems is unnecessary. Based on the proposed controller design methods, it can be obtained that all signals in the closed-loop switched system remain bounded and the tracking error can eventually converge to a small neighborhood of the origin. In the simulation study, two examples are supplied to prove the practicability and feasibility of the developed design schemes.

Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays.

In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.

Adaptive approximation-based design mechanism for non-strict-feedback nonlinear MIMO systems with application to continuous stirred tank reactor.

This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young's inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

Observer-based sliding mode control for synchronization of delayed chaotic neural networks with unknown disturbance.

This paper considers the synchronization of delayed chaotic neural networks with unknown disturbance via observer-based sliding mode control. We design a sliding surface involving integral structure and a discontinuous control law such that the trajectories of error system converge to the sliding surface in finite time and remain on it thereafter. Then, by constructing Lyapunov-Krasovskii functional and using the linear matrix inequality (LMI) technique, some sufficient conditions are derived to guarantee the synchronization of chaotic neural networks. The advantages of our proposed results include:(i) It can be applied to synchronous control for drive and response systems with different structures; (ii) It can be applied to the response system with unknown disturbance. Finally, a simulation example is shown to illustrate the proposed methods.

Review of stability and stabilization for impulsive delayed systems.

This paper reviews some recent works on impulsive delayed systems (IDSs). The prime focus is the fundamental results and recent progress in theory and applications. After reviewing the relative literatures, this paper provides a comprehensive and intuitive overview of IDSs. Five aspects of IDSs are surveyed including basic theory, stability analysis, impulsive control, impulsive perturbation, and delayed impulses. Then the research prospect is given, which provides a reference for further study of IDSs theory.

Measuring Spectral Inconsistency of Multispectral Images for Detection and Segmentation of Retinal Degenerative Changes.

Multispectral imaging (MSI) creates a series of en-face fundus spectral sections by leveraging an extensive range of discrete monochromatic light sources and allows for an examination of the retina's early morphologic changes that are not generally visible with traditional fundus imaging modalities. An Ophthalmologist's interpretation of MSI images is commonly conducted by qualitatively analyzing the spectral consistency between degenerated areas and normal ones, which characterizes the image variation across different spectra. Unfortunately, an ophthalmologist's interpretation is practically difficult considering the fact that human perception is limited to the RGB color space, while an MSI sequence contains typically more than ten spectra. In this paper, we propose a method for measuring the spectral inconsistency of MSI images without supervision, which yields quantitative information indicating the pathological property of the tissue. Specifically, we define mathematically the spectral consistency as an existence of a pixel-specific latent feature vector and a spectrum-specific projection matrix, which can be used to reconstruct the representative features of pixels. The spectral inconsistency is then measured using the number of latent feature vectors required to reconstruct the representative features in practice. Experimental results from 54 MSI sequences show that our spectral inconsistency measurement is potentially invaluable for MSI-based ocular disease diagnosis.

An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping.

In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.