Switching Problem Research Articles

A mathematical model for optimal switching problem of production line based on the OCRA method for ergonomics

A mathematical model for optimal switching problem of production line based on the OCRA method for ergonomics

A dam management problem with energy production as an optimal switching problem

AbstractWe consider an optimal stochastic control problem for a dam. Electrical power production is operating under an uncertain setting for electricity market prices and water level which has to be kept under control. Indeed, the water level inside the basin cannot exceed a certain threshold for safety reasons, and at the same time cannot decrease below another threshold in order to keep power production active. We model this situation as a mixed control problem with regular and switching controls under constraints. We characterize the value function as solution of an HJB equation and provide some numerical approximating methods. We shall illustrate by numerical examples the main achievements of the present approach.

Biased random-key genetic algorithm for the job sequencing and tool switching problem with non-identical parallel machines

We address the job sequencing and tool switching problem associated with non-identical parallel machines – a variation of the well-known sequencing and switching problem (SSP) better adapted to reflect the challenges in modern production environments. The NP-hard problem is approached by considering two isolated objective functions: the minimization of the makespan and the minimization of the total flow time. We present two versions of a parallel biased random-key genetic algorithm hybridized with tailored local search procedures that are organized using variable neighborhood descent. The proposed methods are compared with state-of-the-art methods by considering 640 benchmark instances from literature. For both objective functions considered, the proposed methods consistently outperform the compared methods. All known optimal values for both objectives are achieved, and a substantial gap is reported for all instance groups when compared with the best previously published solution values.

On fuzzy fractional quadratic programming problems with an application in the tourism sector

In the corporations working with logistics, many management issues involving optimization require fractional quadratic programming. Further, crisp parameters are unable to model the real life problems, where impreciseness is involved. In the present study, fuzzy fractional quadratic programming problem is studied using a hybrid method that combines analytic and numerical approaches. The original problem is converted to a crisp multiobjective problem, wherein the constraint coefficients are handled by variation of parameter technique through an interactive manner. The proposed method attempts to alleviate existing problems of straightforward conversion to a deterministic model by making constraint parameters vary as per αr∈(0,1]. The flexibility of choices in constraint parameters with αr=0 leading to best solution and αr=1 resulting in worst solution, indicates the robust nature of proposed algorithm. The theorems have been constructed and proved to show the equivalence between the original and transformed problem. A numerical example and a transportation problem in tourism sector switching between both balanced and unbalanced cases is modelled and later solved using the proposed methodology.

Exponential Stability of Switched Positive Systems With Unstable Subsystems Based on Digraphs

This paper addresses the transfer restricted switching problem for discrete-time switched positive linear systems with all unstable subsystems. To characterize the complex interaction between state components under subsystem switching, the switching digraph and state component digraph are conjoined and a novel switching walk approach is proposed to achieve the exponential stability of switched positive systems. By further deducing the relationship between the obtained decay rate of switched systems and the weight of the switching walk, an optimized stability result is presented and a switching cycle strategy is designed to make the decay rate of switched systems larger. Furthermore, a novel graph-based mode-dependent dwell time method is proposed, which allows subsystems to be activated at any integer multiple of a constant value. Finally, three simulation examples are provided to show the effectiveness of the proposed methods.

Specialized scatter search algorithm for optimal transmission switching with voltage control

This article presents a specialized scatter search (SS) algorithm to solve the optimal transmission switching (OTS) problem considering voltage control, with the objective of reducing operational costs. The nonlinear ac operation of the network is considered, and discrete variables are used to represent the states of the lines, positions of the taps of the transformers, and the states of shunt reactive compensators. The resulting formulation is a mixed-integer nonlinear programming problem, which is nonconvex and hard to solve. The proposed SS algorithm manages the discrete variables of the problem, which becomes a nonlinear programming (NLP) problem when the discrete variables are fixed. The NLP problem is then solved by an optimization solver, which returns all other continuous variables of the problem to the SS algorithm. Tests were conducted using eleven benchmark systems. The results indicated that the SS algorithm is efficient for solving the problem.

A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction

Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed by taking the specified object as the master node and its neighboring objects as the branch nodes. A preprocessing module is applied to exclude unnecessary connections in the graph model based on the speed of the historical trajectory of the object, and to distinguish the features of objects with similar appearances. Feature matching is performed using the Hungarian algorithm, based on the similarity matrix obtained from the features. Post-processing is performed for the temporarily unmatched frames to obtain the final object matching results. The experimental results show that the approach proposed in this paper can achieve the highest MOTA.

A framework for real-time vehicle counting and velocity estimation using deep learning

To better control traffic and promote environmental sustainability, this study proposed a framework to monitor vehicle number and velocity at real time. First, You Only Look Once-v4 (Yolo-v4) algorithm based on deep learning can greatly improve the accuracy of object detection in an image, and trackers, including Sort and Deepsort, resolved the identity switch problem to track efficiently the multiple objects. To that end, this study combined Yolo-v4 with Sort and Deepsort to develop two trajectory models, which are known as YS and YDS, respectively. In addition, different regions of interest (ROI) with different pixel distances (PDs), named ROI-10 and ROI-14, were converted by road marking to calibrate the PD. Finally, a high-resolution benchmark video and two real-time low-resolution videos of highway both were employed to validate this proposed framework. Results show the YDS with ROI-10 achieved 90% accuracy of vehicle counting, when compared to the number of actual vehicles, and this outperformed the YS with ROI-10. However, the YDS with ROI-14 generated relatively good estimates of vehicle velocity. As shown in the real-time low-resolution videos, the YDS with ROI-10 achieved 89.5% and 83.7% accuracy of vehicle counting in Nantun and Daya sites of highway, respectively, and reasonable estimates of vehicle velocity were obtained. In the future, more bus and light truck images could be collected to effectively train the Yolo-v4 and improve the detection of bus and light truck. A better mechanism for precise vehicle velocity estimation and the vehicle detection in different environment conditions should be further investigated.

Multi target tracking algorithm based on anchor free detection

In order to alleviate the problems of frequent identity label switching caused by mutual occlusion between targets and missed detection caused by small target sizes in multi-target tracking, this paper proposes a multi-target tracking algorithm based on anchor-free detection. This algorithm first uses YOLOX for detection and embeds the ReID algorithm to enhance pedestrian recognition. Then, it uses the DeepSORT algorithm for tracking and correcting abnormal trajectory algorithms for occluded targets. Finally, experiments are conducted on both the MOT16 dataset and the self-made dataset. This algorithm has higher tracking accuracy than other algorithms, and its tracking performance for small targets has also been effectively improved.

Set of Factors Projected as Ontological Essence of Theoretical Construct: Statistical Reconstruction of Psychological Tendencies

Statistical reconstruction can be applied to psychological tendencies that project themselves as the ontological essence of a theoretical construct. This research featured the theoretical construct of individual cognitive education space. The research objective was to study the operational possibilities of statistical measurement of a theoretical phenomenon. The general scientific principles of systemic anthropological psychology served as the methodological basis of conceptual interpretation. The theoretical construct of individual cognitive education space was reconstructed through temporal, operational, cognitive, and metacognitive dimensions. The statistical methods included correlation analysis, principal component analysis, scree test, and Shapiro – Wilk test. They revealed three components of linear functions from theoretically identified and empirically measured temporal, operational, cognitive, and metacognitive characteristics. The component behind the interrelation of cognitive and metacognitive processes was identified empirically. It included six main metric elements, namely: temporal-cognitive strategies for solving problems of switching between tasks, dynamic components of positional strategies, motivational self-determination, design, forecasting, and structural components of constructive and conceptual levels. The study proved the empirical efficiency of statistical reconstruction applied to theoretical constructs and defined the psychological specifics of individual cognitive education space.

Asynchronous sampled-data dynamic output feedback control for Markovian jump neural networks via a double-mode-dependent Lyapunov functional

This paper discusses the asynchronous switching problem of Markovian jump neural networks (MJNNs) with mode-dependent time delays. Due to the factors such as communication induced phenomena or the incomplete availability of system modes, asynchronous switching between the system mode and the controller mode is a common phenomenon. Therefore considering the mode mismatch phenomenon and the incomplete measurement of system state, we propose an asynchronous dynamic output feedback control (DOFC) law. Unlike the traditional DOFC, the sampled-data strategy is introduced into DOFC to alleviate the burden of the communication network. In light of the sampled-data DOFC, an augmented dynamic system is modeled. To fully employ the mode information of asynchronous switching and to relax the conservativeness of the results, a double-mode-dependent Lyapunov functional is constructed, which depends both on the system modes and the DOFC modes. Then, a less conservative stability condition is obtained to guarantee mean-square asymptotic stability (MSAS) of the augmented system. On the basis of the stability condition, a design algorithm is attained to solve the gain matrices of the asynchronous DOFC. Finally, a simulation example is given to illustrate the validity and superiority of the developed results.

A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets

A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets

Comparative analysis of NR and DE methods for multilevel inverters: An experimental case study

This paper presents an experimental performance comparison between differential evolution (DE) algorithm and Newton Raphson (NR) method for solving the optimal switching problem for multilevel inverters with the objective of improving the output AC voltage quality. The methods are used to compute optimal switching angles to control a multilevel inverter in such way that allows the elimination of low order harmonics; this control technique is called selective harmonic elimination. The proposed inverter in this article is composed of two H-bridge cells with non-equal DC voltage sources in order to generate seven voltage levels. The differential evolution (DE) optimization and the Newton Raphson (NR) algorithm is used to solve non-linear transcendental equations necessary for the (SHPWM). Computational results obtained from computer simulations presented a good agreement with the theoretical predictions. A laboratory prototype based on STM32F407 microcontroller was built in order to validate the simulation results.

A pedestrian trajectory prediction method based on improved LSTM network

AbstractPedestrian trajectory prediction based on vision is a popular task in autopilot system. As pedestrian trajectories always cross each other, pedestrian targets will frequently obscure each other, which makes the collected pedestrian trajectories produce errors. Moreover, the interaction between pedestrians will have an impact on their trajectories in crowded areas, which leads to great challenge in trajectory prediction. In order to solve the above two problems, a pedestrian trajectory prediction method is proposed. Based on the YOLOv7 algorithm, it is connected with the StrongSORT algorithm. Then adding a feature recognition (ReID) module to the model, the problem of target switching and jumping in the process of pedestrian tracking can be solved. The dot product attention mechanism is also integrated into the long short term memory (LSTM) algorithm, the improved LSTM correlate target position and distance information for the pedestrian trajectory prediction. Extensive experiments on three most challenging datasets showed that this method improves the performance and largely reduces the model size.

A High-Power Density DC Converter for Medium-Voltage DC Distribution Networks

A DC converter is the core equipment of voltage conversion and power distribution in a DC distribution network. Its operating characteristics have a profound impact on the flexible regulation of distributed resources in an active distribution network. It is challenging for the existing single-stage conversion topology to meet the requirements of distributed renewable energy connected to a multi-voltage level, medium-voltage grid. It is necessary to study the multistage transform power unit topology further, which can satisfy high reliability, high efficiency, and wide input range. This paper proposes a high-power density DC converter for medium-voltage DC networks with wide voltage levels. It adopts Buck-LLC integrated modular composition. The input ends of the high isolation resonant power unit are connected in series to provide high voltage endurance, and the output ends are connected in parallel to meet the high-power demand and achieve high-power transmission efficiency. The proposed series dual Buck-LLC resonant power unit topology can adjust the duty cycle of series dual buck circuits to meet the needs of different levels of medium-voltage DC power grids. The soft switching problem within the wide input range of all switching tubes is solved by introducing auxiliary inductors, thereby improving energy transmission efficiency. The auxiliary circuit and control parameters are optimized based on the research of each switching tube’s soft switching boundary conditions. Finally, an experimental prototype of a 6.25~7 kW power unit is designed and developed to prove the proposed topology’s feasibility and effectiveness. Great breakthroughs have been made both in theoretical research and engineering prototype development.

OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video

In today’s era, video analysis is immensely involved in recognizing the sport-related movement that has become a significant part of human’s life. The intent of this approach is to know about the player’s activities with prior information of tracking objects. It also analyzes the player potential or capacity to lead the winning team. When the player frequently changes their location, object tracking and action recognition will become a quite challenging task. Over the game, various athletes or different objects are considered to assist the system to easily recognize the respective actions of the player. Most of the previous models have been implemented, yet, it faces such consequences to provide promising performance. To meet the pre-requisite, a new multi-athlete tracking model for action recognition in soccer sports is designed with deep learning approaches. Initially, the multi-object tracking video is offered as the input to pre-processing phase. Here, occlusion and background clutter removal and contrast enhancement techniques are utilized to perform pre-processing in the videos. Then, the pre-processed video is offered to the multi-object tracking phase, where the jersey number is observed during multi-object tracking to avoid the identity switch problem. Then, effective multi-object tracking is performed by adaptive YOLOv5. The parameters presented in the improved adaptive YOLOv5 are tuned by proposing a new algorithm as the Random-based Cheetah Red Deer Algorithm (RCRDA). Next, in the action recognition phase, the tracked object from the video is taken based on the Region of Interest (ROI) that is subjected to an action recognition model named Optimized Transformer-based Residual Network with Deep Convolutional Network (OTRN-DCN). At first, ROI is offered as the input to TRN for attaining the feature vectors. Then, the optimal weighted vector extraction is performed, where the weight is tuned by the developed RCRDA. Finally, the attained optimally weighted vectors are given to the DCN phase for attaining recognized action as output. Hence, the developed multi-object tracking and action recognition model will secure an improved recognition rate than the traditional framework.

Fault Reconstruction for Continuous-Time Switched Nonlinear Systems via Adaptive Fuzzy Observer Design

This paper investigates the fault reconstruction problem for a class of continuous-time switched systems subject to persistent dwell-time (PDT) switching. In order to relax the rank condition of the perturbations' coefficient matrices in fuzzy fault reconstruction results, a novel adaptive fuzzy design approach is developed by decomposing the perturbations into virtual faults and states. Then, the PDT switching is first considered in the adaptive fuzzy observer to overcome the restricted switching frequency problem, which can guarantee the stability of the estimation error systems with fast and slow switching characteristics simultaneously. Moreover, the unknown nonlinearities, actuator faults and disturbances can be reconstructed by the designed adaptive mechanism, where the switching problem of sliding surfaces in sliding mode observers can be avoided. Finally, a circuit system example is provided to demonstrate the effectiveness of the fault reconstruction scheme.

Multiple satellite and ground clock sources‐based high‐precision time synchronization and lossless switching for distribution power system

AbstractPrecise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M2 and the lossless switching method in time synchronization delay and error.

Asynchronous Switching Control of Discrete Time Delay Linear Switched Systems Based on MDADT

Ideally, switching between subsystems and controllers occurs synchronously. In other words, whenever a subsystem requires switching, its corresponding sub-controller will be promptly activated. However, in reality, due to network delays, system detection, etc., the activation of candidate controllers frequently lags, which causes issues with asynchronous switching between controllers and subsystems. This asynchronous switching problem may affect system performance and even make the system unstable because the state between the subsystem and the controller may be inconsistent, resulting in the controller not being able to control the subsystem correctly. To keep the system stable while using asynchronous switching, this work suggests an asynchronous control technique for a class of discrete linear switching systems with time delay based on the mode-dependent average dwell time (MDADT). First, we construct a state feedback controller and establish a closed-loop system. In the asynchronous and synchronous intervals of subsystems and controllers, different Lyapunov functions are selected, and sufficient conditions for exponential stability and the H∞ performance of the closed-loop system under asynchronous switching are obtained. In addition, using the MDADT switching strategy, the relevant parameters of each subsystem are designed and the corresponding state–feedback controller gain matrix can be obtained. Finally, a switching system with three subsystems is shown. The approach is confirmed by simulating it using the average dwell time (ADT) switching strategy and the MDADT switching strategy separately.

Aperiodic sampled-data synchronization of Markovian jump neural networks with partially known switching transition rates

In this paper, the aperiodic sampled-data synchronization problem of Markovian jump neural networks with partially known switching transition rates is investigated. The problem of asynchronous switching between the system mode and the controller mode is analyzed due to switching of the transition rate matrix. As the size constraint between the dwell time and the sampling interval, a sampling interval is divided into a matched interval and a mismatched interval to establish the error system models, respectively. Then, a set of multiple Lyapunov looped-functionals which consist of mode-dependent single/double/triple integral terms are constructed to analyze the mean-square exponential stability of the error system. Furthermore, a design method for aperiodic sampled-data controller is proposed. Finally, the obtained results are verified by some numerical simulations, and compared with the existing method, it shows that a larger upper bound of sampling interval can be obtained.