Switching Problem Research Articles

Occluded vehicle tracking by integrating full-scale features and trajectory correction

In order to improve the tracking drift and identity switching (IDs) problems caused by occlusion in vehicle tracking, a method for tracking occluded vehicles that integrates full-scale features and trajectory correction is proposed based on the Deep SORT algorithm. Firstly, a full-scale feature extraction network is introduced to extract the features of different scales of the target and realize adaptive dynamic fusion to enhance the target appearance characteristics. Then, a trajectory correction algorithm is proposed to repair the tracking trajectory during occlusion, and the Kalman filter parameters are updated to reduce the accumulated linear error during occlusion and optimize the target motion characteristics. Finally, the occluded vehicle is tracked by combining appearance features and motion features. The feasibility of the proposed method is verified by ablation experiments and visualization analysis. The experimental results on the KITTI dataset show that compared with the existing typical methods, the proposed method achieves the highest comprehensive score of 78.13 and the lowest number of IDs of 192, which effectively improves the IDs problem in occluded vehicle tracking and improves the robustness of vehicle tracking.

Lp-solutions of Multi-dimensional Oblique Reflected BSDEs and Optimal Switching Problem on Finite or Infinite Time Horizon

Lp-solutions of Multi-dimensional Oblique Reflected BSDEs and Optimal Switching Problem on Finite or Infinite Time Horizon

Controller switching with the anti-bump adapter

Switching on a new or retuned controller while keeping the control loop engaged tends to generate transient “bumps” in the commands and plant outputs. For a linear time-invariant (LTI) controller in state–space form, these bumps can be mitigated or even suppressed by resetting the controller state to an appropriate non-zero value adapted to the pre-switching trajectories of the feedback loop signals. This paper proposes a simple way to compute this adapted controller state by minimizing the difference between the commands actually applied immediately before switching and the virtual command trajectory which the new controller would have generated if it had been activated in parallel. This adapted state can be recursively computed as the output of an LTI system in standard state–space form, the anti-bump adapter. Computing the anti-bump adapter involves only standard matrix algebra and control concepts (e.g., observability and controllability matrices/Gramians). Calculations are very simple and can be translated into compact computer code. This bump suppression procedure is applicable to any discrete-time or continuous-time LTI SISO or MIMO controller, without any assumption on or knowledge of either the previously active controller or the plant. Illustrative applications to continuous-time and discrete-time switching problems and a Matlab® code for computing the anti-bump adapter are presented.

Analysis of Electromagnetic Buffer Characteristics of DC Fast Vacuum Switch

In order to solve the contradiction between the rapid isolation of low voltage DC faults and the low bounce of small stroke vacuum switches, taking the fast vacuum switch for 400 V DC power distribution system as the research object, the electromagnetic buffer simulation and experimental research in the opening process are carried out. The influencing factors of the dynamic and reaction mechanism and buffer output characteristics are analyzed, and the electromagnetic buffering effect under typical working conditions is simulated, which proves that electromagnetic buffer is a necessary part of 400 V fast vacuum switch and the motion characteristics of fast switching are optimal when applying 220 V electromagnetic buffer. According to the simulation calculation results, the engineering prototype was made, the experimental research on the electromagnetic buffer under typical working conditions was carried out, and the design criteria of the small stroke electromagnetic buffer were proposed, which solved the problem of fast switching of small stroke and reliable control and low bounce. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

Diagonally quadratic BSDE with oblique reflection and optimal switching

The present paper is devoted to the study of diagonally quadratic backward stochastic differential equation with oblique reflection. Using a penalization approach, we show the existence of a solution by providing some delicate a priori estimates. We further obtain the uniqueness by verifying the first component of the solution is indeed the value of a switching problem for quadratic BSDEs. Moreover, we provide an extension for the solvability and apply our results to study a risk-sensitive switching problem for functional stochastic differential equations.

Fleet Repositioning, Flag Switching, Transportation Scheduling, and Speed Optimization for Tanker Shipping Firms

In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms.

Tight big-Ms for Optimal Transmission Switching

This paper addresses the Optimal Transmission Switching (OTS) problem in electricity networks, which aims to find an optimal power grid topology that minimizes system operation costs while satisfying physical and operational constraints. Existing methods typically convert the OTS problem into a Mixed-Integer Linear Program (MILP) using big-M constants. However, the computational performance of these approaches relies significantly on the tightness of these big-Ms. In this paper, we propose an iterative tightening strategy to strengthen the big-Ms by efficiently solving a series of bounding problems that account for the economics of the OTS objective function through an upper-bound on the generating cost. We also discuss how the performance of the proposed tightening strategy is enhanced if reduced line capacities are considered. Using the 118-bus test system we demonstrate that the proposed methodology outperforms existing approaches, offering tighter bounds and significantly reducing the computational burden of the OTS problem.

Research on indoor and outdoor positioning switching algorithm based on improved PSO-BP

Aiming at the problems of poor positioning accuracy in the indoor and outdoor junction areas and the loss of positioning signals and discontinuous positioning results during the transition from the indoor area to the outdoor area, this paper proposes a machine learning method to solve the positioning problem in the indoor and outdoor junction areas and the switching problem of positioning methods. An indoor and outdoor positioning switching algorithm based on Particle Swarm Optimization-Back Propagation (PSO-BP) and BP neural network is designed. Through this algorithm, the position of the positioning tag can be judged independently and the coordinates of the positioning tag in the indoor and outdoor junction area can be predicted independently. The experimental results show that the accuracy of positioning area judgment based on PSO-BP neural network can reach 99.91%. The indoor and outdoor boundary area coordinates obtained by BP neural network prediction method are lower than the root mean square error of ultra wide band indoor positioning method and BDS positioning method. The algorithm model proposed in this paper effectively improves the positioning problem of the indoor and outdoor junction area and the positioning problem of the transition from the indoor area to the outdoor area, improves the positioning accuracy and positioning stability of the indoor and outdoor junction area, reduces the positioning cost, and has strong practicability.

SCGTracker: object feature embedding enhancement based on graph attention networks for multi-object tracking

Multi-object tracking (MOT) is a task to identify objects in videos, however, objects with similar appearance or occlusion may cause frequent ID switching, which is the main challenge of current MOT. In this paper, we propose a novel self-cross graph neural network-based multi-object tracking method, which we termed as SCGTracker. This method seamlessly integrates object detection and tracking through graph neural networks, building upon the foundation of the JDE paradigm. Specifically, we construct graph structures to capture the correlation between objects in both spatial and temporal dimensions. To further tackle the frequent ID switching problem, we employ an attention mechanism to aggregate object context information within the same frame and across different frames, updating the object information via graph neural networks to derive highly distinctive appearance features. Ultimately, the obtained strongly distinguishable object appearance features serve to mitigate the issue of frequent object ID switches. In experiments conducted on the MOT17 test set, our proposed method yields promising results, achieving a 73% Multiple Object Tracking Accuracy (MOTA) and a 73.2% ID F1 score. Furthermore, it demonstrates a substantial reduction in ID switches compared with state-of-the-art methods.

Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links.

Learning-assisted optimization for transmission switching

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

Adaptive virtual leader–leader–follower based formation switching for multiple autonomous tracked mobile robots in unknown obstacle environments

This article investigates the formation switching problem of a group of autonomous tracked mobile robots (ATMRs) traveling in unknown obstacle environments. Firstly, a novel formation structure model of virtual leader–leader–follower is designed, where the leader tracks the virtual leader determined by a reference trajectory, while the followers track their desired positions dictated by the virtual leader. Subsequently, a dynamic optimization formation switching strategy is developed, in which the leader calculates a formation adjustment factor in real-time to dynamically adjust the formation parameters according to the environmental constraints. Furthermore, a comprehensive performance evaluation index system is presented to assess formation switching efficiency, leveraging the inherent simplicity of the ATMR’s kinematics. Finally, extensive numerical simulations and physical experiments are carried out to validate the effectiveness of the proposed strategies.

Research on simultaneous switch and multiple switch processing methods

In the simulation of complex power electronic equipment containing multiple power electronic switches, it is often encountered that the same switch occurs several times in one step, or there are multiple switch actions in one step, that is, multiple switches. When the switching event occurs between two moments, because the voltage value can only be updated at the end of the simulation step, there is a cumulative error in the current obtained by the integration. In traditional electromagnetic transient simulation, the interpolation algorithm and its deformation are commonly used to solve this problem. However, due to the complexity of the conventional interpolation algorithm, it will seriously affect the efficiency in large-scale power grid simulation, and can only be applied to offline simulation, which cannot meet the needs of real-time simulation. In this paper, a method is presented to deal with synchronous switches and multiple switches, which can reliably solve the problem of synchronous switches and multiple switches encountered in real-time simulation of complex power electronic systems.

Exploiting symmetry for the job sequencing and tool switching problem

The Job Sequencing and Tool Switching Problem (SSP), a well-known combinatorial optimization problem in the domain of Flexible Manufacturing Systems (FMS), is studied in this article. The aim of this research is to improve the currently known exact solution methodology for this NP-hard problem. We propose a new integer linear programming approach with symmetry-breaking and tightening cuts that provably outperformed the existing methodology described in the literature. The computational study conducted on a data set with 1,050 instances showed that the developed method can solve hard instances that were not solvable with the state-of-the-art methods presented in the literature.

Phase shift control of dual active bridge DC‐DC converter based on feedforward compensation and transient optimization strategy

SummaryIn the context of the dual active bridge (DAB) bidirectional DC‐DC converter using the traditional dual‐phase‐shift (DPS) control method, a transient DC bias occurs during power co‐direction and commutation conversion. This bias negatively impacts the converter's transient performance and can lead to harsh effects, such as hard switching problems, resulting in power loss and excessive current stress. In a closed‐loop control system, poor transient response may destroy the stability of the converter. In this paper, the transient‐optimized‐dual‐phase‐shift (TODPS) control strategy is proposed to eliminate the transient DC bias and to maintain soft switching during transient states. It reduces power loss, eliminates excessive current stress, and allows for smooth transitions between single‐phase‐shift (SPS) and DPS control strategies without introducing DC bias. A closed‐loop control system based on TODPS control is established with feed‐forward compensation to adjust the primary‐side current and modify transmission power. This adjustment enhances the transient performance of the DAB converter. The proposed strategy is versatile and applicable to various power conversion scenarios involving DAB converters. It enables quick switching of the magnitude and direction of the primary‐side current during transient states, resulting in a smooth transient effect without noticeable overshoot or oscillation. The experiment results verify the effectiveness of the proposed transient optimization strategy.

Problems of transition of Russia’s energy sector to using domestically produced fire-resistant fluids

Fire-resistant fluids and lubricants are products that are used in various industries or energy sector to ensure the reliability and safety of equipment operating under high temperature and pressure conditions. Aspects of the use of fire-resistant fluids (hydraulic or lubricating agents), including those based on xylenol, in the Russian Federation energy industry (control and lubrication systems for automatic control systems of steam or gas turbines), and the problems of switching from imported materials to domestic analogues are considered. The range of issues raised at the All-Russian Scientific and Technical Conference “Modern trends in the development of the market for fire-resistant turbine oils and the record of their operation at thermal power plants and nuclear power plants,” held in March 2023, as well as the decisions made there, are covered. Besides, measures taken by energy companies to legitimize the use of domestic fire-resistant fluids for operation in thermal power equipment in Russia are indicated. The results of laboratory studies of new domestic fire-resistant fluids are presented, conclusions are drawn about the possibility of use, operation, and approval of new domestic lubricating or hydraulic fluids in the energy sector. The article was prepared in cooperation between two large electric-heat-generating companies and with the participation of Power Machines, PJSC. One of the goals set by the authors was to promote the production of domestic fire-resistant fluids synthesized from raw materials available in the Russian Federation, ensuring the reliable functioning of oil systems of turbine units.

A Problem of Finite-Horizon Optimal Switching and Stochastic Control for Utility Maximization

A Problem of Finite-Horizon Optimal Switching and Stochastic Control for Utility Maximization

Solving the problem of switching from one track gauge to another in the context of the requirements of interoperability specifications for classical infrastructure

The article discusses issues related to solving the problem of switching from one gauge to another. A progressive solution to the problem of different track gauges is the use of a combined gauge design. The combined track solves the problems of overloading, downtime, and wheel set problems. The design of the combined gauge has set new challenges for track infrastructure workers and scientists. The first task is the strength of the combined track structure. The second task is the stability of the combined track plates. The third task is to lay switches in the combined track and determine the permissible train speeds.

The mean field optimal switching problem: Variational inequality approach

The mean field optimal switching problem: Variational inequality approach