Switching Algorithm Research Articles

Optimizing Patient Selection for Physiological Pacing in Bradyarrhythmia: Factors Associated With High Ventricular Pacing Burden.

Right ventricular (RV) pacing is established as the most common ventricular pacing (VP) strategy for patients with symptomatic bradyarrhythmia. Some patients with high VP burden suffer deterioration of left ventricular (LV) function, termed pacing-induced cardiomyopathy (PICM). Patients who pace > 20% of the time from the RV apex are at increased risk of PICM, but independent predictors of increased RV pacing burden have not been elucidated in those who have a permanent pacemaker (PPM) inserted for bradyarrhythmia. We aimed to identify factors that are associated with increased VP burden > 20%, hence determining those at risk for resultant PICM. In this retrospective cohort study, we identified the most recent 300 consecutive cardiac implantable electronic device (CIED) implants in our center and collected past medical history, electrocardiogram (ECG), echo, medication and pacemaker check data. A total of 236 individuals met inclusion criteria. Of the patients, 35% had RV pacing burden < 20%, while 65% had VP burden ≥ 20%; 96.2% of patients with complete heart block (CHB) paced > 20% (P = 0.002). Utilization of DDD or VVI (75.2% and 89.2% of patients, respectively) without mode switch algorithms was associated with VP > 20% (P < 0.001). Male or previous coronary artery bypass grafting (CABG) patients also statistically paced > 20%. Other factors trending towards significance included prolonged PR interval, atrial fibrillation or more advanced age. High-grade atrioventricular (AV) block was associated with an RV pacing burden > 20% over 3 years but this was not consistent in patients with only transient episodes of high-grade AV block. We found a significant association between high VP% and male sex, previous CABG and the absence of mode switching algorithms.

Integrated Power-Efficient Time-Frequency Operations With DRX and BWP Switching

Bandwidth part (BWP) and discontinuous reception (DRX) are critical designs to help devices save power. Unlike DRX, a time-domain power-saving mechanism, the BWP is a frequency-domain mechanism newly proposed in the 3GPP NR system. The interaction between the two mechanisms needs investigation for performance optimization. Therefore, this work proposed a joint design for the BWP switching algorithm and analytical models. The simulation results showed that when the DRX cycle is 320 ms, and the packet arrival rate is 0.01/ms, our method improves 31.4% of packet delay or 19.5% of power consumption compared to the baselines. We also showed the range of the traffic arrival rate that a UE with both mechanisms configured is sensitive to the BWP switch strategy.

Grey wolf optimization tuned drivetrain vibration controller with backlash compensation strategy using time-dependent-switched Kalman filter

To improve the performance and durability of vehicle components, efforts have been made to reduce driveline oscillations using advanced active control algorithms. However, existing methods often rely on subjective parameter adjustments, which can be burdensome for designers. This study introduces an effective tuning algorithm for a driveline vibration controller that accounts for nonlinear backlash effects. Initially, a driveline dynamics model is developed to focus on transient oscillations resulting from changes in driving force and the presence of nonlinear backlash. The backlash impact is incorporated into the model through a discontinuous dead-zone region. Two operational dynamics, which are the contact mode and the backlash mode, are considered. A dynamic output feedback [Formula: see text] controller is designed as a baseline controller to mitigate low-frequency resonance in the driveline. A solution for managing the nonlinear backlash challenges is introduced, involving the use of a simple control mode switching algorithm in conjunction with the controller. This algorithm relies on a time-dependent-switched Kalman filter. Additionally, the optimal settings for the parameters needed by the mode-switching algorithm are autonomously determined using the grey wolf optimizer (GWO). The proposed active controller can be implemented in real vehicles by using an on-vehicle acceleration sensor and electronic control unit (ECU). In a simulation environment, the vehicle body vibration is online fed back to the resultant controller, and an actuator is supposed to apply control commands to the driveline. The effectiveness of this newly proposed active controller is confirmed through comparative tests, revealing the superior vibration control.

Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach

The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting de facto its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.

A Comprehensive Review on the Power Supply System of Hydrogen Production Electrolyzers for Future Integrated Energy Systems

Hydrogen energy is regarded as an ideal solution for addressing climate change issues and an indispensable part of future integrated energy systems. The most environmentally friendly hydrogen production method remains water electrolysis, where the electrolyzer constructs the physical interface between electrical energy and hydrogen energy. However, few articles have reviewed the electrolyzer from the perspective of power supply topology and control. This review is the first to discuss the positioning of the electrolyzer power supply in the future integrated energy system. The electrolyzer is reviewed from the perspective of the electrolysis method, the market, and the electrical interface modelling, reflecting the requirement of the electrolyzer for power supply. Various electrolyzer power supply topologies are studied and reviewed. Although the most widely used topology in the current hydrogen production industry is still single-stage AC/DC, the interleaved parallel LLC topology constructed by wideband gap power semiconductors and controlled by the zero-voltage switching algorithm has broad application prospects because of its advantages of high power density, high efficiency, fault tolerance, and low current ripple. Taking into account the development trend of the EL power supply, a hierarchical control framework is proposed as it can manage the operation performance of the power supply itself, the electrolyzer, the hydrogen energy domain, and the entire integrated energy system.

Research on force/position switching control of servo actuator for hydraulically driven joint robot

According to the robot’s walking motion characteristics, the position/force switching control is studied to realize the segmental control of the robot stroke. This stroke is controlled by position when the foot end of the robot descends from the suspension to the ground. To avoid excessive contact force when the robot touches the ground, force control is carried out when the foot touches the ground. Due to the force and position control methods and control parameters of the hydraulic quadruped robots are different, the precise mathematical model for the joint position control and joint force control of the leg joints of the hydraulic quadruped robot is established using the system identification method. A fuzzy multi-model switching algorithm is proposed to solve the problem of jumping and jitter of system parameters in the process of force/position switching. Through simulation and prototype experiments, fuzzy multi-model switching is compared with direct switching and multi-model switching, and the switching effect of the algorithm is verified.

Supercapacitor‐based coordinated synthetic inertia scheme for voltage source converter‐based HVDC integrated offshore wind farm

AbstractA supercapacitor‐based coordinated synthetic inertia (SCSI) scheme for a voltage source converter‐based HVDC (VSC‐HVDC)‐integrated offshore wind farm (OWF) is proposed. The proposed SCSI allows the OWF to provide a designated inertial response to an onshore grid. Under the SCSI scheme, a supercapacitor is added to the DC side of each wind turbine generator via a bidirectional DC/DC converter, varying its voltage along with the offshore frequency to synthesise the desired inertial response. The HVDC grid side VSC employs a DC voltage/frequency droop control to convey the onshore frequency information to DC voltage without communication. Meanwhile, the wind farm side VSC regulates the offshore frequency to couple with the conveyed onshore frequency, considering voltage drop across the DC cables. An offshore frequency switching algorithm is incorporated to avoid undesired SCSI maloperation under offshore faults. The key parameters of the proposed SCSI are optimised through a small signal stability analysis. The effectiveness of the SCSI scheme is evaluated using a modified IEEE 39‐bus test system. The results show that the proposed SCSI scheme can provide required inertial support from WTG‐installed supercapacitors to the onshore grid through the VSC‐HVDC link, significantly improving the onshore frequency stability.

Control system for noise-resistant electronic speed controller of a brushless electric motor for an unmanned aerial vehicle

Objectives. The high demand for unmanned aircraft and their efficiency makes the production of their components a matter of relevance. One of these components is the speed controller of the brushless electric motor of the propeller motor group. At the current time, Russian industry, however, does not mass-produce them. In order to start production, control methods and algorithms for the hardware and software parts of devices of this type are needed. Criteria for selecting the main components also need to be formalized. The aim of this work is to develop a method for the software control of electric motors. This includes block diagrams and invariant algorithms and methods for the calculated selection of parameters of the main microcontroller of the electronic speed controller.Methods. Methods of algorithmization, expert assessments, linear computational processes and experimental studies were used.Results. The paper presents the theoretical basis for controlling the required motors. It proposes a block diagram of the implementation of the controller, and a technique for switching windings when controlling with a trapezoidal signal is proposed. Examples are given in the form of an oscillogram. Based on theoretical research, an invariant algorithmic apparatus was developed for building software for various types of microcontrollers. Block diagrams of all the main modules of the software are also presented. The main ones include: the event switching algorithm; and the main endless loop of the microcontroller. The requirements for microcontrollers to create the various types of speed controllers are formalized herein and presented in the form of a set of mathematical expressions. They enable the number of required peripheral devices and microcontroller ports to be calculated according to the requirements for the microcontroller, as well as the computing power of the core used.Conclusions. Experimental studies show the reliability of the theoretical research presented herein. The results obtained can be used to select the optimal element base and develop software for speed controllers of electric motors of the propellers of unmanned aircraft.

On Hybrid Prescribed-Time Concurrent Learning with Switching Datasets

We introduce a novel concurrent learning (CL) algorithm designed to solve parameter estimation problems within a user-prescribed time frame and by utilizing alternating datasets during the learning process. The algorithm can tackle applications involving switching data sets (including data sets that are completely uninformative) that are updated in real-time as the algorithm operates. To achieve parameter estimation within a specified time independent of the dataset’s richness, the switching algorithm employs dynamic gains. The main result establishes uniform global exponential ultimate boundedness, with an ultimate bound that shrinks to zero as the magnitude of the measurement disturbances decreases. The stability analysis leverages tools from hybrid dynamical systems theory, along with a recently introduced dilation/contraction argument on the hybrid time domains of the solutions. The algorithm and main results are illustrated via a numerical example.

Re-routable and Low-latency All Optical Switching Algorithm for Next Generation DCN

Digital infrastructure is now indispensable to the function of society and quality of life of the citizen. All over the world there is leveraging of digital infrastructure that comprises use of data, computerized devices, methods and systems. During COVID-19 pandemic it comes more prominently in front. India being the most popular and populated country in the world, comprises nearly half billion internet users expected to transform the digital ecosystem. With the increasing demand of smartphone application, online activities gigantic amount of data has been generated, so evolution and development of data centers be the high importance not only for India but as well as for the world. Thus, it is the need to promote and create robust data center network infrastructure that supports emerging technologies such as 5G, IoT, AI, machine learning, adaptive manufacturing, smart agriculture, and automation and so on. In this paper we propose an all Optical re-routable, low latency Data Center Network (DCN) architecture using Passive Optical Datacenter Switch (PODS). The PODS consists of an Arrayed Waveguide Grating Router (AWGR) in association with a controller unit. An efficient wavelength assignment algorithm is integrated in PODS, to reroute the packet for congestion avoidance and packet loss.. This feature makes the DCN architecture more scalable, proficient with high throughput, low latency, power efficient under high traffic and bursty traffic conditions. The performance of the proposed PODS-based DCN is compared with existing passive optical DCN architectures PODCA and finds improved characteristics in terms of delay, throughput and blocking probability. Simulation of the framework is done in Python platform and executed on google COLAB in the Windows environment and the result shows 46.3% improvement in latency and throughput in terms of 90% network load compared with PODCA architecture.

The revolution of matrix converter: moving towards decarbonization technology

The matrix converter is a power electronic device that can be used to control the flow of electricity in various industrial and commercial applications. In recent years, the matrix converter has become increasingly popular as a means of decarbonizing energy production. One of the primary benefits of the matrix converter is its ability to efficiently convert AC power to DC power, and vice versa. This is critical in renewable energy systems that rely on DC power, such as solar panels and wind turbines. Therefore, this paper presents the revolution of the matrix converter, which focuses on the single-phase topology, and its applications, especially for renewable energy and battery storage. The operations of a matrix converter to convert DC power from solar panels and wind turbine sources to AC power suitable for use on the power grid are presented as well as the appropriate switching algorithms. To achieve this, the SPMC can be designed to handle multiple input voltages and frequencies and to generate multiple output voltages and frequencies. This can be done by using a combination of modulation techniques, such as carrier based PWM, along with appropriate switching patterns. Therefore, a multi-input multi-output converter (MIMO) using matrix converter is a power electronics converter that can handle multiple inputs and multiple outputs voltages and frequencies using only one circuit topology. Thus, the topology can offer several advantages over traditional power converters such as reduced size and cost, high efficiency, improved power quality and flexibility. This is crucial for a decarbonized energy system because it makes renewable energy sources more effective. Its ability to efficiently convert power, regulate power flows, and operate reliably makes it an important tool in the transition to a decarbonized energy system.

Ab initio molecular dynamics study of intersystem crossing dynamics for MH2 (M = Si, Ge, Sn, Pb) on spin-pure and spin-mixed potential energy surfaces.

Recently, surface-hopping ab initio molecular dynamics (SH-AIMD) simulations have come to be used to discuss the mechanisms and dynamics of excited-state chemical reactions, including internal conversion and intersystem crossing. In dynamics simulations involving intersystem crossing, there are two potential energy surfaces (PESs) governing the motion of nuclei: PES in a spin-pure state and PES in a spin-mixed state. The former gives wrong results for molecular systems with large spin-orbit coupling (SOC), while the latter requires a potential gradient that includes a change in SOC at each point, making the computational cost very high. In this study, we systematically investigate the extent to which the magnitude of SOC affects the results of the spin-pure state-based dynamics simulations for the hydride MH2 (M = Si, Ge, Sn, Pb) by performing SH-AIMD simulations based on spin-pure and spin-mixed states. It is clearly shown that spin-mixed state PESs are indispensable for the dynamics simulation of intersystem crossing in systems containing elements Sn and Pb from the fifth period onward. Furthermore, in addition to the widely used Tully's fewest switches (TFS) algorithm, the Zhu-Nakamura (ZN) global switching algorithm, which is computationally less expensive, is applied to SH for comparison. The results from TFS- and ZN-SH-AIMD methods are in qualitative agreement, suggesting that the less expensive ZN-SH-AIMD can be successfully utilized to investigate the dynamics of photochemical reactions based on quantum chemical calculations.

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.

An adaptive solution strategy for Richards' equation

Flow in variably saturated porous media is typically modeled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative solver for Richards' equation. The solver relies on an adaptive switching algorithm, based on rigorously derived a posteriori indicators, between two linearization methods: L-scheme and Newton. Although a combined L-scheme/Newton strategy was introduced previously in [1], here, for the first time we propose a reliable and robust criteria for switching between these schemes. The performance of the solver, which can be in principle applied to any spatial discretization and linearization methods, is illustrated through several numerical examples.

The vector error correction index model: representation, estimation and identification

Summary This paper extends the multivariate index autoregressive model to the case of cointegrated time series of order (1,1). In this new modelling, namely the vector error-correction index model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction with reference to the vector error correction model. We show that the VECIM allows one to decompose the reduced-form errors into sets of common and uncommon shocks, and that the former can be further decomposed into permanent and transitory shocks. Moreover, we offer a switching algorithm for optimal estimation of the VECIM. Finally, we document the practical value of the proposed approach by both simulations and an empirical application, where we search for the shocks that drive the aggregate fluctuations at different frequency bands in the US.

Resolution of the IV International Menopause Expert Forum

The IV International Menopause Expert Forum took place on November 24, 2022. The expert panel included leading endocrine gynaecologists from Armenia, Azerbaijan, Georgia, Kazakhstan, Moldova, Ukraine, Uzbekistan, and Ukraine. The experts discussed interdisciplinary cooperation in the management of menopausal women, the eligibility criteria for menopausal hormone therapy (MHT) and their implementation in routine clinical practice and developed an algorithm for switching from combined oral contraceptives (COCs) to MHT. Due to the lack of awareness and access to information and services related to menopause, interdisciplinary cooperation aimed at early detection of symptoms and diagnosis of menopausal disorders for timely treatment is paramount. The Forum participants highlighted the need for a series of steps to ensure comprehensive management of menopausal women. The Expert Forum reviewed the 2022 «Eligibility criteria for menopausal hormone therapy: a position statement from a consortium of scientific societies for the use of MHT in women with medical conditions». The developed criteria for the acceptability of different MHT types for women with comorbidities is an essential step in expanding the possibilities for the use of MHT by physicians of various therapeutic areas. Moreover, the criteria help to identify populations for whom certain types of MHT are associated with risks. Importantly, the discussed eligibility criteria are not yet universally accepted. Therefore, in routine clinical practice, when deciding whether to prescribe a particular type of MHT, it is vital to take an individual approach and assess the benefit/risk balance, considering the patient’s combination of comorbidities, medical history, as well as the strengths and components of combination MHT, particularly the gestational component.Perimenopausal women may need contraception, including COCs. In such circumstances, physicians are faced with such questions as the acceptability of prescribing or continuing to use COCs for menopausal women, as well as when and how to switch from COCs to MHT. Thus, the Forum developed an algorithm for switching from COCs to MHT.

Influence of Pre-Crash Vehicle Maneuvers on Front Passenger Safety Performance Response

&lt;div&gt;Pre-crash vehicle maneuvers are known to affect occupant posture and kinematics, which consequently may influence injury risks during a collision. In this study, the influence of pre-crash vehicle maneuvers on the injury risks of front-seated occupants during a frontal crash was numerically evaluated. A generic buck vehicle model was developed based on a publicly available FE model, which included the vehicle interior and the front passenger airbag (PAB). The pre-crash phase was simulated using specific rigid-body human models with active joints (GHBMCsi-pre models) developed based on exterior shapes of the simplified deformable human model (GHBMCsi) representing a 50th male subject. Two pre-crash maneuvers representing (1) a generic 1g braking and (2) turning-and-braking scenarios were simulated. Then, the kinematics data of belted GHBMCsi-pre models were transferred using a developed switch algorithm to the corresponding GHBMCsi models, which can predict occupant injury risks. Finally, an FMVSS 208 pulse (NCAP pulse with delta V of 56 km/h) was applied to simulate the in-crash phase. Injury metrics were recorded for the belted GHBMCsi model to evaluate the passenger injury risks. Overall, it was concluded that pre-crash braking decreased the severity of injury sustained by the passenger. The success of the methodology used in this study, to simulate reasonable and computationally efficient pre-crash and in-crash phases, suggests using it for more advanced studies where additional parameters (e.g., BMI, age, etc.) could also be taken into consideration.&lt;/div&gt;

Metamodel-based dynamic algorithm configuration using artificial neural networks

We consider the problem of configuring algorithms dynamically by selecting algorithm parameter values adaptively. The research is motivated by the time dependency of system parameters throughout algorithm runtime in servicing systems: Depending on the customer arrival rate, switching algorithm parameters may be advisable to maintain quality of service. To this end, we develop a metamodel-based methodology for dynamic algorithm configuration: We first record algorithm performance under static system parameters. This knowledge is then translated into an artificial neural network (ANN) predicting performance for given system and algorithm parameters. The ANN finally serves as a metamodel determining optimal algorithm parameters dynamically when there is system parameter variation. Overall, the developed generic methodology for dynamic algorithm control facilitates a structured model-based approach to suitably respond to changing system conditions. The outline is adept to practical instantiation as demonstrated in two service systems where control parameters are adjusted adaptively to customer arrival rates.

Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation

AbstractOnline recommendation systems process large amounts of information to make personalized recommendations. There has been some progress in research on incorporating knowledge graphs in reinforcement learning for recommendation; however, some challenges still remain. First, in these approaches, an agent cannot switch paths intelligently, because of which, the agent cannot cope with multi-entities and multi-relations in knowledge graphs. Second, these methods do not have predefined targets and thus cannot discover items that are closely related to user-interacted items and latent rich semantic relationships. Third, contemporary methods do not consider long rational paths in knowledge graphs. To address these problems, we propose a deep knowledge reinforcement learning (DKRL) framework, in which path-guided intelligent switching was implemented over knowledge graphs incorporating reinforcement learning; this model integrates predefined target and long logic paths over knowledge graphs for recommendation systems. Specifically, the designed novel path-based intelligent switching algorithm with predefined target enables an agent to switch paths intelligently among multi-entities and multi-relations over knowledge graphs. In addition, the weight of each path is calculated, and the agent switches paths between multiple entities according to path weights. Furthermore, the long logic path has better recommendation performance and interpretability. Extensive experiments with actual data demonstrate that our work improves upon existing methods.The experimental results indicated that DKRL improved the baselines of NDCG@10 by 3.7%, 9.3%, and 4.7%; of HR@10 by 12.39%, 20.8%, and 13.86%; of Prec@10 by 5.17%, 3.57%, 6.2%; of Recall@10 by 3.01%, 4.2%, and 3.37%. The DKRL model achieved more effective recommendation performance using several large benchmark data sets compared with other advanced methods.

A Universal Model Predictive Control Strategy for Dual Inverters Fed OW-PMSM Drives

This study proposed a universal model predictive control (MPC) strategy for dual inverters fed open-winding permanent magnet synchronous motor (OW-PMSM) drives where two isolated buses have arbitrary DC voltages. Diverse and unordered space vector distribution caused by different voltage ratios of DC buses that greatly increases the calculation burden for the conventional MPC algorithm is analyzed in detail. Based on this, a novel MPC strategy that is universally applicable to any voltage ratio is put forward, which could decrease the number of candidate vectors from 49 to less than 15 in every cycle. Although the computational burden is reduced, the output power quality is unaffected owing to the effective simplification of the vectors. Besides, an optimized switching algorithm is designed to adapt to the situation when the voltage ratio changes during the operation state. The experimental results are presented to verify the effectiveness of the proposed method.