Switching Strategy Research Articles

A Hybrid Caching Scheme with Dynamic Forwarding Strategy in Information‐Centric Network

In this era of huge data traffic, the information‐centric network (ICN) is a switching strategy from conventional host‐centric communication to a new data‐centric one. In‐network caching an integral part of ICN can reduce the shortcomings of today's location‐based internet paradigm. As ICN has limited caching capability, the decision of optimal routing and content placement are the major challenges. In this study, we suggest a hybrid caching with a dynamic forwarding (HCDF) strategy which takes advantage of the off‐path hash routing scheme and on‐path caching scheme. To facilitate both caching schemes, a fraction of the caching space is allotted in each cache node for local caching. At the user‐end segment, an estimation‐based content placement (ECP) policy is designed using content popularity state and cache node position. This policy helps to cache popular content near the consumer's location using the shared cache space. At the server‐end segment, the HCDF scheme dynamically selects the most appropriate routing strategy among three strategies (symmetric, asymmetric, and multicast) based on hop count and path stretch. To demonstrate the effectiveness of HCDF, extensive simulation on real‐world internet topologies has been carried out. From our investigation, it is observed that the suggested approach exhibits an impressive performance in terms of cache hit, latency, and average link load compared to other standard hash routing and well‐known on‐path caching schemes. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A new optimal space vector modulation with DTC switching strategy for induction motor control

Efforts to achieve swift and precise dynamic torque control have been central in AC drive research. Recent advancements in embedded computer systems have highlighted direct torque control (DTC) and field-oriented control (FOC) as key methods for enhancing torque dynamics, both utilizing space vector modulation (SVM) to optimize voltage source inverter positioning. This study introduces a novel synthesis by integrating DTC with SVM to address limitations in conventional DTC, which suffers from limited voltage vector availability, leading to undesirable torque behavior and significant current fluctuations. The primary goal is to develop an optimal switching modulator for the fastest torque response through the combined application of DTC and SVM. The proposed strategy optimizes DC bus usage, reduces torque fluctuations, minimizes total harmonic distortion in AC motor current, decreases switching losses, and ensures seamless digital system integration. Simulations using MATLAB/SIMULINK demonstrate significant torque, current, and flux linkage ripple reductions, validating the approach's effectiveness. This integration overcomes established limitations, extending the capabilities of motor control methodologies and offering enhanced performance and operational integrity in induction motor drive systems.

Research on Omnidirectional Gait Switching and Attitude Control in Hexapod Robots

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional–integral–derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit–Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot’s endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot’s dynamic stability during its motion, enabling it to adapt to complex and changing environments.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Formation collaborative obstacle avoidance with multiple UGVs in restricted environments based on adaptive DWA

Multiple unmanned ground vehicles (UGVs) can collaborate to complete tasks in complex scenarios and are increasingly attracting attention due to their wide applications. Developing a formation cooperative obstacle avoidance policy for multiple UGVs in restricted environments is still challenging. To deal with the problem, we propose a formation cooperative obstacle avoidance method based on adaptive DWA called Multi-ADWA. First, the adaptive DWA algorithm is proposed to address the global navigation task for a single UGV, which dynamically adjusts the trajectory prediction time of the DWA algorithm according to the danger level of the current environment and the density of obstacles. To solve the deadlock problem between UGVs, a dual-priority mechanism is proposed to coordinate the motion order of UGVs. Then, a distributed optimization method is proposed to compute the optimal formation configuration under byte-level shared information. It provides each follower with its easy-to-reach desired goal in real time to improve the formation recovery efficiency. Furthermore, an event-triggered behavior switching strategy is designed and combined with the leader–follower method to make multiple UGVs system to coordinate global navigation, local obstacle avoidance, and formation maintenance. Finally, the numerical simulation and virtual simulation experiments are conducted to verify the effectiveness and superiority of Multi-ADWA. Moreover, a real-world experiment further proves the effectiveness of our approach in practical applications.

Long-Acting Cabotegravir Plus Rilpivirine in People with HIV with Adherence Challenges and Viremia: Current Data and Future Directions.

Long-acting injectable cabotegravir plus rilpivirine (LA CAB/RPV) is currently US Food and Drug Administration (FDA)-approved and HIV treatment guideline-endorsed as a switch strategy for patients with HIV (PWH) who are virologically suppressed on oral ART without a history of treatment failure. Recent changes to the International Antiviral Society-USA (IAS-USA) and U.S. Department of Health and Human Services' (DHHS) Panel on Antiretroviral Guidelines recommend the consideration of LA CAB/RPV in select PWH with viremia who are unable to achieve suppression with oral ART due to suboptimal medication adherence. In this article, we review the existing data on this off-label use of LA CAB/RPV, discuss the motivations and specific caveats implicit in the guidelines change, and propose next steps in exploring this novel treatment in this vulnerable patient population.

A Programmable Gate Driver Module-Based Multistage Voltage Regulation SiC MOSFET Switching Strategy

Silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs), as a new material, have the advantages of low drain-source resistance, high thermal conductivity, low leakage current, and high switching frequency compared with silicon (Si)-based MOSFETs. Therefore, in many industrial applications, Si MOSFETs have been replaced by SiC MOSFETs. However, as the switching speed increases exponentially, some problems are amplified, the most serious of which is the overshoot of current and voltage. The increase in voltage and current slope caused by high switching speeds inevitably leads to overshoot, oscillations, and additional losses in the circuit. This paper focusses on the actual performance of the optimised switching strategy (OSS) in circuit testing and combines the existing simulation results to verify the practicability of OSS. In this paper, the optimised switching strategy is introduced first, and then, the LTspice model of SiC MOSFET is established in detail and verifies the feasibility of the OSS through half-bridge circuit simulation. Finally, the test platform is built using a programmable gate drive module (2ASC-12A1HP). Through a 400 V/30 A double-pulse test, the practicality of the OSS is verified. The experiments show that the OSS can greatly improve the switching performance of SiC MOSFETs.

Resilient and robust control strategy against deception attacks in the platoon of vehicles

AbstractA platoon must ensure the security of conducting missions due to the susceptibility of the vehicles to cyber‐attacks. The deception attack is a common type of cyber‐attack that might be introduced during data exchange through communication channels. Therefore, it can divert each vehicle by injecting uncertainty into the communication link between the agents in the cyber domain. This paper employs a leader–follower consensus system as a reference model to control the platoon vehicles. Accordingly, each agent in the reference model sends its information as a reference trajectory to its corresponding vehicle within the platoon. The vehicles are equipped with a robust local controller, enabling them to follow their desired trajectory in the presence of external disturbances. Furthermore, each vehicle employs an unknown input observer to detect a deception attack while decoupling it from external disturbances. Moreover, by introducing a switching strategy based on mode‐dependent average dwell time to the leader–follower consensus system, a novel resilient control strategy against the deception attack is implemented in the cyber domain. The resultant resilient leader–follower consensus system in the platoon retrieves and restores the attacked vehicle in the platoon to its associated states. The applicability of the proposed method is shown via simulation.

Examination of mathematics teachers’ strategic flexibility in solving mathematical problems

One of the fundamental skills teachers aim to impart in mathematics education is problem-solving. Developing and sustaining this skill is only possible if teachers possess strategic flexibility. Assessing the current level of teachers’ strategic flexibility is crucial, as it also plays a key role in their professional development. The research aims to explore the strategic flexibility of mathematics teachers in solving mathematical problems–both routine and non-routine problems. Using a qualitative research methodology, specifically the case study design, this study engaged a cohort of 12 mathematics teachers who were actively teaching during the autumn semester of the 2022–2023 academic year. These participants represented various educational levels, possessed different postgraduate qualifications, and had diverse years of professional experience. Data collection tools include a Problem Solving Flexibility Test with four math problems and semi-structured interviews to gather teachers’ perspectives on routine and non-routine problems. The semi-structured interviews aimed to reveal all the strategies that teachers use or can use when solving problems. In the initial stages of data analysis, the distinct strategies employed by each participating teacher in addressing the problems were classified. In the context of intra-task strategic flexibility, teachers often used different strategies simultaneously while solving a problem. Teachers were able to switch strategies or use a different strategy because they were able to tackle routine difficulties more readily. The most commonly favoured problem-solving approaches among participants was “Reasoning” and “Make a Drawing or Diagram”. Interestingly, strategies like “Look for a Pattern” and “Simplifying the Problem” were predominantly employed in non-routine scenarios, which is a significant finding of the research. It was found that teachers’ inter-tasks strategic flexibility paralleled their teaching experience and postgraduate education status. In summary, this study investigates how mathematics teachers adapt their problem-solving strategies when faced with different types of mathematical challenges, highlighting the prevalence of specific approaches in routine and non-routine contexts.

A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.

Almost Sure Stability and Stabilization of Composite Switched Systems With Persistent Dwell Time Constraint.

This article is devoted to the exponential almost sure (EAS) stability and stabilization of continuous-time composite switched systems (CSSs) by employing a novel dual-switching mechanism, which incorporates both persistent dwell time (PDT) switching and a set of Markov processes. The interrelation between the parameters under the PDT constraint is established by ensuring that switching frequency remains within the maximum permissible range, which serves as a pivotal consideration in the stability analysis of CSSs subject to above-mentioned dual-switching mechanism. Furthermore, the sufficient conditions for achieving EAS-stability and stabilization of CSSs are established with the designed switching strategy. Our results, in contrast to previous works that are special cases of our study, are more general and less conservative. Three examples are presented to validate the developed theoretical results.

HBV Antigen-Guided Switching Strategy From Nucleos(t)ide Analogue to Interferon: Avoid Virologic Breakthrough and Improve Functional Cure.

Little is known for factors associated with virologic breakthrough (VBT) after switching from nucleos(t)ide analogue (NA) to pegylated interferon alpha (Peg-IFN-α) for patients with chronic hepatitis B (CHB). Eighty patients who received 48-week Peg-IFN-ɑ and NA combination therapy followed by Peg-IFN-ɑ monotherapy for additional 48 weeks were included in this study. HBV-related markers including HBV DNA, HBsAg, HBcrAg, HBeAg, cccDNA, and immunological biomarkers were dynamically evaluated. Twelve (15.0%) patients experienced VBT after switching to Peg-IFN-ɑ and exhibited significantly lower rates of HBsAg loss after therapy completion (0% vs. 35.3%, p = 0.014). The patients with HBcrAg≥ 5 log10U/mL and HBsAg≥ 100 IU/mL had the highest risk of VBT and failed to achieve subsequent HBsAg clearance. Intrahepatic cccDNA level was significantly higher in patients with HBcrAg≥ 5 log10U/mL than those with HBcrAg< 5 log10U/mL. Notably, in contrast to patients with HBcrAg< 5 log10U/mL or with HBsAg< 100 IU/mL who had obviously restored HBV-specific CD8+T cell, Tfh or B cell responses before NA cessation, those with HBcrAg≥ 5 log10U/mL or with HBsAg≥ 100 IU/mL exhibited lackluster immunities before NA cessation and notable diminished immune responses thereafter. Monitoring HBcrAg and HBsAg levels, which correlated with poor immune responses during sequential Peg-IFN-ɑ strategy, may help to avoid VBT and improve functional cure of CHB.

Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing

ABSTRACTNowadays, cloud computing (CC) has been utilized broadly owing to the services it provides which can be received from any location at any time on the basis of the customer's requirements. A huge amount of data transmission is made from both user to host as well as host to the customer in the cloud environment, but here placing the virtual machine (VM) on a suitable host and data transferring is a challenging task. In this research, a harmonic migration algorithm (HMA) is developed by combining both the migration algorithm (MA) and the harmonic analysis (HA) for migrating a VM from an overloaded to an under‐loaded physical machine (PM) and enabling or disabling the VM through switching strategies in CC. The tasks are allocated to the corresponding VM in a round‐robin (RR) manner and subsequently, the load of the VM is predicted through the gated recurrent unit (GRU). The HMA technique migrates the VM when the predicted load is higher than the value of threshold and also, it enables or disables the VM when necessary. Thus, the performance of the developed HMA is improved over the other previous schemes at tasks 100, 200, 300, and 400 by varying the iterations. Therefore, the predicted load, makespan, and resource utilization of the developed HMA are 0.148, 0.327 s, and 0.482% for task 100 at iteration 100.

A Nonlinear Suspension Road Roughness Recognition Method Based on NARX-PASCKF.

Road roughness significantly impacts vehicle safety and dynamic responses. For nonlinear suspension systems, the nonlinear characteristics often make it challenging for estimators to identify the actual road roughness accurately. This paper proposes a hybrid road roughness identification algorithm based on nonlinear auto-regressive with exogenous inputs (NARX) and a process noise adaptive square root cubature Kalman filter (PASCKF) to address this issue. Driven by vehicle acceleration data, an NARX-based road roughness identification system is constructed to mitigate the model uncertainties. Furthermore, a hybrid strategy is proposed. On the one hand, the accurate road roughness estimated by the NARX is converted into process noise covariance, enhancing the estimator's accuracy and convergence rate. Another switching strategy is proposed to optimize the non-convergence issues of the PASCKF. Finally, simulation and actual vehicle experiment data demonstrate that this approach offers superior identification accuracy and adaptability compared to the standalone SCKF algorithm.

The acute procedural results and subsequent clinical outcomes after CTO-PCI: main-analysis of the japanese CTO-PCI expert registry

Abstract Background The prospective and nonrandomized registry by Japanese Board of chronic total occlusion (CTO) experts has been finished. The large-scale clinical data related with acute procedural results and subsequent clinical outcomes after CTO-percutaneous coronary intervention (PCI) has received limited study. Objectives The aim of this study was to make clear the procedural results comparing the different primary CTO-PCI approaches and the clinical outcomes after successful CTO-PCI. Methods Between January 2014 and December 2022, a total of 11,674 CTO-PCI patients were consecutively enrolled in this registry. We compared the acute procedural results between 2 groups; primary antegrade approach (PAA) versus primary retrograde approach (PRA), following the intention-to-treat principle. The key outcome of interest was a technical success that was defined as successful stent implantation, residual diameter stenosis &amp;lt;30% within the treated segment, and restoration of TIMI grade 3 flow. The clinical outcomes after achieving technical success of CTO-PCI were assessed in terms of major adverse clinical events (MACE) that were composed of all-cause death, myocardial infarction (MI), and target lesion revascularization (TLR). Moreover, the technical failed factors were also investigated with multivariate analyses. Results Of overall, the technical success rate was 91.9%, but there were significant differences between PAA and PRA groups in terms of the success rate (93.2% vs 88.3%; p &amp;lt; 0.001). The success rates of switching strategy to final antegrade approach after retrograde failure were very low among both groups (PAA; 55.8%, PRA; 68.1%). Of retrograde success group, the successful channel tracking tended to using septal and graft route (70.8% vs 56.0%; p &amp;lt; 0.001). Severe calcification and tortuosity of CTO lesion were stronger predictors of failed CTO-PCI than the other factors of J-CTO score. Also, The median follow-up duration was 446.7±613.4 days. At 1 year, the clinical incidence of MACE was 9.4%. The individual components of MACE were also shown lower rates (All-cause death; 2.6%, MI; 0.8%, TLR; 6.3%). Conclusions In this large cohort of Japanese CTO-PCI registry, CTO-PCI by highly experienced specialists achieved a high technical success rate and clinical impacts. Retrograde approach has been a key to technical success of CTO-PCI in this study.

Enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery in wireless ad hoc networks

The utilization of directional antennas for neighbor discovery in wireless ad hoc networks brings notable benefits, such as extended transmission range, reduced transmission interference, and enhanced antenna gain. However, when nodes use directional antennas for neighbor discovery, the communication range is limited, resulting in a lack of knowledge of potential neighbors. Hence, it is necessary to design a special antenna direction switching strategy for neighbor discovery based on directional antennas. Traditional methods of switching antenna directions are often random or follow predefined sequences, overlooking the historical knowledge of sector exploration for antenna directions. In contrast, existing machine learning approaches aim to leverage observed historical knowledge to adjust antenna directions for faster neighbor discovery. Nonetheless, the latency of neighbor discovery is still high because the node cannot fully utilize the observed historical knowledge (i.e.., only using the knowledge observed by the node in transmission mode, ignoring the knowledge observed by the node in reception mode). Meanwhile, the corresponding reward and penalty mechanisms are still not detailed enough (i.e.., these reward and penalty mechanisms only consider the sectors of discovered and undiscovered neighboring nodes, ignoring the scenario of sectors that have been rewarded). In this paper, the neighbor discovery process is modeled as a reinforcement learning-based learning automaton. We propose an enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery algorithm, called ERTTND. The algorithm consists of a two-way transmit-receive reinforcement learning mechanism (TTRL) and an enhanced reward-and-penalty mechanism (ERAP). This algorithm leverages insights from nodes in transmission and reception modes to refine their tactical decisions. Then, through an enriched reward-and-penalty framework, nodes optimize their strategies, thus expediting neighbor discovery based on directional antennas in wireless ad hoc networks. Simulation results demonstrate that compared to existing representative algorithms, the proposed ERTTND algorithm can achieve over 30% savings in terms of average discovery delay and energy consumption.

Lightweight Flow‐Based Policy Enforcement for SDN‐Based Multi‐Domain Communication

ABSTRACTAlthough software‐defined networking (SDN) is commonly employed for intra‐domain communication, inter‐domain communication still heavily relies on conventional routing methods, specifically BGP‐based routers. The BGP router plays a crucial role in managing control and data planes, but this traditional approach hinders the exploitation of SDN advantages. Previous studies demonstrated the use of BGP for inter‐domain and end‐to‐end communication. This paper advocates for the adoption of a fully SDN‐based data plane packet switching strategy through the introduction of LPEES, a lightweight policy framework tailored for SDN‐based inter‐domain communication. LPEES strategically confines BGP's functionality to the control plane, preserving SDN benefits. Evaluation results confirm the effectiveness of LPEES compared to the BGP routing approach, as measured by throughput and various network quality of service (QoS) metrics. Additionally, LPEES streamlines inter‐domain communication by utilizing a trust‐based routing policy approach that can establish trust between communicating domains. The presented solution's main advantage is that it loosens the burden on the administrator by requiring less human interference to check the inter‐domain communication security and privacy. Our evaluations show LPEES outperform the BGP‐based in terms of throughput as LPEES achieves a 27 Gbps versus 22 Gbps in the traditional approach. Based on our experiments, LPEES also enhances the communication delay by an average of 17% compared to the traditional BGP‐based approach.

Cycling versus swapping strategies with TNF-α inhibitors and IL-17 inhibitors in psoriatic arthritis in clinical practice

The availability of a number of bDMARDs with different mechanism of action increases potential treatment pathways in psoriatic arthritis (PsA). In clinical practice, following the failure of one bDMARD, it is normal to consider which options are the best for switching strategy. In most cases this choice involves IL17i and TNFi. The main aim of this study was to compare the effectiveness of cycling (from TNFi to another TNFi) and swapping (from TNFi to IL17i or vice versa) strategies. In this monocentric retrospective observational study, all PsA patients treated with TNFi or IL17i between January 2016 and January 2022 were enrolled. The prescriptions were clustered in one cycling group (CG), and two swap groups: from TNFi to IL17i (SG1) and from IL17i to TNFi (SG2). The Kaplan–Meier method and Cox regression models were applied to compare the drug retention rates and to identify factors affecting treatment persistence. A total of 122 patients were enrolled. The CG, SG1 and SG2 2-years retention rates were 51%, 58% and 34% (p = 0.1), respectively. SG1 strategy (HR 0.53; CI 0.31–0.89; p = 0.02), age (HR 0.98; CI 0.96–0.99; p = 0.003), Disease Activity PsA (HR 1.11; CI 1.08–1.13; p < 0.0001), year of switch (HR 1.78; CI 1.39–2.22; p < 0.0001) influenced the retention rate. The findings of this real-world study, even if burdened by bias related to its observational nature, support the hypothesis that in PsA patients swapping from TNFi to IL17i might be more effective than cycling TNFis.

On-line and real-time controller tuning architecture based on Youla–Kucera controller parameterization

This paper presents an on-line and real-time controller tuning architecture based on Youla–Kucera controller parameterization. First, a coarse controller is designed to satisfy nominal performance of closed-loop system. Second, Youla parameter is used to construct an on-line and real-time fine tuning mechanism including a group of controller candidates under arbitrary switching strategy. The mechanism can adjust the control performance on-line to achieve the balance between dynamic and steady-state performance without resulting in any closed-loop stability problems.

Performance analysis of MIMO FSO adaptive mode switching in Malaga turbulent channels with pointing error

MIMO FSO (Free Space Optical) systems can provide high data rates through spatial multiplexing or improve transmission reliability using spatial diversity, but atmospheric turbulence is a bottleneck that restricts communication performance. Adaptive transmission technology can effectively suppress the influence of turbulence and improve communication performance. In this paper, switching between spatial multiplexing and diversity is proposed as an adaptive transmission technology for MIMO links to improve the diversity performance of spatial multiplexing of FSO systems. The switching criterion is based on the Minimum Euclidean Distance (MED) of the spatial mapping scheme. The MED expressions for two spatial modes at the receiver are derived, and the bit error rate (BER) approximations are provided, respectively. We evaluate the BER performance of spatial multiplexing and spatial diversity schemes, respectively, taking into account the joint effects of turbulence strengths, pointing error and path loss. To choose the optimal adaptive switching strategy for the system in different signal-to-noise ratio (SNR) ranges, a lookup table for the adaptive switching statistical SNR threshold was created based on the BER curve corresponding to the minimal Euclidean distance. The results show that compared with fixed spatial mode systems, adaptive spatial mode switching systems can achieve significant BER performance gains. The Demmel condition number of matrix channels is considered to provide a sufficient condition for spatial multiplexing to be superior to spatial diversity.