Efficient Integration Research Articles

Efficient Compressor and Encoder Strategies for Cost-Effective Radix-4 Approximate Booth Multipliers

Multiplication operations are essential in digital systems, and the design of efficient and cost- effective multipliers holds significant importance across various applications. This paper presents a novel methodology aimed at improving the affordability of approximate radix-4 Booth multipliers by proposing simplified designs for compressors and encoders. The primary objective is to achieve a balance between computational accuracy and hardware cost, rendering the multiplier suitable for deployment in low-cost embedded systems and applications where a certain degree of approximation is permissible. The proposed approach involves utilization of simplified compressors, meticulously optimized for resource efficiency and seamless integration into the approximate radix-4 Booth multiplier architecture. Furthermore, an innovative encoder design is introduced to further streamline the hardware complexity associated with encoding partial products. These encoder designs are strategically crafted to maintain an acceptable level of accuracy while minimizing resource utilization. Keywords:Approximate Booth multipliers, Booth encoders, Compressors, Xilinx ISE 14.7 tool

Shape optimization of non-matching isogeometric shells with moving intersections

While shape optimization using isogeometric shells exhibits appealing features by integrating design geometries and analysis models, challenges arise when addressing computer-aided design (CAD) geometries comprised of multiple non-uniform rational B-splines (NURBS) patches, which are common in practice. The intractability stems from surface intersections within these CAD models. In this paper, we develop an approach for shape optimization of non-matching isogeometric shells incorporating intersection movement. Separately parametrized NURBS surfaces are modeled using Kirchhoff–Love shell theory and coupled using a penalty-based formulation. The optimization scheme allows shell patches to move without preserving relative location with other members during the shape optimization. This flexibility is achieved through an implicit state function, and analytical sensitivities are derived for the relative movement of shell patches. The introduction of differentiable intersections expands the design space and overcomes challenges associated with large mesh distortion, particularly when optimal shapes involve significant movement of patch intersections in physical space. Throughout optimization iterations, all members within the shell structures maintain the NURBS geometry representation, enabling efficient integration of analysis and design models. The optimization approach leverages the multilevel design concept by selecting a refined model for accurate analysis from a coarse design model while maintaining the same geometry. We adopt several example problems to verify the effectiveness of the proposed scheme and demonstrate its applicability to the optimization of the internal stiffeners of an aircraft wing.

Enhancing Enterprise Efficiency through Cross-Domain Supply Chain Linkage Management

In recent years, with the complexity and variability of international finance, debt, and geopolitical risks, the growth rate of domestic economic operations has slowed down. More enterprise groups are facing the transformation and upgrading from domestic leadership to internationalization, achieving revenue growth of nearly 10 billion yuan per year against the trend. The severe and complex internal and external environment has brought many challenges to the supply chain for enterprises. This article focuses on the goal of an “enterprise supply chain system focusing on value creation, strengthening system linkage, and improving overall chain efficiency,” aiming to find solutions to problems such as strong demand variability, frequent emergency needs, coexistence of project shortages and inventory surplus, long material turnover cycles, and high organizational complexity. By searching for key activities in the entire business chain of demand, design, production, service, and finance, implementing source planning, process control, system linkage, analysis and summary, and team collaboration, we have achieved more scientific demand forecasting, more accurate planning, more effective procurement cost reduction, more stable production performance supply, more convenient inventory sharing services, and more efficient integration of business and financial policies.

N/O-bridge stimulated robust binding energy and fast charge transfer between carbon nanofiber and NiCo LDH for advanced supercapacitors

Layered double hydroxide (LDH)-carbon composites effectively mitigate the inherent issues of agglomeration and poor conductivity in LDH. However, the weak binding energy and insufficient charge transfer capability between LDH and carbon substrate significantly compromise the active substance loading, cyclic stability and practical capacity of the composites. Herein, N/O co-doping porous carbon nanofibers (NOPCNFs) are first prepared by blending diminutive zinc imidazolate framework-8 nanoparticles with polyacrylonitrile for electrospinning, and then densely packed NiCo LDH nanosheets are homogeneously anchored on NOPCNFs to form NiCo LDH@NOPCNFs heterostructure via a hydrothermal method. The experimental findings and density functional theory calculation results indicate that N/O atoms exhibit robust binding forces with metal atoms through enhanced electrostatic adsorption and p-d covalent hybridization, which facilitates the nucleation and development of NiCo LDH on carbon nanofibers. Meanwhile, these heteroatoms also serve as the bridge for electron transfer from NiCo LDH to NOPCNF, leading to a strong interfacial electric field, thus accelerating charge transfer behaviors. Benefitting from the synergistic interaction between NiCo LDH and NOPCNF, the obtained NiCo LDH@NOPCNFs demonstrate an elevated mass loading of active substance (55 wt%), an impressive specific capacitance of 1340 F/g at 1 A/g (based on the mass of NiCo LDH, 2463 F/g), and good cyclic durability for 5000 cycles. Moreover, an all-solid-state asymmetric supercapacitor using NOPCNFs and NiCo LDH@NOPCNFs shows promising practical application prospects. This work gives insights into the important influence of heteroatom doping in carbon, and provides a feasible approach for the efficient integration of electroactive and carbon material.

Characterization of Strategic Deconflicting Service Impact on Very Low-Level Airspace Capacity

European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how safety and operators’ time flexibility in flight planning will impact capacity. This study focuses on the impact of strategic deconflicting services on the capacity of the very low-level airspace, a critical area in the future management of unmanned aerial vehicle traffic. The results validate the assumptions regarding the roles of airspace managers and drone operators through simulation studies; highlight the limitations of the first come, first served policy; and propose a batch policy as a potential optimization strategy for future airspace capacity management. The forecasting model developed using regression techniques provides a general method for predicting airspace capacity under specific conditions, contributing to the safe and efficient integration of unmanned aerial vehicles into European airspace.

Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

Evaluating the effectiveness of large language models in abstract screening: a comparative analysis

ObjectiveThis study aimed to evaluate the performance of large language models (LLMs) in the task of abstract screening in systematic review and meta-analysis studies, exploring their effectiveness, efficiency, and potential integration into existing human expert-based workflows.MethodsWe developed automation scripts in Python to interact with the APIs of several LLM tools, including ChatGPT v4.0, ChatGPT v3.5, Google PaLM 2, and Meta Llama 2, and latest tools including ChatGPT v4.0 turbo, ChatGPT v3.5 turbo, Google Gemini 1.0 pro, Meta Llama 3, and Claude 3. This study focused on three databases of abstracts and used them as benchmarks to evaluate the performance of these LLM tools in terms of sensitivity, specificity, and overall accuracy. The results of the LLM tools were compared to human-curated inclusion decisions, gold standard for systematic review and meta-analysis studies.ResultsDifferent LLM tools had varying abilities in abstract screening. Chat GPT v4.0 demonstrated remarkable performance, with balanced sensitivity and specificity, and overall accuracy consistently reaching or exceeding 90%, indicating a high potential for LLMs in abstract screening tasks. The study found that LLMs could provide reliable results with minimal human effort and thus serve as a cost-effective and efficient alternative to traditional abstract screening methods.ConclusionWhile LLM tools are not yet ready to completely replace human experts in abstract screening, they show great promise in revolutionizing the process. They can serve as autonomous AI reviewers, contribute to collaborative workflows with human experts, and integrate with hybrid approaches to develop custom tools for increased efficiency. As technology continues to advance, LLMs are poised to play an increasingly important role in abstract screening, reshaping the workflow of systematic review and meta-analysis studies.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Joint coordinate attention mechanism and instance normalization for COVID online comments text classification.

The majority of extant methodologies for text classification prioritize the extraction of feature representations from texts with high degrees of distinction, a process that may result in computational inefficiencies. To address this limitation, the current study proposes a novel approach by directly leveraging label information to construct text representations. This integration aims to optimize the use of label data alongside textual content. The methodology initiated with separate pre-processing of texts and labels, followed by encoding through a projection layer. This research then utilized a conventional self-attention model enhanced by instance normalization (IN) and Gaussian Error Linear Unit (GELU) functions to assess emotional valences in review texts. An advanced self-attention mechanism was further developed to enable the efficient integration of text and label information. In the final stage, an adaptive label encoder was employed to extract relevant label information from the combined text-label data efficiently. Empirical evaluations demonstrate that the proposed model achieves a significant improvement in classification performance, outperforming existing methodologies. This enhancement is quantitatively evidenced by its superior micro-F1 score, indicating the efficacy of integrating label information into text classification processes. This suggests that the model not only addresses computational inefficiencies but also enhances the accuracy of text classification.

Short-Term forecasting of floating photovoltaic power generation using machine learning models

Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations.

Flexibility exploitation in Net-Zero Energy Factories. A technical and economic study case for dairy systems located in Central and South Europe

The decarbonization of energy systems (electric, thermal, gas, and transportation) requires flexibility to integrate power generated by volatile and renewable energy sources (vRES). Industrial systems can contribute to exploiting the flexibility needed to integrate the electricity generated by vRES into energy systems. Designing industrial systems as Net-Zero Energy Factories (NZEF) offers the possibility of exploiting the flexibility that could be used internally to integrate the power generated by renewable energy sources. This study addresses, on one hand, the presentation of a methodology to identify and quantify the flexibility potential of continuous processes typical in dairy systems. On the other hand, it aims to demonstrate a methodology for designing additional flexibility options to operate dairy systems as NZEF. A model has been developed in which the energetic and material flows of conventional diary systems are simulated. The model allows for the design of conventional dairy systems as NZEF and the evaluation of their potential to exploit flexibility. Two different European climate zones have been investigated: Bari (Italy) and Magdeburg (Germany). This study is relevant for designers and facility managers who are following their decarbonization plans, aiming to operate their industrial facility as Net-Zero Energy. The results of this study show that for dairy systems designed as Net-Zero Energy Factories and located in southern Europe (Bari, Italy), the operational costs are much lower than those operating in central Europe (Magdeburg, Germany). Specifically, integrating an 800 kW PV system reduced operational costs by 16.3 % in Bari, compared to 3.8 % in Magdeburg. Additionally, achieving 97 % vRES integration in Bari requires a 1000 kWh thermal storage capacity, whereas in Magdeburg, a 500 kWh capacity is sufficient. These findings suggest that NZEF designs can be optimized based on regional climatic conditions to maximize cost efficiency and vRES integration. The analysis indicates that sensible heat storage systems should be preferred over electrochemical batteries when designing additional flexibility options, considering both technical and economic criteria.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

Free-standing millimeter-range 3D waveguides for on-chip optical interconnects

Next-generation energy-efficient photonic integrated systems, such as neuromorphic computational chips require efficient heterogeneous integration of ultracompact light sources and photodetectors through highly dense waveguide circuits. However, interconnecting these devices in emitter-receiver communication circuits remains a challenge and an obstacle towards upscaling heterogeneous photonic chips. Here we report on versatile air-cladded free-standing 3D polymer waveguides (OrmoCore, n≈1.5) spanning up to 900 µm in length without intermediate mechanical support structures, microprinted via two-photon polymerization. The presented waveguides are suitable for on-chip out-of-plane light coupling as well as non-connected 3D crossings, needed for high density optical circuits. The waveguides show optical transmission losses of 1.93 dB mm−1 at λ = 635 nm, and of 3.71 dB mm−1 at λ = 830 nm in the wavelength range of GaAs-based microLEDs spectral emission. On-chip imaging for high-precision alignment and TPP microfabrication are performed seamlessly by utilizing the same laser source for both steps, allowing accurate 3D printing on microstructured substrates. As proof of concept, we interconnect two GaAs-based microLEDs via an on-chip microprinted 3D waveguide. Such combined systems can serve as building blocks of future complex integrated heterogeneous photonic networks.

Risk Evaluation and Software Development of Urban Natural Gas Pipelines Based on Bayesian Networks

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

LiVeR: Lightweight Vehicle Detection and Classification in Real-Time

Detection of vehicles and their classification is a significant component of wide-area monitoring and surveillance, as well as intelligent- transportation . Existing solutions tend to employ heavy-weight infrastructure and costly equipment, as well as largely depend on constant support from the cloud through round-the-clock internet connectivity and uninterrupted power supply. Moreover, existing works mainly concentrate on localized measurement and do not discuss their efficient integration to address the problem over a wide area. For practical use in an outdoor environment, apart from being technically sound and accurate, a solution also needs to be cost-effective , lightweight , easy to install , flexible , low overhead , and easily maintainable , as well as self-sufficient as much as possible. However, fulfilling all these goals together is a challenging task. In this work, we propose an IoT-assisted strategy, LiVeR , to accomplish it. For self-sufficient on-the-fly classification in resource-constrained low-power IoT devices, LiVeR minimizes not only the computational requirements but also the energy consumption, which enables sustained operation in a hostile outdoor environment for a considerably long time solely based on battery power. Through extensive studies based on outdoor measurement and trace-based simulation on empirical data, we demonstrate that LiVeR classifies vehicles of small, medium, and large size with an accuracy of 91.3% up to 98.8%, 92.3% up to 98.5%, and 93.8% up to 98.8%, respectively, for single-lane traffic. We also demonstrate that LiVeR spends only about one-third of the number of RF packets to achieve vehicle detection and classification compared to the state-of-the-art RF-based solution, considerably extending the lifetime of the system.

Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics

Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

(Invited) Emerging Opto-Electronics and Quantum-Photonics Based on Ultra-Low Temperature Epitaxy of Group-IV Nanolayers

Germanium has already successfully entered Si technology through electronics and photonics applications [1,2]. However, due to the inherent lattice mismatch between Ge and Si, high-crystalline SiGe epitaxial layers of a sufficient thickness could only be grown for relatively low Ge concentration (<40%). At higher concentrations, the epitaxial SiGe layers relax the strain through either plastic or elastic relaxation. Thus, for about 2% (and 4%) strain between substrate and epilayer, only layer thicknesses of < 2nm (<0.5 nm) can be grown before elastic relaxation leads to quantum dot formation. In principle, such epitaxial Ge-rich QDs on Si(001) substrates can be successfully used to enhance the light emission properties of SiGe material [3-7]. However, planar Ge-rich two-dimensional layers would be preferred for efficient large-scale integration and reliable addressing of the nanostructures.Here, we demonstrate that ultra-low temperature (ULT) growth, carried out in a temperature window between 100°C and 350°C, is the key to extensive epilayer supersaturation, leading to layer thicknesses that are about one order of magnitude larger than what can be achieved by conventional epitaxy [8]. Thereby, we highlight that molecular beam epitaxy is the suitable choice for ULT growth since, using chemical vapor deposition methods for Si deposition, the ULT range cannot be reached due to the lack of precursor decomposition. However, we also stress that the chamber conditions during the growth need to be excellent in order to limit detrimental point defect formation during epitaxy.If these preconditions are met, ULT growth can lead to up to now unattainable layer structures. These, in turn, can be used, e.g., for the first available double heterostructure (DHS) light-emitting diodes in the group-IV system. We demonstrate that these LEDs, emitting in the telecom band, work efficiently up to room temperature and above. Light emission above room temperature is eventually limited by minority carrier injection.Additionally, novel electronic device concepts such as reconfigurable field effect transistors (RFETs) can be realized using several nanometer-thick Ge layers grown directly on silicon on insulator substrates. We argue that these devices have distinct advantages in performance and integration possibilities as compared to conventional nanoelectronics devices based on Ge-rich material, for which typically vertically grown nanowires are employed [9,10].

Mixed-Layered Lead Halide Frameworks with High Stability and Efficient Room-Temperature Phosphorescence.

Room-temperature phosphorescent (RTP) materials play a crucial role in optical anticounterfeiting science and information security technologies. Ionically bonded organic metal halides have emerged as promising RTP material systems due to their excellent self-assembly and unique photophysical property, but their intrinsic instability largely hinders their advanced practical applications. Herein, we employ a coordination-driven synthetic strategy utilizing organocarboxylates for the synthesis of two isostructural layered lead halide frameworks. The frameworks adopt a new mixed-layered topology, consisting of alternating [Pb10X9]11+ (X = Cl-/Br-) layers and [Pb6XO3]11+ (X = Cl-/Br-) layers that are coordinatively sandwiched by organocarboxylate layers. The frameworks exhibit long-lived green afterglow emission with the long lifetime of up to 45.89 ms and the photoluminescence quantum yield (PLQY) of up to 43.13%. The Pb2+-carboxylate coordination accelerates the metal-to-ligand charge transfer from the light-harvesting lead halide layers to the phosphorescent organic component, promoting efficient spin-orbit coupling and intersystem crossing. Moreover, the coordination networks exhibit good structural robustness under ambient conditions for at least 12 months, as well as stability in boiling water, acidic and basic aqueous environments. The highly efficient afterglow and high structural integrity enable multiple anticounterfeiting applications across diverse chemical environments.

Comparative analysis of control strategies impact on power losses in IGBT power modules with a multiphysics‐circuit coupling method

AbstractPower losses in semiconductor devices present a significant impediment to advancing power electronics systems for achieving higher frequencies, improved efficiency, and greater integration. A major challenge lies in directly coupling power losses with various control strategies, considering the variations in manufacturers, packaging, and process structures within the power electronics field. This paper introduces a field‐circuit coupling simulation methodology of the insulated gate bipolar transistor (IGBT) within the Simulink/COMSOL environment, using a conventional three‐phase six‐switch voltage source inverter (VSI) IGBT module as a case study. A composite transfer function interconnects the electrical and thermal aspects, enabling bidirectional coupling between IGBT temperature and losses. This study aims to comprehensively compare the effects of various control strategies, namely, single‐vector, dual‐vector, and three‐vector current model predictive control (CMPC) and space vector pulse width modulation (SVPWM), on the losses and temperature of IGBT power modules. The findings emphasize that losses are significantly influenced by the selection of weighting factor coefficients and power factor settings under the same CMPC control strategy with a fixed switching frequency. Additionally, the selection of control strategies, such as CMPC and SVPWM, substantially impacts power losses in power electronic devices.