Static Block Research Articles

MODEL OF THREE-PHASE ASYNCHRONOUS MOTOR WITH SHORT CIRCUIT ROTOR IN MATLAB PACKAGE

The presented paper examines the specifics of researching the mathematical apparatus of the model of a three-phase АМ with a short-circuited rotor in the matlab package. It has been proven that three-phase АМ with a short-circuited rotor form the basis of a modern electric drive in many branches of industry, agro-industrial complex and transport. A model was developed in the matlab package, which describes the operation of a frequency-regulated electric drive, taking into account theoretical analysis and experimental studies. In the scientific work, the task of combining mathematical modeling and modern computer technologies, which are based on application packages, is fulfilled, which gives the researcher the opportunity to deeply study the processes that occur in all links of the electric drive. With the help of the proposed model, quantitative and qualitative analysis of electromagnetic and electromechanical processes in transient and established modes of operation, research of dynamic mechanical and operational characteristics, analysis of spectral composition and hodographs of spatial vectors of phase voltage and current of an АМ can be carried out. The considered model can be applied not only when powering an asynchronous motor from a three-phase source with a sinusoidal form of voltage, but also when powering it from power semiconductor converters. The scheme of a virtual installation for modeling and researching processes in a three-phase АМ with a short-circuited rotor was developed in the work. The analyzed installation includes the following blocks: three-phase AC voltage source, three-phase AC voltage meter, Selector block, Voltage Measurement block, three-phase АМ under investigation, static torque assignment block, active and reactive power meter, Subsystem block. An analysis of electromagnetic processes in static and electromechanical converters of electrical energy was carried out using the spatial vector method, which has found wide use. With the help of software packages, instantaneous values of symmetrical three-phase variables such as voltage, current, flux linkage were mathematically transformed into one vector.

Robust rateless space time block coding for mmWave massive MIMO system

In the vast growing wireless communication system creating robust encoding mechanisms using space-time block codes (STBC) for mmWave massive MIMO to overcome uncertainty and ensure reliability is a critical concept to be covered. This article reviews the core concepts behind MIMO communication, Massive MIMO communication, space-time block codes, and rateless codes in the context of wireless communication systems. Building on the foundational concepts of information theory and mmWave massive MIMO, we developed space-time block codes that maintain orthogonality for real-valued symbols. Following a thorough analysis of these codes, we successfully extended them into rateless orthogonal space-time block codes (ROSTBC) for massive MIMO. This extension enables the codes to adapt their rates dynamically based on the unknown channel conditions at the transmitter. The research work was concluded by comparing static G4 encoded Tarokah work and other Orthogonal Space Time Block Codes (OSTBCs) with Rateless Space Time Block Codes (ROSTBCs). The results show that as the number of blocks used to encode the message in Rateless Space Time Block Code (ROSTBC) increases, this coding scheme can outperform static Rateless Space Time Block Codes (OSTBC) in very low SNR values by a minimum of 8.5 %. This work can enhance wireless communication by creating reliable communication over inherently unreliable systems.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

The Effect of Forearm Shortening on Finger Flexion: A Biomechanical Study

Surgeons may shorten the forearm for many indications. We quantified the impact of shortening on finger flexion with a cadaver model. Ten fresh cadaver proximal forearms were pinned to a static block. We pinned each distal forearm/hand to a block that could unlock, slide, and relock on a mounting track. This block allowed wrist-neutral or 30-degree extension. With the sliding block locked, we removed the central 10 cm of the radius/ulna. We placed sutures in the proximal end of each flexor digitorum profundus (FDP). After pretensioning, we simulated near-maximum baseline FDP muscle-generating force by applying 100 N via a load cell at the proximal sutures. We then anchored the load cell system proximally to set the initial length-tension relationship for simulating near-maximum baseline muscle-generating force. We called subsequent load cell readings the simulated muscle force (SMF) and pressure sensor readings between fingertips and the palm the tip-to-palm force (TPF). We shortened the forearm in 1 cm increments with the distal sliding-locking block. At each increment, we recorded SMF and TPF in the wrist-neutral position. Once a specimen lost measurable TPF, we applied 30 degrees wrist extension until again losing TPF. Incremental forearm shortening was associated with exponential decreases in each FDP's SMF and TPF. In wrist-neutral, 3 cm mean shortening had a loss of 99% and 98% SMF and TPF, respectively. Wrist extension marginally improved SMF and TPF up to 4 cm mean shortening, where both lost 99%. Loss of any fingertip touchdown occurred after a mean shortening of 4.9 cm in wrist-neutral and 5.3 cm in 30 degrees wrist extension. Mean forearm shortening of 3 or 4 cm had a near-complete loss of FDP SMF and TPF in wrist-neutral/wrist extension, respectively. With ∼5 cm shortening, there was a complete loss of fingertip touchdown. Surgeons should consider the influence of forearm shortening on the FDPs and contemplate flexor tendon shortening or alternative reconstructions as indicated.

Parameter learning for Wiener systems with time‐delay state‐space model

AbstractThis paper discusses a novel scheme for learning the Wiener output error nonlinear system with time‐delay state‐space model. In the Wiener system, the dynamic linear block is approximated by time‐delay state‐space model, and the static nonlinear block is established using neural fuzzy network. Combined signals designed including separable signal and random signal are devoted to achieving parameters separation learning of the Wiener system, that is, the two blocks are learned independently. Firstly, using the properties of shift operator and transforming state‐space model with time‐delay into a representation with input and output, then linear dynamic block parameters are learned by the virtue of correlation analysis method in the condition of Gaussian signals. Moreover, a recursive extended least squares estimation is carried out to learn parameters of static nonlinear block and colored noise model under the condition of random signals. The efficiency and accuracy of proposed scheme are confirmed on experiment results of a numerical simulation and a typical practical nonlinear process, and experimental simulation results demonstrate that the learning scheme proposed obtains good learning precision.

Data-driven carbon emission accounting for manufacturing systems based on meta-carbon-emission block

In the context of carbon neutrality and sustainable manufacturing, there is an urgent need for carbon emission accounting in the manufacturing industry, especially in high-energy, low-efficiency, and high-carbon emission manufacturing systems. However, the dynamics and coupling of carbon emissions caused by the multiple characteristics of manufacturing systems increase the challenges of carbon emission accounting. To this end, this research aims to develop a generic methodology to reveal the carbon emission characteristics and implement carbon emission accounting for manufacturing systems, so as to support the lean diagnosis and management of carbon emission hotspots. Firstly, the concept of the “meta-carbon-emission block” consisting of both static and variable carbon emission blocks is proposed to characterize the carbon emission behaviors of each device in various operating states. Then, a data-driven carbon emission accounting approach based on the meta-carbon-emission block is presented to calculate the carbon emission and diagnose the carbon emission hotspots. Finally, a typical manufacturing system, that is, a laser welding system for butt welding of aluminum alloy 6061 with multi-source of the carbon emission, multi-device of the processing system, multi-state of the sub-device, and multi-stage of the production, is conducted to validate the proposed approach, and the results revealed the effectiveness and practicality of this approach. This research provides an appropriate theoretical and methodological basis for carbon emission accounting of manufacturing systems and also supports the diagnosis and reduction of carbon emissions for manufacturing industries.

Occupancy map-based low complexity motion prediction for video-based point cloud compression

This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset.

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

AbstractThis paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

PerBlocks: A Reconfigurable Blockchain for Service Provisioning in Industrial Environment

In this work, we propose a reconfigurable blockchain (BC) – “PerBlocks” – for handling data from heterogeneous activities and achieving scalability as well as throughput in the industrial environment. Typically, industries deal with pervasive deployment of various sensors, resulting in heterogeneous data with varying services. The adoption of conventional BC satisfies transparency, immutability, and security. However, static block size and stringent consensus algorithms are unable to serve the needs of heterogeneous activities. In order to resolve this, we propose reconfigurable service-oriented BC, which dynamically selects the consensus algorithm and block size according to the requirement of heterogeneous activities. Through experimental results, we demonstrate that the proposed method can utilize the resources efficiently and provide user-oriented services as well as scalability. In particular, the proposed method uses <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$28.2\%$</tex-math></inline-formula> CPU and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$82\%$</tex-math></inline-formula> memory while reducing response time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\%$</tex-math></inline-formula> , compared to a conventional BC and improves the quality of services by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60\%$</tex-math></inline-formula> .

Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor.

This paper focuses on two types of neural network-based Hammerstein model identification methods for the acrylamide polymerization reaction of a batch reactor process. The first neural-based identification type formulates the weights of the multilayer network directly as parameters of the nonlinear static and linear dynamic blocks of the Hammerstein model and trains the weights using a gradient-based backpropagation algorithm. In the second identification type, the nonlinear static block of the Hammerstein model is framed as a single hidden-layer feedforward network and both nonlinear and linear block parameters are trained using an extreme learning machine, where the training procedure is exempted from gradient calculation. The primary focus of the paper is neural-based model identification of a complex nonlinear system, which facilitates ease of linear/nonlinear controller design with good learning speed and less computations. A future work toward the machine learning-based nonlinear model predictive controller implementation using the Jetson Orin Nano board is also described.

Managing the OR: Tools, resources and guidelines to effectively manage operative suites

As modern medicine offers novel surgical therapies for conditions simple and complex, the proper resource allocation of operating rooms, surgical staff and supporting services is becoming increasingly important for healthcare organisations. Surgical services are one of the most essential parts of any healthcare system, substantially influencing patient choice of healthcare services. These services are typically the centre for revenue but also impart considerable costs. Given the complexity of surgical schedule planning, expectations of high reliability and demand on resources, many healthcare organisations have resorted to using a static block schedule based on historic utilisation patterns. This is based on the dogma that surgical schedules are not predictable, surgical blocks are stable, and surgical yield has no direct correlation with outpatient schedules. Here we share our two-year experience with a Surgical Predictor Tool using our surgical practice management principles and the cost-effective use of these means to meet the needs of patients and our surgical staff in an integrated community health system.

Is Robust Optimization Essential When Planning Pencil Beam Scanned Proton Therapy for Intracranial Lesions?

Creating intensity modulated proton therapy (IMPT) plans usually involves a robust optimization step to account for uncertainties in proton range and positioning instead of using the PTV margins typically seen in photon IMRT planning. Because robust optimization adds significant planning time per iteration, and proton planning typically involves many iterations to obtain an optimal plan, this project evaluates whether a PTV approach can be used to more efficiently create plans for intracranial lesions by comparing plan quality from both approaches to determine if they produce equivalent plans. Five patients with intracranial lesions treated with IMPT at our center were randomly chosen for this study. 3-4 beams were used to treat CTVs ranging between 46 and 246 cc. Patients were treated on a Mevion S250i single-vault proton machine with Hyperscan. Static blocks (7mm conformed to the CTV) were used for all cases and plans were created in a treatment planning system using robustness criteria of 3mm (position uncertainty) and 3.5% (range uncertainty). For each patient, the CTV was uniformly grown by 3 mm to create a PTV. The optimization criteria used in the clinical plan were used as baseline to create two plans - one using robust optimization for CTV coverage and one without robust optimization for PTV coverage. A script was used to time two otherwise identical optimization runs. All plans were normalized so that the prescription was delivered to 95% of the CTV. The plan was robustly evaluated under conditions of 3.5% range uncertainty and 2mm positional uncertainty. For each nominal plan, the CTV dose heterogeneity and conformity as well as the following dose metrics were collected: CTV D99% and D98%, Normal Brain V100%, V90%, V80%, V50% as well as overall plan Dmax (to 0.03cc). For each plan, the minimum CTV D99 and maximum Brain Dmax were also collected for the robust evaluation scenarios. All collected metrics were compared between the robust CTV-based and non-robust PTV-based plans using paired t-tests with 5% significance. For the five cases investigated here, all dosimetric metrics investigated were not significantly different between the CTV-based and PTV-based plans except for the plan maximum dose (CTV-based: 104.6% Rx - 110.0% Rx, PTV-based: 105.6% Rx-110.6% Rx, p = 0.029). The optimization times were also significantly different, averaging 1532 s for CTV-based plans versus 252 s for the PTV-based plans (p = 0.004). For the plans investigated here, non-robust PTV planning approach creates plans of very similar quality to a robust CTV-based plan, while having significantly shorter planning times.

Fraction Execution Resolver Using a Hybrid Multi-CPU/GPU Encoding Scheme

Modern video coding standards make use of sub-pixel motion estimation to improve the video quality and reduce the bitrate. It is known that the fraction motion estimation (FME) part follows the integer motion estimation (IME) and adds an extra computational overhead due to the interpolation and the additional motion searches. In this paper, we propose a fraction execution resolver (FER) algorithm that lets the encoder skip the fraction part when specific criteria are met by introducing a preliminary fast test decision point (pFTDP) function for the IME part. If the pFTDP returns zero motion vectors (MVs) and the displacement search area center is also zero, then the fraction part is skipped. The pFTDP decision maker is executed only once, when a 2N × 2N block is first met, while all subsequent blocks follow this initial decision either by receiving the necessary MVs and RD from the pFTDP function or by using the precalculated IME values from the GPU kernel. For our experiments, we use a multithreaded CPU environment that also makes use of GPUs only for the integer part. Our evaluations provide a greater than 1600% encoding time saving at its peak in comparison with the default HEVC sequential mode and ideally a saving of greater than 2286% for still video frame sequences. The total average speedup for both Class A and Class B video sequences is ×13.45. The gain of the FER itself is more than ×3.9 compared with the same multithreaded setup environment. The PSNR and bitrate overhead observed are proportional to the tiling scheme used and are more related to the way CABAC works internally. The FER’s negative effects on coding efficiency are proven to be negligible. A balance between speed and quality achieved by using a lower tiling pattern is shown to minimize the negative effects of the encoding scheme pattern. The experimental results confirm the validity of our motivation, namely, that we can benefit from a software fraction execution resolver without any extra hardware costs. The gain is further increased when video sequences have more static blocks than others.

High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point.

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications. Although various domain-specific accelerators have been proposed to address this challenge, most of them only focus on accelerating 2-D CNNs, without considering their computational efficiency on 3-D CNNs. In this article, we propose a unified hardware architecture to accelerate both 2-D and 3-D CNNs with high hardware efficiency. Our experiments demonstrate that the proposed accelerator can achieve up to 92.4% and 85.2% multiply-accumulate efficiency on 2-D and 3-D CNNs, respectively. To improve the hardware performance, we propose a hardware-friendly quantization approach called static block floating point (BFP), which eliminates the frequent representation conversions required in traditional dynamic BFP arithmetic. Comparing with the integer linear quantization using zero-point, the static BFP quantization can decrease the logic resource consumption of the convolutional kernel design by nearly 50% on a field-programmable gate array (FPGA). Without time-consuming retraining, the proposed static BFP quantization is able to quantize the precision to 8-bit mantissa with negligible accuracy loss. As different CNNs on our reconfigurable system require different hardware and software parameters to achieve optimal hardware performance and accuracy, we also propose an automatic tool for parameter optimization. Based on our hardware design and optimization, we demonstrate that the proposed accelerator can achieve 3.8-5.6 times higher energy efficiency than graphics processing unit (GPU) implementation. Comparing with the state-of-the-art FPGA-based accelerators, our design achieves higher generality and up to 1.4-2.2 times higher resource efficiency on both 2-D and 3-D CNNs.

A Dynamic and Static Binary Translation Method Based on Branch Prediction

Binary translation is an important technique for achieving cross-architecture software migration. However, mainstream dynamic binary translation frameworks, such as QEMU, often generate a large amount of redundant code, which degrades the efficiency of the target code. To this end, we propose a dynamic–static binary translation method based on branch prediction. It first identifies parts of translation blocks following static branch prediction techniques. Then it translates these translation blocks into less-redundant native code blocks by canonical static translation algorithms. Finally, it executes all code blocks that are translated either statically or dynamically by correctly maintaining and switching their running contexts. In order to correctly weave the two types of translation activities, the proposed method only translates the next translation block that is data-independent from the current one by the active variable analysis algorithm, and records and shares the intermediate states of the dynamic and static translation activities via a carefully designed data structure. In particular, a shadow register-based context recovery mechanism is proposed to correctly record the running context of static translation blocks, and to correctly recover the context for dynamically translating and running blocks that were not statically translated. We also designed an adaptive memory optimization mechanism to dynamically release the memory of the mispredicted translation blocks. We implemented a dynamic–static binary translation framework by extending QEMU, called BP-QEMU (QEMU with branch prediction). We evaluated the translation correctness of BP-QEMU using the testing programs for the ARM and PPC instruction sets from QEMU, and evaluated the performance of BP-QEMU using the CoreMark benchmark code. The experimental results show that BP-QEMU can translate the instructions from the ARM and PPC architectures correctly; moreover, the average execution efficiency of the CoreMark code on BP-QEMU improves by 13.3% compared to that of QEMU.

A Hierarchical Identification Method for Lithium-Ion Battery SOC Based on the Hammerstein Model

Two-input one-output Hammerstein model consists of two parallel nonlinear static blocks followed by a linear dynamic part. By using Hammerstein structure to map relation between a battery State of Charge (SOC) and its terminal voltage/current, a hierarchical stochastic gradient algorithm is studied to estimate parameters of Hammerstein SOC model, so as to predict battery SOC. Firstly, the Hammerstein model is transformed into a bilinear parameter system with the least number of required parameters. Then, a hierarchical stochastic gradient algorithm with a forgetting factor is used to update the two sets of model parameters of the bilinear parameter system, so as to realize SOC estimation. Furthermore, the experiment platform of lithium-ion battery was built and the data of the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile were collected. Finally, the MATLAB simulation results show that the proposed parameter optimized method based Hammerstein model has the advantages of fast convergence speed and high SOC estimation accuracy.

Identification of series–parallel systems composed of linear and nonlinear blocks

AbstractMost works on system identification of block‐oriented nonlinear systems were devoted to Wiener and Hammerstein systems. This article is focused on a more general, and so more complex, nonlinear model structure. Specifically, the system under study is a series connection of two subsystems each one being constituted of linear dynamic block and a nonlinear static block coupled in parallel. Clearly, this system structure generalizes those of Wiener and Hammerstein systems. Interestingly, the linear blocks can be parametric or not and are allowed to be of unknown structure. We develop a two‐stage frequency‐type identification method that provides accurate estimates of the all system parts. The proposed identification method enjoys the simplicity and the consistency of developed estimators. Furthermore, the parameters of linear dynamic blocks can be easily decoupled without any further experiments. The performances of the proposed identification method are highlighted by several simulation results. As perspectives, one or the two nonlinearities can be considered non‐static, for example, of backlash type.

Separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis

AbstractThis article develops a novel separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein‐Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein‐Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering‐based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms.

Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise

In this paper, a novel correlation analysis-based parameter learning scheme for Hammerstein nonlinear systems with output noise is presented. The developed Hammerstein system contains a static nonlinear block approximated by neural fuzzy network and a linear dynamic block modeled by transfer function, and parameter separation learning of the nonlinear block and linear block are realized by using combined signals. Firstly, based on the input and output of the separable signal, the correlation analysis algorithm is applied to estimate linear block parameters, thereby the interference of moving average noise is dramatically handled. Moreover, to improve parameter estimation precision of the learned Hammerstein system, multi-innovation theory and data filtering technology are introduced, and a data filtering-based multi-innovation stochastic gradient learning scheme is implemented for jointly estimating nonlinear block parameters and noise model using random signals. Studies with a numerical simulation and a practical nonlinear process demonstrate the efficiency. In the numerical simulation, when the noise to signal ratio is less than 33.37%, estimate error of the linear block parameters using least squares method is less than 0.0796. In contrast, estimate error of the proposed method is less than 0.0338. For the modeling ability of the nonlinear block, the proposed method has an improvement of 67.07% than the MI-ESG method with innovation length of six. With regard to practical nonlinear process, when the concentration set value is 0.1, the rise time of the proposed method is 0.021 h, while the rise time of MPC and traditional PI controller and is 0.055 h and 0.143 h, respectively.

Loss Analysis of Magnetic Gear with Slotted in Magnetic Modulation Ring

In order to improve the operation efficiency of coaxial magnetic gear (CMG), in this paper, a CMG model with slotted in magnetic modulation ring is proposed. In this model, the permanent magnets (PMs) of internal and external rotors are distributed in Halbach array, the inner rotor PMs are equally divided into 3 small pieces, and the outer rotor PMs are equally divided into 2 small pieces. At the same time, the static magnetic modulation ring iron blocks are slotted, each iron block has 3 slots, the width of the slot is 0.4°, and the depth of the single side slot is 1mm. Finally, a two-dimensional model is established, and the eddy current loss and iron loss of the model are optimized, compared with the conventional CMG model, it is found that the changed pattern can increase the internal and external output torque by 4% and 4.12%, respectively. The eddy current loss is reduced by 66.57%, and the iron loss is reduced by 8.9%, which significantly improve the operation efficiency of the CMG.