Sequence Optimization Method Research Articles

A Novel Dose Rate Optimization Method to Maximize Ultrahigh-Dose-Rate Coverage of Critical Organs at Risk Without Compromising Dosimetry Metrics in Proton Pencil Beam Scanning FLASH Radiation Therapy

PurposeTo investigate a dose rate optimization framework based on the spot scanning patterns to improve ultra-high dose rate coverage of critical organs-at-risk (OARs) for proton PBS FLASH radiotherapy, and to present implementation of a genetic algorithm (GA) method for spot sequence optimization to achieve PBS FLASH dose rate optimization under relatively low nozzle beam currents. Materials and MethodsFirstly, a multi-field FLASH plan was developed to meet all the dosimetric goals and optimal FLASH dose rate coverage by considering the deliverable minimum MU (MMU) constraint. Then, a GA method was implemented into the in-house treatment platform to maximize the dose rate by exploring the best spot delivery sequence. A phantom study was performed to evaluate the effectiveness of the dose rate optimization. Then, 10 consecutive plans for lung cancer patients previously treated using PBS intensity-modulated proton therapy (IMPT) were optimized using 45 GyRBE in 3 fractions both transmission and Bragg peak FLASH-RT for further validation. The spot delivery sequence of each treatment field was optimized using this GA. The ultra-high dose rate volume histogram (DRVH) and dose rate coverage V40GyRBE/s were investigated to assess the efficacy of dose rate optimization quantitatively. ResultsUsing a relatively low MU/spot of 150, corresponding to nozzle beam current of 65 nA, the FLASH dose rate ratio V40GyRBE/s of the OAR contour of the core was increased from 0 to ∼60% in the phantom study. In the lung cancer patients, the ultra-high dose rate coverage V40GyRBE/s were improved from 15.2%, 15.5%, 17.6%, and 16.0% before the delivery sequence optimization, to 31.8%, 43.5%, 47.6%, and 30.5% after delivery sequence optimization, in the lungs-GTV, spinal cord, esophagus, and heart (p-values all<0.001). When beam current increased to 130 nA, V40GyRBE/s was improved from 45.1%, 47.1%, 51.2%, and 51.4%, to 65.3%, 83.5%, 88.1%, and 69.4% (p-values<0.05). The averaged V40GyRBE/s for the target and OARs was increased from 12.9% to 41.6%, and 46.3% to 77.5% for 65 and 130 nA, respectively, showing significant improvements based on a clinical proton system. After optimizing the dose rate for the Bragg peak FLASH technique with a beam current of 340 nA, the V40GyRBE/s for the lung-GTV, spinal cord, esophagus, and heart were increased by 8.9%, 15.8%, 22%, and 20.8%, respectively. ConclusionAn optimal plan quality can be maintained as the spot delivery sequence optimization is a separate independent process after the plan optimization. Both the phantom and patient results demonstrated that novel spot delivery sequence optimization can effectively improve the ultra-high dose rate coverage for critical OARs, which can potentially be applied in clinical practice for better OARs sparing efficacy.

A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption

Existing methods for optimizing the LLVM compiler face challenge in quickly obtaining high-quality solutions due to the large and discrete optimization space of option sequences, time-consuming energy consumption evaluation, and complex interactions among options. To address these challenges, we present a Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption (TSOMFEC). In the first stage, we propose an Option Selection algorithm based on Pattern Mining (OSPM). OSPM integrates a pattern mining algorithm and CRC algorithm to precisely preselect options, with the goal of reducing the search space of option sequences while guaranteeing optimization quality. In the second stage, we present an Option Sequence Evolutionary Algorithm (OSOA). OSOA encodes options and their positions in the sequence as individual items and then constructs an item interaction graph to capture complex interactions among different items. The item interaction graph is utilized in a genetic algorithm to reduce actual energy consumption evaluations and rapidly obtain high-quality option sequences. We evaluate the performance of TSOMFEC by comparing it with four state-of-the-art methods across eight typical embedded programs in terms of solution quality and optimization time. Furthermore, we validate the effectiveness of both OSPM and item interaction graph. The experimental results demonstrate that TSOMFEC not only achieves the best solution quality but also spends the least optimization time. It obtains average energy consumption improvement percentages relative to the -O3 level of 0.075, along with average optimization time of 3178 s. Moreover, the effectiveness of both OSPM and the item interaction graph has been validated.

Sequence optimization for MRI acoustic noise reduction

The large acoustic noise of 80-110 dB during magnetic resonance imaging (MRI) scanning harms patients’ comfort and health. The noise can be reduced by hardware modification or active noise control, but these methods are expensive, difficult, or not very effective. In this study, a sequence optimization method is used to mitigate the acoustic noise problem while maintaining image quality. The 4th order polynomial function is applied to design the new quiet pulse sequences, decreasing the gradient slew rate and higher time derivatives of the original trapezoidal lobes. A sound pressure level (SPL) estimation method is proposed to predict the acoustic noise loudness from the gradient and is used for genetic algorithm sequence optimization. The original and quiet gradient recalled echo (GRE) sequences are applied on a 1.5 T MRI scanner. The average SPL is reduced by 18.6 dBA, and the images show small differences and have similar SNR values. This method is also applied for the scouting and shimming GRE sequences in common clinical applications with significant noise reduction.

Efficient Dynamic Reconfigurable CNN Accelerator for Edge Intelligence Computing on FPGA

This paper proposes an efficient dynamic reconfigurable CNN accelerator (EDRCA) for FPGAs to tackle the issues of limited hardware resources and low energy efficiency in the deployment of convolutional neural networks on embedded edge computing devices. First, a configuration layer sequence optimization method is proposed to minimize the configuration time overhead and improve performance. Second, accelerator templates for dynamic regions are designed to create a unified high-speed interface and enhance operational performance. The dynamic reconfigurable technology is applied on the Xilinx KV260 FPGA platform to design the EDRCA accelerator, resolving the hardware resource constraints in traditional accelerator design. The YOLOV2-TINY object detection network is used to test the EDRCA accelerator on the Xilinx KV260 platform using floating point data. Results at 250 MHz show a computing performance of 75.1929 GOPS, peak power consumption of 5.25 W, and power efficiency of 13.6219 GOPS/W, indicating the potential of the EDRCA accelerator for edge intelligence computing.

A Mixing Sequence Optimization Method for Focused Compressed Sampling Based on Modulated Wideband Converter

In modulated wideband converter (MWC), all signals are aliased and sampled indiscriminately, which reduces the reconstruction accuracy of target signal. The focused compressed sampling uses optimized mixing sequence to increase the mixing gain of the target signal. To establish the sequence library with selective characteristics, this brief analyzes the signal components aliased into baseband, then proposes a sequence optimization method based on the greedy algorithm. Simulation and experiments show that the optimized sequence possesses a high gain for a desired frequency band signal. In the three-carrier signal test, as the approximate desired signal power ratio (DSR) is raised to 0.99 in baseband, the normalized mean square error (NMSE) of the reconstructed desired signal is improved by nearly 10 dB compared to using pseudorandom sequences.

Merging Sequence Optimization Based on Reverse Auction Theory and Merging Strategy with Active Trajectory Adjustment of Heterogeneous Vehicles

This paper investigates the optimized merging sequence (MS) and on-ramp merging strategy in mixed traffic with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). To this end, a cooperative merging sequence optimization method is first proposed based on the reverse auction. In the method, a hybrid optimized computing structure is proposed to provide the foundation for connected and human-driving vehicles (CHVs) and connected and automated vehicles (CAVs) to obtain the MS more efficiently. And a cooperative merging strategy based on all cooperative merging vehicles under mixed traffic conditions is proposed. In particular, the downstream vehicles in the strategy can change their original velocities to actively participate in the cooperative merging process according to the merging requirements of on-ramp vehicles. And the vehicles in this strategy are all subject to state constraints to avoid the adverse effects of cooperative merging behavior on the following traffic on the main road. Results of numerical experiments illustrate that the merging sequence optimization method can reduce the time to obtain the optimal MS, and the increased computational efficiency is affected by CAV penetration. In addition, in mixed traffic conditions, the cooperative merging strategy can reduce fuel consumption and the time required for merging.

Genetic algorithm-based optimization of pulse sequences.

The performance of pulse sequences in vivo can be limited by fast relaxation rates, magnetic field inhomogeneity, and nonuniform spin excitation. We describe here a method for pulse sequence optimization that uses a stochastic numerical solver that in principle is capable of finding a global optimum. The method provides a simple framework for incorporating any constraint and implementing arbitrarily complex cost functions. Efficient methods for simulating spin dynamics and incorporating frequency selectivity are also described. Optimized pulse sequences for polarization transfer between protons and X-nuclei and excitation pulses that eliminate J-coupling modulation were evaluated experimentally using a surface coil on phantoms, and also the detection of hyperpolarized [2-13 C]lactate in vivo in the case of J-coupling modulation-free excitation. The optimized polarization transfer pulses improved the SNR by ~50% with a more than twofold reduction in the B1 field, and J-coupling modulation-free excitation was achieved with a more than threefold reduction in pulse length. This process could be used to optimize any pulse when there is a need to improve the uniformity and frequency selectivity of excitation as well as to design new pulses to steer the spin system to any desired achievable state.

Fast activation maximization for molecular sequence design

BackgroundOptimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence.ResultsHere, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp’s capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor.ConclusionsFast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.

Research on Optimization of Single-phase Reclosing Sequence to Alleviate Voltage Sag of Sensitive Load Node

In order to reduce the voltage sag depth of the sensitive load node when the single-phase reclosing reclose fails, this paper proposes a single-phase reclosing sequence optimization method. When single-phase short-circuit to ground fault occurs on branch, the influence of reclosing sequence, including disconnecting and connecting, of circuit breakers at both branch sides on voltage sag of sensitive load node is analyzed. According to this, the reclosing sequence optimization scheme is put forward. Then, taking sensitive load node as the center, a branch stratification method and criterion of branch needing reclosing sequence optimization are proposed. The proposed method is verified in IEEE 30-bus system, the result shows the method is effective and feasible.

A Switching Sequence Optimization Method (SSOM) to Eliminate the Dead-Time Unexpected Output Levels for Four-Level Nested Neutral Point Clamped Converter

The four-level nested neutral point clamped (4L-NNPC) converter is a new multilevel converter with a simple structure and no power device in series, which is suitable for medium-voltage high-power applications. However, it experiences severe dead-time unexpected output levels problem in the output voltage when the low-output-frequency modulation method is applied. This article proposes a switching sequence optimization method (SSOM) on the basis of carrier-based 3 level (CB3L) PWM that can eliminate the dead-time unexpected levels and ensure the voltage balance of the flying capacitors at the low output frequency. The reasons for the unexpected levels generated by the state transition are first analyzed, and the relationship between the dead time and unexpected levels is investigated. Then, the basic principle and implementation of the proposed SSOM are elaborated in detail. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

The interrupted transmitting and receiving (ITR) can be used in anechoic chamber to solve the coupling between the transmitted and reflected signals. When the ITR periods are random, the fake peaks in high-resolution range profile (HRRP) of ITR echo can be reduced. Then, by utilizing the piecewise sparse property of ITR echo, the HRRP is reconstructed based on compressive sensing (CS). However, the periods of ITR determine the restricted isometry property (RIP) condition and the HRRP reconstruction performance. In order to improve the HRRP reconstruction performance, the ITR period sequence optimization method based on a genetic algorithm (GA) is proposed in this paper. The correlation coefficient of the sensing matrix columns is minimized after optimization so that the RIP of the sensing matrix can be well satisfied. Simulation and comparison results illustrate that the optimization method converges fast and the HRRP reconstruction performance is improved with the optimized ITR periods.

Collaborative Mining Sequence Optimization for Multiple Stopes under Intensive Mining

The optimization of a mining sequence not only reduces stress concentration in surrounding rock but also prevents underground debris flows, significantly improving safety. Firstly, the 870–898 m level of the eastern mining area in the Tiaoshuihe phosphate mine was divided into 25 ore blocks, and six different mining sequences were designed for this area. Then, it was calculated that five ore blocks must be processed simultaneously to reach the annual production output. The distances between the five simultaneously mined ore blocks will inevitably affect the efficiency of the equipment for any scheme. So, a collaborative model considering both the area stability and production capacity was established by combining the distance between the centers of the five ore blocks as an index. Differences in stability, deformation, and plastic zone size between the schemes are compared. The calculation results show that a mining scheme with a convex stepped shape produces the best results. These results provide a general method for entropy‐based mining sequence optimization and an optimal solution for the Tiaoshuihe phosphate mine.

Research on action sequence optimization method of Micro-Crystal resonator spot welding device

To realize spot welding process of the id and base for micro-crystal resonator quickly and accurately, the action sequence is need to be designed for its spot welding device reasonably. The device’s performance indexes are proposed and its preliminary action sequence is designed based the functional structure and process flow. Stateflow tool is used to the device’s simulation model is established and the state transition diagram is obtained by Stateflow with the finite state machine theory. According to the simulation result, the action sequence for the spot welding device is optimized, and its final action sequence is determined. It shows that he optimized design can meet the function requirements for micro-crystal resonator spot welding device through the simulation analysis and prototype test.

Feeder Layout Optimization and Feeding Sequence Optimization in High Speed and High Precision Placement Machine

As for the multi-optimization mutual coupling process optimization for mounting production with multiple 1210 or1812 resistor components and the relatively low nozzle requirement, a fast multi-objective hierarchical optimization method with low matching requirement for parallel nozzle and placement sequence optimization is proposed. In the hierarchical structure, the nozzle optimization is constructed as a 0-1 integer programming optimization model for the first master hierarchy. In the second hierarchical, feeder optimization and picking order are parallel optimized based on the first hierarchy optimization. The respective problems related algorithm strategies have been proposed. The engineering experiments show that this Feeder layout optimization and Feeding sequence optimization method can significantly improve the overall performance of the multi-head placement machine with high speed and high precision.

Energy principle and material removal sequence optimization method in machining of aircraft monolithic parts

In the machining process of aircraft monolithic parts, the initial residual stress redistribution and structural stiffness evolution often lead to unexpected distortions. On the other hand, the stress redistribution and stiffness reduction during the machining process depend on the material removal sequence. The essence of the stress redistribution is releasing the initial elastic strain energy. In the present study, the influence of the material removal sequence on the energy release is studied. Moreover, a novel optimization method is proposed for the material removal sequence. In order to evaluate the performance of the proposed method, the mechanism of the machining distortion is firstly analyzed based on the energy principle. Then a calculative model for the machining distortion of long beam parts is established accordingly. Moreover, an energy parameter related to the bending distortion and the procedure of the material removal sequence optimization is defined. Finally, the bending distortion analysis and material removal sequence optimization are performed on a long beam with a Z-shaped cross-section. Furthermore, simulation and experiments are carried out. The obtained results indicate that the optimized sequence results in a low distortion fluctuation and decreases the bending distortion.

Efficient Spot Welding Sequence Optimization in a Geometry Assurance Digital Twin

Abstract A digital twin for geometry assurance contains a set of analyses that are performed to steer the real production for securing the geometry of the final assembly. In sheet metal assemblies, spot welding is performed to join the parts together. The sequence of the welding has a considerable influence on the geometrical outcome of the final assembly. In industry, the sequence of welding to secure the geometry is mainly derived by tacit manufacturing knowledge. Including such knowledge to mimic the production process requires extensive knowledge management, and the result might be just a good enough solution. Theoretically, spot welding sequence optimization for the optimal geometrical quality is among NP-hard combinatorial problems. In a geometry assurance digital twin, where assembly parameters are selected for the individual assemblies, time constraints define the quality of the optimal sequence. In this paper, an efficient method for spot welding sequence optimization with regards to the geometrical quality is introduced. The results indicate that the proposed method reduces 60–80% of the time for the sequencing of the spot welding process to achieve the optimal geometrical quality.

A multi-fidelity competitive sampling method for surrogate-based stacking sequence optimization of composite shells with multiple cutouts

In order to explore the global optimizing ability of the surrogate-based stacking sequence optimization of composite shells with multiple cutouts, a multi-fidelity competitive sampling (MFCS) method is developed in this paper, which is composed of a low-fidelity sampling level and a high-fidelity sampling level. In the low-fidelity sampling level, competitive sampling points are determined by means of the prior screening criterion and the further screening criterion in sequence. Based on the POD-based buckling method, a novel reduced-order model is established as the low-fidelity model for fast linear buckling analysis of composite shells with multiple cutouts. In the high-fidelity sampling level, the explicit dynamic method is employed to calculate the post-buckling response of competitive sampling points. Based on these sampling results, a surrogate model is built. Next, a surrogate-based stacking sequence optimization framework is established for minimum material cost of composite shells with multiple cutouts. A simple optimization example of Mishra's Bird test function is performed and the effectiveness of the MFCS method is demonstrated. Finally, the proposed method is used for the surrogate-based stacking sequence optimization of composite shells with multiple cutouts. The optimal result of total material cost by the MFCS method decreases by 23.0% than the optimal result by the traditional sampling method, which indicates that the MFCS method contributes to improving the global optimizing ability of the surrogate-based stacking sequence optimization of composite shells with multiple cutouts.

A new efficient decoupled reliability-based design optimization method with quantiles

The problems of reliability-based design optimization (RBDO) can generally be solved by double-loop methods, single-loop methods or decoupled methods. The sequence optimization and reliability assessment (SORA) method is a widely used decoupled method due to its good efficiency and stability. However, most research on SORA is the most probable point (MPP) based, which may cause the unavoidable error or even fail in convergence, especially for the problems with high nonlinearity, multiple MPPs, and nonnormally distributed variables. This paper presents a new decoupled method based on quantile instead of MPP to overcome the intrinsic shortcomings of MPP-based methods. In comparison with SORA which decouples RBDO in the design space, the proposed method decouples RBDO in the probability space. The quantile is obtained by sampling methods with the Kriging model, in which a sample updating strategy is utilized by choosing the updating samples in the region significant for solving the RBDO problem. The proposed method is compared with SORA and the performance measure approach (PMA) using several examples, and the results show that the proposed method can solve the RBDO problems efficiently and accurately with high nonlinearity, multiple MPPs, or high dimensions.

Test Sequencing Optimization Method based on MDP under Multi-value Tests

For the optimization of multi-value test in complex systems, this paper proposes a novel method based on Markov decision process, since the fault diagnosis process is a typical Markov process through analysing. The optimal test sequencing can be obtained as the solution of MDP is got. And the example also illustrates that the proposed method is efficient and scientific to design the optimal test sequence.

Fast acquisition of full-range auditory event-related potentials using an interleaved deconvolution approach.

This work relates to recent advances in the field of auditory event-related potentials (ERP), specifically deconvolution-based ERP acquisition and single-trial processing. An efficient stimulus sequence optimization method for ERP deconvolution is proposed, achieving consistent noise attenuation within a broad designated frequency range. Furthermore, a stimulus presentation paradigm for the fast, interleaved acquisition of auditory brainstem, middle-latency and late responses featuring alternating periods of high-rate deconvolution sequences, and subsequent low-rate stimulation is investigated in 20 normal hearing subjects. Deconvolved sequence responses containing early and middle-latency ERP components are fused with subsequent late responses using a time-frequency resolved weighted averaging method based on cross-trial regularity, yielding a uniform signal-to-noise ratio of the full-range auditory ERP across investigated timescales. Obtained average ERP waveforms exhibit morphologies consistent with both literature values and reference recordings acquired in 15 normal hearing subjects using a prior art approach to full-range auditory ERP acquisition, with all prominent waves being visible in the grand average waveforms. Results suggest the proposed interleaved stimulus presentation and associated ERP processing methodology to be suitable for the fast, reliable extraction of full-range auditory processing correlates in future ERP studies.