Number Of Iterations Research Articles

Phantom test procedures for a new neuro-oncological amino acid PET tracer: [18F]fluciclovine.

Amino acid positron emission tomography (PET) examinations using anti-1-amino-3-[18F]-fluorocyclobutane-1-carboxylic acid ([18F]FACBC) were allowed for routine clinical use in July 2024. However, phantom test procedures for [18F]FACBC reconstruction parameters have not yet been established. The present study aimed to establish new phantom test procedures for [18F]FACBC brain PET imaging to determine optimal reconstruction parameters. Background (BG) activity as well as hot sphere and target-to-background ratios (TBRs) of [18F]FACBC were estimated based on brain activity and tumor-to-normal tissue ratios (TNR) in a Japanese clinical trial of [18F]FACBC. Phantom experiments proceeded under [18F]FACBC or L-[methyl-11C]-methionine ([11C]MET) conditions. The number of iterations and the Gaussian filter parameters were determined from the reconstruction parameters %contrastmean and coefficients of variation (CVs) in ordered subset expectation maximization (OSEM) and time-of-flight (TOF) with or without point-spread-function (PSF) correction. The amounts of activity in the hot spheres and BG were 1.1 and 5.5kBq/mL, respectively, and the TBR was 5.0 at the start of acquisition. The %contrastmean of all hot spheres was higher with [18F]FACBC than [11C]MET, and %contrastmean converged between 4 and 6 iterations in hot spheres with diameters < 10mm. We used four iterations for OSEM + TOF and five for OSEM + TOF + PSF correction for [18F]FACBC and [11C]MET images. The CV was higher for [18F]FACBC than [11C]MET. The optimal sizes of Gaussian filters for OSEM + TOF and OSEM + TOF + PSF correction of image reconstruction were 5mm for [18F]FACBC, and 4 and 3mm, respectively, for [11C]MET images. We estimated phantom activity and TBR based on brain activity in a Japanese clinical trial and established new phantom test procedures for [18F]FACBC. We recommend that the optimal reconstruction parameters for [18F]FACBC should be set to the same number of iterations as [11C]MET and that the FWHM of Gaussian filter should have a few mm higher than [11C]MET to reduce image noise from brain normal tissue.

Robustness of higher‐order network pertinent to first‐ and higher‐order dependency

AbstractRobustness of higher‐order network (HON) with dependency is different from first‐order network (FON), as the attack orders and the number of iterations are totally different. To measure the robustness of HON with dependency, this study proposed new attack strategies on node and edge failure through measuring the size of the giant component and global efficiency. And two real‐world systems of hazardous materials transportation accident causation system and Chinese high‐speed railway system with different network size were empirically studied. Results show that HON is more fragile to robustness attacks than FON. The number of attack iterations is uncertain in HON, which depends on the attack strategies and the network structure. Meanwhile, nodes and edges with high dependency have greater impacts on network robustness, providing valuable insights into capturing critical information. This study may be helpful to design the robust system, improve the system's reliability and serve as a valuable reference for future exploration of HONs robustness.

Optimization of the FPM iterative process based on bright-field spectral overlap rate analysis.

Fourier ptychographic microscopy (FPM) reconstructs high-resolution images through multiple iterations on a large number of sub-images at different angles, a process that is time-consuming. For a long time, various methods for optimizing the efficiency of FPM based on the acquisition process and algorithms have been proposed. However, there has been no specific analysis of the impact that the sub-images involved in the reconstruction have on the final result. In this Letter, we conduct the first, to our knowledge, analysis of the impact of a single sub-image on the reconstruction result of a high-resolution image in different numbers of iterations and obtain a curve depicting the change in image quality after the sub-images are involved in the reconstruction in different cycles. By analyzing this curve, the sub-images that exert a negative impact on the resulting image are exported along with their corresponding LED positions. On this basis, we propose the concept of bright-field spectral overlap ratio to distinguish whether the sub-images have a positive impact on the reconstruction results under different acquisition conditions and remove the sub-images that have a negative impact on the results during the iterative process. Both simulation and real experimental results demonstrate that our algorithm can reduce the reconstruction time while maintaining image quality. Moreover, it can be combined with other methods to improve reconstruction efficiency.

Using a random cross-validation technique to compare typical regression vs. Random Forests for modelling pan evaporation

Pan evaporation (Epan) of class A pan evaporimeter under local semi-arid conditions was modelled in this study based on meteorological observations as input data using an integrated regression approach that includes three steps: a) first step: appropriate selection of transformations for reducing normality departures of independent variables and ridge regression for selecting variables with low collinearity based on variance inflation factors, b) second step (RCV-REG): regression (REG) of the final model with selected transformed variables of low collinearity implemented using an iterative procedure called “Random Cross-Validation” (RCV) that splits multiple times the data in calibration and validation subsets considering a random selection procedure, c) robustness control of the estimated regression coefficients from RCV-REG by analyzing the sign (+ or -) variation of their iterative solutions using the 95% interval of their Highest Posterior Density Distribution (HPD). The iterative procedure of RCV can also be implemented on machine learning methods (MLs) and for this reason, the ML method of Random Forests (RF) was also applied with RCV (RCV-RF) as an additional case in order to be compared with RCV-REG. Random splitting of data into calibration and validation set (70% and 30%, respectively) was performed 1,000 times in RCV-REG and led to a respective number of solutions of the regression coefficients. The same number of iterations and random splitting for validation was also used in the RCV-RF. The results showed that RCV-REG outperformed RCV-RF at all model performance criteria providing robust regression coefficients associated to independent variables (constant signs of their 95% HPD interval) and better distribution of validation solutions in the iterative 1:1 plots from RCV-RF (RCV-RG: R2=0.843, RMSE=0.853, MAE=0.642, MAPE=0.081, NSE=0.836, Slope(1:1 plot)=0.998, Intercept(1:1 plot)=0.011, and RCV-RF: R2=0.835, RMSE=0.904, MAE=0.689, MAPE= 0.088, NSE=0.818, Slope(1:1 plot)=1.120, Intercept(1:1 plot)=-1.011, based on the mean values of 1,000 iterations). The use of RCV approach in various modelling approaches solves the problem of subjective splitting of data into calibration and validation sets, provides a better evaluation of the final modelling approaches and enhances the competitiveness of typical regression models against machine learning models.

Distributed event-triggered algorithm for convex optimization with coupled constraints

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of O(1/k) for general convex objective functions, where k>0 is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

Integrating Null Controllability and Model-Based Safety Assessment for Enhanced Reliability in Drone Design

The increasing use of drones for safety-critical applications, particularly beyond visual lines of sight and over densely populated areas, necessitates safer and more reliable designs. To address this need, this paper introduces a novel methodology integrating Null Controllability with the Model-Based Safety Assessment (MBSA) framework AltaRica 3.0 to optimize propulsor configurations and system architectures. The main advancement of this method lies in the automation of reliability modeling and the integration of controllability assessment, eliminating restrictions on the types of propulsor configurations and system architectures that can be evaluated and significantly reducing the effort required for each design iteration. Through a hexarotor drone case study, the proposed method enabled a high number of design iterations, efficiently exploring various aspects of the design problem simultaneously, such as configuration, system architecture, and controllability hypothesis, which is not possible with state-of-the-art techniques. This approach demonstrated significant reliability improvements by implementing and optimizing redundancies, reducing the probability of loss of control by up to 99%. The case study also highlighted the increasing difficulty of enhancing reliability with each iteration and confirmed that it is unnecessary to consider more than two simultaneous failures for design optimization. A comparison of reliability figures with previous studies highlights the crucial role of system architecture in effectively enhancing drone design reliability. This work advances the field by providing an effective multidisciplinary modeling framework for drone design, enhancing reliability in safety-critical applications.

A hybrid approach to extract conceptual diagram from software requirements

Employing rules for the automatic extraction of conceptual diagrams from software requirements has been in practice for some time. However, considering only rules for extraction makes the system complex to handle. Moreover, the rules are predominantly based on the syntactic structure such as Part of Speech tags along with Dependency Grammar of sentences and rarely on semantics. In this paper, we propose to use a probabilistic approach in configuration with the rule-based technique and the Word embeddings to preserve the semantics of the sentence. Hence, reduces the complexity of the extraction procedure. Further, we advocate the use of a divide-and-conquer policy of extraction instead of extracting classes for one entire use case description. We extract the class diagram from small use cases and then merge it to obtain the class diagram. As generated class diagram corresponding to small use cases can be utilized in another similar software design, thus, it increases the scalability and decreases the extraction time. The proposed hybrid approach integrates the knowledge from the experiences. Thus, the proposed approach achieved 90% as F1-score whereas the F1-Score for the existing methods ranged between 79-88%. The proposed hybrid approach also shows a 19.44% reduction in terms of the number of iterations performed to carry out extraction procedures for individual use cases. Hence, reduces the extraction procedure complexity.

Universal methods for variational inequalities: Deterministic and stochastic cases

In this paper, we propose universal proximal mirror methods to solve the variational inequality problem with Hölder-continuous operators in both deterministic and stochastic settings. The proposed methods automatically adapt not only to the oracle’s noise (in the stochastic setting of the problem) but also to the Hölder continuity of the operator without having prior knowledge of either the problem class or the nature of the operator information. We analyzed the proposed algorithms in both deterministic and stochastic settings and obtained estimates for the required number of iterations to achieve a given quality of a solution to the variational inequality. We showed that, without knowing the Hölder exponent and Hölder constant of the operators, the proposed algorithms have the least possible in the worst-case sense complexity for the considered class of variational inequalities. We also compared the resulting stochastic algorithm with other popular optimizers for the task of image classification.

Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting

Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selection. Notwithstanding, centralized optimizers overcome the performance of distributed channel assignment algorithms, so their study is of utmost importance. One of the most successful optimization proposals in the state-of-the-art for Wi-Fi 4 is based on simulated annealing (SA). However, SA presents a significant limitation: its parameters must be adjusted manually for each scenario, otherwise yielding suboptimal solutions. In this paper, we propose a new technique for channel assignment optimization of Wi-Fi dense networks, called Thermodynamic Threshold Accepting (TTA), including modifications inspired by Threshold Accepting (TA) and Thermodynamic Simulated Annealing (TSA). With respect to SA, TTA avoids manual tuning, being able to adapt to the specific setting automatically. We evaluate TTA in a dense Wi-Fi model and, compared to SA, we show that our approach reaches optimal results without requiring any prior fine-tuning. We also show that TTA is even able to slightly overcome the performance of an accurately calibrated SA optimizer for the same number of iterations.

Non-ergodic convergence rate of an inertial accelerated primal–dual algorithm for saddle point problems

In this paper, we design an inertial accelerated primal–dual algorithm to address the convex–concave saddle point problem, which is formulated as minxmaxyf(x)+〈Kx,y〉−g(y). Remarkably, both functions f and g exhibit a composite structure, combining “nonsmooth” ＋ “smooth” components. Under the assumption of partially strong convexity in the sense that f is convex and g is strongly convex, we introduce a novel inertial accelerated primal–dual algorithm based on Nesterov’s extrapolation. This algorithm can be reduced to two classical accelerated forward–backward methods for unconstrained optimization problem. We show that the proposed algorithm achieves a non-ergodic O(1/k2) convergence rate, where k represents the number of iterations. Several numerical experiments validate the efficiency of our proposed algorithm.

A New Porosity Evaluation Method Based on a Statistical Methodology for Granular Material: A Case Study in Construction Sand

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

Modeling of Growth in Turkeys by Nonlinear Regression Models

Nonlinear regression models are commonly used in many areas, such as physics, chemistry, biology, and engineering. In these models, the solution of normal equations is more difficult than normal equations of linear regression models. Iterative algorithms are used to solve these equations. It is highly important to choose an appropriate initial value while using these algorithms. The values obtained during the study or from previous studies can be used as an initial value. With an inappropriately chosen initial value, the number of iterations will increase, and convergence may also not occur. In this study, the nonlinear Gompertz, Richards, and Weibull models of turkey growth were considered by taking the data on female turkey weight, and it was investigated which of these models was the most appropriate model for turkey growth using the R program

Active contour model with improved second-order differential driven term

Driven terms in active contour models (ACMs) play a significant role in edge identification and image segmentation. However, in many existing ACMs, the driven terms are iteratively updated, resulting in slower segmentation speed because the image segmentation time increases with the iteration number. To address this problem, an ACM based on an improved second-order differential driven term (ISDDT) is presented, which can extract the edge information of images. The improved second-order differential driven term is computed only once before the iterations. Therefore, the computational complexity of our presented ACM is reduced, leading to a faster image segmentation speed. In addition, an improved regularization method with mean filtering is presented to improve the robustness of our ISDDT model. As an application, a target contour tracking method is developed based on our ISDDT model. Experimental results show that our ISDDT model segments images with inhomogeneous intensities well. The image segmentation speed of our proposed model has obvious advantages.

Prediction through Simulation-Based Corrective Models of Contra-Directional Couplers’ Experimental Results

Lithography variation presents one of the biggest challenges for photonic component optimization, especially for fabless designers. Lithography prediction models are a crucial tool for minimizing the necessary number of fabrication iterations for a device’s optimization. This paper presents one of these models specifically adapted for the contra-directional coupler structure. Through the experimental characterization of devices with a specific range of design parameters, it was possible to observe how the lithography process impacts their performance. A correction model based on effective refractive index variation and its impact on the Bragg condition of the structure was developed to predict the performance variation of a device based on the expected design variation induced by fabrication. The contra-directional couplers fabricated at CORNERSTONE foundry show a tendency to be redshifted as the gap decreases, due to an increase in waveguide width as a result of a diffraction-limited lithography process. Based on these and other findings, it was possible to correlate the design parameters to the posterior fabricated structure and ultimately predict the expected experimental response.

Conversion of An Existing 3 – Phase BLDC Hub motor to A 6 – Phase BLDC Hub Motor and their Performance Analysis for EV Application

In this research work a six–phase BLDC Hub motor designed, developed by using an existing 3–phase BLDC Hub motor material. This conversion orients towards design modifications in the stator winding layout, the number of phases, the number of hall-sensors, placement of hall-sensors and stator winding commutation. Also, a new controller is designed to commutate the six-phase stator winding. The rotor is kept the same without any modification in geometry and number of magnets. The existing 3–phase BLDC Hub motor, which is used in two – wheel EV application, has 48 slots and 52 magnets and a concentrated double layer winding layout is observed in it. This paper presents a novel direct approach for the conversion of the existing 3–phase BLDC Hub motor to a six–phase BLDC Hub motor. Using this proposed approach, a six-phase winding layout is designed and developed for the existing 48 slots stator of the BLDC Hub motor. This approach reduces the number of iterations to determine the proper phase sequence and phase offset among the phase windings compared to regular Cros’ method. In this work, first the existing 3–phase BLDC Hub motor is dismantled, and its stator is rewound with the six – phase winding layout, which is determined by author’s presented direct approach. The winding was designed in such a way to keep the stator slot fill factor in practical limits. Six hall-sensors placed in the stator slots to identify the position of the rotor. Secondly, a new controller is designed to run the developed six-phase BLDC Hub motor. Six-phase and 3–phase BLDC Hub motors are loaded at common load conditions, for performance comparison, and experimental results of both the motors presented. The results show that the six-phase BLDC Hub motor performs better than the existing 3–phase BLDC Hub motor. Hence, this work proves that the conversion of existing 3–phase BLDC Hub motor to a six–phase BLDC Hub motor is viable and can be adopted commercially.

Optimizing Wireless Sensor Network Localization: Hybrid Natural Inspired with Bat Swarm Algorithm

In wireless sensor networks, the data received from sensor nodes is processed and communicated to the next node or cluster head. Sensed data are meaningful only when location information is embedded with the data. Therefore, the sensor should be able to estimate its location information and embed the location information with the sensed data. This research work proposes three hybrid nature-inspired localization optimization algorithms for location estimation of sensor nodes, namely the Hybrid genetic –Bat Localization Algorithm (GA-BAT LA), Hybrid Bat- Particle Swarm Optimization Localization Algorithm (BAT-PSO LA), and Hybrid genetic -Bat- Particle Swarm Optimization Localization Algorithm (GA-BAT-PSO LA). The localization algorithms are developed, implemented, and compared to ensure the accuracy of the estimation of location information. A fixed number of location-known anchor nodes with a variable location-unknown sensor node are considered. The algorithms are compared for a minimum number of iterations, average number of iterations, standard deviation, maximum number of iterations, time complexity, and accuracy to estimate the location of the sensor nodes.

Dual-polarization carrier-assisted differential detection with asymmetric twin-SSB modulation and simplified receiver structure.

Carrier-assisted differential detection (CADD) is a promising solution for high-capacity and cost-sensitive short-reach application scenarios, in which the optical field of a complex-valued double-sideband (CV-DSB) signal is reconstructed without using a local oscillator laser. In this work, we propose a polarization division multiplexed asymmetric twin single-sideband CADD (PDM-ATSSB CADD) scheme to realize the optical field recovery of the PDM CV-DSB signals. The polarization fading is solved by using a pair of optical bandpass filters (OBPFs) to suppress the unwanted other polarized offset carrier and signal, and the dual-polarization optical field is recovered by the CADD receiver. An asymmetric twin-SSB signal is used to relax the sharpness requirement of optical filter edges. We also propose a joint signal-signal beat interference (SSBI) iterative mitigation algorithm, which can effectively mitigate intra- and inter-polarization SSBI. The proposed PDM-ATSSB CADD scheme is validated for 30 Gbaud PDM asymmetric twin-SSB 16-ary quadrature amplitude modulation signals. We illustrate the parameter optimization process for PDM-ATSSB CADD, including optical delay and the number of iterations. The impacts of phase noise and relative intensity noise for laser and polarization impairment on PDM-ATSSB CADD are evaluated through numerical simulation. Compared with the PDM-symmetric-TSSB CADD (PDM-STSSB CADD), the required frequency gap to reach the 7% FEC threshold can be reduced by 1 GHz, and the OSNR sensitivity is improved by about 3 dB. Moreover, two simplified PDM-ATSSB CADD schemes are proposed and discussed, which could be considered hardware-efficient and integrative candidates for metro and inter-data center interconnects.

Grabbing Path Extraction of Deep-Sea Manganese Nodules Based on Improved YOLOv5

In an effort to enhance the efficiency and accuracy of deep-sea manganese nodule grasping behavior by a manipulator, a novel approach employing an improved YOLOv5 algorithm is proposed for the extraction of the shortest paths to manganese nodules targeted by the manipulator. The loss function of YOLOv5s has been improved by integrating a dual loss function that combines IoU and NWD, resulting in better accuracy for loss calculations across different target sizes. Additionally, substituting the initial C3 module in the network backbone with a C2f module is intended to improve the flow of gradient information while reducing computational demands. Once the geometric center of the manganese nodules is identified with the improved YOLOv5 algorithm, the next step involves planning the most efficient route for the manipulator to pick up the nodules using an upgraded elite strategy ant colony algorithm. Enhancements to the ACO algorithm consist of implementing an elite strategy and progressively decreasing the number of ants in each round. This method reduces both the number of iterations and the time required for each iteration, while also preventing the occurrence of local optimal solutions. The experimental findings indicate that the improved YOLOv5s detection algorithm boosts detection accuracy by 2.3%. Furthermore, when there are fewer than 30 target planning points, the improved algorithm requires, on average, 24% fewer iterations than the ACO algorithm to determine the shortest path. Additionally, the speed of calculation for each iteration is quicker while still providing the optimal solution.

Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm.

Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.

Performance optimization of latent heat storage device based on surrogate-assisted multi-objective evolutionary algorithm and CFD method

The design of fin structures and the formulation of heat conductivity enhancers have always been critical factors influencing the performance of latent heat storage devices. However, achieving the optimal parameters of fin structures and the fraction of heat conductivity enhancers when adjusting multiple parameters simultaneously remains a time-consuming and labor-intensive task. In this paper, a combination of machine learning methods and computational fluid dynamics (CFD) was employed to optimize the parameters of fin structures and the mass fraction of expanded graphite with minor computational resources and time costs, thereby obtaining a set of Pareto optimal latent heat storage (LHS) devices that simultaneously considers energy storage power and energy storage capacity. The combination essentially is a surrogate assistant multi-objective evolutionary optimization algorithm, which utilizes batch recommendation strategies to recommend multiple high-quality candidate solutions that achieve a balance between two conflicting optimization objectives in CFD simulations. This method significantly reduces the number of model iterations and saves the time and computational resources required for multi-objective optimization. The results showed that the proposed algorithm only required 80 cases of CFD calculations to obtain two well-fitted Gaussian process models, leading to a 91 % improvement in computational efficiency. According to the recommendations from the algorithm, the LHS device with 78 fins of 2.0 mm thickness and 20 % mass fraction of expanded graphite could achieve the optimal trade-off of energy storage power and energy storage capacity. Compared to the prototype, the energy storage power and total energy storage capacity increased by 46.7 % and 22.1 %, respectively. The method proposed in this study can provide guidance for the optimal configuration design of LHS devices.