Selection Of Optimal Parameters Research Articles

Digital signal processing for neutron-gamma discrimination using a liquid scintillator detector

Digital signal processing has shown promise as a reliable approach for nuclear radiation spectroscopy. In this paper, the charge-to-digital conversion technique was applied to neutron-gamma discrimination. The method is based on the direct comparison of the charge at the fast and slow components of the anode signal of the scintillator detector. The performance of the technique was tested using experimental investigation by 252Cf source and D-D neutron generator. The minimum detectable energy for reasonable separation was found to be around 0.2 MeVee. The FOM of the discrimination parameter was maximized by the optimum selection of the charge integration parameters. Excellent discrimination between neutron and gamma signals was confirmed by the presented setup. The results confirmed that the presented technique can replace analog nuclear electronics conventionally uses for pulse shape discrimination.

Instantaneous cross-correlation function-[formula omitted]-Wigner distribution: Theory and application

To raise the noisy linear frequency-modulated (LFM) signal detection threshold without sacrificing the computing performance, this study employs the well-known τ-Wigner distribution (τ-WD) in extending further the notion of the instantaneous cross-correlation function WD (ICFWD) to the so-called ICF-τ-WD. We extend some essential properties of the ICFWD to those of the ICF-τ-WD. We analyze the computational complexities of the ICFWD and τ-WD, based on which we derive the computational complexity of the ICF-τ-WD. We address an important concern regarding the ICF-τ-WD’s optimal parameters selection for the noisy single component LFM signal processing, in accordance with the output signal-to-noise ratio (SNR) optimization models including the single-objective (expectation-SNR) optimization model and the double-objective (expectation-SNR and variance-SNR) optimization model. We also provide numerical simulations for verifying the advantage of the double-objective optimization model over the single-objective one and the ICF-τ-WD over the ICFWD in the improvement of detection performance.

Distributed Dynamic Load Identification of Beam Structures Using a Bayesian Method

The distributed dynamic load is difficult to obtain due to the complexity of loads in practical engineering, such as the aerodynamic loads of aircraft and the distributed dynamic loads of sea-crossing bridges. Thus, distributed dynamic load identification is important to deal with these difficulties, which is generally an ill-posed problem considering the inversion of the infinite dynamic loads. The traditional Tikhonov regularization technique is limited on the optimal regularization parameters selection. Consequently, in this paper, we develop a novel distributed dynamic load identification algorithm in combination with the orthogonal polynomials and the Bayesian framework. Thus, the orthogonal polynomial coefficients in the load identification model are regarded as the prior probability distribution of unknown variables in the Bayesian inference. Simultaneously, the posterior probability distribution of the orthogonal polynomial coefficients is derived based on the Bayesian formula and the likelihood function. The regularization parameters and the standard deviation of the response error are also treated as random variables to obtain the corresponding prior distribution in the multi-level Bayesian model. Moreover, the maximum posterior estimate is applied aiming at determining the regularization parameters, as well as the orthogonal polynomial coefficients to reconstruct the distributed dynamic loads. Compared with the Tikhonov regularization, a series of numerical simulations are studied to verify the effectiveness and high accuracy, as well as the noise resistance, and the results illustrate that this approach is effective to reconstruct the distributed dynamic loads.

The Concept of WC-CrC-Ni Plasma-Sprayed Coating with the Addition of YSZ Nanopowder for Cylinder Liner Applications.

In the article, the new concept of plasma-sprayed coatings for the cylinder liner was presented. The new type of powder containing WC-CrC-Ni with a 5 and 10 wt. % addition of nano-YSZ powder was plasma-sprayed on aluminum 2017 alloy samples. The selection of optimal plasma-spraying parameters was made taking into account the thickness, porosity, and hardness of the coatings. For the coatings obtained according to the developed parameters, the analysis of their microstructure, chemical, and phase composition was performed. At the next stage, the friction coefficient of the developed coatings was tested and compared with the properties of a classic cast-iron cylinder liner. The obtained results suggest that the developed type of coating might be used for cylinder liner applications after a deeper friction analysis.

Influence of Base-Angle Bolt Support Parameters and Different Sections on Overall Stability of a Roadway under a Deeply Buried High Stress Environment Based on Numerical Simulation

Strengthening the base-angle of a roadway can have a beneficial impact on its overall stability, but the specific optimal parameter selection range is still unclear. Fast Lagrangian Analysis of Continua 3D (FLAC3D) software is used to carry out the stability analysis of 5 kinds of roadway models with different section shapes under the conditions of no support and different base-angle bolt support angles, and the simulation verification is carried out under the actual working conditions of the Sanshandao Gold Mine. The conclusion is as follows: without support, the self-stability of a tri-centric arch roadway is the best, and that of a rectangular roadway is the worst. When the base-angle bolt dip angle is between 15° and 45°, a better supporting effect can be obtained under the established 5 kinds of roadway sections. In the straight wall circular arch roadway of the Sanshandao Gold Mine, the roadway stability is the best when the angle of the base-angle bolt is 15°. However, changing the length of the base-angle bolt, even if the length of the base-angle bolt is increased to a certain extent, will decrease the overall supporting effect of the supporting structure. This paper can provide a reference for the selection of tunnel bottom corner bolt support parameters.

Moment-based density estimation of confidential micro-data: a computational statistics approach

Providing access to synthetic micro-data in place of confidential data to protect the privacy of participants is common practice. For the synthetic data to be useful for analysis, it is necessary that the density function of the synthetic data closely approximate the confidential data. Hence, accurately estimating the density function based on sample micro-data is important. Existing kernel-based, copula-based, and machine learning methods of joint density estimation may not be viable. Applying the multivariate moments’ problem to sample-based density estimation has long been considered impractical due to the computational complexity and intractability of optimal parameter selection of the density estimate when the true joint density function is unknown. This paper introduces a generalised form of the sample moment-based density estimate, which can be used to estimate joint density functions when only the information of empirical moments is available. We demonstrate optimal parametrisation of the moment-based density estimate based solely on sample data by employing a computational strategy for parameter selection. We compare the performance of the moment-based estimate to that of existing non-parametric and parametric density estimation methods. The results show that using empirical moments can provide a reasonable, robust non-parametric approximation of a joint density function that is comparable to existing non-parametric methods. We provide an example of synthetic data generation from the moment-based density estimate and show that the resulting synthetic data provides a reasonable disclosure-protected alternative for public release.

Numerical Study of a Miniaturized, 1–3 Piezoelectric Composite Focused Ultrasound Transducer

This study aimed to develop an optimal methodology for the design of a miniaturized, 1–3 piezoelectric composite focused ultrasound transducer. Miniaturized focused ultrasound (FUS) devices, generally guided through catheters, have received considerable attention in the biomedical and ultrasound fields as they can overcome the technical restrictions of typical FUS transducers. However, miniaturized transducers cannot readily generate a high acoustic intensity because of their small aperture sizes and the vibration mode coupling. As such, 1–3 composite transducers, having a high electromechanical coupling and efficient vibration directivity, break through the current technical restrictions. However, the systematic methodology for designing miniaturized FUS transducers has not been thoroughly discussed so far. Therefore, in this study, we designed 1–3 piezoelectric composite transducers using analytical and numerical methods. Specifically, extensive parametric studies were performed through finite element analysis under the coupled field with piezoelectricity, structural vibration, and acoustic pressure. The simulation results confirmed that the optimal design of the 1–3 composite type transducer produces much higher (>160%) acoustic pressure output at the focal point than the single-phase device. Furthermore, the array type of the interstitial transducer was predicted to produce an unprecedented acoustic intensity of approximately 188 W/cm2 under a short duty cycle (1%). This study will provide valuable technical methodology for the development of interstitial, 1–3 composite FUS transducers and the selection of optimal design parameters.

Improved parameter optimization method for measurement device independent protocol

The optimal selection of parameters in practical quantum key distribution can greatly improve the key generation rate and maximum transmission distance of the system. Owing to the high cost of global search algorithm, local search algorithm is widely used. However, there are two shortcomings in local search algorithm. One is that the solution obtained is not always the global optimal solution, and the other is that the effectiveness of the algorithm is greatly dependent on the choice of initial value. This paper uses the Monte Carlo method to prove whether the key generation rate function is convex, and also simulates and analyzes the projection of the key generation rate function on each dimension of the parameter, in contrast to the approach in previous article. In order to eliminate the effect of the initial value, this paper proposes the particle swarm local search optimization algorithm which integrates particle swarm optimization algorithm and local search algorithm. The first step is to use the particle swarm optimization to find a valid parameter which leads to nonzero key generation rate, and the second step is to adopt the parameter as the initial value of local search algorithm to derive the global optimal solution. Then, the two algorithms are used to conduct simulation and their results are compared. The results show that the key generation rate function is non-convex because it does not satisfy the definition of a convex function, however, since the key generation rate function has only one non-zero stagnation point, the LSA algorithm can still obtain the global optimal solution with an appropriate initial value. When the transmission distance is relatively long, the local search algorithm is invalid because it is difficult to obtain an effective initial value by random value method. The particle swarm optimization algorithm can overcome this shortcoming and improve the maximum transmission distance of the system at the cost of slightly increasing the complexity of the algorithm.

Numerical study of modified centrifugal cyclone

A cyclone separator is an equipment that separates particles contained in a liquid or gas without filters. Dust particles in flue gases can be separated using centrifugal forces and different particle densities so that the exhaust gases must be cleaned before being discharged into the environment. The selection of optimal geometric dimensions and operation parameters of industrial equipment is always a topical issue. In the work, numerical modeling and research of a modified centrifugal cyclone dust collector are carried out. A modified centrifugal cyclone was developed by one of the article's authors at the Institute of Mechanics and Seismic Resistance of Structures of the Academy of Sciences of the Republic of Uzbekistan, which has a high efficiency of at least 98%. The study was carried out in the Comsol Multiphysics 5.6 engineering software package using the Flow Simulation add-on, designed to simulate hydrodynamic processes. The relevance of the chosen research topic was indicated. The standard k-ɛ model is chosen as a turbulence model, which is often used in engineering problems. The computational experiment was carried out at three flow rates in the centrifugal cyclone. Based on the obtained numerical data, it is shown that the efficiency of the modified cyclone is significantly higher than that of the classical cyclone.

Research on Electroacupuncture Parameters for Cancer Pain Based on Data Mining.

Cancer pain, a common complication of this disease, has been widely treated by electroacupuncture in recent years. However, there are numerous treatment parameters that are not conducive to clinical translation applications. This study aims to summarize the stimulation parameters commonly used in electroacupuncture treating cancer pain by data mining and visualization techniques to provide a basis for the future acupuncture technology transformation and selection of optimal stimulation parameters. Nine databases, including Pubmed, Cochrane Library, Embase, Web of Science, OVID, China National Knowledge Infrastructure Database, China Biology Medicine disk, China Science and Technology Journal Database, and Wanfang Database, were searched for clinical studies on electroacupuncture treatment cancer pain published between January 2012 and September 2022. A database was established using Microsoft Excel 2020 and analyzed with SPSS Modeler 18.1 software and SPSS statistics 26.0 software. Twenty-four articles were included according to the established criteria. The most used electroacupuncture stimulation parameters were a dilatational wave, the current frequency of 2/100 Hz, stimulation duration of 30 minutes per treatment, and frequency of treatment once a day. Fifty-eight acupoints were mentioned, and the total frequency of acupoints involved was 156 times. The most used ones include Zusanli (ST36), Sanyinjiao (SP06), Hegu (LI04), Neiguan (PC06), Quchi (LI11), Taichong (LR03), Ashi point, Jiaji point, and those most generally used acupoints that are closely arranged on the Stomach Channel of Foot Yangming and the Spleen Channel of Foot Taiyin. The association analysis of acupoints revealed that the most supported acupoint pair was Sanyinjiao (SP06) and Zusanli (ST36). Cluster analysis demonstrated 3 groups, 1 for obligatory acupoints, 1 for Ashi point, and the third for Jiaji point. A dilatational wave, the current frequency of 2/100 Hz, 30-minute stimulation, and acupoints of the Stomach Channel of Foot Yangming and the Spleen Channel of Foot Taiyin selection are frequently used in electroacupuncture for treating cancer pain. Due to the limitations of this study, further research and more standardized, multi-center, large-sample clinical trials can be carried out to guide optimizing acupuncture treatment schemes and promote the formation of TCM-characteristic technologies for cancer pain.

Design and Analysis of RSA and Paillier Homomorphic Cryptosystems Using PSO-Based Evolutionary Computation

Non-lattice based homomorphic encryption schemes usually involve huge modular exponentiation operations. Thus, improving the efficiency of modular exponentiation for large exponents is a real-world issue. Modular exponentiation can be computed by a series of modular multiplications. However, performing a series of modular multiplications is computationally expensive. This paper proposes hardware /software co-design for efficient modular exponentiation. This article first explores the use of particle swarm optimization (PSO) for the modular exponentiation in software, and present an efficient hardware co-design utilizing FPGA. Our findings reveal that the suggested PSO approach surpasses all other deterministic and non-deterministic approaches already in use. Further, we also demonstrate a comprehensive analysis for the optimal performance and parameter selection of our proposed PSO approach. Finally, we implement homomorphic encryption schemes, such as RSA and Paillier, using our PSO-based hardware /software co-design. Our approach gains resource savings for 1024-bit as follows: RSA encryption/decryption - 60.7% (area) and 65.3% (DSP); Paillier encryption - 46.3% (area) and 40% (DSP) and Paillier decryption - 73.7% (area) and 66.6% (DSP). We have obtained area-time improvements of 1024-bit as follows: RSA encryption/decryption - 2.7x; Paillier encryption - 2x and Paillier decryption - 4.6x using Xilinx Virtex-7 FPGAs.

Performing parameter optimization and variable selection simultaneously in Fourier transform infrared and laser-induced breakdown spectroscopy data fusion

Performing parameter optimization and variable selection simultaneously in Fourier transform infrared and laser-induced breakdown spectroscopy data fusion.

Построение плана траектории перемещения манипуляционного робота на основе модифицированного метода муравьиных колоний

Manipulators are widely used to automate a broad range of operations in industry. It is required to compute a motion plan for a manipulator to move the manipulator from the initial configuration to the goal configuration so that the manipulator can accomplish the planned operations. The motion planning problem is further complicated by the fact that there are obstacles in the work zone of the manipulator, collision with which is to be avoided. There exist many motion planning methods both for obstacle-free environments and for environments with obstacles. The group of motion planning methods based on heuristic algorithms is of a particular interest. In this paper, a research on the possibility of modification of a motion planning method based on ant colony optimization algorithm for building a motion plan for a manipulator is carried out. A modification of the original method, using probabilistic roadmap (PRM) method, is proposed. An analysis on results of modified algorithm with varied values of parameters of the algorithm is performed. Recommendations on the optimal selection of parameters of the algorithm are provided based on results of the analysis.

Application of the WASPAS method in a selected technological process

The article presents the results of work on the application of the WASPAS method in the selection of optimal parameters of the water-abrasive cutting process, which significantly affect the course of the research process, while allowing the user to omit those factors that have a negligible impact on the results. WASPAS is a multi-criteria decision-making technique. This is a weighted aggregate product evaluation sum method for parametric optimization. A method can indicate one or more optimization responses. It combines the Weighted Sum Model (WSM) and the Weighted Product Model (WPM). The article shows that WASPAS effectively determines a set of optimal combinations of parameters for many responses. The material used in the study was Inconel-625- a nickel-chromium alloy. It was found that the rate of material removal varies directly with feed rate and abrasive flow rate. Increasing the feed rate and abrasive flow rate increased the rate of workpiece material removal during waterjet cutting. The abrasive flow rate and the feed speed had the greatest influence on the roughness. The advantage of proceeding with the WASPAS scheme is the selection of parameters that can be very carefully optimized by applying an additional factor .Ñú.. The method was considered more meticulous compared to others.

ПОДХОДЫ ТЕХНОЛОГИЧЕСКОГО ИНТЕГРАТОРА ПРИ ВЫБОРЕ ПАРАМЕТРОВ РЕАЛИЗАЦИИ ОБЪЕКТОВ НА ПРЕДПРОЕКТНОЙ СТАДИИ

The paper is devoted to selection of optimal parameters of railway infrastructure objects on the concept design stage. The author considers problems that arise at designing and applying normative documentation and lead to excess of the initial cost of objects, as well as tools that can be used for solving such problems by a technological integrator - independent division of the JSC «RZD». As an important aspect of the analysis of concept design documentation the paper presents possible approaches to determination of stage-by-stage development of railway infrastructure objects. Moreover, it provides specific examples and a complex of technological measures.

Suppression of absence seizures by using different stimulations in a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model.

Coupled neural network models are playing an increasingly important part in the modulation of absence seizures today. However, it is currently unclear how basal ganglia, corticothalamic network and pedunculopontine nucleus can coordinate with each other to develop a whole coupling circuit, theoretically. In addition, it is still difficult to select effective parameters of electrical stimulation on the regulation of absence seizures in clinical trials. Therefore, to develop a coupled model and reduce computation cost, a new model constructed by a simplified basal ganglion, two corticothalamic circuits and a pedunculopontine nucleus was proposed. Further, to seek better inhibition therapy, three electrical stimulations, high frequency stimulation (HFS), 1:0 coordinate reset stimulation (CRS) and 3:2 CRS, were applied to the thalamic reticular nucleus (RE) in the first corticothalamic circuit in the coupled model. The simulation results revealed that increasing the frequency and pulse width of an electrical stimulation within a certain range can also suppress seizures. Under the same parameters of electrical stimulation, the inhibitory effect of HFS on seizures was better than that of 1:0 CRS and 3:2 CRS. The research established a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model, which lays a theoretical foundation for future optimal parameters selection of electrical stimulation. We hope that the findings will provide new insights into the role of theoretical models in absence seizures.

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this article, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.

Optimization of drilling parameters based on Copula function method for the Chepai area

In areas with complex stratigraphic lithology, the relationship between the penetration rate and drilling parameters should be fully considered to optimize the drilling process and improve drilling efficiency. The most frequently utilized methods for performing parameter optimization through correlation analysis are the correlation coefficient, principal component analysis, and grey correlation. The correlation coefficient method solely evaluates the extent of linear correlation between two variables, it cannot be applied to the non-linear connection between penetration rate and drilling parameters. The application of principal component analysis may produce inaccurate experimental findings due to the intricate and poorly co-varying nature of drilling parameters. The grey correlation method can lead to the substantial bias in the results because of the vast quantity of data analysed. Based on the vast quantity of data, using the copula function, the big data analysis method analyses the nonlinear relationship between penetration rate and drilling parameters. It constructs a united distribution function expression to determine the optimal parameter selection criteria. The in-situ drilling data from dozens of wells in the Chepaizi area are collected and optimized six types of parameters. The optimal parameter combination is determined. Following field investigation, there was a noteworthy increase of 34.83% in the average penetration rate.

Improved Metaheuristic Algorithms for Optimal Parameters Selection of Proton Exchange Membrane Fuel Cells: A Comparative Study

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy; However, HSOA could be more stable. The source code of this study is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://drive.matlab.com/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69</uri> .

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.