Theoretical Optimal Value Research Articles

Efficient knowledge model for whale optimization algorithm to solve large-scale problems

In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage.

A comparative study on the acceleration techniques for solving finite difference discretization poisson’s equation in the PIC/MCC Method

A wide variety of plasma phenomena have been investigated during the past decades using the particle-in-cell/Monte Carlo collisions (PIC/MCC) method. As an important component of the PIC/MCC method, solving Poisson’s equation is crucial for the accuracy and efficiency of calculations. Different acceleration techniques for solving finite difference discretization Poisson’s equation are investigated and compared, including direct method, iterative method, multigrid (MG) method, parallel computing and inherited initial value. The charge density distribution with a known analytical solution is used to validate the algorithm and code. The optimal relaxation factor for the successive over-relaxation (SOR) method in 2D Poisson’s equation with unequal grid node numbers in different dimensions is derived, which is only related to the dimension with the largest grid number. Although there will be a ‘more optimal’ relaxation factor deviated from in some simulation cases, selecting the optimal relaxation factor derived always leads to a not slow solving speed. However, when SOR is used in MG for smoothing, the optimal relaxation factor will shift to 0.5–1.2 from the theoretical optimal value derived with the increase of MG levels. By comparing the convergence order under different relaxation factors and MG levels, the suitable MG level is proposed as log2[min(N x , N y )]−2. Combining the optimal SOR relaxation factor, MG, parallel computing and inherited initial values, the computational cost may decrease by 5 orders of magnitude than that by the simple Gaussian elimination (GE). Based on the optimal acceleration techniques mentioned above, a benchmark simulation case electron cyclotron drift instability (ECDI) in magnetized plasmas was run to further validate the developed PIC/MCC code. The distributions of electric field in the x-direction, electron density and electron temperature are all consistent with the literatures. This paper provides a reference for the acceleration strategy selection for solving Poisson’s equation quickly in plasma simulations.

Precision agriculture with AI-based responsive monitoring algorithm

Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within ± 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.

High strength and high toughness Csf/SiC composites prepared by laser powder bed fusion/ reactive melt infiltration with the impregnation of boron-and silicone-containing phenolic resin

Laser powder bed fusion (LPBF) has emerged as an effective method for preparing complex structured silicon carbide (SiC) ceramics. However, the SiC components demonstrate low strength and high brittleness, which remain major challenges limiting its application. To overcome this limitation, short carbon fiber reinforced silicon carbide (Csf/SiC) composites are prepared by combining LPBF and phenolic resin binder impregnation (PRBI)-reactive melt infiltration (RMI), in which high char yield boron-and silicone-containing phenolic resin (BSiPF) impregnation is used to adjust the carbon density of green bodies and to reduce the pore size of green bodies. By optimizing the impregnation process, the carbon density can be brought close to the theoretical optimal value of 0.84 g/cm3, and the reduction of the pore size is conducive to the improvement of the reaction rate at the early stage of the reaction, which enhance the SiC phase content of Csf/SiC composites obtained by RMI. Meanwhile, the application of BSiPF results in a protective pyrolytic carbon (PyC) coating on the surface of Csf, which can reduce the erosion of Csf by molten Si during the RMI process, thus improving the fracture toughness of Csf/SiC composites. The final Csf/SiC composites exhibit the enhanced bulk density, bending strength and fracture toughness, reaching 2.90 ± 0.01 g/cm3, 257 ± 20 MPa, 3.52 ± 0.15 MPa m1/2, respectively. This study provides a new strategy for effective impregnation technique for the additive manufacturing (AM) of high strength and high toughness SiC composites.

A Simplified One-Parallel-Element Automatic Impedance-Matching Network Applied to Electromagnetic Acoustic Transducers Driving

Ultrasonic waves generated and received by electromagnetic acoustic transducers (EMATs) are advantageous in non-destructive testing, mainly due to the ability to operate without physical contact with the medium under test. Nevertheless, they present a main drawback of less efficiency, which leads to a lower signal-to-noise ratio. To overcome this, the L-network impedance-matching network is often used in order to ensure maximum power transfer to the EMAT from the excitation electronics. There is a wide range of factors that affect an EMAT’s impedance, apart from the transducer itself; namely, the properties of the specimen material, temperature, and frequency. Therefore, to ensure optimal power transfer, the matching network’s configuration needs to be fine-tuned often. Therefore, the automation of the laborious process of manually adjusting the network is of great benefit to the use of EMAT transducers. In this work, a simplified one-parallel-element automatic matching network is proposed and its theoretical optimal value is derived. Next, an automatic matching network was designed and fabricated. Experiments were performed with two different EMATs at several frequencies obtaining good agreement with theoretical predictions. The automatic system was able to determine the best configuration for the one-element matching network and provided up to 5.6 dB gain, similar to a standard manual solution and considerably faster.

Abstract 16610: A New Left Ventricle Vortex Formation Time for Clinical Assessment of Diastolic Filling Efficiency Based on Direct Mechanical Ventricular-Vascular Coupling: Evaluation in Heart-Failure and Healthy Cohort

Introduction: The mechanical coupling of the left ventricle (LV) and aorta creates the apical motion of the atrioventricular plane (AVP), resembling a piston-cylinder system, impacting early diastolic filling (J Am Heart Assoc. 2017;6:e004903). Furthermore, several clinical studies have suggested the vortex formation time (VFT) as an indicator of LV diastolic function (Eur. Heart J. Cardiovasc. Imaging. 2012;13(5):385-93). However, conflicting results exist in previous clinical studies, potentially due to deviations from the original fluid dynamics definition of VFT in a piston-in-cylinder setting. In this study, we propose a new VFT approach to assess LV diastolic filling performance based on direct mechanical ventricular-vascular coupling. Methods: Cardiac MRI was used on 69 healthy individuals and 19 heart failure (HF) patients. The clinical cohort (37% women) had a mean age of 46 [± 17.5] years and BMI of 24.8 [±4.5]. LV valvular diseases were excluded. VFT was assessed using the original L/D definition, where L represents ascending aortic recoil motion (piston displacement) and D represents the mitral valve orifice. Aortic displacement was measured on the three and four-chamber MRI scans by tracing the AVP. Mitral valve orifice was determined as the average of commissure distance (short-axis images) and the leaflet tip distance (three-chamber view). Results: The new VFT approach yielded a mean[± std.] of 4.73[±0.89] for the healthy group, and considerably lower values (3.24±0.83) for the patients with HF (figure 1). Conclusions: The proposed VFT based on the mechanical ventricular-vascular coupling and original fluid dynamic formulation, demonstrates a theoretical optimal value of approximately 4 in healthy individuals, while significantly lower in HF. This novel VFT has the potential to address existing discrepancies regarding the clinical significance of VFT and establish it as a promising indicator of left ventricular filling efficiency.

Reliability-Guaranteed Uplink Resource Management in Proactive Mobile Network for Minimal Latency Communications

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

<p>In order to make the railroad inspection robot better adapt to its complex working environment, it is especially important to study the robot object avoidance algorithm. The WOA algorithm has simple and understandable structure and strong optimization ability but is prone to local convergence. IWOA-PSO is used for railway inspection robots. The performance of IWOA-PSO in the experimental results is better than that of WOA and PSO, and the average accuracy and standard deviation of the IWOA-PSO can better reach the theoretical optimal value in the function tests, and it has performance close to the theoretical value. In the simple environment object avoidance route planning, the minimum path length of IWOA-PSO is 850 mm, which is 53.6% less than that of the PSO algorithm, and the search time is 13.12 seconds, which is 5.11 seconds less than that of PSO algorithm; in the ordinary environment object avoidance route planning, the minimum path length of IWOA-PSO is 830 mm, while the path length of PSO algorithm is 1339 mm, the former is 38% less than the latter, and the search time of IWOA-PSO is 14.05 seconds less than PSO algorithm, so the method has better effect on object avoidance.</p> <p> </p>

Practical continuous-variable quantum key distribution with feasible optimization parameters

Continuous-variable quantum key distribution (CV-QKD) offers an approach to achieve a potential high secret key rate (SKR) in metropolitan areas. There are several challenges in developing a practical CV-QKD system from the laboratory to the real world. One of the most significant points is that it is really hard to adapt different practical optical fiber conditions for CV-QKD systems with unified hardware. Thus, how to improve the performance of practical CV-QKD systems in the field without modification of the hardware is very important. Here, a systematic optimization method, combining the modulation variance and error correction matrix optimization, is proposed to improve the performance of a practical CV-QKD system with a restricted capacity of postprocessing. The effect of restricted postprocessing capacity on the SKR is modeled as a nonlinear programming problem with modulation variance as an optimization parameter, and the selection of an optimal error correction matrix is studied under the same scheme. The results show that the SKR of a CV-QKD system can be improved by 24% and 200% compared with previous frequently used optimization methods theoretically with a transmission distance of 50 km. Furthermore, the experimental results verify the feasibility and robustness of the proposed method, where the achieved optimal SKR achieved practically deviates <1.6% from the theoretical optimal value. Our results pave the way to deploy high-performance CV-QKD in the real world.

Control Analysis of Vehicle Steering System based on Closed-Loop Control Algorithm

When a four-wheel vehicle turns and the steering centre of the front and rear wheels is one point, it is regarded as the theoretical optimal steering of the vehicle and the goal of optimal control. Aiming at the commonly used multi-link steering system, the system control was analysed, according to the structural characteristics and angle relationship. Based on the closed-loop control algorithm, the optimal control of the steering system was designed and analysed, according to the steering control quantity obtained by the target path and parameter feedback. At the same time, the state variables and prediction parameters of the system were analysed. Based on the actual vehicle and steering optimal control system, the changes of various parameters of vehicle operation under no-load condition and turning at 30 km/h were analysed and compared with the theoretical optimal value to verify the effectiveness of the control system. The results show that: the state variables of the control system are predicted by discrete analysis and half step integration, and the accuracy is high. In the steady state turning condition, with the lifting of the vehicle, the radius of the steering track gradually increases, and the vehicle shows obvious understeer characteristics. Under the two working conditions, the characteristic curves obtained by the steering control system are highly consistent with the theoretical values, and the peak error of the two is controlled within 5%, indicating the reliability of the optimal control algorithm, which provides a reference for such design optimization.

An advanced control strategy for engine thermal management systems with large pure time delay

Stable and precise temperature control can effectively improve the engine’s fuel consumption and emissions. An advanced engine thermal management system (AETMS) composed of electric actuators has great potential in increasing the temperature tracking control accuracy and decreasing the system energy consumption. Time-varying disturbance and pure time delay in the system make it challenging to harmoniously control multiple actuators to ensure efficient temperature tracking performance. In addition, a reasonable power distribution of the actuators is beneficial to reduce the parasitic energy consumption of the system and further improve the system efficiency. In this article, an adaptive optimization control strategy consisting of a state predictor, a disturbance estimator, an energy consumption optimization block and a state tracking controller is proposed for the temperature tracking and energy consumption optimization of AETMS. Experimental verification was performed on a range extender bench of an extended-range electric vehicle, and the results show that the system achieves high-precision temperature control and energy consumption near the theoretical optimal value under changing working conditions. The temperature steady-state error is within 0.3 oC, and the temperature adjustment time is within 100 s after the first overshoot.

Link assignment of time-reference transfer from the space station to the BeiDou satellite constellation

With the successful in-orbit operation of the cold atomic optical clock in the Mengtian laboratory cabin, a highly precise space time-reference will be formed. The BeiDou navigation satellite constellation has the ability of global coverage. By establishing time-transfer links with BeiDou satellites, the space station can realize precise transfer of its time-reference to wide area users. Aiming at the problem of highly precise and robust time-transfer link assignment from the space station to the BeiDou constellation, a link assignment model was established with taking the maximum hop and the connectivity as the evaluation indexes respectively of the accuracy and robustness of the time-transfer network. A topology optimization algorithm based on the genetic algorithm (GA) was further designed. Taking the time-reference transfer from the space station to the BeiDou Walker constellation as an example, the link assignment simulation was implemented in a minimum common orbit period. The precise time-reference transfer was realized using about 30% of inter-satellite link (ISL) timeslots. The maximum hop of the time-transfer links reached the theoretical optimal value, and the network connectivity was not less than 3. It was demonstrated that with the complex constraints of the ISL system, the payload quantity and visibility, the proposed assignment method could realize highly precise and robust time-reference transfer from the space station to the BeiDou constellation in any operation period.

Robust 4-D Hyperchaotic DNA Framework for Medical Image Encryption

With the integration of cloud computing approaches in the healthcare systems, medical images are now processed and stored remotely on third-party servers. For such digital medical image data, privacy, protection, and security must be maintained by using image encryption methods. The aim of this paper is to design and apply a robust medical encryption framework to enhance the protection of medical image transformation and the patient information confidentiality. The proposed Framework encrypt the digital medical images using DNA computation and hyperchaotic RKF-45 random sequence approach. For which, the DNA computation is enhanced by applying hyperchaoticRKF-45 random key to the different Framework phases. The simulation results on different medical images were measured with various security analyses to prove the proposed framework randomness and coherent. Simulation results showed the ability of the hyperchaotic DNA encryption framework to withstand multiple electronic attacks with high performance compared to its counterparts of encryption algorithms. Finally, simulation and comparative studies have shown that, the proposed cryptography framework reported UACI and NPCR values 33.327 and 99.603 respectively, which are near to the theoretical optimal value.

A Method Based on Spectrum Superposition for Minimizing the Hazards of Blasting Vibration

The selection of delay time will directly affect the effect of vibration reduction in millisecond delay blasting. The commonly used frequency spectrum control methods have some problems such as the difficulty of seed wave selection and the complexity of nonlinear superposition model. Based on the principle of waveform linear superposition, this study proposes a new spectrum superposition method for minimizing the vibration hazards in an open deep-hole bench blasting. This method, with no need to select the seed waves, mainly uses the ratio of the Fourier spectrum of the combined vibration waveform and the single hole vibration waveform to quantify the suppressed vibration. By defining the suppression ratio K, the suppression ratio with frequency range of 10 – 60 Hz under different delay times was calculated, and the theoretical optimal vibration reduction delay time value of 10 – 15 ms was given. A high precision digital electronic detonator was used to verify the proposed spectrum superposition method. The test results show that the best delay time of field vibration reduction is 5 – 10 ms, which is lower than the theoretical best delay time of 10 – 15 ms. It is the authors’ belief that this phenomenon is mainly caused by the joints and cracks in the rock mass, which will prolong the actual superposition time of blasting seismic waves between adjacent holes. In addition, it is found that there is a negative correlation between the delay time and the main vibration frequency, which is mainly caused by the low-pass filtering characteristic of rock medium. In order to minimize the probability of resonance failure, an optimal delay of 5 ms based on the main vibration frequency was given.

A Hybrid Cellular Genetic Algorithm for the Traveling Salesman Problem

The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. Genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. However, it has some issues for solving TSP, including quickly falling into the local optimum and an insufficient optimization precision. To address TSP effectively, this paper proposes a hybrid Cellular Genetic Algorithm with Simulated Annealing (SA) Algorithm (SCGA). Firstly, SCGA is an improved Genetic Algorithm (GA) based on the Cellular Automata (CA). The selection operation in SCGA is performed according to the state of the cell. Secondly, SCGA, combined with SA, introduces an elitist strategy to improve the speed of the convergence. Finally, the proposed algorithm is tested against 13 standard benchmark instances from the TSPLIB to confirm the performance of the three cellular automata rules. The experimental results show that, in most instances, the results obtained by SCGA using rule 2 are better and more stable than the results of using rule 1 and rule 3. At the same time, we compared the experimental results with GA, SA, and Cellular Genetic Algorithm (CGA) to verify the performance of SCGA. The comparison results show that the distance obtained by the proposed algorithm is shortened by a mean of 7% compared with the other three algorithms, which is closer to the theoretical optimal value and has good robustness.

A Feasible Approach to Determine the Optimal Relaxation Parameters in Each Iteration for the SOR Method

The paper presents a dynamic and feasible approach to the successive over-relaxation (SOR) method for solving large scale linear system through iteration. Based on the maximal orthogonal projection technique, the optimal relaxation parameter is obtained by minimizing a derived merit function in terms of right-hand side vector, the coefficient matrix and the previous step values of unknown variables. At each iterative step, we can quickly determine the optimal relaxation value in a preferred interval. When the theoretical optimal value is hard to be achieved, the new method provides an alternative choice of the relaxation parameter at each iteration. Numerical examples confirm that the dynamic optimal successive over-relaxation (DOSOR) method outperforms the classical SOR method.

Research on data mining and prediction of large-scale competitions based on online data migration model

To improve the data mining effect of large-scale (sports) competitions and improve the results of competition prediction and analysis, based on the online data migration model, this paper establishes a system model for processing applications of transmitting nodes, relay nodes and receiving nodes in the competition network, and proposes an online distributed cost optimization control strategy to be responsible for the operation and processing of applications in the communication system. The control strategy realizes that while ensuring the stability of the application queue, the optimization target system overhead is infinitely close to the theoretical optimal value. In addition, according to the competition data mining and prediction requirements, this paper constructs a system structure model, and designs experiments to verify the system performance. The research results show that the performance of the data mining and prediction system of large-scale (sports) competition constructed in this paper meets actual needs.

Renography with a semiautomated algorithm for diuretic decision 7 min postradiopharmaceutical administration: a feasibility study.

The F+10 method for diuretic renography (diuretics given 10 min after the radiopharmaceutical) could be a time-conserving method. This method involves a 30-min dynamic acquisition where diuretics are administered only when necessary by the Nuclear Medicine technologist performing the examination. The purpose of this study was to assess the method's performance and to discover the optimal threshold of residual activity for a diuretic administration 7 min into the F+10 renography by reprocessing raw data from prior performed examinations with 20-min acquisitions without diuretics. Retrospectively, raw data from 320 original examinations of adult patients performed from 2013 to 2015 were reprocessed into 7-min series and categorized as requiring diuretic or not. The diuretic decisions made by an expert panel were used as a reference. A receiver-operating characteristic curve was drawn to assess the optimal cutoff value for the residual renal activity. Sensitivity, specificity, positive and negative predictive values, as well as the Youden J index were calculated. The experts classified 50% (160 examinations) as in need of diuretics. The receiver-operating characteristic curve demonstrated the theoretical optimal cutoff value at 7 min to be 94% of maximum activity (sensitivity 0.93, specificity 0.81, Youden J index 0.73). A clinically acceptable threshold is suggested to be 85% (sensitivity 0.99, specificity 0.59, Youden J index 0.58). Tc-mercaptoacetyltriglycine renography with the F+10 method and the threshold 85% for diuretic decision 7 min into the renography is a feasible and acceptable method in clinical practice.

Charge Scheduling of Electric Vehicles in Smart Parking-Lot Under Future Demands Uncertainty

In this study, a public parking-lot is assumed to schedule the charging of Electric Vehicles (EVs). Each EV owner upon arriving gives the energy demand as well as departure time to the system and immediately receives feedback; fulfilling or adjusting the request. The system designed in this study decides based on the previously admitted requests and the uncertain future demands in both Admission Control (AC) and Charge Scheduling (CS) mechanisms. We formulate a multi-stage stochastic programming model to minimize the expected total energy costs over the finite time horizon. Next, we approximate the model using a finite scenario tree. However, this model is computationally intractable, even for a moderate number of stages. Therefore, we customize a well-known decomposition procedure, Stochastic Dual Dynamic Programming (SDDP), to be matched the time-dependent charging conditions. Since the procedure takes several hours to obtain a high-quality solution, we run it once in the offline mode and employing the results for the online mode. The simulation results indicate that the proposed method outperforms the myopic approach, and obtains a close solution to the theoretical optimal value in terms of total costs and rejected demands.

Neural Network-Based Relay Selection in Two-Way SWIPT-Enabled Cognitive Radio Networks

In this paper, we investigate the relay selection problem for two-way simultaneous wireless information and power transfer (SWIPT) enabled cognitive radio networks. The system we consider includes a pair of primary users and multiple secondary user transceivers, where the secondary transmitters are energy-constrained and employ the decode-and-forward scheme to assist the transmission between the primary users. Specifically, in order to improve the spectrum efficiency of the considered system, we design a novel two-way communication protocol for SWIPT-enabled cognitive radio networks. Furthermore, different from the traditional relay selection scheme, under this protocol, we propose two data-driven relay selection methods based on the neural network for the fixed number of relays and the variable number of relays, respectively. The simulation results demonstrate that the proposed methods can achieve better performance than the traditional relay selection as well as the traditional machine learning methods, and approach to the theoretical optimal value in most cases.