Double Algorithm Research Articles

Numerical–Experimental Analysis to Optimize the Geometry of a Prosthetic Abutment

This paper aims to identify methods for optimizing the geometry of dental prosthesis preparation, through both an analytical study and a numerical investigation. Assuming a 10° inclined load with respect to the median sagittal plane, a double algorithm was developed that was capable of calculating the axial resistance area and the cusp resistance area (if present) of a prosthetic abutment as the main geometric parameters vary. In particular, a coupling model with a pseudo-elliptical base was proposed, which is better than the circular shape representing the real shape assumed by the abutment following the dental intervention. The model also allows the presence of a cusp. This model was first analyzed using FEA, also simulating the presence of coupling cement between the abutment and the crown, and then physically made with a 10:1 scale prototype. The convergence of the experimental results found with the numerical–theoretical studies is an indication of the validity of the proposed model. A key contribution of this research using the proposed algorithm allowed to demonstrate that a superior limit of 0.25 for the ratio (HR) between the heights of the cusp sulcus (h-hc) and the abutment (h) is required to achieve good stability of the coupling. The methodology developed in this study is applicable to a variety of teeth, making it versatile and highly adaptable for broader clinical applications.

Fast Location and Recognition of Green Apple Based on RGB-D Image.

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.

Observer-based lateral stability adaptive control method for distributed drive electric vehicles

Aiming at the lateral instability of distributed drive electric vehicles (DDEV) under extreme conditions, a nonlinear adaptive lateral stability control strategy is designed based on Cubature Kalman filter (CKF) observer and adaptive fuzzy sliding mode control (FSMC) theory. To solve the problem that it is difficult to measure some parameters of road and vehicle state directly, a vehicle state observer based on a double CKF algorithm is designed. The joint sliding mode surface is designed by the deviation between the ideal and observed values of yaw rate and sideslip angle. The SMC algorithm is combined with the adaptive fuzzy algorithm to design an adaptive FSMC to accurately obtain the additional yaw moment of the vehicle. Simulation verification is carried out based on CarSim and MATLAB/Simulink simulation platforms. The results show that the proposed lateral stability adaptive method can effectively improve the robustness of the DYC system.

Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model

Cloud model and sensor network are the research hotspots in recent years. This paper proposes a congestion and rate control strategy for wireless sensor networks based on cloud model. It adjusts the input rate of nodes based on cloud model through node congestion detection. Aiming at the problem of network congestion control, two congestion adjustment algorithms based on red are improved. Congestion threshold and congestion degree are used as the basis of packet transmission rate adjustment to realize network support plug control. In this paper, the congestion control strategy NP starts to alleviate congestion at about 40 s and controls the packet loss rate at about 116 packets/s, which is 45.3% lower than the multipath congestion control strategy and is more stable. However, when β = 0 , the packet loss rate of the double congestion threshold algorithm is 20% lower than that of the single congestion threshold algorithm. The reason is that the double congestion threshold algorithm allows a stronger source rate control mechanism earlier than the single congestion threshold algorithm a large number of packets are lost in a point, but this also makes the network performance relatively low.

Intensity-Averaged Double Three-Step Phase-Shifting Algorithm with Color-Encoded Fringe Projection

Fringe projection profilometry (FPP) has been broadly employed for three-dimensional shape measurements. However, the measurement accuracy suffers from gamma nonlinearity. This paper proposes an intensity-averaged double three-step phase-shifting (IDTP) algorithm making use of color-encoded fringe projection, which does not require complex calibration processes or extra fringe patterns. Specifically, two phase maps with π/2 phase shift are encoded into the red and blue channels of color fringe patterns. The average fringe patterns of the red and blue channels are approximately in sinusoidal waveform with little harmonics, thus can be directly used for accurate phase recovery. Additionally, an adaptive weight is also estimated for average operation to suppress the effect of color crosstalk. Both simulations and experiments demonstrate that the proposed IDTP algorithm can effectively eliminate nonlinear phase errors.

Single base station hybrid TOA/AOD/AOA localization algorithms with the synchronization error in dense multipath environment

Mobile station (MS) localization in a cellular network is appealing to both industrial community and academia, due to the wide applications of location-based services. The main challenge is the unknown one-bound (OB) and multiple-bound (MB) scattering environment in dense multipath environment. Moreover, multiple base stations (BSs) are required to be involved in the localization process, and the precise time synchronization between MS and BSs is assumed. In order to address these problems, hybrid time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurement model from the serving BS with the synchronization error is investigated in this paper. In OB scattering environment, four linear least square (LLS), one quadratic programming and data fusion-based localization algorithms are proposed to eliminate the effect of the synchronization error. In addition, the Cramer-Rao lower bound (CRLB) of our localization model on the root mean-square error (RMSE) is derived. In hybrid OB and MB scattering environment, a novel double identification algorithm (DIA) is proposed to identify the MB path. Simulation results demonstrate that the proposed algorithms are capable to deal with the synchronization error, and LLS-based localization algorithms show better localization accuracy. Furthermore, the DIA can correctly identify the MB path, and the RMSE comparison of different algorithms further prove the effectiveness of the DIA.

Double iterative algorithm for solving different constrained solutions of multivariate quadratic matrix equations

<abstract><p>Constrained solutions are required in some practical problems. This paper studies the problem of different constrained solutions for a class of multivariate quadratic matrix equations, which has rarely been studied before. First, the quadratic matrix equations are transformed into linear matrix equations by Newton's method. Second, the different constrained solutions of the linear matrix equations are solved by the modified conjugate gradient method, and then the different constrained solutions of the multivariate quadratic matrix equations are obtained. Finally, the convergence of the algorithm is proved. The algorithm can be well performed on the computers, and the effectiveness of the method is verified by numerical examples.</p></abstract>

Image encryption using improved Cubic map and Henon map

In this paper, constructing the improved chaotic map which multiplies the output value of the chaotic map by a large value, and subtracts its integer part. Simulation results show that the chaos range of the improved chaotic map is enlarged. The generated chaotic sequence has strong randomness. A double chaotic image encryption algorithm is proposed by combining the improved chaotic maps with the permutation and diffusion encryption structure. The algorithm can reduce the complexity while ensuring the encryption effect. The simulation results show that the encryption algorithm can resist statistical attack and has excellent robustness, and has a good development prospect in information security.

Network Security Defense Decision-Making Method Based on Stochastic Game and Deep Reinforcement Learning

Aiming at the existing network attack and defense stochastic game models, most of them are based on the assumption of complete information, which causes the problem of poor applicability of the model. Based on the actual modeling requirements of the network attack and defense process, a network defense decision-making model combining incomplete information stochastic game and deep reinforcement learning is proposed. This model regards the incomplete information of the attacker and the defender as the defender’s uncertainty about the attacker’s type and uses the Double Deep Q-Network algorithm to solve the problem of the difficulty of determining the network state transition probability, so that the network system can dynamically adjust the defense strategy. Finally, a simulation experiment was performed on the proposed model. The results show that, under the same experimental conditions, the proposed method in this paper has a better convergence speed than other methods in solving the defense equilibrium strategy. This model is a fusion of traditional methods and artificial intelligence technology and provides new research ideas for the application of artificial intelligence in the field of cyberspace security.

Phase Error Analysis and Correction for Crossed-Grating Phase-Shifting Profilometry.

Crossed-grating phase-shifting profilometry (CGPSP) has great utility in three-dimensional shape measurement due to its ability to acquire horizontal and vertical phase maps in a single measurement. However, CGPSP is extremely sensitive to the non-linearity effect of a digital fringe projection system, which is not studied in depth yet. In this paper, a mathematical model is established to analyze the phase error caused by the non-linearity effect. Subsequently, two methods used to eliminate the non-linearity error are discussed in detail. To be specific, a double five-step algorithm based on the mathematical model is proposed to passively suppress the second non-linearity. Furthermore, a precoding gamma correction method based on probability distribution function is introduced to actively attenuate the non-linearity of the captured crossed fringe. The comparison results show that the active gamma correction method requires less fringe patterns and can more effectively reduce the non-linearity error compared with the passive method. Finally, employing CGPSP with gamma correction, a faster and reliable inverse pattern projection is realized with less fringe patterns.

Estimation of Nuclear Medicine Exposure Measures Based on Intelligent Computer Processing.

This paper provides an in-depth discussion and analysis of the estimation of nuclear medicine exposure measurements using computerized intelligent processing. The focus is on the study of energy extraction algorithms to obtain a high energy resolution with the lowest possible ADC sampling rate and thus reduce the amount of data. This paper focuses on the direct pulse peak extraction algorithm, polynomial curve fitting algorithm, double exponential function curve fitting algorithm, and pulse area calculation algorithm. The detector output waveforms are obtained with an oscilloscope, and the analysis module is designed in MATLAB. Based on these algorithms, the data obtained from six different lower sampling rates are analyzed and compared with the results of the high sampling rate direct pulse peak extraction algorithm and the pulse area calculation algorithm, respectively. The correctness of the compartment model was checked, and the results were found to be realistic and reliable, which can be used for the analysis of internal exposure data in radiation occupational health management, estimation of internal exposure dose for nuclear emergency groups, and estimation of accidental internal exposure dose. The results of the compartment model of the respiratory tract and the compartment model of the digestive tract can be used to calculate the distribution and retention patterns of radionuclides and their compounds in the body, which can be used to assess the damage of radionuclide internal contamination and guide the implementation of medical treatment.

A Double Sky-Hook Algorithm for Improving Road-Holding Property in Semi-Active Suspension Systems for Application to In-Wheel Motor

This paper presents a double sky-hook algorithm for controlling semi-active suspension systems in order to improve road-holding property for application in an in-wheel motor. The main disadvantage of the in-wheel motor is the increase in unsprung masses, which increases after shaking of the wheel, so it has poor road-holding that the conventional theoretical sky-hook algorithm cannot achieve. The double sky-hook algorithm uses a combination of damper coefficients, one from the chassis motion and the other from the wheel motion. Computer simulations using a quarter and full car dynamic models with the road conditions specified by ISO2631 showed the effectiveness of the algorithm. It was observed that the algorithm was the most effective in the vicinity of the wheel hop frequency. This paper also proposed the parameter set of the double sky-hook algorithm to differentiate the driving mode of vehicles under advanced development.

The acquisition method of the user’s Kansei needs based on double matrix recommendation algorithm

In the development of product design, one of the elements of market competition for products is to meet the Kansei needs of users. Compared to features, users pay more attention to whether products can match their emotions, which is Kansei needs. The product developers are eager to get the Kansei needs of users more accurately and conveniently. This paper takes the computer cloud platform as the carrier and based on the collaborative filtering algorithm. We used personalized double matrix recommendation algorithm as the core, and the adjectives dimensionality reduction method to filter the image tags to simplify the users’ rating process and improve the recommendation efficiency. Finally, we construct a Kansei needs acquisition model to quickly and easily obtain the Kansei needs of users. We verify the model using the air purifier as a subject. The results of the case show that the model can find out the user’s Kansei needs more quickly. When the data is more, the prediction will be more accurate and timely.

Realisation of the double sweep method by using a Sleptsov net

The present article is focused on the application of Sleptsov net (SN) for solving system of linear algebraic equations with a tridiagonal matrix. SN, which implements the double sweep algorithm for solving a system of linear equations with a tridiagonal matrix, is constructed. The work of the constructed network is considered when solving a specific example. The number of operations carried out by the net in performing computations has been calculated and the performance of the parallel double sweep method implemented by the net is compared with the sequential double sweep method. We also consider representation of integer and real numbers by a SN and implementation of basic arithmetic operations over these numbers because the implementation of the algorithm requires floating point operations.

Design of an Adaptive Boost Energy-Saving Fuzzy Control System Driven by the Finite State Machine

Aiming at the challenging problem of the traditional warp knitting machine electronic jacquard control system with complex structure of multiple circuit boards layered cascade, such as large physical space occupation, high power consumption, and independent high-voltage power supply voltage, we proposed an embedded circuit and control strategy design for the piezoelectric jacquard needle (PJN) with adaptive boost and energy recovery functions. Firstly, the electromechanical dynamics model of PJN was established. Secondly, the fuzzy PI double closed-loop control algorithm driven by a finite state machine is proposed. Thirdly, with the help of a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET), the PJN is integrated with the drive circuit. The drive circuit of PJN uses an energy storage inductor to replace the current limiting resistor of the traditional drive circuit, which can not only limit the forward charging current of the PJN and reduce energy loss but also can use the energy absorbed from the low-voltage power supply to adaptively boost the power supply of the PJN to the high voltage required for working conditions. The simulation results show that the new PJN drive circuit has an adaptive self-boost function. The PWM signal modulated by the fuzzy PI double closed-loop control algorithm can efficiently and accurately control the adaptive boost power supply and the voltage across the PJN. The mode of the circuit can be correctly switched through the sequential logic of the finite state machine and realize the energy recovery function.

A Data Encryption and Approximate Recovery Strategy Based on Double Random Sorting and CGAN

The measurement data in the power system may be attacked and tampered during the transmission. For this problem, a data encryption and approximate recovery strategy based on double random sorting and CGAN is proposed. The double random sorting encryption algorithm is proposed based on the form of measurement data in plaintext. The randomness of pseudo-random number, random sequence and random sequence insertion will ensure the uncertainty of data block number, sorting order and insertion location of random sequence, which will definitely improve the security of measurement data. Besides, an approximate recovery strategy used to approximately recovery abnormal samples is proposed on the base of CGAN, which can ensure the accuracy of decryption. Finally, the feasibility, and effectiveness of the proposed method are analyzed on wind power and photovoltaic power historical dataset.

3D Modeling Design of Multirole Virtual Character Based on Visual Communication in Wireless Sensor Networks

In order to solve the problems of poor design effect and time consumption of traditional virtual character modeling design methods, a three‐dimensional (3D) modeling design method of multirole virtual characters based on visual communication is studied. Firstly, the wireless sensor network is used to locate, scan, and collect the human body structure information and convert the coordinates to bind the 3D skeleton. Secondly, according to different human postures, we switch the linear hybrid skin algorithm, spherical hybrid skin algorithm, and double quaternion hybrid skin algorithm; design the geometric surface; and attach it to the 3D skeleton to generate 3D modeling. Finally, based on the influence of visual communication on human eye observation and psychological feeling, the geometric surface is divided twice, and the virtual character is rendered in the way of display, coating, and splicing to obtain a complete 3D modeling of the virtual character. The results show that the positioning coverage of this method is higher, the rendering effect of the hand and head is better, the design time is significantly shortened, and the maximum time is no more than 35 min.

Integrated resource management pipeline for dynamic resource-effective cloud data center

Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

Multiple mini-robots navigation using a collaborative multiagent reinforcement learning framework

In this work we investigate the use of a reinforcement learning (RL) framework for the autonomous navigation of a group of mini-robots in a multi-agent collaborative environment. Each mini-robot is driven by inertial forces provided by two vibration motors that are controlled by a simple and efficient low-level speed controller. The action of the RL agent is the direction of each mini-robot, and it is based on the position of each mini-robot, the distance between them and the sign of the distance gradient between each mini-robot and the nearest one. Each mini-robot is considered a moving obstacle that must be avoided by the others. We propose suitable state space and reward function that result in an efficient collaborative RL framework. The classical and the double Q-learning algorithms are employed, where the latter is considered to learn optimal policies of mini-robots that offers more stable and reliable learning process. A simulation environment is created, using the ROS framework, that include a group of four mini-robots. The dynamic model of each mini-robot and of the vibration motors is also included. Several application scenarios are simulated and the results are presented to demonstrate the performance of the proposed approach.

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.