Local Minimum Value Research Articles

Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem.Graphical abstract

A Comparison of Heuristic Algorithms for Solving the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a challenging computational problem in combinatorial optimization that aims to visit all cities exactly once and return to the first city. Despite that numerous theoretical solutions have been proposed in the literature, finding the exact optimal solution remains computationally infeasible due to the NP-hard nature of the problem. To address this, many heuristic and optimization approaches have been developed to generate probabilistic results that are often approximations. This paper presents a comparison between four popular algorithms: steepest ascent hill climbing, simulated annealing, genetic algorithm with partially matched crossover, and Particle Swarm Optimization (PSO). The study examines how these algorithms can solve the TSP and avoid local minimum values while achieving a balance between research exploration and exploitation for an optimal solution. For a relatively large number of cities, the simulated annealing algorithm and genetic algorithm produce promising results whilst the genetic algorithm takes longer time to execute due to the iterative application of its variation operators.

Unraveling the Mechanical Response of Short-Chain Branched Amorphous Polyethylene: Insights from Molecular Dynamics Simulations

The deformation behavior of short-chain branched, amorphous polyethylene (PE) was investigated using united atom molecular dynamics (MD) simulations under uniaxial stretching. Our research reported in this paper examined the internal mechanisms underlying the mechanical response of the PE by analyzing potential energies, dihedral angle distributions, chain orientation and entanglements. The findings indicated that the short-chain branching generally enhanced Young’s modulus, except for sample B60 with a critical ethyl branch content of 60/1000 (60 ethyl branches per 1000 backbone carbons). The Young’s modulus of B60 was the local minimum value in the various samples. Analysis of the evolution of dihedral angles shows that the short-chain branching hinders the transition from gauche to trans configurations, leading to a decrease in the trans population as the branch content increases. Regarding the sample B60 with critical branch content 60/1000, the orientation parameter and entanglement parameter of the molecular chains along the backbone were lowest in the initial structure, and the efficiency of chain disentanglement was at its lowest during deformation. The simulation results elucidated the deformation mechanism related to the conformational evolution of the molecular chains, which can provide atomic-scale insights into the mechanical properties and processability of branched polyethylene, thereby allowing for optimization in its design and application.

Hermite-Hadamart Integral Inequality for Differentiable m- Convex Functions

Convexity in connection with integral inequalities is an interesting research domain in recent years. The convexity theory plays a fundamental role in the development of various branches of applied sciences since it includes the theory of convex functions that possesses two important attributes viz. a boundary point is where the maximum value is reached and any local minimum value is a global one. Convexities and inequalities are connected which has a basic character in many branches of pure and applied disciplines. The most important inequality related to convex function is the Hermite-Hadamard integral inequality. The extensions, enhancements and generalizations of this inequality has motivated the researchers in recent years. This paper is an extension of some inequalities connected with difference of the left-hand part as well as the right-hand part from the integral mean in Hermite- Hadamard’s inequality for the case of m- convex functions.

Propagation Analysis of an RFID System in the UHF Band in the Honeycomb Frame of a Beehive.

In recent years, communication systems, including RFID, have been used in intelligent beehives for beekeeping. RFID systems in the UHF frequency band offer reading distances of tens of centimetres, allowing the localisation and identification of the queen bee inside the hive. With this purpose, this work proposes an analysis of an environment of propagation that consists of a honeycomb frame, where the reader is placed within the frame, and the tag is placed in different positions over it. A honeycomb frame consists of a wooden box containing a honey wax panel, supported by metallic wires. The environment is modelled theoretically using its S-parameters and simulated in CST Studio. An analysis of these results and empirical measurements is performed. The results show that a periodicity in the received power of the tag is found with respect to the distance to the reader when the tag is located in a direction parallel to the wire, where local maximum and minimum values are found. Additionally, when the tag is placed over a wire of the frame, a higher received power is obtained compared to the case where the tag is placed between two wires. Furthermore, it has been observed that the reading range has increased with respect to free space, covering the full frame.

A New X-ray Diffraction Spectrum-Based Untargeted Strategy for Accurately Identifying Ancient Painted Pottery from Various Dynasties and Locations in China

X-ray diffraction (XRD) is extensively used in archaeometric investigation. Herein, we provide a novel XRD spectrum-based untargeted strategy for the classification of ancient painted pottery for various dynasties. It was accomplished using the original spectrum without a phase identification. To eliminate the influence of baseline drift, a new baseline drift correction algorithm was specifically designed for XRD spectra. The algorithm was implemented using local minimum values in the analyzed signal in an iterative optimization manner. The results indicated that with the aid of the algorithm, the baseline drift problem can be successfully resolved, and the classification of ancient painted pottery can be greatly improved. Finally, the developed strategy was successfully used to discriminate ancient painted pottery from the Han and Tang dynasties in the cities of Guyuan and Zhongwei, China. The developed untargeted strategy had the remarkable advantage of almost automatic data analysis. The toolbox of our strategy can be obtained from the authors.

Bearing Fault Diagnosis based on Convolution Neural Network with Logistic Chaotic Map

AbstractBearing is the most basic component of motor, and prone to failure. Bearing fault diagnosis is paramount for improving the reliability and safety in motor‐drive systems. Therefore, convolutional neural network (CNN) is proposed with Logistic chaotic map and its corresponding fault diagnosis approach, which can effectively advance the accuracy of bearing fault diagnosis. Specifically, the Logistic chaotic map and Sigmoid function are combined into a non‐monotonic excitation function, which is employed to the full connection layer of the CNN. The proposed chaotic CNN can solve two issues that the conventional neural network inclines to get the local minimum value and the gradient of Sigmoid excitation function disappears. It is applied to fault data from the center of Western Reserve University and from the American Society for Mechanical Failure Prevention technology (in noiseless and noisy conditions). The results indicate the diagnosis accuracy of the algorithm outperforms other classical bearing diagnosis algorithms. Moreover, the chaotic CNN exhibits better anti‐noise performance.

Numerical investigation on the effects of slotted height on performance of louver and slit fins at different fin pitches

In this paper, the effects of slotted height at different fin pitches are analyzed in detail, aiming to investigate the function and optimal ratio of slotted height to fin pitch. In the cases of high Reynolds number for louver fin, the ?bimodal phenomenon? of heat transfer coefficient begins to appear with the increasing slotted height. As the slotted height is about half of the fin pitch, the heat transfer coefficient has a local minimum value, of which there are two peaks on both sides. How-ever, the pressure drop has been increasing with the increasing slotted height. The optimal slotted height under different fin pitches is more instructive than the optimal louver angle. For slit fin, the heat transfer coefficient increases first and then decreases with the increasing slotted height, so does the pressure drop. Meanwhile, as the slotted height is about 0.3~0.45 times and 0.5~0.65 times of fin pitch for louver fin and slit fin, respectively, the comprehensive performance can reach a maximum. The optimal comprehensive performance under 1.2 mm fin pitch is greater than the optimal comprehensive performance under 1.8 mm fin pitch. For lower Reynolds numbers, the optimal comprehensive performance of louver fin is roughly similar to the slit fin. For higher Reynolds numbers, the optimal comprehensive performance of louver fin is greater than that of slit fin.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Spline manipulations for empirical mode decomposition (EMD) on bounded intervals and beyond

Empirical mode decomposition (EMD), introduced by N.E. Huang et al. in 1998, is perhaps the most popular data-driven computational scheme for the decomposition of a non-stationary signal or time series f(t), with time-domain R:=(−∞,∞), into finitely many oscillatory components {f1(t),⋯,fK(t)}, called intrinsic mode functions (IMFs), and some “almost monotone” remainder r(t), called the trend of f(t). The core of EMD is the iterative “sifting process” applied to each function mk−1(t) to compute fk(t), for k=1,⋯,K, where m0(t):=f(t) and mk(t):=mk−1(t)−fk(t), with trend r(t):=mK(t). For the computation of each IMF, the sifting process depends on cubic spline interpolation of the local maxima and local minima for computing the upper and lower envelopes, respectively, and on subtracting the mean of the two envelopes from the result of the previous iterative step. Since it is not feasible to search for all local extrema in the entire time-domain (−∞,∞), implementation of the sifting process is commonly performed on some desired truncated bounded interval [a,b]. The main objective of this paper is to introduce and develop four “cubic spline manipulation engines”, called “quasi-interpolation (QI)”, “enhanced quasi-interpolation (EQI)”, “local interpolation (LI)”, and “improved global interpolation (IGI)” cubic spline manipulation engines, in order to significantly improve the performance of EMD on the truncated time-domains with minimal boundary artifacts, computational efficiency, accuracy, and consistency. Introduction and construction of the “fundamental quasi-interpolation” (FQI) splines as basis functions of the QI manipulation engine eliminates the need of matrix inversion for computing (global) cubic spline interpolation, since the local maximum values and local minimum values are used as coefficients of their FQI spline series representations, respectively. For the EQI spline manipulation engine, the FQI functions are formulated in terms of the same cubic B-spline basis for both the upper and lower envelopes; and for the LI spline manipulation engine, the “cubic spline blending” operation is applied to further modify the FQI splines to enable true cubic spline interpolation by “correcting the approximate interpolation error” of the EQI engine. As a consequence, the EQI and LI manipulation engines have the common property that in computing the means of the upper and lower envelopes, the only computation is averaging the B-spline coefficients, instead of computing the upper and lower envelopes separately. Furthermore, fast cubic spline pre-processing of the given f(t) is also introduced to assure numerical stability in the computation of the Hilbert transform of the first IMF f1(t) on the truncated time-domain. The theory, along with methods and explicit formulas, developed in this paper are intended for other applications beyond EMD.

Analysis of the Corneal Geometry of the Human Eye with an Artificial Neural Network

In this paper, a hybrid cuckoo search technique is combined with a single-layer neural network (BHCS-ANN) to approximate the solution to a differential equation describing the curvature shape of the cornea of the human eye. The proposed problem is transformed into an optimization problem such that the L2–error remains minimal. A single hidden layer is chosen to reduce the sink of the local minimum values. The weights in the neural network are trained with a hybrid cuckoo search algorithm to refine them so that we obtain a better approximate solution for the given problem. To show the efficacy of our method, we considered six different corneal models. For validation, the solution with Adam’s method is taken as a reference solution. The results are presented in the form of figures and tables. The obtained results are compared with the fractional order Darwinian particle swarm optimization (FO-DPSO). We determined that results obtained with BHCS-ANN outperformed the ones acquired with other numerical routines. Our findings suggest that BHCS-ANN is a better methodology for solving real-world problems.

Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

Validation through field data of LineCore, a lightweight Eddy-current sensor for the early detection of corrosion of ACSRs

Since 2014, Hydro-Québec is using LineCore technology for the early detection of corrosion in ACSRs. This paper features, for the first time, an extensive validation based on measures on in-lab prepared samples and metallographic analysis on field collected samples. The experimental validation protocol is presented which resulted in 21 cuts and thousands of zinc layer thickness measurements. Key finding is the very high variability of zinc layer thicknesses observed on all samples. Correlation between LineCore data and metallographic cuts is established. More specifically, LineCore measurement features a bias toward local minimum values of zinc thickness on the strands, which can be indicative of localized corrosion initiation. By sharing these results, the authors wish to initiate discussion that could help standardize the usage of this novel NDT technology for the assessment of transmission line conductors.

Asymptotics for exponential functionals of random walks

This paper provides a detailed description for the asymptotics of exponential functionals of random walks with light/heavy tails. We give the convergence rate based on the key observation that the asymptotics depends on the sample paths with either slowly decreasing local minimum or final value below a low level. Also, our thoughtful analysis of the interrelationship between the local minimum and the final value provides the exact expression for the limiting coefficients in terms of some transformations of the random walk.

A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects

ABSTRACT Lumbar disc degenerative disc disease with back pain and its severity is a leading health issue in society and MRI is the best modality to detect the severity and degree of disc degeneration. The most critical component of degenerative disc disease deals with triggering rapid action for real-time-based system identification. The input is obtained from the non-invasive device called finger pulse plethysmography to assess the stiffness and its correlation with body composition in lumbar disc degeneration. The recent methodology contributions aim at predicting the stiffness which uses pulse wave velocity and reflection on signal features. As the signals are very sensitive to differences between high and low ranges, finger pulse plethysmography effectively detects irregularities at early stages. Based on the severity of degeneration, shown by the MRI report, subjects were grouped into the disc bulging group (DBG) and the nerve compression group (NCG). The supervised features help in training the signals to correct the limitations of prediction. Finally, the Radial Basis Function neural network approach helps in diminishing the local minimal values in the signal. It helps in the effective categorization of anomalous and ordinary stiffness index measurements for lumbar disc degeneration.

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

There are abundant deep coal resources in northern Shaanxi, but the fragile natural environment in this area hinders the large-scale exploitation of oil-rich coal. In-situ thermal conversion of deep coal to oil and gas will become an environmentally friendly technology for oil-rich coal mining. Accurate prediction of oil-rich coal tar yield in various regions is a prerequisite. Based on a particle swarm optimization algorithm and two machine learning algorithms, BP neural network and random forest, a prediction model of tar yield from oil-rich coal is constructed in this paper. Combined with the particle swarm optimization method, the problem of slow convergence speed and possibly falling into local minimum value of BP neural network is solved and optimized. The results showed that the PSO-BP had a convergence speed about five times faster than that of the BP neural network. Furthermore, the predicted value of the PSO-BP was consistent with the measured value, and the average relative error was 4.56% lower than that of the random forest model. The advantages of fast convergence and high accuracy of the prediction model are obviously apparent. Accurate prediction of tar yield would facilitate the research process of in-situ fluidized mining of deep coal seams.

UAV Path Planning Based on Improved Artificial Potential Field Method

The obstacle avoidance system of a drone affects the quality of its flight path. The artificial potential field method can react quickly when facing obstacles; however, the traditional artificial potential field method lacks consideration of the position information between drones and obstacles during flight, issues including local minima, unreachable targets, and unreasonable obstacle avoidance techniques that lengthen flight times and consume more energy get encountered. Therefore, an improved artificial potential field method is proposed. First, a collision risk assessment mechanism was introduced to avoid unreasonable obstacle avoidance actions and reduce the length of unmanned aerial vehicle flight paths. Then, to solve the problem of local minimum values and unreachable targets, a virtual sub-target was set up and the traditional artificial potential field model was modified to enable the drone to avoid obstacles and reach the target point. At the same time, a virtual sub-target evaluation factor was set up to determine the reasonable virtual sub-target, to achieve a reasonable obstacle avoidance path compared to the traditional artificial potential field method. The proposed algorithm can plan a reasonable path, reduce energy consumption during flight, reduce drone turning angle changes in the path, make the path smoother, and can also be applied in complex environments.

Effect of tip clearance flow on pressure oscillation and vortex patterns of a high-speed centrifugal pump

In a micro centrifugal pump with tip clearance working at high speed, the pressure fluctuation and complex vortex structures related to the tip leakage flow are conducted. The external characteristics calculated by the numerical simulation and experiment method agree well, and the maximum deviation of the simulated pump head and efficiency compared with the experimental data is less than 5%. The vortex structures induced by the tip clearance flow, which is less influenced by the flow conditions, can be divided into four categories, namely backflow vortex, passage vortex, primary tip clearance flow (PTLV), and separation TLV (STLV). The evolution of these vortex structures is described by the relative vorticity transport equation, using the relative vortex stretching, Coriolis force, and viscous diffusion. The PTLV starts from the corner of blade tip gap near the leading edge (LE), and breaks away into the STLV in the region of 0.7 R* to 0.8 R* owing to the pushing effect of the secondary tip leakage flow. To analyze the leakage flow quantitatively, the relative velocity in the gap region is decomposed. The results show that the local maximal and minimal values of the normal component correspond to the start position of the PTLV and the separation region of the STLV, respectively. Besides, the passage vortex has the greatest contribution to the pressure pulsation of the passage from main blade to main blade (MB). But the dominant frequency of the pressure alterations has little to do with the vortex, and mainly depends on the rotor-stator interaction (RSI) between the impeller and volute tongue. The results could be referred to improve the efficiency and operational stability of the high-speed centrifugal pumps.

Numerical investigation on effects of rib shapes on oscillating flow and heat transfer in heat exchanger channels

Ribs are straightforward and cost-effective to improve the heat exchange efficiency, and the impact of cooling medium driven by the movement of the channel itself can also enhance the heat exchange capacity. This paper investigates the flow patterns of the gas–liquid two-phase flows in a smooth cubic and a cubic with semi-circular ribs by using a high-speed camera under different low-frequency oscillations. A numerical simulation method was used to study the impact of different ribs on each wall at various frequencies. The microscopic flow and heat transfer characteristics of the ribs were analyzed. Reflux occurred near ribs, and the fluid accumulated near the wall under the ribs with a liquid coverage ratio of approximately 79%. After the gas–liquid two-phase flow fully oscillated in the channel, the heat transfer contribution of the top wall to the entire channel reached the greatest value of approximately 40%, and the left and right walls contributed to the entire channel with 21% and 27%, respectively. An additional inertial force of the two-phase flow was sustained in the oscillating environment to form a periodic impingement flow on the rib and hence improve heat transfer. Due to the reflux near the rib, the local minimum values of the heat transfer coefficient appeared on the downwind of the rib. The difference in rib design affected the fluid motion and caused different heat transfer distributions on the top surface. These findings can support the application design of ribs in dynamic heat exchange channels for optimizing heat transfer.

Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm

To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility.