Efficient ECG classification based on Chi-square distance for arrhythmia detection

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

Optimal Nonlinear Distance Toll for Cordon-Based Congestion Pricing Considering Equity Issue

In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network with elastic demand,to evaluate any given toll charge function. A bi-level programming model is developed for determining the optimal toll levels,with the TUE being represented at the lower level.The upper level optimizes the total equity level over the transport network,represented by the Gini coefficient,where a constraint is imposed to the total travel impedance of each OD pair after the levy. A genetic algorithm( GA) is implemented to solve the bi-level model,which is verified by a numerical example.

An Algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing

We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. .

Solution-Distance-Based Migration Rate Calculating for Biogeography-Based Optimization

Biogeography-based optimization(BBO),a natureinspired optimization algorithm(NIOA),has exhibited a huge potential in optimization.In BBO,the good solutions have a large probability to share information with poor solutions,while poor solutions have a large probability to accept the information from others.In original BBO,calculating for migration rates is based on solutions' ranking.From the ranking,it can be known that which solution is better and which one is worse.Based on the ranking,the migration rates are calculated to help BBO select good features and poor features.The differences among results can not be reflected,which will result in an improper migration rate calculating.Two new ways are proposed to calculate migration rates,which is helpful for BBO to obtain a suitable assignment of migration rates and furthermore affect algorithms ' performance.The ranking of solutions is no longer integers,but decimals.By employing the strategies,the ranking can not only reflect the orders of solutions,but also can reflect more details about solutions' distances.A set of benchmarks,which include 14 functions,is employed to compare the proposed approaches with other algorithms.The results demonstrate that the proposed approaches are feasible and effective to enhance BBO's performance.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides

This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.

Optimization Route Algorithm Based on the Minimal Transfer Time and Distance

The transfer system,an important subsystem in urban citizen passenger transport system,is a guarantee of public transport priority and is crucial in the whole urban passenger transport traffic.What the majority of bus passengers consider is the convenience and comfort of the bus ride,which reduces the transfer time of bus passengers."Transfer time" is considered to be the first factor by the majority of bus passengers who select the routes.In this paper,according to the needs of passengers,optimization algorithm,with the minimal distance being the first goal,namely,the improved Dijkstra algorithm based on the minimal distance,is put forward on the basis of the optimization algorithm with the minimal transfer time being the first goal.

基于粒子群优化的DV-Distance改进算法

DV-Distance算法通过未知节点与锚节点间的多跳协作,实现节点位置估计,简单实用,但定位精度不高。为了提高算法性能,本文提出一种基于粒子群优化的DV-Distance改进算法,针对RSSI测距引起的误差,利用链路质量指示(LQI)对RSSI的测量值进行惯性滤波,建立基于LQI的RSSI测距模型;并针对位置估计方法所引起的误差,通过粒子群算法对节点位置进行优化。结合DV-Distance算法自身特点,确定了粒子群算法适应值函数的选取和关键算法参数的确定。最后,仿真和实验证明改进算法有效可行,具有较高的定位精度。

基于IFOA优化DV-distance算法的无线传感器网络定位研究

对无线传感器网络节点定位问题进行研究,提出一种基于IFOA优化DV-distance算法的WSNs定位方法。针对DV-distance算法定位精度低、噪声影响大,受限于网络拓扑结构等问题,将改进的果蝇优化算法(IFOA)引入到DVdistance设计中,实现了节点位置的精确定位,为进一步提高算法定位的精度,引入动态加权修正因子,并给出动态误差修正策略,最后对WSNs节点定位问题进行实验仿真,仿真结果表明,基于IFOA优化的DV-distance定位算法较DV-distance和传统定位算法在定位精度上有明显改善。

Minimum Time Overtaking Problem of Vehicle Handling Inverse Dynamics Based on Two Kinds of Safe Distances

Overtaking accidents caused by improper operations performed by a driver occur frequently. However, most stud?ies on overtaking safety have neglected research into driver control input. A novel method is proposed to obtain the driver control input during the overtaking process. Meanwhile, to improve the safety of overtaking, two types of safe distances, and the time of the overtaking are considered. Path constraints are established when considering the two types of safe distances. An optimal control model is established to solve the minimum time maneuver under multiple constraints. Using the Gauss pseudospectral method, the optimal control problem is converted into a nonlinear pro?gramming problem, which is then solved through sequential quadratic programming(SQP). In addition, the e ective?ness of the proposed method is verified based on the results of a Carsim simulation. The simulation results show that by adopting an inverse dynamics method to solve the manipulation problem of the vehicle’s minimum overtaking time, the manipulation capability of a vehicle in completing an overtaking safely within the minimum time can be obtained. This method can provide a reference for research into the active safety of manned and unmanned vehicles.

Optimal Guidance Law to Maximize Terminal Velocity for Missiles with Impact Angle Constraint

In this paper,an optimal guidance law for missiles with impact angle and miss distance constraints is proposed to achieve the maximal terminal velocity. The normal acceleration command that includes the timevarying coefficients is introduced to satisfy the desired impact angle as well as zero miss distance according to the geometric relation and relative motion parameters between missile and target. The problem is formulated as an optimal control problem by defining the angle of velocity error and flight-path angle as state variables and maximizing a performance index of the terminal velocity. The analytical form of the proposed guidance law is obtained as the solution of the optimal control problem combining optimal control theory and numerical value computation method. Nonlinear simulations of various situations demonstrate the performance and feasibility of the proposed optimal guidance law.

Distance Matrix and Markov Chain Based Sensor Localization in WSN

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of interest.Relevant geometries of the surrounding environment are essential to establish a successful WSN topology.But it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging task.In this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the node.The method further employs a Markov Chain Model(MCM)for energy optimization and interference reduction.Experiments are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing models.Transition probability is used in the Markova chain to sustain higher energy nodes.Finally,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA methods.The experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

Comparison of Gluteal Muscle Intramuscular Injection Sites of Japanese Healthy Subjects: Considerations for Optimal Insertion of Injection Needle Length

Long Acting Injectable (LAI) medications for patients with schizophrenia is commonly administered to relieve their symptoms. Through shared decision-making and clinical evidence-based, psychiatrists should systematically offer LAIs to all patients requiring long-term antipsychotic treatment as a first-line treatment. Gluteal intramuscular (IM) injection requires accurate insertion of needles into the specific muscle area, often the outer upper quadrant of the buttocks, in order to achieve the required blood concentration. The purposes of this study were to compare the “Distance from the Epidermis to the Under-Fascia (DEUF)” and “Distance from the Epidermis to the Iliac Bone (DEB)” of the buttocks IM injection sites at the dorsogluteal and ventrogluteal sites among healthy Japanese volunteer subjects, and to identify the optimal insertion injection needle length. The DEUF and DEB at the gluteal regions were measured by ultrasonography. Welch’s one-way analysis of variance was used to compare the DEUF and the DEB at the gluteal IM injection regions. There was no statistically significant difference observed between the right and left mean values of DEUF for Hochstetter and Clark’s point at the ventrogluteal sites, and the Four and Three-way split or Double Cross point at the dorsogluteal sites. However in the DEB, the Hochstetter’s point (P < 0.01) at ventrogluteal site on the right side, and Clark’s point (P < 0.05) were significantly shorter than the Double Cross point at dorsogluteal sites (F = 4.38). The left buttocks Hochstetter’s point was significantly shorter than the Double Cross point (F = 4.38, P < 0.01). These results, however, did not establish a statistically significant difference in the DEUF among injection sites. It was considered that the difference in the DEB depended on muscle volume and thickness in the gluteal injection sites.

Un-powered gliding aircraft's ballistic missile optimal design

In this dissertation, un-powered gliding aircraft＇s optimal extended rangeproblem is discussed. The aircraft movement model was built. According to the degree of coupling, the model can be classified into a simple model or a complicated model. Using an optimal control method, two different movement models gave out the aircraft＇s attitude angle optimal flight path. Complicated model＇s optimal solution can be found by the genetic algorithm. This method can transfer the analytic solution of complicated model to a numerical value solution. Comparing the simulation results of different methods, it showed that the genetic algorithm combined with the complicated model＇s numerical value solution had the best performance in control strategy. This method solved the problem in which the highly coupling complicated model＇s analytic solution was hard to obtain. It verified that the genetic algorithm has validity in the field of extended range solution searching.

Advanced optimal guidance law with maneuvering targets

Based on optimal theory, the advanced optimal guidance law （AOGL） is derived for the interception endgame of maneuvering targets in step mode. The guidance system dynamics, target maneuvering dynamics and acceleration, gravity acceleration are considered and their effects are dy- namically cancelled out in guidance law. A four states Kalman filter is designed to estimate the re- quired states for AOGL. Simulation results show the AOGL is less sensitive to errors caused by target maneuvering and guidance system lag, and it needs less missile acceleration in most time of guidance especially at the end of intercept than other guidance laws. Especially its acceleration is zero at the end of intercept when attacking maneuvering target.

DISTINIT:Data poISoning atTacks dectectIon usiNg optIized jaCcard disTance

Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.

A Novel PSO Algorithm for Optimal Production Cost of the Power Producers with Transient Stability Constraints

The application of a novel Particle Swarm Optimization (PSO) method called Fitness Distance Ratio PSO (FDR PSO) algorithm is described in this paper to determine the optimal power dispatch of the Independent Power Producers (IPP) with linear ramp model and transient stability constraints of the power producers. Generally the power producers must respond quickly to the changes in load and wheeling transactions. Moreover, it becomes necessary for the power producers to reschedule their power generation beyond their power limits to meet vulnerable situations like credible contingency and increase in load conditions. During this process, the ramping cost is incurred if they violate their permissible elastic limits. In this paper, optimal production costs of the power producers are computed with stepwise and piecewise linear ramp rate limits. Transient stability limits of the power producers are also considered as addi-tional rotor angle inequality constraints while solving the Optimal Power Flow (OPF) problem. The proposed algo-rithm is demonstrated on practical 10 bus and 26 bus systems and the results are compared with other optimization methods.

海上风电半潜式基础初步选型 Pareto⁃Optimal 评价

针对半潜漂浮式风电基础初步选型,采用Pareto⁃Optimal评价方法对不同吃水、平台立柱直径、立柱间距和垂荡板直径四个参数的不同组合进行分析比较。基于浮体动力学频域计算方法,采用我国阳江某海域极限波浪条件计算得到叶轮中心水平加速度,同时考虑完整稳性的计算结果。对比分析表明平台吃水和立柱直径宜选择适中的取值,较大的排水量和立柱总体积并不会显著减小叶轮中心水平加速度。垂荡板对于改善平台整体性能是较为敏感的,垂荡板与立柱的直径比存在一定的最佳范围。平台立柱间距是影响平台运动性能最大的因素,增大立柱间距可以有效地降低叶轮中心水平加速度,但立柱间距的增大对立柱间的撑杆结构强度以及平台整体的建造和下水提出了较大的挑战。

A Multi-objective Genetic Algorithm Bas on Individual Density Distance

The uniform and extension distribution of the optimal solution are very important criterion for the quality evaluation of the multi-objective programming problem. A genetic algorithm based on agent and individual density is used to solve the multi-objective optimization problem. In the selection process, each agent is selected according to the individual density distance in its neighborhood, and the crossover operator adopts the simulated binary crossover method. The self-learning behavior only applies to the individuals with the highest energy in current population. A few classical multi-objective function optimization examples were used tested and two evaluation indexes U-measure and S-measure are used to test the performance of the algorithm. The experimental results show that the algorithm can obtain uniformity and widespread distribution Pareto solutions.

VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.