Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients

Virtual reality is nowadays used to facilitate motor recovery in stroke patients. Most virtual reality studies have involved chronic stroke patients; however, brain plasticity remains good in acute and subacute patients. Most virtual reality systems are only applicable to the proximal upper limbs （arms） because of the limitations of their capture systems. Nevertheless, the functional recovery of an affected hand is most difficult in the case of hemiparesis rehabilitation after a stroke. The recently developed Leap Motion controller can track the fine movements of both hands and fingers. Therefore, the present study explored the effects of a Leap Motion-based virtual reality system on subacute stroke. Twenty-six subacute stroke patients were assigned to an experimental group that received virtual reality training along with conventional occupational rehabilitation, and a control group that only received conventional rehabilitation. The Wolf motor func- tion test （WMFT） was used to assess the motor function of the affected upper limb; functional magnetic resonance imaging was used to measure the cortical activation. After four weeks of treatment, the motor functions of the affected upper limbs were significantly improved in all the patients, with the improvement in the experimental group being significantly better than in the control group. The action perfor- mance time in the WMFT significantly decreased in the experimental group. Furthermore, the activation intensity and the laterality index of the contralateral primary sensorimotor cortex increased in both the experimental and control groups. These results confirmed that Leap Motion-based virtual reality training was a promising and feasible supplementary rehabilitation intervention, could facilitate the recovery of motor functions in subacute stroke patients. The study has been registered in the Chinese Clinical Trial Registry （registration number： ChiCTR-OCH- 12002238）.

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

Improved Shuffled Frog Leaping Algorithm Optimizing Integral Separated PID Control for Unmanned Hypersonic Vehicle

To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)control is designed for hypersonic vehicle,and an improved shuffled frog leaping algorithm is presented to optimize the control parameters.A nonlinear model of hypersonic vehicle is established to examine the dynamic characteristics achieved by the flight control system.Simulation results demonstrate that the proposed optimized controller can effectively achieve better flight control performance than the traditional controller.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

Sequencing of Mixed Model Assembly Lines Based on Improved Shuffled Frog Leaping Algorithm

Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment

The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.

Leaping prediction oftwo big earthquakes MS>7 in 1996～1997

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Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.

地震液化离心机振动台模型试验与LEAP研究进展

离心机振动台模型试验是研究地震液化问题的重要手段,一方面可重现场地地震响应和揭示液化致灾规律,为发展工程设计方法提供依据;另一方面可以作为特定边值问题的标准模型试验数据,验证数值方法与土体本构模型及其参数选取的合理性。介绍了离心机振动台模型试验的试验原理,回顾了离心机振动台模型试验在地震液化领域的主要研究进展。围绕由中国、美国、日本和英国等国家高校与研究所联合开展的国际合作项目“液化试验与分析”(LEAP),重点介绍了若干为提高离心机振动台模型试验可重复性而发展的物理模拟新技术,包括离心机振动台的台面振动控制技术、模型土体弹性波速测试技术和基于图像分析的动态位移监测技术等。最后针对地震液化领域的若干工程和研究需求,探讨了离心机振动台模型试验领域的发展趋势。

安徽省居住建筑终端能耗及碳排放达峰研究

以安徽省既有居住建筑运行阶段为研究对象,采用能源平衡表法、LEAP模型、碳排放因子法和STIRPAT模型等工具和方法,预测碳排放峰值及影响因素,并提出技术路径和政策建议。研究结果表明:安徽省居住建筑实现碳达峰的最佳实施路径应采取蓝图情景。该情景下居住建筑运行阶段碳排放2025年达到峰值3429.29万t, 2060年回落至918.69万t。在影响因素方面,常住人口是居住建筑碳排放的主导因素,大小为-2.646;能源结构也对居住建筑碳排放产生了不小的影响,大小为-1.465,这得益于安徽省建筑消费能源结构的持续优化。因此,开展建筑电气化、发展可再生能源、限制一次能源的使用、优化能源结构至关重要。

White Rabbit Leaping Toward a New Field

You may still remember the story about the race between the big white rabbit and little tortoise, in which the over-proud rabbit lost. However, the White Rabbit toffees produced by the Shanghai Guansheng Yuan (Group) Co., Ltd. are famous-brand products in China. This White Rabbit not only is the cham-

基于情景分析法的安徽省能源消耗及碳排放分析

为了从区域层面开展能源消耗和碳排放量预测分析,以安徽省能源消耗和碳排放量为研究对象,通过构建LEAPAnhui预测模型,设计了基准情景(BAS)、发展规划情景(DPS)、节能减排情景(ERS)3种情景,预测了安徽省未来3种不同的发展路径。结果显示:到2035年,BAS情景下能源消耗总量达到23459×10^(4)t(以吨标准煤计),相比BAS情景,DPS与ERS情景能源消耗总量分别下降了20.6%、30.7%,其中ERS情景的能源消耗总量于2030年实现达峰,峰值为16416×10^(4)t;BAS情景下,安徽省碳排放总量到2035年已达到512.2×10^(6)t,年均增长率为2.6%。DPS与ERS情景下,碳排放总量分别于2030年(389.1×10^(6)t)、2025年(357.2×10^(6)t)达到峰值,均能够完成我国提出的争取在2030年前实现碳达峰的目标承诺。基于碳约束目标对于安徽省能源结构进行了优化分析,在约束路径下,分配给三大系统清洁能源的比例分别为:第三产业39.3%、居民生活23.3%、第二产业37.4%,最小清洁能源需求量为107×10^(16)J,在能源需求总量中的占比为20.6%。

Three Big Leaps by Anhui Province-An Interview with the Provincial Governor

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甘肃省碳排放预测及减排情景分析

基于LEAP模型和情景分析法构建甘肃-LEAP模型,设置基准情景、发展情景、目标达峰情景和尽早达峰情景,预测2020-2060年不同情景下的甘肃省能源消费与碳排放情况.结果表明,目标达峰情景下甘肃省能源需求量将在2037年达峰,碳排放量于2032年左右达峰;优化产业结构、降低能耗强度、提升能源转化效率和使用清洁能源是降低能耗和碳排放的主要方法.

基于LEAP模型的西安市交通污染物排放预测分析

为了探讨交通运输部门的低碳发展方向,基于LEAP(longrange energy alternatives planning system)模型建立西安市道路交通运输部门运输能源与环境模型,模拟2021—2050年不同情景下交通运输部门的能源需求、CO_(2)和污染物排放变化趋势以及减排潜力。结果表明,低碳情景(LC)下能源消耗和CO_(2)排放在2031年左右达到峰值,2050年相对基准情景(BAU)的削减率分别为32.62%、30.21%,对CO、NO_(x)、PM_(10)减排效果较好,相对BAU削减率分别为33.88%、36.27%、40.33%;各子情景中,运输结构调整情景(TSA)节能减排贡献最大,其次为绿色汽车情景(GC)和技术性节能情景(TES);要实现交通运输部门碳减排和污染物的排放控制,需调整交通结构,淘汰老旧车型和大力发展公共交通,并不断完善相应的基础设施,提高新能源汽车的市占率。

基于就业的高职会展英语LEAP教学模式应用探析

在经济全球化背景下,会展经济愈发受到重视,由此衍生出的会展专业人才培养工作也逐渐受到教育界关注。在高职会展专业教学中,英语学科处于核心地位,是高素质、综合型会展人才职业规划和就业发展中不可或缺的专业技能培养课程。在高职会展专业教学中重点做好英语教学,也能够全面助力高职会展专业人才就业。本文围绕高职会展专业英语教学的改革优化,结合LEAP教学模式,探讨了该教学模式的应用重点,提出了几种可行性较高的教学方法,总结了LEAP教学模式的应用成效,以期为相关教职人员提供参考。