Design Method for Optimizing the Interactive Interface of Live Broadcasting Platform for the Elderly Users

In the era of network live broadcasting for everyone,the development of live broadcasting platforms is also more intelligent and diversified.However,in the face of a large group of elderly users,the interface interaction design mode used is still mainly based on the interaction mode for young groups,and is not designed for elderly users.Therefore,a design method for optimizing the interaction interface of live broadcasting platform for elderly users was proposed in this study.Firstly,the case study method and Delphi expert survey method were used to determine the design needs of elderly users and the design mode was analysed.Secondly,the orthogonal design principle was used to design a test sample of the interactive interface of live broadcasting platform applicable for the elderly users,and then a user evaluation system was established to calculate the weights of the design elements using hierarchical analysis,and then the predictive relationship between the design mode of the interactive interface of live broadcasting platform and the elderly users was established by Quantitative Theory I.Finally,Genetic Algorithm was applied to generate the optimized design scheme.The results showed that the design method based on the Genetic Algorithm and the combination of Quantitative Theory can scientifically and effectively optimize the design of the interactive interface of the live broadcasting platform for the elderly users,and improve the experience of the elderly users.

Interactive Genetic Algorithms with Fitness Adjustment

Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.

Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

Improved particle filtering techniques based on generalized interactive genetic algorithm

This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.

An Interactive Online Platform for Algorithms Teaching

Learning programming has become an important part of education.However,most students have extreme difficulty learning programming and complex algorithms.This is because programming has a hierarchical logic.Solving complex problems requires students to develop skills in decomposing problems.To this end,this paper describes an effective method to develop an online platform for teaching complex algorithms.

A hybrid contact approach for modeling soil-structure interaction using the material point method

The grid-based multi-velocity field technique has become increasingly popular for simulating the Material Point Method(MPM)in contact problems.However,this traditional technique has some shortcomings,such as(1)early contact and contact penetration can occur when the contact conditions are unsuitable,and(2)the method is not available for contact problems involving rigid-nonrigid materials,which can cause numerical instability.This study presents a new hybrid contact approach for the MPM to address these limitations to simulate the soil and structure interactions.The approach combines the advantages of point-point and point-segment contacts to implement contact detection,satisfying the impenetrability condition and smoothing the corner contact problem.The proposed approach is first validated through a disk test on an inclined slope.Then,several typical cases,such as granular collapse,bearing capacity,and deformation of a flexible retaining wall,are simulated to demonstrate the robustness of the proposed approach compared with FEM or analytical solutions.Finally,the proposed method is used to simulate the impact of sand flow on a deformable structure.The results show that the proposed contact approach can well describe the phenomenon of soil-structure interaction problems.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Hybrid partheno-genetic algorithm and its application in flow-shop problem

In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high rank schemas at the subsequent generation decrease exponentially even though its fitness is more optimal than the average one in the population and the low rank schemas at the subsequent generation increase exponentially when its fitness is more optimal than the average one in the population. In order to overcome the shortcoming that the optimal high rank schema can be deserted arbitrarily, the HGA (hybrid partheno-genetic algorithm) is proposed, that is, the hill-climbing algorithm is integrated to search for a better individual. Finally, the results of the simulation for facility layout problem and no-wait schedule problem are given. It is shown that the hybrid partheno- genetic algorithm is of high efficiency.

Interactive Multi-objective Optimization Design for the Pylon Structure of an Airplane

The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization （IMOPSO） is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%.

Implementation of n-qubit Deutsch-Jozsa algorithm using resonant interaction in cavity QED

We propose a scheme to implement the n-qubit Deutsch-Jozsa algorithm based on resonant interaction between the atoms and a single-mode cavity. In the scheme, the resonant transitions between two ground states and one excited state of an atom are changed alternately by adjusting the cavity frequency appropriately, and the operations required to complete the algorithm can be significantly simplified following the increment of the number of qubits. The implementation of the scheme in experiment would show the full power of quantum algorithm and would be significative and important for more complicated quantum algorithm in cavity quantum electrodynamics.

Generation of Interactive Questionnaires Using YAWL-based Workflow Models

A concept is introduced in this article which has strong practical impact for computer aided system configuration. System configuration is a cumbersome and fault sensitive task while setting up systems in a broad range of business applications like ERP （enterprise resource planning） and other workflow-systems. Given a generic process or workflow model in YAWL-notation （yet another workflow language） or any other process modeling language like business process model and notation or WFMC （workflow management coalition）, it could be stated that, by using a set of reduction rules as introduced, it is possible to generate a hierarchically structured tree of sub graphs of the workflow graph-representation. According to the notation used, authors call these sub graphs facts. The tree structure of the graph-representation on one hand and the logical relation between the branches and leafs of the tree on the other can be utilized to create a set of constraints and dependencies among the single facts. Some researchers showed that the nested branches can be associated to （predefined） questions with respect to the configuration of a workflow management system, for instance an ERP-application. They presented an algorithm which dynamically sorts the questions and answers in a maximum efficient configuration path, while working through the corresponding questionnaire. By combining the different elements as facts, constraints on questions, and configuration space, it is thus possible to algorithmically generate the efficient structured and interactive questionnaire for the configuration of workflow systems and algorithmically check the consistency （dead lock free, free of synchronization structural conflict） of the underlying workflow model. The concept was tested in the prototype of the interactive questionnaire for configuration of the web-service based ERP-Application Posity.

Dynamic constraint and objective generation approach for real-time train rescheduling model under human-computer interaction

Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.

基于深度表征学习和遗传算法的军用座舱色彩设计方法

军用座舱色彩设计作为载人军事设备工业设计中较为主观的一部分,设计的合理性至关重要。为提高军用座舱色彩设计的科学性,提出一种基于深度表征学习和遗传算法的军用座舱配色方法。利用深度表征学习模型预测军用座舱配色方案,并根据色彩感知理论建立军用座舱配色模型,将其作为生成方案的限制条件。同时,引入交互式遗传算法到智能配色系统中,通过人工引导的方式优化神经网络的参数,对预测的配色方案进行有效迭代。实验结果表明:该方法生成的配色方案符合军用座舱配色模型,结合遗传算法的模型预测准确率比单一的深度表征模型提高了16%~18%。相较于人工色彩设计方案,军用座舱智能配色方法生成的方案满意度略优、设计周期缩短了80%~88%,色彩稳定性提高了6%~12%。

非线性量测下的机动多目标跟踪

为了解决非线性量测下机动多目标跟踪实时性差、跟踪误差大以及对杂波变化鲁棒性较差的问题,基于随机有限集理论,提出了一种采用量测转换和模糊算法改进的多模型δ-广义标签多伯努利滤波器。首先,推导了交互多模型的δ-GLMB滤波器,通过去相关无偏量测转换实现位置量测从极坐标系到笛卡尔坐标系的无偏转换,并通过预测值去除量测误差和其协方差的相关性造成的滤波估计偏差,实现了非线性场景下的机动多目标跟踪;然后,通过航迹和量测的关联新息以及目标的机动约束构建联合波门,降低了杂波量测的数量;最后引入改进的模糊算法,以目标的模型后验概率为输入,根据模型的分离程度自适应调节运动模型的过程噪声,增加滤波精度。研究结果表明:在杂波环境下,通过与CKF-JMS-δ-GLMB、CKF-IMM-δ-GLMB等非线性多模型滤波器对比,所提算法计算时间较小,且跟踪精度更高,鲁棒性强。所提算法避免了传统的非线性处理方式计算量较大的问题,并且具有较好的杂波抑制特性,提升了非线性量测下机动多目标跟踪的性能。

基于RepNet的自闭症健康监测方法研究

自闭症谱系障碍(ASD)患者在社交互动、语言交流和兴趣等方面表现出正常行为的偏差。随着自闭症患者数量的增加,迫切需要在早期进行自闭症筛查,以尽早提供专业治疗。目前自闭症筛查主要仰赖家长填写调查问卷和医疗专业人员进行人工观察和评估,这种方式效率较低且需要耗费较长的时间。机器算法在自闭症筛查领域的应用尚未得到广泛采纳。为提高筛查效率和准确性,探讨机器视觉算法RepNet在识别自闭症患者重复动作方面的有效性,使用RepNet算法分析包含重复性和非重复性动作的视频,评估Repnet的准确性;通过分析包含自闭症患儿重复动作的视频,评估RepNet在自闭症重复性动作筛查中的准确性和匹配性。结果表明:RepNet在检测自闭症患者的重复行为方面表现出极高的有效性和准确性。

基于虚拟现实的立管横焊机器人辅助焊接工艺设计与实现

焊接工艺参数的选择对焊接质量有重要影响。针对立管J型铺设窄坡口横焊熔池下淌特性,提出基于横焊焊道厚度及热输入恒定策略的焊接工艺参数匹配算法。基于Unity3D开发平台和工艺参数匹配算法,通过C#开发了立管横焊CAPP模块,扩展了虚拟现实人机交互系统的功能模块;开展立管横焊计算机辅助焊接工艺实验,焊后焊缝成形良好,无焊接缺陷。实验结果表明:CAPP模块可有效获取匹配的焊接工艺参数,能缩短焊接工艺开发周期,降低工艺开发成本,有效提升系统的智能化水平。

基于激光测距仪的智能物联网体感交互感知研究

当前的智能物联网设备在识别用户指令方面存在不足,时常需要用户多次尝试,降低了用户的使用体验和对设备的信任度。为了解决问题,提出了一种基于激光测距仪的智能物联网体感交互感知方法。首先,拟合激光波绘制结果,并利用距离变换算法设定智能物联网的体感交互方式。然后,通过误差传递公式,计算激光测距仪的协方差矩阵,依据此调整参数和测量点。最后,融合激光图像特征,识别操作者的动态指令。实验结果表明,所提方法能够显著提升智能物联网设备对体感指令的采集完整度,正确完成所有体感指令,且平均体感动作类别划分精准度高达97.02%,说明所提方法具有较高的体感交互感知精度。

精英引导和信息交互的多目标狼群算法

鉴于狼群算法在单目标优化问题上的优越表现,结合狼群的生物习性将其运用到多目标优化问题上,提出一种精英引导和信息交互的多目标狼群算法(MOWPA-EGII)。首先,提出精英引导策略,利用外部档案中的精英狼和当前子种群的头狼共同引导种群移动,让人工狼均匀地分布在整个搜索空间,增强算法的全局搜索能力;其次,设计信息交互机制,模拟狼群捕猎过程中的信息传递,具有不同优势的个体可以相互传递信息,保证狼群的捕猎效率,提高算法勘探Pareto最优解的能力;最后,加入变异算子,扰动人工狼的移动方向,让算法跳出局部最优,增强算法的局部搜索能力。为了验证MOWPA-EGII的有效性,将其与5种经典算法和10种新近算法进行比较,结果表明MOWPA-EGII拥有良好的收敛性和多样性,证明了所提算法具有较好的优化性能。

面向用户偏好的动态网页数据交互式查询算法

为提高网页数据查询速度、精度及工作效率,提出一种面向用户偏好的动态网页数据交互式查询算法.首先,构建用户偏好模型,增加偏好组合的演化个体适应性,综合计算适配值;其次,为防止数据冗余和重复,基于兴趣相似性,分离相似度高的查询数据和重复数据,识别出网络数据的性质;最后,利用粒子群优化算法寻找最优的动态网页数据交互式查询方案.实验结果表明:在数据集基数影响下,该算法的查询结果集质量在0.95以上;在查询最大维数影响下,该算法的查询结果集质量在0.96以上,表明其查询使用时间短、结果集精度高、自适应能力强.