A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method.In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.

A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Virological course of hepatitis A virus as determined by real time RT-PCR: Correlation with biochemical, immunological and genotypic profiles

瞄准：承担肝炎 A 的分析，并且相关病毒的负担，丙氨酸 aminotransferase (中高音) ，和有病毒血的持续时间的病毒的遗传型有控制调停房间的免疫的 CD4 (+)/ CD8 (+) 淋巴细胞人口的这些参数。方法：房间计数用用荧光在乙二胺四乙酸小瓶收集的新鲜全血被执行激活的房间 sorter。肝炎 A 病毒(HAV ) RNA 从血浆液被提取，抄录进 cDNA 并且由实时聚合酶链反应确定了并且是 genotyped 的颠倒。结果：在 11 个病人之中， 10 能完全被分析。这些， 3 有严重尖锐肝炎(s -- 啊) 并且剩余物有自我限制尖锐肝炎 A (啊哈) ，在第 4 d 上与有暴发性的疾病(脑病等级 IV ) 的一个病人一起死。中高音水平在啊哈两个都是显著地更高的(1070.9 +/- 894.3；P = 0.0014 ) 并且 s -- 啊(1713.9 +/- 886.3；P = 0.001 ) 与正常控制相比(23.6 +/- 7.2 ) 。在 s 的前凝血酶时间 -- 啊病人(21.0 +/- 2.0；P = 0.02 ) 比在啊哈显著地高(14.3 +/- 1.1；P = 0.44 ) 。在啊哈病人的 CD4 (+)/CD8 (+) 比率(1.17 +/- 0.11；P = 0.22 ) 并且 s -- 啊(0.83 +/- 0.12；P = 0.0002 ) 比在正常健康控制(1.52 ) 看低。有的自我限制盒子达到顶点在分析的开始的病毒的负担当时在 s -- 啊病人这发生在第 15 或第 30 d。在敏锐、严格的组，一耐心的各个属于遗传型 IA，与仍然是 8 个盒子属于遗传型 IIIA。唯一的暴发性的肝的失败大小写属于遗传型 IA。在自我限制感染的全部功课期间收集的 HAV 病毒的负担和中高音价值直接为 s 被相关，但是这不是事实 -- 啊病人。结论：基于小规模的研究， s 的固执地更高的病毒的负担 -- 啊可能由于减少的细胞免疫和溶血。病毒血的持续时间依赖于主人，当病毒的遗传型没在 AVH 和 s 的临床的结果有明显的角色 -- 啊盒子。

Real-time crash prediction on freeways using data mining and emerging techniques

Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.

Research on fast real-time adaptive audio mixing in multimedia conference

In multimedia conference, the capability of audio processing is basic and requires more for real-time criteria. In this article, we categorize and analyze the schemes, and provide several multipoint speech audio mixing schemes using weighted algorithm, which meet the demand of practical needs for real-time multipoint speech mixing, for which the ASW and AEW schemes are especially recommended. Applying the adaptive algorithms, the high-performance schemes we provide do not use the saturation operation widely used in multimedia processing. Therefore, no additional noise will be added to the output. The above adaptive algorithms have relatively low computational complexity and good hearing perceptibility. The schemes are designed for parallel processing, and can be easily implemented with hardware, such as DSPs, and widely applied in multimedia conference systems.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

PointMLP-FD:基于多级自适应下采样的点云分类模型

针对受硬件条件、物体遮挡和背景杂波等客观因素的影响,传感器采集的目标点云具有较强的稀疏性和密度不均匀性,导致分类模型对点云特征的学习效率低、分类泛化能力差的问题,提出了一种基于多级自适应下采样的点云分类模型PointMLP-FD。该模型设计了多个MLP模块作为网络分支,以点云的浅层特征为输入得到每个点云类别维度上的特征表达,之后再根据特征表达进行排序,选择具有更强语义特征的点构成下采样点集。通过过滤背景和与目标相关性低的信息来自适应保留反应目标本质特征的信息。最后分别计算分支网络的损失,与骨干网络并行训练来优化点云特征,减少模型参数。该方法在ScanObjectNN数据集上进行测试,结果表明相较于PointMLP-elite分类精度更高,mAcc提升1%,OA提升0.8%,以更少的参数量接近SOTA模型的性能。

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Self - Crosslinking Behavior of Self - Emulsion Water - Based Polyure-thane Coating Agents

The relation between structures and properties of polyurethane are investigated by modern physical and chemical methods.The results obtained are as follows:the effects of the content of self-crosslinking agent on the properties of polyurethane,i.e.,dispersion stability,dynamical viscoelasticity and mechanical properties are discussed.It is found that the optimum molar ratio of epichlorohydrin and diethylenetriamine is 1:2.A mois-

Culture Self- Consciousness in Transmission of Chinese Philosophy from the Perspective of English Globalization -- Taking the Belt and Road Initiative for Example

The Belt and Road Initiative contains the aspiration of Chinese nation to pursue ideological freedom and desire of strengthening international exchanges and cooperation. This paper takes the Belt and Road Initiative for example,emphasizing the interpretation of the inheritance and development of Chinese culture so as to study the importance of Culture Self-Consciousness in Chinese Philosophy,in the context of English globalization. That is to say,this paper stresses the spirits and the stand of nation 's demonstration on pursuing peace,cooperation and sustainable development in the civilization continuity from traditional China to the contemporary China,with practical philosophic view.

Neuroimaging Phenomenology of the Central Self-Regulation Mechanisms

Functional MRI was used to map the brains of subjects on-line during the process of media training for the acquisition and improvement of self-regulation mechanisms. The temporal and spatial dynamics of the new neural network formation were studied in real and simulated (false) biofeedback game, and their qualitative characteristics were discussed. It has been shown that immersion into a virtual competitive game, controlled by physiological responses, causes a wide involvement of the cortices, characterized by a high volume of activation in the mid-temporal, occipital and frontal areas, the cuneus and the precuneus. In both forms of media training, high values of activation volume were identified in the cerebellar structures.

Interferon-free treatments in patients with hepatitis C genotype 1-4 infections in a real-world setting

AIM To investigated the real-world effectiveness and safety of various regimens of interferon-free treatments in patients infected with hepatitis C virus(HCV).METHODS We performed an observational study to analyze different antiviral treatments administered to 462 HCV-infected patients, of which 56.7% had liver cirrhosis. HCV RNA after 4 wk of treatment and at 12 wk after treatment sustained virologic response(SVR) as well as serious adverse events(SAEs) was analyzed first for the whole cohort and then separately in patients who met or did not meet the inclusion criteria of a clinical trial(CT-met and CT-unmet, respectively).RESULTS The most frequently prescribed treatment was simeprevir/sofosbuvir(36.4%), followed by sofosbuvir/ledipasvir(24.9%) and ombitasvir/paritaprevir/ritonavir(r)/dasabuvir(19.9%). Ribavirin(RBV) was administered in 198 patients(42.9%). SVRs occurred in 437/462 patients(94.6%). The SVRs ranged between 93.3% and 100% for genotypes 1-4. SVRs were achieved in 96.2% patients in the CTmet group vs 91.9% patients in the CT-unmet group(P = 0.049). Undetectable HCV RNA at week 4 occurred in 72.9% of the patients. In the univariate analysis, the factors associated with SVRs were lower liver stiffness, absence of cirrhosis, higher platelet count, higher albumin levels, no RBV dose reduction, undetectable HCV RNA at week 4 and CT-met group. In the multivariate analysis, only albumin was an independent predictor of treatment failure(P = 0.04). Eleven patients(2.4%) developed SAEs; 5.2% and 0.7% of the patients in the CT-unmet and CT-met groups, respectively(P = 0.003).CONCLUSION A high proportion of patients with HCV infection achieved SVRs. For patients who did not meet the CT criteria, treatment regimens must be optimized.

Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

新时代青年大学生爱国主义的变迁与启示

新时代青年大学生爱国主义的积极变化主要表现在:展现空间上,新时代青年大学生更加注重在合作开放的全球化中谋取正当的民族权益,网络爱国行动更加积极主动;表现形式上,新时代青年大学生的理性爱国成分占比逐步增加,爱国行为更为匹配爱国认知;心理状态上,新时代青年大学生正在从外显自尊向真实自豪转变,反向表达多是为了抒发正向情感。这些变迁启示我们:新时代青年大学生的爱国主义教育未来应以复杂的全球化和加速的网络化为主要场域,应以言行理性化和实践指向性为主要方向,应以国家现代化和民众获得感为主要基础。

自适应SR-RM调度算法

在分析RM调度算法的基础上,提出了一种自适应SR RM调度算法,通过跟踪任务的实际执行情况以及处理器的繁忙程度自适应地调整任务的执行周期,使任务得到较合理的调度,达到理想的服务响应时间,以提高系统的实时性;同时监视和预测环境的变化是否造成不可调度任务集,通过自动调节任务的执行周期来减少不可调度任务集的发生,提高系统的可靠性·通过仿真实验,证明SR RM能得到较小的任务调度错失率、较高的可调度利用率和实时性能·

基于BiLevelNet的实时语义分割算法

针对语义分割网络参数量过大导致其难以部署在内存受限的边缘设备等问题,本文提出一种基于BiLevelNet的轻量级实时语义分割算法。首先,利用空洞卷积扩大感受野,并结合特征复用策略增强网络的区域感知能力。接着,嵌入两阶段的PBRA注意力机制,建立远距离相关物体之间的依赖关系以增强网络的全局感知能力。最后,引入结合浅层特征的FADE算子以改善图像上采样效果。实验结果表明,在输入图像分辨率为512×1024的情况下,本文网络在Cityscapes数据集上以121 f/s的速率获得了75.1%的平均交并比,模型大小仅为0.7 M。同时在输入图像分辨率为360×480的情况下,在Camvid数据集上取得68.2%的平均交并比。同当前其他实时语义分割方法相比,该网络性能取得速度与精度的均衡,符合自动驾驶应用场景对实时性的要求。

真实世界二叶式主动脉瓣重度狭窄患者应用Venus-A瓣膜行经导管主动脉瓣置换术的多中心经验

目的:探索我国真实世界中采用第一代自膨胀式Venus-A瓣膜行经导管主动脉瓣置换术(TAVR)治疗二叶式主动脉瓣(二叶瓣)重度狭窄患者的临床疗效。方法:连续入选2017年4月至2020年3月共22个中心行TAVR的二叶瓣重度狭窄患者,根据CT核心实验室判断将患者按Sievers分型分为0型二叶瓣组和1型二叶瓣组。通过基线、术中及短期随访指标评价其安全性及有效性。主要终点为30 d全因死亡及致残性脑卒中。次要终点包括术中即刻不良事件指标;30 d随访指标:延迟冠状动脉阻塞及瓣环破裂、主要出血、新发永久起搏器植入、NYHA心功能分级以及经胸超声心动图结果。结果:共入选199例二叶瓣重度狭窄患者,平均年龄(71.77±8.13)岁,男性113例(56.8%),0型二叶瓣组121例(60.8%),1型二叶瓣组78例(39.2%)。所有患者均采用Venus-A瓣膜行TAVR治疗,术中无死亡、无冠状动脉闭塞、无瓣环破裂,1例(0.5%)患者急诊外科开胸,7例(3.5%)患者出现瓣膜移位,24例(12.1%)患者进行了第二枚瓣膜置换。释放后即刻4例(2.0%)患者出现中度及以上瓣周反流,6例(3.0%)患者仍有残余狭窄。术后30 d随访,全因死亡率0.5%,致残性脑卒中发生率0.5%,无延迟冠状动脉闭塞及瓣环破裂。新发永久起搏器植入19例(9.5%),1型二叶瓣组发生率高于0型二叶瓣组(19.2%vs.3.3%,P=0.023)。30 d随访超声心动图提示:主动脉瓣平均跨瓣压差、主动脉瓣峰值流速及左心室射血分数较基线有明显改善。5例(2.5%)患者仍存在中量及以上瓣周反流,0型二叶瓣组发生率高于1型二叶瓣组(4.1%vs.0%,P=0.034),1例(0.5%)患者仍有残余狭窄。结论:真实世界中采用Venus-A瓣膜行二叶瓣TAVR短期整体效果满意,体现出了良好的安全性及有效性。

基于SAPSO-BP网络模型的港口潮汐实时预报

为了提高港口码头潮汐预报的精度,提出一种自适应变异的粒子群优化算法SAPSO,将SAPSO优化算法与BP神经网络结合,用以潮汐水位的实时预报。SAPSO-BP网络模型运用自适应变异的PSO算法优化BP神经网络的网络参数,克服了传统BP神经网络所具有的对初始权值阈值敏感、容易陷入局部极小值的缺点,最后选用Isabel港口的实测潮汐值数据进行潮汐水位的实时预报仿真试验,用以验证SAPSO-BP预测模型的实用性和可靠性。