Fast-track program vs traditional care in surgery for gastric cancer

AIM:To systematically review the evidence for the effectiveness of fast-track program vs traditional care in laparoscopic or open surgery for gastric cancer.METHODS:PubMed,Embase and the Cochrane library databases were electronically searched for published studies between January 1995 and April 2013,and only randomized trials were included.The references of relevant studies were manually searched for further studies that may have been missed.Search terms included"gastric cancer","fast track"and"enhanced recovery".Five outcome variables were considered most suitable for analysis:postoperative hospital stay,medical cost,duration to first flatus,C-reactive protein(CRP)level and complications.Postoperative hospital stay was calculated from the date of operation to the date of discharge.Fixed effects model was used for meta-analysis.RESULTS:Compared with traditional care,fasttrack program could significantly decrease the postoperative hospital stay[weighted mean difference(WMD)=-1.19,95%CI:-1.79--0.60,P=0.0001,fixed model],duration to first flatus(WMD=-6.82,95%CI:-11.51--2.13,P=0.004),medical costs(WMD=-2590,95%CI:-4054--1126,P=0.001),and the level of CRP(WMD=-17.78,95%CI:-32.22--3.35,P=0.0001)in laparoscopic surgery for gastric cancer.In open surgery for gastric cancer,fast-track program could also significantly decrease the postoperative hospital stay(WMD=-1.99,95%CI:-2.09--1.89,P=0.0001),duration to first flatus(WMD=-12.0,95%CI:-18.89--5.11,P=0.001),medical cost(WMD=-3674,95%CI:-5025--2323,P=0.0001),and the level of CRP(WMD=-27.34,95%CI:-35.42--19.26,P=0.0001).Furthermore,fast-track program did not significantly increase the incidence of complication(RR=1.39,95%CI:0.77-2.51,P=0.27,for laparoscopic surgery;and RR=1.52,95%CI:0.90-2.56,P=0.12,for open surgery).CONCLUSION:Our overall results suggested that compared with traditional care,fast-track program could result in shorter postoperative hospital stay,less medical costs,and lower level of CRP,with no more complications occurring in both laparoscopic and open surgery for gastric cancer.

Optimal set integer programming algorithm for multiple maneuvering targets tracking in clutter

The aim of this paper is to solve the problems of multitarget tracking in clutter. Firstly, the data association of measurement-to-target is formulated as an integer programming problem. Through using the linear programming （LP） based branchand-bound method and adjusting the constraint conditions, an optimal set integer programming （OSIP） algorithm is then proposed for tracking multiple non-maneuvering targets in clutter. For the case of maneuvering targets, this paper introduces the OSIP algorithm into the filtering step of the interacting multiple model （IMM） algorithm resulting in the IMM based on OSIP algorithm. Extensive Monte Carlo simulations show that the presented algorithms can obtain superior estimations even in the case of high density noises.

Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

Analysis of Nuclear Track Parameters of CN-85 Detector Irradiated to Thermal Neutrons by Using MATLAB Program

CN-85 detector which covered with boric acid H3Bo3 pellete has been irradiated by thermal neutrons from (241Am-9Be) source with activity 12 Ci and neutron flux 105 n. cm-2. s-1. The irradiation times-TD for detector were 4 h, 8 h, 16 h and 24 h. The track detector has been etched with sodium hydroxide. After chemical etching of the irradiated CN-85 detector, the images have been taken from a digital camera connected to the optical microscope. Image processing for the output images has been performed using MATALB program, and these images were analyzed and we had found the following relations: a) The relation between summation of opened track or surface density for tracks (intensity-IT) varies with radius of opening (track radius-RT). b) The relation between the tracks number-NT varies with the tracks diameter-DT (in micrometer) and tracks area-AT. That analysis of image processing was obtained, and the track intensity-IT was decreased with increase of track radius-RT at all of the irradiation time-TD. And the track intensity-IT was increased with increasing irradiation time-TD (h) for different track radius-RT (0.4225, 0.845, 1.2675 and 1.69 μm). The study indicates the possibility of using the analysis of image processing to CN-85 detector for classification of α-particle emitters through limitation of radius of track-RT, in addition to the contribution of these techniques in preparation of nano-filters and nono-membrane in nanotechnology fields.

Zenith Pass Problem of Inter-satellite Linkage Antenna Based on Program Guidance Method

While adopting an elevation-over-azimuth architecture by an inter-satellite linkage antenna of a user satellite, a zenith pass problem always occurs when the antenna is tracing the tracking and data relay satellite （TDRS）. This paper deals with this problem by way of, firstly, introducing movement laws of the inter-satellite linkage to predict the movement of the user satellite antenna followed by analyzing the potential pass moment and the actual one of the zenith pass in detail. A number of specific orbit altitudes for the user satellite that can remove the blindness zone are obtained. Finally, on the base of the predicted results from the movement laws of the inter-satellite linkage, the zenith pass tracing strategies for the user satellite antenna are designed under the program guidance using a trajectory preprocessor. Simulations have confirmed the reasonability and feasibility of the strategies in dealing with the zenith pass problem.

Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP

Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.

Multi-view video color correction using dynamic programming

Color inconsistency between views is an important problem to be solved in multi-view video systems. A multi-view video color correction method using dynamic programming is proposed. Three-dimensional histograms are constructed with sequential conditional probability in HSI color space. Then, dynamic programming is used to seek the best color mapping relation with the minimum cost path between target image histogram and source image histogram. Finally, video tracking technique is performed to correct multi-view video. Experimental results show that the proposed method can obtain better subjective and objective performance in color correction.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof

In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

This paper estimates an off-policy integral reinforcement learning（IRL） algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman（HJB） equation, an off-policy IRL algorithm is proposed.It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

基于功率谱流形的信息几何DP-TBD算法

针对复杂杂波背景下目标信杂比起伏而导致的漏检问题,本文结合信息几何检测器的性能优势与动态规划检测前跟踪(Dynamic Programming Track Before Detect,DP-TBD)的多帧信息积累能力,提出了基于功率谱流形的信息几何DP-TBD算法.该算法利用功率谱流形与矩阵流形对偶关系,设计了功率谱信息几何检测器,将信息几何检测器的计算复杂度降低了近两个数量级.通过实测数据实验验证,功率谱DP-TBD算法可实现与矩阵DP-TBD算法相近的检测性能,并将运算时间降低为矩阵DP-TBD算法的3%~8%.此外,相较于信息几何检测器,功率谱DP-TBD可将检测信杂比(Signal-to-Clutter Ratio,SCR)提高2~3 dB.

基于虚拟编组技术的单线重载铁路通过能力计算方法研究

针对我国现阶段单线重载铁路能力紧张问题,提出应用虚拟编组技术从运输组织层面提升运输能力。对虚拟编组技术基本概念进行介绍,细致分析了虚拟编组技术下列车追踪运行的全过程,将其分为4种状态,并分别给出各状态下追踪间隔计算方法。通过分析虚拟编组技术应用下单线重载线路列车在站作业及会让方式,给出区间运行图周期计算通式,构建虚拟编组下单线重载铁路最小运行图周期计算模型,并将其改写为混合整数线性规划模型。模型结构简单,且规模随区间数多项式有界,可直接采用商业优化软件求解。选取1条实际运营线路进行案例分析,计算结果显示,虚拟编组技术下其通过能力可提升55%,表明计算模型有效可靠,且虚拟编组技术的应用对单线重载铁路能力提升显著。

双线束铁路集装箱中心站“列车-轨道”分配策略研究

为了满足铁路集装箱中心站日益增长的作业需求,部分中心站已建成或规划在建第二线束,多线束的建设会大幅提升中心站的作业能力,但相应的也可能会产生线束间的作业均衡和集装箱的场内转运问题。以最小化线束间轨道门吊的工作量差异和集装箱场内转运数量为目标,考虑轨道的时空容量、列车的进出站时间要求及装卸作业要求等约束以保证列车按时完成作业,建立双目标整数规划模型来研究双线束集装箱中心站内的“列车-轨道”分配问题。设计启发式规则生成初始可行解,并利用存档式多目标模拟退火算法处理双目标优化问题,以得到一组帕累托近似最优解。设计算例验证了算法的可行性和有效性,并研究了装卸线的数量和轨道门吊的配置对实验结果的影响。结果表明:本文提出的双线束铁路集装箱中心站“列车-轨道”分配策略能够有效均衡线束间的工作量并降低集装箱场内转运数,灵敏度分析结果能够为未来双线束集装箱中心站的规划建设提供借鉴。

单线铁路区段开行一对摘挂列车运行图优化研究

单线铁路摘挂列车运行图优化,有助于加速车辆周转和货物送达。由于顺向车流在车站等待时间确定,故列车开行时只考虑上行和下行的逆向车流。当每日开行一对摘挂列车时可采用上开口、下开口和交叉方案等3种运行图形式,使用0-1变量将其转换成1种。考虑摘挂列车交叉车站选择逻辑约束,列车在车站到达和出发时刻、停站时间、区间运行时间等运行过程约束,逆向车流在中间站停留总时间、所需货物作业时间等约束,建立以逆向车流在中间站停留车分钟之和最小为目标的非线性整数规划模型。引入大M将其非线性约束转换成线性,得到线性整数规划模型。设计6个车站算例,使用数学软件求得最优解,根据求解结果从不同角度对摘挂列车运行图优化提出建议,为运行图编制和调整提供参考。

无人驾驶混合动力汽车轨迹跟踪节能控制融合研究

为进一步提高无人驾驶混合动力汽车轨迹跟踪精度和能耗经济性,提出了一种轨迹跟踪节能控制融合策略。首先,建立车辆运动学模型,采用模型预测控制策略对车辆进行轨迹跟踪控制;在此基础上,以速度为交互变量,提出了一种三阶段动态规划节能控制策略,在线优化最优经济性函数,以降低整车能耗总成本;最后,选择相互独立的纯跟踪轨迹跟踪算法与功率跟随节能控制策略进行比较。结果表明,所提出的轨迹跟踪节能控制融合策略提高了轨迹跟踪效果,降低了整车能耗总成本,轨迹跟踪精度提高了70.47%,纯电动和混合驱动模式下能耗总成本分别下降了4.52%和25.10%。

基于STM32的智能搬运小车控制系统设计

为了直观展示智能搬运小车的控制系统工作原理,硬件模块的功能设计以及相互作用,研究分析一般智能搬运小车的硬件模块组成和普遍用途,并设计出一种简单而基础功能完整的智能搬运小车理论方案,主要包括控制模块所需的单片机、灰度传感器、舵机、颜色识别传感器、电机等功能模块的选型及其依据。基于STM32芯片,采用C语言编程,为实现智能搬运小车自动循迹、物块的颜色识别、抓取等功能,编写了相应的程序,并进行了程序测试。结果表明,智能搬运小车的控制系统可通过程序的不断调整优化,实现灵活地运行和功能优化。所提设计方案可用于科技馆直观展示一般智能小车控制系统的工作原理以及C语言编程的应用和优势。

凸近似避障及采样区优化的智能车辆轨迹规划

针对结构化道路下作匀速运动的智能车辆避障轨迹规划问题,提出一种基于凸近似避障原理及采样区域优化的智能车辆轨迹规划方法。引入凸近似避障原理,得到轨迹可行域范围;将采样区域分为静态采样区、动态采样区两部分,并根据障碍物运动状态,另外划分动态、静态障碍物采样区;采用“动态规划(DP)+二次规划(QP)”思想求解轨迹:利用五次多项式对采样点依次连接,建立动态规划代价函数并筛选得到粗略轨迹;通过二次规划及约束条件的构造,对粗略轨迹进行平滑,最终得到最优轨迹。仿真结果表明:对于静态、低速、动态三种障碍物,该车能够有效地得到平滑轨迹并避开障碍物。

具有未知参数的拉格朗日系统的安全跟踪控制

针对具有未知参数的拉格朗日系统,研究了安全跟踪控制问题。首先采用backstepping方法,设计跟踪控制器用于解决系统的轨迹跟踪问题。其次构造了自适应控制障碍函数,通过该函数得到的安全控制器确保系统状态远离安全集的边界。然后结合所提出的跟踪控制器以及自适应控制障碍函数约束产生了二次规划,基于二次规划设计安全跟踪控制器,使得系统安全的同时尽可能地跟踪到参考信号。最后仿真结果验证了所提出方法的有效性。

基于航迹消除与策略迭代的无人机集群区域目标搜索方法

无人机集群区域搜索在军事领域以及民用领域的搜救、巡逻、监测、环境勘测等方面有着广泛的应用,但如何保证不同场景下无人机集群搜索方法的效率问题依然是个难题。为了更好地解决搜索目标先验信息已知的无障碍区域内多无人机集群搜索航迹规划问题,提高无人机集群搜索效率,本文根据目标区域热度以及传感器探测概率等先验信息,提出了一种基于无人机航迹消除策略的概率计算方法,并在此基础上结合策略迭代算法动态规划无人机航迹,找到单个无人机航迹覆盖率最优策略;进而通过适当组合顺序实现无人机集群区域目标搜索整体覆盖率最优;最后,通过仿真计算验证了算法的有效性。

深度确定性策略梯度下运动目标识别及无人机跟随

针对无人机(unmanned aerial vehicle,UAV)平台采集运动目标图像信息过程中因UAV自身的飞行状态、环境的干扰、目标的随机性等原因易产生运动目标丢失等问题,提出了一种基于运动目标识别的深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法UAV跟随方法。面向高速公路的车辆目标,分析了UAV高度、位姿与高速车辆运动之间的关系,建立了移动平台目标检测帧率的速度自适应模型,根据目标的运动状态计算能够相匹配UAV的飞行状态,实时修正飞行姿态与速度,使UAV能够保持与目标的相对位置和角度。继而基于DDPG算法价值网络估计UAV在不同状态下采取特定动作的价值,策略网络生成UAV在给定状态下采取动作的策略,给予UAV飞行高度、速度控制参数用于目标跟踪,使UAV能够根据目标的运动变化自动调节飞行状态,实现运动目标的自适应跟随。仿真实验表明:DDPG算法能够提供稳定的飞行姿态数据,为UAV的跟随任务提供了可靠的控制基础;通过在真实场景下实验验证,UAV能够实时跟踪速度范围0~33 m/s、半径为120 m的圆形面积内的地面运动目标,且在续航范围内能够实现持续稳定跟随。