Load-balanced parallel streamline generation on large scale vector fields

Because of the ever increasing size of output data from scientific simulations, supercomputers are increasingly relied upon to generate visualizations. One use of supercomputers is to generate field lines from large scale flow fields. When generating field lines in parallel, the vector field is generally decomposed into blocks, which are then assigned to processors. Since various regions of the vector field can have different flow complexity, processors will require varying amounts of computation time to trace their particles, causing load imbalance, and thus limiting the performance speedup. To achieve load-balanced streamline generation, we propose a workload-aware partitioning algorithm to decompose the vector field into partitions with near equal workloads. Since actual workloads are unknown beforehand, we propose a workload estimation algorithm to predict the workload in the local vector field. A graph-based representation of the vector field is employed to generate these estimates. Once the workloads have been estimated, our partitioning algorithm is hierarchically applied to distribute the workload to all partitions. We examine the performance of our workload estimation and workload-aware partitioning algorithm in several timings studies, which demonstrates that by employing these methods, better scalability can be achieved with little overhead.     

Coordination control of greenhouse environmental factors

Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO₂ concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

应用词法分析方法抽取Web查询接口模式

Deep Web中蕴含着大量高质量的数据,然而只有通过Web查询接口对Web数据库提交查询才能获取这些数据,因此,自动获取Web查询接口模式是实现Web数据库集成的关键.将Web查询接口模式的抽取过程看作一个词法分析的过程,通过构建EGLM-FA(元素分组及标签匹配有限状态自动机)来完成对Web查询接口模式的抽取.首先应用Html呈现引擎将Web查询接口所在页面进行解析,利用查询接口Form中的DOM节点及其坐标信息构建相应的NSS(节点空间结构),之后再将所有的NSS组成NSS列表,将NSS列表作为EGLM-FA的输入,进而抽取出Web查询接口的模式.     

地理信息数据变化检测系统的研究与实现

针对地理信息数据变化的需求,提出一种对地理信息数据进行变化检测的地理信息系统设计方案。探讨变化检测系统的设计思想、功能及系统架构,运用ArcObjects, ArcSDE及Oracle数据库等相关技术进行实现,检测出不同时期地理数据之间的变化信息。实例应用结果表明,该系统对地理信息数据变化的检测具有较高的准确性。     

一种面向PKI/PMI的电子政务边界数据防护系统

电子政务边界上的数据安全防护变得越来越重要.提出一种基于PKI/PMI的电子政务边界数据安全防护系统BDSPS(Boundary Data Security Protection System),研究了系统架构、组成、中间件、及实现方式.系统从电子政务系统业务边界、客户端、服务端对数据进行综合的安全防护.最后,给出系统对一个大规模电子政务边界数据进行保护的实例.     

B样条曲面重构中的轮廓匹配和分支问题

通过建立轮廓树,确定轮廓的内外属性以及轮廓之间的相互嵌套关系,并缩小轮廓匹配的搜索范围;在轮廓树的基础上,利用阈值半径寻找轮廓,确定相邻切片上轮廓的对应关系; 通过对轮廓树节点的数量及位置判断确定分支部位,利用加权补分法,进行母轮廓分割,并结合层间插值,解决分支问题,进行曲面重构.     

基于自适应学习的文本信息过滤系统

面对实时网络信息过滤的新挑战,自适应信息过滤能够解决问题。针对现有自适应系统的不足,提出了提高模板准确性的学习和过滤阈值优化的新方法。采用增量迭代学习算法来逼近真实的过滤模板,结合非法网页的分布函数自适应调整阈值,不断提高过滤精度,并运行于一个校园网关之上,取得了较好的效果。     

基于几何流形能量最小化的图像检索算法

提出了一种基于几何流形能量GEOMEN(geometric manifold energy)最小化的图像检索算法。许多基于流形的检索算法都是在图像的特征空间提取相应的语义流形空间,进而在语义空间中进行图像检索排序。将图像的检索看作一个图像数据库中搜索一个最优图像能量环的问题。图像能量环表示了图像之间的联系和相关性,通过最小化GEOMEN可以得到最优图像环。最小化的求解涉及到一个组合优化的问题,传统的禁忌搜索算法在选择最优候选集时非常耗时,提出一种智能的积极禁忌搜索算法求解最优环,实验表明提出的算法检索性能高,可以得到较高的查全率与准确率。     

南方电网CNGI驻地网的设计与实现

根据南方电网CNGI驻地网的建设情况,介绍了南方电网CNGI驻地网的设计原则及目标,对接入网技术、骨干网技术、路由协议的选择、IPv6地址分配等作具体的研究和说明,提出了适合电力应用环境需求的IPv6驻地网建设方法,为今后网络向IPv6过渡提供了指导性建议.