Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms

Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.     

合成氢化松香乙酯的新工艺研究

以DRC为催化剂,在无溶剂条件下,通过氢化松香与乙醇的直接酯化反应合成氢化松香乙酯。探索了反应温度、催化剂用量、反应时间、物料配比等因素对反应酯化率的影响,确定最佳反应条件为:反应温度180℃,催化剂用量为原料氢化松香质量的6%,反应时间6h,醇与酸的摩尔比为3∶1。在最佳条件下酯化率达92.2%。还探讨了用阴离子交换树脂柱层析分离纯化氢化松香乙酯粗产物的方法。此外,利用红外光谱对氢化松香乙酯精制产物进行了表征;用GC-MS分别对氢化松香乙酯粗产物及其精制产物进行了定性和定量分析,比较了柱层析前后化学组成的变化。     

Towards a Trajectory Planning Concept: Augmenting Path Planning Methods by Considering Speed Limit Constraints

Trajectory planning is an essential part of systems controlling autonomous entities such as vehicles or robots. It requires not only finding spatial curves but also that dynamic properties of the vehicles (such as speed limits for certain maneuvers) must be followed. In this paper, we present an approach for augmenting existing path planning methods to support basic dynamic constraints, concretely speed limit constraints. We apply this approach to the well known A* and state-of-the-art Theta* and Lazy Theta* path planning algorithms. We use a concept of trajectory planning based on a modular architecture in which spatial and dynamic parts can be easily implemented. This concept allows dynamic aspects to be processed during planning. Existing systems based on a similar concept usually add dynamics (velocity) into spatial curves in a post-processing step which might be inappropriate when the curves do not follow the dynamics. Many existing trajectory planning approaches, especially in mobile robotics, encode dynamic aspects directly in the representation (e.g. in the form of regular lattices) which requires a precise knowledge of the environmental and dynamic properties of particular autonomous entities making designing and implementing such trajectory planning approaches quite difficult. The concept of trajectory planning we implemented might not be as precise but the modular architecture makes the design and implementation easier because we can use (modified) well known path planning methods and define models of dynamics of autonomous entities separately. This seems to be appropriate for simulations used in feasibility studies for some complex autonomous systems or in computer games etc. Our basic implementation of the augmented A*, Theta* and Lazy Theta* algorithms is also experimentally evaluated. We compare (i) the augmented and basic A*, Theta* and Lazy Theta* algorithms and (ii) optimizing of augmented Theta* and Lazy Theta* for distance (the trajectory length) and duration (time needed to move through the trajectory).     

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper.     

Point-based online value iteration algorithm in large POMDP

Partially observable Markov decision process (POMDP) is an ideal framework for sequential decision-making under uncertainty in stochastic domains. However, it is notoriously computationally intractable to solving POMDP in real-time system. In order to address this problem, this paper proposes a point-based online value iteration (PBOVI) algorithm which involves performing value backup at specific reachable belief points, rather than over the entire belief simplex, to speed up computation processes, exploits branch-and-bound pruning approach to prune the AND/OR tree of belief states online, and proposes a novel idea to reuse the belief states that have been searched to avoid repeated computation. The experiment and simulation results show that the proposed algorithm can simultaneously satisfy the requirement of low errors and high timeliness in real-time system.     

基于EIGamal签名的隐私保护网上交易方案

针对网上交易时的用户个人隐私安全问题,依据不经意传输理论,基于ElGamal签名的不经意签名电子信封(OSBE)模型,提出了一个可保护用户隐私的网上交易数字商品方案.首先用户匿名付费给银行,然后银行将用户订购商品的数字签名发送给用户,接着用户使用数字签名与商家进行不经意信息交互,最后用户得到且只能得到自己订购的数字商品,商家并不知道用户订购何种数字商品.正确性证明和安全性分析结果表明,可以有效地保护用户在网上交易过程中的个人隐私,同时防止商家恶意欺诈行为.该方案签名相对较短,计算量较小,密钥动态变化,安全性更强.     

Efective Error-Tolerant Keyword Search for Secure Cloud Computing简

The existing solutions to keyword search in the cloud can be divided into two categories： searching on exact keywords and searching on error-tolerant keywords. An error-tolerant keyword search scheme permits to make searches on encrypted data with only an approximation of some keyword. The scheme is suitable to the case where users＇ searching input might not exactly match those pre-set keywords. In this paper, we first present a general framework for searching on error-tolerant keywords. Then we propose a concrete scheme, based on a fuzzy extractor, which is proved secure against an adaptive adversary under well-defined security definition. The scheme is suitable for all similarity metrics including Hamming distance, edit distance, and set difference. It does not require the user to construct or store anything in advance, other than the key used to calculate the trapdoor of keywords and the key to encrypt data documents. Thus, our scheme tremendously eases the users＇ burden. What is more, our scheme is able to transform the servers＇ searching for error-tolerant keywords on ciphertexts to the searching for exact keywords on plaintexts. The server can use any existing approaches of exact keywords search to search plaintexts on an index table.