Scenario generation using adaptive sampling: The case of resource scarcity

Static input-oriented sampling approaches are often used for generating model-based scenarios. However, for models of deeply uncertain and dynamically complex issues, there is no guarantee that such approaches reveal the total behavioral spectrum that could be generated by simulating them. In this paper, we present an adaptive output-oriented sampling approach for exploring the full behavioral spectrum that could be generated by computational models in view of generating interesting, even previously undiscovered, scenarios. In this paper, we use a resource scarcity model to illustrate the approach, show the difference between static sampling and adaptive sampling, and demonstrate the usefulness for scenario discovery of the latter combined with other methods. We show that this approach can be used for revealing the behavioral spectrum of models, identifying regions of the input space that generate particular behaviors, and selecting (sets of) scenarios that are representative in terms of output and input spaces.     

面向智能驾驶测试的仿真场景构建技术综述

随着汽车智能化程度的不断提高,智能汽车通过环境传感器与周边行驶环境的信息交互与互联更为密切,需应对的行驶环境状况也越来越复杂,包括行驶道路、周边交通和气象条件等诸多因素,具有较强的不确定性、难以重复、不可预测和不可穷尽。限于研发周期和成本、工况复杂多样性,特别是安全因素的考虑,传统的开放道路测试试验或基于封闭试验场的测试难以满足智能驾驶系统可靠性与鲁棒性的测试要求。因此,借助数字虚拟技术的仿真测试成为智能驾驶测试验证一种新的手段,仿真场景的构建作为模拟仿真的重要组成部分,是实现智能驾驶测试中大样本、极限边界小概率样本测试验证的关键技术,这对提升智能驾驶系统的压力和加速测评水平显得尤为重要。面向智能驾驶测试的仿真场景构建技术已成为当前汽车智能化新的研究课题和世界性的研究热点,作为一种新兴技术仍面临许多挑战。本文提出了面向智能驾驶测试的仿真场景构建方法,系统阐述了国内外研究工作的进展与现状,包括场景自动构建方法和交通仿真建模方法,重点分析一些值得深入研究的问题并围绕场景构建技术的发展趋势进行了讨论分析,最后介绍了团队相关研究在2020中国智能驾驶挑战赛仿真赛和世界智能驾驶挑战赛的仿真场景应用情况。     

Data mining with various optimization methods

Road traffic represents the main source of noise in urban environments that is proven to significantly affect human mental and physical health and labour productivity. Thus, in order to control noise sound level in urban areas, it is very important to develop methods for modelling the road traffic noise. As observed in the literature, the models that deal with this issue are mainly based on regression analysis, while other approaches are very rare. In this paper a novel approach for modelling traffic noise that is based on optimization is presented. Four optimization techniques were used in simulation in this work: genetic algorithms, Hooke and Jeeves algorithm, simulated annealing and particle swarm optimization. Two different scenarios are presented in this paper. In the first scenario the optimization methods use the whole measurement dataset to find the most suitable parameters, whereas in the second scenario optimized parameters were found using only some of the measurement data, while the rest of the data was used to evaluate the predictive capabilities of the model. The goodness of the model is evaluated by the coefficient of determination and other statistical parameters, and results show agreement of high extent between measured data and calculated values in both scenarios. In addition, the model was compared with classical statistical model, and superior capabilities of proposed model were demonstrated. The simulations were done using the originally developed user friendly software package.     

Finding hidden independent sets in interval graphs

We design efficient competitive algorithms for discovering hidden information using few queries. Specifically, consider a game in a given set of intervals (and their implied interval graph G) in which our goal is to discover an (unknown) independent set X by making the fewest queries of the form “Is point p covered by an interval in X?” Our interest in this problem stems from two applications: experimental gene discovery with PCR technology and the game of Battleship (in a 1-dimensional setting). We provide adaptive algorithms for both the verification scenario (given an independent set, is it X?) and the discovery scenario (find X without any information). Under some assumptions, these algorithms use an asymptotically optimal number of queries in every instance.     

Using the agent-based model to simulate and evaluate the interaction effects of agent behaviors on groundwater resources,a case study of a sub-basin in the Zayandehroud River basin

The present study aims to identify and assess the parameters affecting agent behaviors and to simulate their effects on groundwater resources under the existing water regulations and policies. For this purpose, agent-based models are exploited to simulate the different groups of agents including Regional Water Authority, Public Prosecutor's Office, and the stakeholders as well as their interactions. The systems dynamic model is employed to investigate the effects of changes in agent behaviors on groundwater resources. The Any Logic software package is also used as a powerful tool for simulating both models and identifying the relationships between human behavior and water resources. Two efficient and inefficient scenarios are defined for assessing the effects of such parameters as ‘meetings of regional water agencies held with other public organizations”. Results revealed that changes in the behavior and interactions of Regional Water Authority with other public organizations under the first (inefficient) scenario led to increased renewable storage by 11.6 MCM while elimination of this parameter in the second (efficient) scenario led to a decrease of 52.6 MCM in renewable storage. Results of both scenarios showed that proper interactions of the Regional Water Authority with other stakeholders led to the control of illegal water abstractions and the rise of water table in the aquifers from the scenario one 1777.4 m–1778.1 m under the second scenario.     

Scenario Violation in Nursing Activities: Nursing Risk Management from the Viewpoint of Chance Discovery

This paper introduces scenarios that from a time series of events under a coherent context of performing nursing risk management. First, we describe general nursing risk management procedures. Then we review our previous nursing accident or incident protection model based on abduction. This paper extends the nursing accident or incident protection model by using the concept of scenario. That is, the model introduces chronological information in knowledge presentation. Then this paper regards a set of nursing activities as a scenario and characterizes a (nursing) accident or incident as a scenario violation. The main purpose of this paper is to present nursing risk management from the viewpoint of scenario violation in the context of chance discovery.     

Distributed attitude synchronization using backstepping and sliding mode control

We consider the problem of attitude synchronization for systems of rigid body agents with directed topologies. Two different scenarios for the rotation matrices of the agents are considered. In the first scenario, the rotations are contained in a convex subset of SO(3), which is a ball of radius less than π/2, whereas in the second scenario the agents are contained in a subset of SO(3), which is a ball of radius less than π. Using a control law based on backstepping and sliding mode control, we provide distributed, semi-global, torque control laws for the agents so that the rotations asymptotically synchronize. The control laws for the agents in the first scenario only depend on the relative rotations between neighboring agents, whereas the control laws in the second scenario depend on rotations defined in a global coordinate frame. Illustrative examples are provided where the synchronization is shown for both scenarios.     

Capacity Gain of Mixed Multicast/Unicast Transport Schemes in a TV Distribution Network

This paper presents three approaches to estimate the required resources in an infrastructure where digital TV channels can be delivered in unicast or multicast (broadcast) mode. Such situations arise for example in Cable TV, IPTV distribution networks or in (future) hybrid mobile TV networks. The three approaches presented are an exact calculation, a Gaussian approximation and a simulation tool. We investigate two scenarios that allow saving bandwidth resources. In a static scenario, the most popular channels are multicast and the less popular channels rely on unicast. In a dynamic scenario, the list of multicast channels is dynamic and governed by the users' behavior. We prove that the dynamic scenario always outperforms the static scenario. We demonstrate the robustness, versatility and the limits of our three approaches. The exact calculation application is limited because it is computationally expensive for cases with large numbers of users and channels, while the Gaussian approximation is good exactly for such systems. The simulation tool takes long to yield results for small blocking probabilities. We explore the capacity gain regions under varying model parameters. Finally, we illustrate our methods by discussing some realistic network scenarios using channel popularities based on measurement data as much as possible.     

Performance extrapolation in discrete-event systems simulation

The use of simulation as an engineering tool to design complex computer stochastic systems is often inhibited by cost. Extensive computer processing is needed to recompute performance functions for changes in input parameters. Moreover, simulation models are often subject to errors caused by the input data in estimating the parameters of the input distributions. The ‘;what if analysis is needed to establish confidence in a models validity with respect to small changes in the system’s parameters. To solve the ‘what if problem for several scenarios requires a separate simulation run for each scenario. A method to estimate the performance for several scenarios using a single simulation run based on the efficient score function is developed. The approximate function is an exponential random function which is tangential to the performance function in expectation. We study the algorithm's properties and examine the validity of the estimates based on this single run procedure by performing some experimental studies on some simple queueing and pert models. Implementation ‘details’ necessary to package this method with existing simulation software are provided. Finally, there is a set of recommended directions for future research     

Automating test generation for discrete event oriented embedded systems

A method for the automatic generation of test scenarios from the behavioral requirements of a system is presented in this paper. The generated suite of test scenarios validates the system design or implementation against the requirements. The approach proposed here uses a requirements model and a set of four algorithms. The requirements model is an executable model of the proposed system defined in a deterministic state-based modeling formalism. Each action in the requirements model that changes the state of the model is identified with a unique requirement identifier. The scenario generation algorithms perform controlled simulations of the requirements model in order to generate a suite of test scenarios applicable for black box testing. Measurements of several metrics on the scenario generation algorithms have been collected using prototype tools.     

Minimizing worst-case and average-case makespan over scenarios

We consider scheduling problems over scenarios where the goal is to find a single assignment of the jobs to the machines which performs well over all scenarios in an explicitly given set. Each scenario is a subset of jobs that must be executed in that scenario. The two objectives that we consider are minimizing the maximum makespan over all scenarios and minimizing the sum of the makespans of all scenarios. For both versions, we give several approximation algorithms and lower bounds on their approximability. We also consider some (easier) special cases. Combinatorial optimization problems under scenarios in general, and scheduling problems under scenarios in particular, have seen only limited research attention so far. With this paper, we make a step in this interesting research direction.     

A fortran program for Monte Carlo simulation of oil-field discovery sequences

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios.     

自动驾驶汽车的高效对抗性场景测试方法研究

在自动驾驶安全性的研究和应用中,测试里程长、暴露危险场景单一的问题使自动驾驶安全性能的提升受到限制。使用对抗性场景进行测试被认为是解决上述问题的重要手段,然而,现有研究采用通用的优化算法作为框架,将大量计算资源浪费在对参数空间的探索过程中,效率低下。在计算成本的约束下,这些算法甚至无法在更复杂的环境中测试出足够多、足够丰富的失效样本。复杂环境中的对抗性场景测试面临三大挑战：信息匮乏;对抗性样本在庞大的参数空间中稀疏分布;搜索过程中探索与利用难以平衡。该文从这三大挑战出发,提出一种高效的对抗性场景测试框架,通过代理模型来获取更多关于参数空间的信息,精选小样本,以打破庞大空间中稀疏事件的制约,对未知区域和对抗性样本附近的目标进行有针对性的搜索和更新,以实现探索和利用的平衡。实验证明,该文提出方法的搜索效率是随机采样的4倍,与通用遗传算法相比,效率提升一倍以上,在有限的仿真测试次数下,生成了更多容易使被测自动驾驶系统失效的对抗性测试用例。特别地,该文提出的方法能够找出许多离群的对抗性样本,揭示出现有算法无法识别的失效模式。此外,该文提出的方法能够快速、全面地定位出被测算法的脆弱场景,为自动驾...     

Evaluating methods to simulate crop rotations for climate impact assessments – A case study on the Canterbury plains of New Zealand

Most climate impact assessments for food production simulate single crops with re-initialised soil conditions. However, crop rotations with multiple crops are used in many agricultural regions worldwide. This case-study compares methods to aggregate outputs from simulations of multi-crop systems for climate impact assessments. The APSIM model was used to simulate four crops as monocultures (re-initialised or continuous) or as (single or multiple) instances of continuous rotations. We considered two contrasting climates and two soil types, with four production intensification scenarios (high/low water and nitrogen input). Results suggest that differences among the methods depend on the impact variable of interest and the degree of intensification. Detailed simulations (i.e. multiple runs of continuous rotations) were especially valuable for soil-related variables and limiting growth conditions. These results can indicate sources of uncertainty for large scale impact and adaptation assessments where simplifications of crop rotations are often necessary.     

Frequent substructure-based approaches for classifying chemical compounds

Computational techniques that build models to correctly assign chemical compounds to various classes of interest have many applications in pharmaceutical research and are used extensively at various phases during the drug development process. These techniques are used to solve a number of classification problems such as predicting whether or not a chemical compound has the desired biological activity, is toxic or nontoxic, and filtering out drug-like compounds from large compound libraries. This paper presents a substructure-based classification algorithm that decouples the substructure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topological and geometric substructures present in the data set. The advantage of this approach is that during classification model construction, all relevant substructures are available allowing the classifier to intelligently select the most discriminating ones. The computational scalability is ensured by the use of highly efficient frequent subgraph discovery algorithms coupled with aggressive feature selection. Experimental evaluation on eight different classification problems shows that our approach is computationally scalable and, on average, outperforms existing schemes by 7 percent to 35 percent.     

自动驾驶软件测试技术研究综述

自动驾驶系统（autonomous driving system,ADS）是一种集成高精度传感器、人工智能和地图导航系统等模块的信息—物理融合系统。该类系统中的自动驾驶软件完成了从高级辅助驾驶到无人驾驶任务中关键的感知、定位、预测、规划和控制任务。随着深度学习和强化学习等人工智能技术的发展和车载硬件设备的不断升级,高级别的自动驾驶软件已经逐渐应用于多种安全攸关的场景中,保障其运行稳定性与可靠性的测试技术逐渐成为学术界和产业界的研究重点。本文在广泛调研国内外文献基础上,对自动驾驶软件测试技术进行了深入分析与梳理。结合自动驾驶软件的架构特点及系统特征,讨论了面向自动驾驶系统的仿真测试和实景测试,以及面向组件的测试技术。其中,在仿真方法方面,分析了软件仿真、半实体仿真和在环仿真等技术;在仿真对象方面,讨论了静态环境仿真、动态场景仿真、传感器仿真和车辆动力学仿真等。同时,本文介绍了当前实景测试的进展与情况,重点分析了实景测试案例中的得失优劣。在面向自动驾驶软件组件的测试技术方面,重点讨论了当前数据驱动技术在感知组件、决策规划组件,以及控制组件测试方面的进展。最后,本文总结分析了自动驾驶软件测试当前面临的挑战,并对未来自动驾驶软件测试技术的研究方向和研究重点进行了展望。     

Explanation-Based Scenario Generation for Reactive System Models

Reactive systems control many useful and complex real-world devices. Tool-supported specification modeling helps software engineers design such systems correctly. One such tool, a scenario generator, constructs an input event sequence for the spec model that reaches a state satisfying given criteria. It can uncover counterexamples to desired safety properties, explain feature interactions in concrete terms to requirements analysts, and even provide online help to end users learning how to use a system. However, while exhaustive search algorithms such as model checkers work in limited cases, the problem is highly intractable for the functionally rich models that correspond naturally to complex systems engineers wish to design. This paper describes a novel heuristic approach to the problem that is applicable to a large class of infinite state reactive systems. The key idea is to piece together scenarios that achieve subgoals into a single scenario achieving the conjunction of the subgoals. The scenarios are mined from a library captured independently during requirements acquisition. Explanation-based generalization then abstracts them so they may be coinstantiated and interleaved. The approach is implemented, and I present the results of applying the tool to 63 scenario generation problems arising from a case study of telephony feature validation.     

基于堆的无向带权图最小生成树的PRIM方法

通过引用堆的思想,达到了在选最小边时能充分使用原有比较信息的目的,对PRIM算法进行了探讨.     

Chameleon: hierarchical clustering using dynamic modeling

Clustering is a discovery process in data mining. It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. Many advanced algorithms have difficulty dealing with highly variable clusters that do not follow a preconceived model. By basing its selections on both interconnectivity and closeness, the Chameleon algorithm yields accurate results for these highly variable clusters. Existing algorithms use a static model of the clusters and do not use information about the nature of individual clusters as they are merged. Furthermore, one set of schemes (the CURE algorithm and related schemes) ignores the information about the aggregate interconnectivity of items in two clusters. Another set of schemes (the Rock algorithm, group averaging method, and related schemes) ignores information about the closeness of two clusters as defined by the similarity of the closest items across two clusters. By considering either interconnectivity or closeness only, these algorithms can select and merge the wrong pair of clusters. Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters. Chameleon finds the clusters in the data set by using a two-phase algorithm. During the first phase, Chameleon uses a graph partitioning algorithm to cluster the data items into several relatively small subclusters. During the second phase, it uses an algorithm to find the genuine clusters by repeatedly combining these subclusters