On controlling robots with redundancy

Recently there has been considerable interest in increasing the applicability and utility of robots by developing manipulators which possess kinematic and/or actuator redundancy. This paper presents a unified approach to controlling these redundant robots. The proposed control system consists of two subsystems: an adaptive position controller which generates the Cartesian-space control force FR^m required to track the desired end-effector position trajectory, and an algorithm that maps this control input to a robot joint torque vector TRⁿ. The F → T map is constructed so that the robot redundancy (kinematic and/or actuator) is utilized to improve the performance of the robot. The control scheme does not require knowledge of the complex robot dynamic model or parameter values for the robot or the payload. As a result, the controller is very general and is computationally efficient for on-line implementation. Computer simulation results are given for a kinematically redundant robot, for a robot with actuator redundancy, and for a robot which possesses both kinematic and actuator redundancy. In each case the results demonstrate that accurate end-effector trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed scheme.     

Development of a Sensor Fusion Strategy for Robotic Application Based on Geometric Optimization

Fusion of multi-sensor information is an important technology, which is growing exponentially due to its tremendous application potential in many areas. Effective fusion of data from sensors is very critical in increasing an intelligent system's capability to accomplish complex tasks. Appropriate fusion technologies are needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty has been modeled using classical Lagrangian optimization techniques. However, the uniqueness and effectiveness of the present technique lies on the fact that starting from the optimized value as initial estimate the accuracy of the sensory information has further been improved up to any pre defined bounded range, by developing two architectures – FFA (fission–fusion architecture) and FDD (fusion in differential domain). Sufficient evidences and analyses have been provided in the paper to show its effectiveness in various applications.     

嵌入式存储器的多故障自修复方法

使用冗余行覆盖占故障总数70%的单故障,导致冗余资源的浪费.为提高冗余资源的利用率,提出一种高效的修复方案,即冗余行覆盖多故障,纠错码修复单故障.当采用码率大于1/2的纠错码修复单故障时,校验住的长度小于冗余行的长度,节约了面积开销.通过2~4×8比特静态随机存取存储器(SRAM)的自修复实验,验证了新方案的可行性.实验结果表明,与冗余行结构相比,新的修复方案可以减小面积开销,提高芯片的最大工作频率.     

Calculation of H2+ eigenparameters in arbitrary dimensions

An interdimensional degeneracy linking the orbital angular momentum projection |m| and spatial dimension D yields D-dimensional eigenstates for H₂⁺ by simple correspondence with suitably scaled D = 3 excited states. The wave-equation for fixed nuclei is separable in D-dimensional spheroidal coordinates, giving generalized two-center differential equations with parametric dependence on the internuclear distance R. A computer program is presented which is capable of calculating the two eigenparameters, the energy E_D(R), and the separation constant A_D(R) related to the total orbital angular momentum and the Runge-Lenz vector, to high accuracy over a large range in R for dimensions up to D = 100, or equivalently, for projections up to |m| = 50 for D = 3. The program can be implemented on personal computers to give accuracies up to 16 digits. The execution times required are modest. Normalization constants, wavefunctions and expectation values can be calculated as well, but at a cost of much larger execution times for even moderate accuracy.     

On partitions of functions on finite sets

Let D and R be finite sets with cardinality n and m respectivelyR ^D be the set of all functions from D into R, and G and H be permutation groups acting on D and R respectively. Two functions f and g in R ^D are said to be related if there exists a σ in G and a τ in H with f(σd) = τg(d) for every d in D. Since the relation is an equivalence relation, R ^D is partitioned into disjoint classes. Roughly, by using the cycle indices of G and H, de Bruijn's theorem determines the number of equivalence classes, and Pólya's theorem, with H being the identity group, gives the function counting series, Pólya-de Bruijn's theorem has many applications (for instance, see Pólya and Read [6]). The theorem and its applications, basically, centered around the partitions of functions. Here, we present an algorithm to determine which functions in R ^D belong to the same equivalent class. Our algorithm does not use the cycle indices of G and H (to compute the cycle index of a given group, in general, is difficult), but it uses the generators of G and H, and the m-nary numbers to code the functions in R ^D . Our algorithm also gives the function counting series and the number of equivalence classes. An important application is that for each positive integer n, we use our algorithm and the symmetric group S _n to determine all isomorphic and nonisomorphic graphs and directed graphs with n vertices.     

Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

Nitrogen (N) is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest (RF) regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear (SML) regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both non-linear RF regression (coefficient of determination, R ²?=?0.67; root mean square error of validation (RMSEV)?=?0.15%; 8.44% of the mean) and SML regression models (R ²?=?0.71; RMSEV?=?0.19%; 10.39% of the mean) derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.     

An A Posteriori Error Estimate for the Local Discontinuous Galerkin Method Applied to Linear and Nonlinear Diffusion Problems

In this paper we present a new residual-based reliable a posteriori error estimator for the local discontinuous Galerkin approximations of linear and nonlinear diffusion problems in polygonal regions of R ². Our analysis, which applies to convex and nonconvex domains, is based on Helmholtz decompositions of the error and a suitable auxiliary polynomial function interpolating the Dirichlet datum. Several examples confirming the reliability of the estimator and providing numerical evidences for its efficiency are given. Furthermore, the associated adaptive method, which considers meshes with and without hanging nodes, is shown to be much more efficient than a uniform refinement to compute the discrete solutions. In particular, the experiments illustrate the ability of the adaptive algorithm to localize the singularities of each problem.Mathematics Subject Classifications (1991). 65N30This revised version was published online in July 2005 with corrected volume and issue numbers.     

Cartesian control of redundant robots

This article presents a Cartesian-space position/force controller for redundant robots. The proposed control structure partitions the control problem into a nonredundant position/force trajectory tracking problem and a redundant mapping problem between Cartesian control input F ? R ^m and robot actuator torque T ? R ⁿ(for redundant robots, m < n). The underdetermined nature of the F → T map is exploited so that the robot redundancy is utilized to improve the dynamic response of the robot. This dynamically optimal F → T map is implemented locally (in time) so that it is computationally efficient for on-line control; however, it is shown that the map possesses globally optimal characteristics. Additionally, it is demonstrated that the dynamically optimal F→T map can be modified so that the robot redundancy is used to simultaneously improve the dynamic response and realize any specified kinematic performance objective (e.g., manipulability maximization or obstacle avoidance). Computer simulation results are given for a four degree of freedom planar redundant robot under Cartesian control, and demonstrate that position/force trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed controller.     

基于Define-Use分析的冗余通信消除算法

针对并行代码自动生成过程中产生的大量冗余通信代码,提出基于Define-Use分析的冗余通信消除算法。将中间代码的每一个过程划分为不同的块,同时收集各块中对数组变量的定义和引用信息。以块为节点,按控制流关系构造控制流图。以控制流图为基础,根据块间各数组变量的Define-Use关系,确定需要通信的位置,从而消除冗余通信代码,达到优化通信的目的。测试结果表明,该算法可有效提高并行程序的执行效率。     

Value Numbering

Value numbering is a compiler-based program analysis method that allows redundant computations to be removed. This paper compares hash-based approaches derived from the classic local algorithm¹ with partitioning approaches based on the work of Alpern, Wegman and Zadeck.² Historically, the hash-based algorithm has been applied to single basic blocks or extended basic blocks. We have improved the technique to operate over the routine's dominator tree. The partitioning approach partitions the values in the routine into congruence classes and removes computations when one congruent value dominates another. We have extended this technique to remove computations that define a value in the set of available expressions (AVAIL).³ Also, we are able to apply a version of Morel and Renvoise's partial redundancy elimination⁴ to remove even more redundancies. The paper presents a series of hash-based algorithms and a series of refinements to the partitioning technique. Within each series, it can be proved that each method discovers at least as many redundancies as its predecessors. Unfortunately, no such relationship exists between the hash-based and global techniques. On some programs, the hash-based techniques eliminate more redundancies than the partitioning techniques, while on others, partitioning wins. We experimentally compare the improvements made by these techniques when applied to real programs. These results will be useful for commercial compiler writers who wish to assess the potential impact of each technique before implementation. © 1997 John Wiley & Sons, Ltd.     

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied: linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PL S and PL S/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of 'soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.     

Hierarchical codes: A flexible trade-off for erasure codes in peer-to-peer storage systems

Redundancy is the basic technique to provide reliability in storage systems consisting of multiple components. A redundancy scheme defines how the redundant data are produced and maintained. The simplest redundancy scheme is replication, which however suffers from storage inefficiency. Another approach is erasure coding, which provides the same level of reliability as replication using a significantly smaller amount of storage. When redundant data are lost, they need to be replaced. While replacing replicated data consists in a simple copy, it becomes a complex operation with erasure codes: new data are produced performing a coding over some other available data. The amount of data to be read and coded is d times larger than the amount of data produced, where d, called repair degree, is larger than 1 and depends on the structure of the code. This implies that coding has a larger computational and I/O cost, which, for distributed storage systems, translates into increased network traffic. Participants of Peer-to-Peer systems often have ample storage and CPU power, but their network bandwidth may be limited. For these reasons existing coding techniques are not suitable for P2P storage. This work explores the design space between replication and the existing erasure codes. We propose and evaluate a new class of erasure codes, called Hierarchical Codes, which allows to reduce the network traffic due to maintenance without losing the benefits given by traditional erasure codes.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Web应用中冗余代码检测方法研究

为了提高Web开发效率,开发人员常常复用已有系统框架或成熟项目中现有的代码,但因此也导致了Web应用中总存在大量的冗余代码,冗余代码不仅影响程序的可读性和运行效率同时还会隐藏软件缺陷。通过研究Web应用源代码逻辑和框架的特性,提出了Web应用系统中基于源代码分析的冗余代码检测方法。从应用程序入口开始,根据代码之间的逻辑调用关系构建Web应用调用树,进而得到有效页面集、有效类与方法节点集;然后根据冗余检测算法检测出Web应用系统中冗余页面、冗余处理类与处理方法。为了评估冗余检测方法的有效性,包括漏检率与误检率,对两个JavaWeb应用进行冗余检测并通过人工注入冗余实验验证检测的有效性。实验结果证明,提出的冗余代码检测方法可以达到较高的检测效率。     

Learning spatio-temporal relational structures

We introduce a rule-based approach for learning and recognition of complex actions in terms of spatio-temporal attributes of primitive event sequences. During learning, spatio-temporal decision trees are generated which satisfy relational constraints of the training data. The resulting rules are used to classify new dynamic pattern fragments, and general heuristic rules are used to combine classification evidences of different pattern fragments.     

Redundant disequalities in the Latin Square problem

This paper studies the redundancy properties of the constraints used when formulating the well known Latin Square problem. This problem is often formulated using either (N???1)*N ² binary disequalities or 2*N all_different global constraints. Both formulations contain redundant constraints. A complete classification of all redundant sets of constraints, be they binary or global, is performed for any N.     

Test overlay in an emerging software product line – An industrial case study

ContextIn large software organizations with a product line development approach, system test planning and scope selection is a complex task. Due to repeated testing: across different testing levels, over time (test for regression) as well as of different variants, the risk of redundant testing is large as well as the risk of overlooking important tests, hidden by the huge amount of possible tests.AimsThis study assesses the amount and type of overlaid manual testing across feature, integration and system test in such context, it explores the causes of potential redundancy and elaborates on how to provide decision support in terms of visualization for the purpose of avoiding redundancy.MethodAn in-depth case study was launched including both qualitative and quantitative observations.ResultsA high degree of test overlay is identified originating from distributed test responsibilities, poor documentation and structure of test cases, parallel work and insufficient delta analysis. The amount of test overlay depends on which level of abstraction is studied.ConclusionsAvoiding redundancy requires tool support, e.g. visualization of test design coverage, test execution progress, priorities of coverage items as well as visualized priorities of variants to support test case selection.     

基于纠删编码P2P存储系统的数据维护算法

纠删编码是一种通过产生数据冗余来提高P2P存储系统容错性和可用性的有效方法。对基于纠删编码的非结构化P2P存储系统来说,使用洪泛算法进行数据维护会在网络中产生大量冗余消息,系统效率低。本文提出一种使用二叉树来保存文件块的信息的算法。二叉树结构被建立以后,更新消息在二叉树中的节点间传播,不需要在网络中洪泛。分析表明,相较于洪泛算法,本算法有效减少了冗余的消息数量,提高了数据维护的效率,而付出的是极小的存储代价。     

A systematic literature review on the use of machine learning in code clone research

Context:Research related to code clones includes detection of clones in software systems, analysis, visualization and management of clones. Detection of semantic clones and management of clones have attracted use of machine learning techniques in code clone related research.Objective:The aim of this study is to report the extent of machine learning usage in code clone related research areas.Method:The paper uses a systematic review method to report the use of machine learning in research related to code clones. The study considers a comprehensive set of 57 articles published in leading conferences, workshops and journals.Results:Code clone related research using machine learning techniques is classified into different categories. Machine learning and deep learning algorithms used in the code clone research are reported. The datasets, features used to train machine learning models and metrics used to evaluate machine learning algorithms are reported. The comparative results of various machine learning algorithms presented in primary studies are reported.Conclusion:The research will help to identify the status of using machine learning in different code clone related research areas. We identify the need of more empirical studies to assess the benefits of machine learning in code clone research and give recommendations for future research.     

Requirements simulation for early validation using Behavior Trees and Datalog

ContextThe role of formal specification in requirements validation and analysis is generally considered to be limited because considerable expertise is required in developing and understanding the mathematical proofs. However, formal semantics of a language can provide a basis for step-by-step execution of requirements specification by building an easy to use simulator to assist in requirements elicitation, validation and analysis.ObjectiveThe objective of this paper is to illustrate the usefulness of a simulator that executes requirements and captures system states as rules and facts in a database. The database can then be queried to carry out analysis after all the requirements have been executed a given number of timesMethodBehavior Trees (BTs)¹ are automatically translated into Datalog facts and rules through a simulator called SimTree. The translation process involves model-to-model (M2M) transformation and model-to-text (M2T) transformation which automatically generates the code for a simulator called SimTree. SimTree on execution produces Datalog code. The effectiveness of the simulator is evaluated using the specifications of a published case study – Ambulatory Infusion Pump (AIP)².ResultsThe BT specification of the AIP was transformed into SimTree code for execution. The simulator produced a complete state-space for a predetermined number of runs in the form of Datalog facts and rules, which were then analyzed for various properties of interest like safety and liveness.ConclusionQueries of the resultant Datalog code were found to be helpful in identifying defects in the specification. However, probability values had to be manually assigned to all the events to ensure reachability to all leaf nodes of the tree and timely completion of all the runs. We identify optimization of execution paths to reduce execution time as our future work.