A neural network--based methodology for the recreation of high-speed impacts on metal armours

The prediction of the consequences of a ballistic impact is highly relevant in the advanced material engineering. Traditionally, the solution of this kind of problems was made by means of experimental tests, analytical models or numerical simulations. In this domain, the particularities of the phenomenon at high speed increase the difficulty of the mathematical resolution of the equations associated, and the complexity of the mechanical behaviour of the materials at high strain rates complicates the numerical simulation of the problem. Therefore, this paper describes a neural network--based methodology applied to recreate the ballistic impact phenomenon. The objective of this study is threefold. Firstly, to obtain the most precise prediction possible, minimizing the amount of data used. Secondly, to discover and analyse the influence of each of the variables on the entire neuronal model. Finally, to compare the precision and performance of this methodology with other alternatives of learning machine. The empirical results have shown that the proposed methodology is an interesting approach to reliably solving ballistic impact problems.     

Expert Recommender: Designing for a Network Organization

Recent knowledge management initiatives focus on expertise sharing within formal organizational units and informal communities of practice. Expert recommender systems seem to be a promising tool in support of these initiatives. This paper presents experiences in designing an expert recommender system for a knowledge-intensive organization, namely the National Industry Association (NIA). Field study results provide a set of specific design requirements. Based on these requirements, we have designed an expert recommender system which is integrated into the specific software infrastructure of the organizational setting. The organizational setting is, as we will show, specific for historical, political, and economic reasons. These particularities influence the employees’ organizational and (inter-)personal needs within this setting. The paper connects empirical findings of a long-term case study with design experiences of an expertise recommender system.     

An integrated cross-layer framework of adaptive FEedback REsource allocation and Prediction for OFDMA systems

Orthogonal frequency division multiple access (OFDMA) technology has been adopted by 4th generation (a.k.a. 4G) telecommunication systems to achieve high system spectral efficiency. A crucial research issue is how to design adaptive feedback mechanisms so that the base station can use adaptive modulation and coding (AMC) techniques to adjust its data rate based on the channel condition. This problem is even more challenging in resource-limited and heterogeneous multiuser environments such as Mobile WiMAX and long-term evolution (LTE) networks. In this paper, we develop an integrated cross-layer framework of adaptive FEedback REsource allocation and Prediction (FEREP) for OFDMA systems. The proposed framework, implemented at the base station side, is composed of three modules. The feedback window adaptation (FWA) module dynamically tunes the feedback window size for each user based on the received automatic repeat request (ARQ) messages that reflect the current channel condition. The priority-based feedback scheduling (PBFS) module then performs feedback resource allocation by taking into account the feedback window size, the user profile and the total system feedback budget. To choose adapted modulation and coding schemes (MCS), the channel quality indicator prediction (CQIP) module performs channel prediction by using recursive least square (RLS) algorithm for the users whose channel feedback has not been granted for schedule in current frame. Through extensive simulations, the proposed framework shows significant performance gain especially under stringent feedback budget constraints.     

Juggling with Pattern Matching

In the late 1980s it was shown that juggling patterns can be described by strings of numbers with fascinating combinatorial properties that have since then been studied by many mathematicians and computer scientists. In this paper we propose to study juggling patterns from a pattern matching point of view. Inspired by pattern matching algorithms based on convolution, we propose a fast algorithm for finding transitions between juggling states. Apart from being a fun application of pattern matching theory, it provides a practical tool in the experimental design of (large) juggling patterns. We also provide a compact formula for counting the number of transitions of a given length between two juggling states.     

Performance Evaluation of Data Value Prediction Schemes

Data value prediction has been widely accepted as an effective mechanism to break data hazards for high performance processor design. Several works have reported promising performance potential. However, there is hardly enough information that is presented in a clear way about performance comparison of these prediction mechanisms. This paper investigates the performance impact of four previously proposed value predictors, namely last value predictor, stride value predictor, two-level value predictor and hybrid (stride two-level) predictor. The impact of misprediction penalty, which has been frequently ignored, is discussed in detail. Several other implementation issues, including instruction window size, issue width and branch predictor are also addressed and simulated. Simulation results indicate that data value predictors act differently under different configurations. In some cases, simpler schemes may be more beneficial than complicated ones. In some particular cases, value prediction may have negative impact on performance.     

Setup Scheduling of Manufacturing Systems as the Art of Juggling

The problem of detailed scheduling of complex flexible manufacturing systems is addressed by optimal flow control. A model problem of scheduling parallel machines is considered to obtain necessary setup conditions. Studying the conditions results in a new solution approach that takes advantage of a juggling analogy of the production/setup scheduling. This analogy is used in the paper to direct construction of a solution method. The method searches for a globally optimal schedule by means of both a juggling strategy and a method of global optimization. The results obtained for a model problem are then generalized to systems with complex production and setup operations. Computational examples demonstrate the validity of the approach.     

Let's be a virtual juggler

Juggling, which uses both hands to keep several objects in the air at once, is admired by anyone who sees it. However, skillful real‐world juggling requires long, hard practice. Therefore, we propose an interesting method to enable anyone to juggle skillfully in the virtual world. In the real world, the human motion has to follow the motion of the moving objects; in the virtual world, the objects' motion can be adjusted together with the human motion. By using this freedom, we have generated a juggling avatar that can follow the user's motion. The user simply makes juggling‐like motions in front of a motion sensor. Our system then searches for juggling motions that closely match the user's motions and connects them smoothly. We then generate moving objects that both satisfy the laws of physics and are synchronized with the synthesized motion of the avatar. In this way, we can generate a variety of juggling animations by an avatar in real time. Copyright © 2016 John Wiley & Sons, Ltd.     

时间窗设置对综采工作面矿压预测精度的影响研究

综采工作面矿压预测对保障煤矿安全开采具有重要意义。深度学习领域的长短时记忆网络已被证实可以提高综采工作面矿压预测的精度。然而，时间窗设置（包括历史数据长度和预测数据长度）直接影响长短时记忆网络模型的预测性能。为此研究时间窗设置对矿压预测精度的影响。首先，采用长短时记忆网络的深度学习方法对平煤股份八矿14160综采工作面矿压数据进行训练，建立矿压预测模型。其次，研究用于预测的历史数据长度对模型预测性能的影响，确定最佳历史数据长度。再者，研究在精度允许范围内的最长预测数据长度。最后，采用最佳时间窗设置，对模型的预测精度进行分析。实验结果表明，时间窗设置对预测结果有显著影响，通过优化时间窗设置可提高综采工作面矿压预测的精度。     

The reliability estimation, prediction and measuring of component-based software

Reliability is a key driver of safety-critical systems such as health-care systems and traffic controllers. It is also one of the most important quality attributes of the systems embedded into our surroundings, e.g. sensor networks that produce information for business processes. Therefore, the design decisions that have a great impact on the reliability of a software system, i.e. architecture and components, need to be thoroughly evaluated. This paper addresses software reliability evaluation during the design and implementation phases; it provides a coherent approach by combining both predicted and measured reliability values with heuristic estimates in order to facilitate a smooth reliability evaluation process. The approach contributes by integrating the component-level reliability evaluation activities (i.e. the heuristic reliability estimation, model-based reliability prediction and model-based reliability measuring of components) and the system-level reliability prediction activity to support the incremental and iterative development of reliable component-based software systems. The use of the developed reliability evaluation approach with the supporting tool chain is illustrated by a case study. The paper concludes with a summary of lessons learnt from the case studies.     

Composite adaptive dynamic surface control using online recorded data

This paper presents an online recorded data‐based design of composite adaptive dynamic surface control for a class of uncertain parameter strict‐feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed‐loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time‐interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright © 2016 John Wiley & Sons, Ltd.     

MARSW:一种高效的基于滑动窗口数据流关联规则挖掘方法

数据流中的关联规则在预测和在线分析系统中有重要应用.现有的研究大多集中在事务数据模型上,鲜有对数据项之间的关联规则挖掘.由于数据的实时性特点,用户又往往对新产生的数据所包含的信息更感兴趣.为了实时而准确地挖掘最近一段时间内数据项间的关联规则,提出了MARSW(mining association rules on sliding window)算法,利用滑动窗口模型对数据流进行关联规则挖掘.MARSW算法在给定的误差范围内,能够有效去除历史数据的影响,并以有限的空间代价快速挖掘大量数据间存在的关联规则.大量仿真实验结果表明,MARSW算法具有较高的效率和优良的可扩展性.     

Analogy between Juggling and Hopping: Active Object Manipulation Approach

Dynamic manipulation of an active object is introduced as a general model of hopping and juggling tasks. In this setting, juggling and hopping are two extreme cases of this general model. Behavioral resemblance of these two tasks is afterwards extended to a detailed mathematical analogy between them. Then the analogy is exploited to develop a unified and abstract planning framework for juggling and hopping. To this end, dynamic manipulation of an active object is decomposed into three distinct phases and two transitions: Carry I, Free flight and Carry II phases. These phases are analogous to Lift off, Free flight and Touch down in hopping. In the next step, a mathematical model for each phase is developed. It is shown that dynamic grasp (in Carry phases of juggling) and foot stability (in Support phases of hopping) conditions share similar sets of dynamic equations. Accordingly, Lift off/Release and Touch down/Catch conditions in hopping/juggling are derived. It is shown that analogous strategies can be developed for Lift off and Release. The analogy is held for Touch down and Catch conditions as well. It is discussed that in the planning framework the initial and the goal configurations of the three phases are set in a model-based and forward manner. To do so, Touch down/Landing time, Free flight duration and robot/object maneuvers during Free flight are used as free parameters for planning in order to ensure foot stability in hopping and dynamic grasp in juggling along with other constraints.     

Automated monitoring system for drive motors of gas-compressor units

The present-day techniques for design and development of the embedded systems for monitoring and prediction of a technical condition of MW electrically driven units are examined with a case study at synchronous machines at gas-compressor stations. The statistical analysis of damageability and operational factors of aging of the insulation at stator windings of gas-compressor unit motors is cited. The example of an automated monitoring system for the STD-12 500 synchronous motor, based on innovative electric equipment and an automated process control system (APCS), is shown.     

Sensorless stabilization of bounce juggling

The paper studies the properties of a sinusoidally vibrating wedge billiard as a model for 2-D bounce juggling. It is shown that some periodic orbits that are unstable in the elastic fixed wedge become exponentially stable in the nonelastic vibrating wedge. These orbits are linked with certain classical juggling patterns, providing an interesting benchmark for the study of the frequency-locking properties in human rhythmic tasks. Experimental results on sensorless stabilization of juggling patterns are described.     

Priori information and sliding window based prediction algorithm for energy-efficient storage systems in cloud

One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.     

Floor shape optimization for green building design

Shape is an important consideration in green building design due to its significant impact on energy performance and construction costs. This paper presents a methodology to optimize building shapes in plan using the genetic algorithm. The building footprint is represented by a multi-sided polygon. Different geometrical representations for a polygon are considered and evaluated in terms of their potential problems such as epistasis, which occurs when one gene pair masks or modifies the expression of other gene pairs, and encoding isomorphism, which occurs when chromosomes with different binary strings map to the same solution in the design space. Two alternative representations are compared in terms of their impact on computational effectiveness and efficiency. An optimization model is established considering the shape-related variables and several other envelope-related design variables such as window ratios and overhangs. Life-cycle cost and life-cycle environmental impact are the two objective functions used to evaluate the performance of a green building design. A case study is presented where the shape of a typical floor of an office building defined by a pentagon is optimized with a multi-objective genetic algorithm.     

基于MARTE模型的系统可靠性预测

系统的可靠性是系统的重要非功能属性之一。传统的可靠性分析在系统开发结束后进行,可能会发现由于系统开发早期的架构设计不合理而导致的问题,这时再修改系统架构并重做后继开发步骤,将会浪费大量人力和物力。如果能在开发的早期阶段,在系统模型层面进行分析并预测,则可以尽早地发现系统可靠性方面的问题并将其修复。UML是一种通用的、标准化的建模语言,MARTE是UML在嵌入式实时系统领域的扩展。提出了基于MARTE模型的系统可靠性预测方法,该方法考虑的MARTE模型包括用例图、活动图、部署图。先将MARTE模型转换为马尔可夫决策过程网络模型,再利用概率模型检测工具PRISM进行分析,得到系统可靠性的预测结果。实例研究表明,所提方法不仅能够预测系统可靠性的最大值和最小值,还能通过调整各个资源的可靠性值,考察其对系统可靠性的影响,为设计人员的进一步工作提供参考。     

A Framework for Automatic Generation of a Contact State Graph for Robotic Assembly

Dynamic manipulation of an active object is introduced as a general model of hopping and juggling tasks. In this setting, juggling and hopping are two extreme cases of this general model. Behavioral resemblance of these two tasks is afterwards extended to a detailed mathematical analogy between them. Then the analogy is exploited to develop a unified and abstract planning framework for juggling and hopping. To this end, dynamic manipulation of an active object is decomposed into three distinct phases and two transitions: Carry I, Free flight and Carry II phases. These phases are analogous to Lift off, Free flight and Touch down in hopping. In the next step, a mathematical model for each phase is developed. It is shown that dynamic grasp (in Carry phases of juggling) and foot stability (in Support phases of hopping) conditions share similar sets of dynamic equations. Accordingly, Lift off/Release and Touch down/Catch conditions in hopping/juggling are derived. It is shown that analogous strategies can be developed for Lift off and Release. The analogy is held for Touch down and Catch conditions as well. It is discussed that in the planning framework the initial and the goal configurations of the three phases are set in a model-based and forward manner. To do so, Touch down/Landing time, Free flight duration and robot/object maneuvers during Free flight are used as free parameters for planning in order to ensure foot stability in hopping and dynamic grasp in juggling along with other constraints.     

A Controlled Expeniment on the Impact of Software Structure on Maintainability

This paper describes a study on the impact of software structure on maintainability aspects such as comprehensibility, locality, modifiability, and reusability in a distributed system environment. The study was part of a project at the University of Kaiserslautern, West Germany, to design and implement LADY, a LAnguage for Distributed systems. The study addressed the impact of software structure from two perspectives. The language designer's perspective was to evaluate the general impact of the set of structural concepts chosen for LADY on the maintainability of software systems implemented in LADY. The language user's perspective was to derive structural criteria (metrics), measurable from LADY systems, that allow the explanation or prediction of the software maintenance behavior. A controlled maintenance experiment was conducted involving twelve medium-size distributed software systems; six of these systems were implemented in LADY, the other six systems in an extended version of sequential Pascal. The benefits of the structural LADY concepts were judged based on a comparison of the average maintenance behavior of the LADY systems and the Pascal systems; the maintenance metrics were derived by analyzing the interdependence between structure and maintenance behavior of each individual LADY system.