A Simple Robotic System for Neurorehabilitation

In the recent past, several researchers have shown that important variables in relearning motor skills and in changing the underlying neural architecture after stroke are the quantity, duration, content, and intensity of training sessions. Unfortunately, when traditional therapy is provided in a hospital or rehabilitation center, the patient is usually seen for few hours a week. Robot-mediated therapies could improve this situation but even if interesting results have been achieved by several groups, the use of robot-mediated therapy has not become very common in clinical practice. This is due to many different reasons (e.g., the “technophobia” of some clinicians, the need for more extensive clinical trials) but one of the more important is the cost and the complexity of these devices which make them difficult to be purchased and used in all the clinical centers. The aim of this work was to verify the possibility of improving motor recovery of hemiparetic subjects by using a simple mechatronic system. To achieve this goal, our system (named “MEchatronic system for MOtor recovery after Stroke” (MEMOS)) has been designed with the aim of using mainly “off-the-shelf products” with only few parts simply manufactured with standard technology, when commercial parts were not available. Moreover, the prototype has been developed taking into account the requirements related to the clinical applicability such as robustness and safety. The MEMOSsystem has been used during clinical trials with subjects affected by chronic hemiparesis (<6 months from the cerebrovascular accident). The results obtained during these experiments seem to showthat notwithstanding the simple mechatronic structure characterizing theMEMOSsystem, it is able to help chronic hemiparetics to reduce their level of impairment. Further clinical experiments with acute and chronic subjects will be carried out in order to confirm these preliminary findings. Moreover, experiments for tele-rehabilitation of patients will be also carried out. Silvestro Micera was born in Taranto, Italy, on August 31, 1972. He received the University degree (Laurea) in electrical engineering from the University of Pisa, Pisa, Italy, in 1996, and the Ph.D. degree in biomedical engineering from the Scuola Superiore Sant'Anna, Pisa, Italy, in 2000. From 1998 to 2001, he was the Project Manager of the EU GRIP Project (ESPRIT LTR Project 26322, “An integrated system for the neuroelectrIic control of grasp in disabled persons”). During 1999, he was a Visiting Researcher at the Center for Sensory-Motor Interaction, Aalborg University. Since May 2000, he has been an Assistant Professor of Biomechanical Engineering at the Scuola Superiore Sant'Anna. He is currently involved in several projects on neuro-robotics and rehabilitation engineering. His research interests include the development of neuro-robotic systems (interfacing the central and peripheral nervous system with robotic artefacts) and the development of mechatronic and robotic systems for function restoration in disabled persons. Dr. Micera is an Associate Editor of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING and member of the IEEE Engineering in Medicine and Biology and Robotics and Automation Societies. M. Chiara Carrozza received the Laurea degree in physics from the University of Pisa, Pisa, Italy, in 1990. Since 2001, she has been an Associate Professor of biomedical robotics at the Scuola Superiore Sant'Anna, Pisa, Italy. She is the co-cordinator of the Advanced Robotics Technology and Systems Laboratory where she is responsible for some national and international projects in the fields of biorobotics. Her research interests are in the fields of biorobotics (artificial hands, upper limb exoskeletons), rehabilitation engineering (neurorehabilitation, domotic, and robotic aids), and biomedical microengineering (microsensors, tactile sensors). She is an author of several scientific papers and international patents. Eugenio Guglielmelli received the Laurea degree and the PhD in electronics engineering from the University of Pisa, Italy, in 1991 and in 1995, respectively. He is currently Associate Professor of Bioengineering at Campus Bio-Medico University in Rome, Italy, where he teaches the courses of Bio-Mechatronics and of Rehabilitation Bioengineering, and where he also recently co-founded the new Research Laboratory of Biomedical Robotics & Electro-Magnetic Compatibility. He has been working in the field of biomedical robotics over the last fifteen years at Scuola Superiore Sant'Anna where he also served from 2002 to 2004 as the Head of the Advanced Robotics Technology & Systems Laboratory (ARTS Lab), founded by prof. Paolo Dario in 1991. His main current research interests are in the fields of novel theoretical and experimental approaches to human-centered robotics and to biomporphic control of mechatronic systems, and in their application to robot-mediated motor therapy, assistive robotics, neuro-robotics and neuro-developmental engineering. He serves in the Editorial Board of the International Journal on Applied Bionics and Biomechanics. He has been Guest Co-Editor of the Special Issue on Rehabilitation Robotics of the International Journal ‘Autonomous Robots’. He is member of the IEEE Robotics & Automation Society, of the IEEE Engineering in Medicine & Biology Society, of the Society for Neuroscience, and of the Association for the Advancement of Assistive Technology in Europe (AAATE). He served (2002–03) as the Secretary of the IEEE Robotics & Automation Society (RAS) and he is currently Co-chair of the RAS Technical Committee on Rehabilitation Robotics. He serves in the Programme Committees of several International Conferences, such as ICRA, IROS, ICAR, AIM, BIOROB and others. He was/is a member of the Organizing Committees of ICAR2003, IROS2004, IFAC/SYROCO2006 and ICRA2007. Giovanni Cappiello received the M.E. degree from the University of Pisa, Pisa, Italy. He is currently working towards the Ph.D. degree in robotics at the ARTS Lab of the Scuola Superiore Sant'Anna Pisa. He worked on the RTR IV Prosthetic Hand Project. Among his research interests are rehabilitation technologies, biomedical and surgical devices, osseointegration, and biomimetic artificial sensors. He is involved in the design of antropomorphic hands and arm and in the exploitation of compliant joints. Franco Zaccone was born in Policoro, Italy. He received the University degree (Laurea) in electrical engineering from the University of Pisa, Pisa, Italy, in 2000. Since June 2000, he has been a Research Assistant at the Advanced Robotics Technologies and Systems Laboratory, Scuola Superiore Sant'Anna, Pisa. His research interests include the design of hardware systems for rehabilitation engineering and motion analysis. Cinzia Freschi was born in Caserta, Italy, on December 25, 1969. She received the University degree (Laurea) in computer engineering from the University of Pisa, Pisa, Italy, in 1998. Since 1998, she has been research assistant at the Advanced Robotics Technology and Systems Laboratory (ARTSLAB), Scuola Superiore Sant'Anna. Her research interests are in the filed of rehabilitation engineering and neuro-robotics. Roberto Colombo received the Dr. Eng. degree in electrical engineering from the Politecnico of Milano, Milan, Italy, in 1980. Since 1981, he has been a Research Engineer in the Bioengineering Department of the “Salvatore Maugeri” Foundation, IRCCS, Rehabilitation Institute, Veruno, Italy. From 1998 to 2001, he was a Partner of the European Community project “Prevention of muscular disorders in operation of computer input devices (PROCID).” From 2001 to 2004, he was the Coordinator of the project “Tecniche robotizzate per la valutazione ed il trattamento riabilitativo delle disabilitá motorie dell'arto superiore,” 2001-175, funded by the Italian Ministry of Health. His research interests include robot-aided neurorehabilitation, muscle tone and spasticity evaluation, muscle force and fatigue assessment, speech production mechanisms study, cardiovascular control assessment by spectral analysis of heart rate variability signals, and respiratory mechanics assessment. He has taught several national courses in the field of neurorehabilitation. He is the author of over 20 papers and the co-editor of one book on the subject of speech production mechanisms. Alessandra Mazzone received the degree (Diploma) in computer science, from the ITIS “Leonardo da Vinci,” Borgomanero, Italy, in 1988. Since 1989, she has been a Programmer at the Bioengineering Department, the Fondazione Salvatore Maugeri, Rehabilitation Institute of Veruno (NO), Italy. Her research interests include robot-aided neurorehabilitation, cardiovascular control assessment by spectral analysis of heart rate variability signals, and respiratory mechanics assessment. Carmen Delconte received the Diploma in neurophysiology techniques from the University of Pavia, Pavia, Italy, in 1989. She is currently with the Clinical Neurophysiology Unit, Scientific Institute of Veruno “Salvatore Maugeri” Foundation, Rehabilitation Institute, Veruno, Italy. Her research concerns the quantification of muscle tone, emg-biomechanical studies, and the robotic rehabilitation of upper limb in cerebrovascular diseases. She has been published in the clinical and electrophisiological field of neuromuscular diseases and on the topic of stroke patients rehabilitation. Her current research is focused on the evaluation and treatment of upper limbs disorders like spasticity and paresis. Dr. Delconte is a member of the Italian Neurophysiology Technician Society. Fabrizio Pisano received the M.D. degree from the University of Milan, Milan, Italy, in 1981. In 1986, he completed his training as resident in neurology and became Neurologist at the same University He was a teacher in “Electromyography” from 1991 to 1997 at the School of Physical Medicine and Rehabilitation, the University of Turin, Torino, Italy. He has taught several national and international electromyographic courses on hand neuromotor rehabilitation, occupational pathology, rehabilitation therapy, muscle fatigue, posture and movement, clinical neurophysiology, and EMG Culture. He was a Scientific Project co-leader of a telethon program (1994–1996); speech motor control in ALS; a search for an early marker of disease. He was the Project Leader of “Quantitative Analysis of Spastic Hypertonia” by the Istituto Superiore della Sanitá during 1998–1999. He was the Clinical Scientific Leader of the INAIL project “International clinical survey over functional electrical stimulation.” He was the Scientific Project Leader of the Clinical Neurophysiology Unit of the project “Tecniche robotizzate per la valutazione ed il trattamento riabilitativo delle disabilitá motorie dell'arto superiore,” 2001-175, funded by the Italian Ministry of Health. He is currently a Neurologist and the Head of the Clinical Neurophysiology Unit, ”Salvatore Maugeri” Foundation, IRCCS, Rehabilitation Institute, Veruno, Italy. He has been published in the clinical and electrophysiological field of neuromuscular diseases and on the topic of stroke patients rehabilitation. His current research interests are in evaluation and treatment of upper limb disorders like spasticity and paresis. Dr. Pisano is a Member of the Italian Neurological Society and the Italian Clinical Neurophysiology Society. Giuseppe Minuco received the Dr. Eng. degree in mechanical engineering from the Politecnico Milano, Milan, Italy, in 1972, and a postgraduate degree in biomedical engineering from the Faculty of Medicine, Bologna, Italy, in 1975. He is currently Head of the Bioengineering Department, “Salvatore Maugeri” Foundation, IRCCS, Pavia, Italy. He is Chair of the Technical Scientific Committee of “CBIM” (Medical Informatics and Bioengineering Consortium) Pavia, Italy. He is Member of the Editorial Board of The Monaldi Archives for Chest Disease and of Giornale Italiano di Medicina del Lavoro ed Ergonomia. Has taught several courses in healthcare management. His main interests are in the fields of rehabilitation engineering, clinical engineering, medical informatics, and telemedicine. Paolo Dario received the Dr. Eng. degree in mechanical engineering from the University of Pisa, Pisa, Italy, in 1977. He is currently a Professor of Biomedical Robotics at the Scuola Superiore Sant'Anna, Pisa, Italy. He also teaches courses at the School of Engineering of the University of Pisa, and at the Campus Biomedico University, Rome, Italy. He has been a Visiting Professor at Brown University, Providence, RI, at the Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland, and at Waseda University, Tokyo, Japan. He was the founder of the Advanced Robotics Technologies and Systems (ARTS) Laboratory and is currently the co-cordinator of the Center for Research in Microengineering (CRIM) Laboratory of the Scuola Superiore Sant'Anna, where he supervises a team of about 70 researchers and Ph.D. students. He is also the Director of the Polo Sant'Anna Valdera and a Vice-Director of the Scuola Superiore Sant'Anna. His main research interests are in the fields of medical robotics, mechatronics, and micro/nanoengineering, and specifically in sensors and actuators for the above applications. He is the coordinator of many national and European projects, the editor of two books on the subject of robotics, and the author of more than 200 scientific papers (75 in ISI journals). He is Editor-in-Chief, Associate Editor, and Member of the Editorial Board of many international journals. Prof. Dario served as President of the IEEE Robotics and Automation Society during 2002–2003, and he is currently Co-Chair of the Technical Committees on Bio-robotics and of Robo-ethics of the same society. He is a Fellow of the European Society on Medical and Biological Engineering, and a recipient of many honors and awards, such as the Joseph Engelberger Award. He is also a Member of the Board of the International Foundation of Robotics Research (IFRR).     

Cooperative Mobile Robotics: Antecedents and Directions

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.     

Real-World Robotics: Learning to Plan for Robust Execution

In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.     

The Robotic Autonomy Mobile Robotics Course: Robot Design, Curriculum Design and Educational Assessment

Robotic Autonomy is a seven-week, hands-on introduction to robotics designed for high school students. The course presents a broad survey of robotics, beginning with mechanism and electronics and ending with robot behavior, navigation and remote teleoperation. During the summer of 2002, Robotic Autonomy was taught to twenty eight students at Carnegie Mellon West in cooperation with NASA/Ames (Moffett Field, CA). The educational robot and course curriculum were the result of a ground-up design effort chartered to develop an effective and low-cost robot for secondary level education and home use. Cooperation between Carnegie Mellon's Robotics Institute, Gogoco, LLC. and Acroname Inc. yielded notable innovations including a fast-build robot construction kit, indoor/outdoor terrainability, CMOS vision-centered sensing, back-EMF motor speed control and a Java-based robot programming interface. In conjunction with robot and curriculum design, the authors at the Robotics Institute and the University of Pittsburgh's Learning Research and Development Center planned a methodology for evaluating the educational efficacy of Robotic Autonomy, implementing both formative and summative evaluations of progress as well as an in-depth, one week ethnography to identify micro-genetic mechanisms of learning that would inform the broader evaluation. This article describes the robot and curriculum design processes and then the educational analysis methodology and statistically significant results, demonstrating the positive impact of Robotic Autonomy on student learning well beyond the boundaries of specific technical concepts in robotics.     

Collaboration Through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment

This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground—an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical setup composed of groups of 2 to 6 Khepera robots, and in Webots, a 3D simulator of Khepera robots.The results using these two implementations are compared with the predictions of a probabilistic modeling methodology (A. Martinoli, A. Ijspeert, and F. Mondada, 1999, Robotics and Autonomous Systems, 29:51–63, 1999; A. Martinoli, A. Ijspeert, and L. Gambardella, 1999, in Proceedings of Fifth European Conference on Artificial Life, ECAL99, Lecture Notes in Computer Science, Springer Verlag: Berlin, pp. 575–584) which is here extended for the characterization and the prediction of a collaborative manipulation experiment. Instead of computing trajectories and sensory information, the probabilistic model represents the collaboration dynamics as a set of stochastic events based on simple geometrical considerations. It is shown that the probabilistic model qualitatively and quantitatively predicts the collaboration dynamics. It is significantly faster than a traditional sensor-based simulator such as Webots, and its minimal set of parameters allows the experimenter to better identify the effect of characteristics of individual robots on the team performance.Using these three implementations (the real robots, Webots and the probabilistic model), we make a quantitative investigation of the influence of the number of workers (i.e., robots) and of the primary parameter of the robots' controller—the gripping time parameter—on the collaboration rate, i.e., the number of sticks successfully taken out of the ground over time. It is found that the experiment presents two significantly different dynamics depending on the ratio between the amount of work (the number of sticks) and the number of robots, and that there is a super-linear increase of the collaboration rate with the number of robots. Furthermore, we investigate the usefulness of heterogeneity in the controllers' parameters and of a simple signalling scheme among the robots. Results show that, compared to homogeneous groups of robots without communication, heterogeneity and signalling can significantly increase the collaboration rate when there are fewer robots than sticks, while presenting a less noticeable or even negative effect otherwise.     

Error Detection and Prediction Algorithms: Application in Robotics

This article presents research on error detection and prediction algorithms in robotics. Errors, defined as either agent error or Co-net error, are analyzed and compared. Three new error detection and prediction algorithms (EDPAs) are then developed, and validated by detecting and predicting errors in typical pick and place motions of an Adept Cobra 800 robot. A laser Doppler displacement meter (LDDM™) MCV-500 is used to measure the position of robot gripper in 105 experiment runs. Results show that combined EDPAs are preferred to detect and predict displacement errors in sequential robot motions.     

Pulse-signaling algorithm: a non-device-based robotics control and communication

A pulse-signaling algorithm (PSA) is proposed for robotics control and communication. Using the PSA, an operator can send control signals directly to control a robot or a group of robots. No extra equipment is needed in PSA because the pulse signal can be generated easily from the operator. Three aspects of the algorithm are considered in this paper: communication protocol, hardware prototype and software implementation. The PSA protocol is composed of three parts, i.e. command code, operand code and address code, in order to control the desired robot to do the desired work. A PSA prototype circuit is designed and developed, and PSA software is programmed on the designed prototype circuit to realize the algorithm. Some experiments are performed to test and evaluate the proposed algorithm.     

文本挖掘在主题发现和相关性评估中的应用——以人工智能和机器人领域的专利为例

人工智能和机器人是当前技术发展的重要领域,专利反映了基础研究和技术创新的进展。将两者结合起来进行学科发现与关联性评价以及演化趋势分析,有利于对知识的挖掘,对于理解科学技术的互动与渗透、识别技术机会、发现潜在商业机会具有重要意义。在LDA算法的基础上,通过对专利主题强度和主题内容演变的分析,探索并构建了能够全面揭示专利主题关系的相关进化图。人工智能和机器人专利领域的实证研究表明,该方法能够充分展示领域主题随时间的变化趋势,揭示专利主题之间的相互继承关系。     

Human-Machine Collaboration in Robotics: Integrating Virtual Tools with a Collision Avoidance Concept using Conglomerates of Spheres

This paper describes how virtual tools that represent real robot end-effectors are used in conjunction with a generalized conglomerate-of-spheres approach to collision avoidance in such a way that telerobotic trajectory planning can be accomplished using simple gesture phrases such as put that there while avoiding that. In this concept, an operator (or set of collaborators) need not train for cumbersome telemanipulation on several multiple-link robots, nor do robots need a priori knowledge of operator intent and exhaustive algorithms for evaluating every aspect of a detailed environment model. The human does what humans do best during task specification, while the robot does what machines do best during trajectory planning and execution.Four telerobotic stages were implemented to demonstrate this strategic supervision concept that will facilitate collaborative control between humans and machines. In the first stage, virtual reality tools are selected from a toolbox by the operator(s) and then these virtual tools are computationally interwoven into the live video scene with depth correlation. Each virtual tool is a graphic representation of a robot end-effector (gripper, cutter, or other robot tool) that carries tool-use attributes on how to perform a task. An operator uses an instrumented glove to virtually retrieve the disembodied tool, in the shared scene, and place it near objects and obstacles while giving key-point gesture directives, such as cut there while avoiding that. Collaborators on a network may alter the plan by changing tools or tool positioning to achieve preferred results from their own perspectives. When parties agree, from wherever they reside geographically, the robot(s) create and execute appropriate trajectories suitable to their own particular links and joints. Stage two generates standard joint-interpolated trajectories, and later creates potential field trajectories if necessary. Stage three tests for collisions with obstacles identified by the operator and modeled as conglomerates of spheres. Stage four involves automatic grasping (or cutting etc.) once the robot camera acquires a close-up view of the object during approach. In this paper particular emphasis is placed on the conglomerate-of-spheres approach to collision detection as integrated with the virtual tools concept for a Puma 560 robot by the Virtual Tools and Robotics Group in the Computer Integrated Manufacturing Laboratory at The Pennsylvania State University (Penn State).     

一种基于非线性振荡器的步态轨迹自适应算法

步态训练轨迹是影响康复训练效果的一项重要因素,而自适应性对于下肢康复机器人的临床应用具有重要的意义.振荡器可通过在线调节参数而输出不同波形的周期信号,常用于康复机器人步态轨迹的生成.本文在高斯核函数非线性振荡器的基础上提出了一种下肢康复机器人步态轨迹自适应算法.该算法通过轨迹偏差实现对参考轨迹波形的调节,并且用相位偏差曲线面积实现参考轨迹周期的自适应.本文首先介绍了用于生成步态参考轨迹的非线性振荡器的数学模型;其次,详细描述了基于该模型的参考轨迹波形和周期自适应算法;最后,以悬挂减重式下肢康复机器人为研究对象,建立机器人与人体下肢仿真模型,对所提出的步态参考轨迹自适应算法进行仿真实验,并验证了该算法的可行性.     

Progressive equilibration algorithms: The case of linear transaction costs

In this paper we consider the solution of large-scale market equilibrium problems with linear transaction costs which can be formulated as strictly convex quadratic programming problems, subject to supply and demand constraints. In particular, we introduce two new classes of progressive equilibration algorithms, which retain the simplicity of the original cyclic ones in that at each step either the supply or demand market equilibrium subproblem can be solved explicitly in closed form. However, rather than equilibrating the markets in cyclic manner, the next market to be equilibrated is selected in a more strategic fashion.We then provide qualitative results for the entire family of progressive equilibration algorithms, i.e., the rate of convergence and computational complexity. We discuss implementation issues and give computational results for large-scale examples in order to illustrate and give insights into the theoretical analysis. Furthermore, we show that one of the new classes of algorithms, the good-enough one, is computationally the most efficient. Theoretical results are important in that the relative efficiency of different algorithms need no longer be language, machine, or programmer dependent. Instead, the theory can guide both practitioners and researchers in ensuring that their implementation of these algorithms is, indeed, good.Since an equivalent quadratic programming problem arises in a certain class of constrained matrix problems, our results can be applied there, as well. Finally, since more general asymmetric multicommodity market equilibrium problems can be solved as series of the type of problems considered here, the result$ are also applicable to such equilibrium problems.     

Design Issues for Therapeutic Robot Systems: Results from a Survey of Physiotherapists

Over the years, research into rehabilitation robots has increased considerably. Using robots for rehabilitation can improve persons with physical disabilities to perform the basic activities of daily living. However, rehabilitation robots are not welcome yet in clinical environments. While surveys concerning how patients respond to robots used for rehabilitation have been conducted, no survey exists in the literature concerning how the therapists themselves think of these robotic devices, and what functionality they should possess in order to be effective. This paper presents a survey of physiotherapists concerning their thoughts, experience, and what functionality should be included in robots used for rehabilitation. In particular, the therapists were asked about the development of an intelligent robotic device capable of performing repetitive tasks for patients who suffer from reduced upper and lower limb mobility. In general, the results from this survey suggest that therapists respond positively to the idea of robotic devices in a clinical setting. Furthermore, the majority of respondents are interested in rehabilitation robotics. The results of the survey will be very helpful in the design of robotic systems for use during physiotherapy.     

数字图像配准快速算法：阈值序列的自适应生成研究

本文基于配准点的误差特点，指出初始阈值序列的合理形式是直线；基于后验信息（匹配过程），提出了两种自适应阈值序列算法，理论分析与实验结果均表明所提出的算法是有效的。     

LCFAA:一个低代价的完全自适应路由算法

大规模并行处理机系统（ＭＰＰ）中路由算法对互联网络通信性能和系统性能起着重要作用。自适应路由算法具有灵活性好、网络的通道利用率高和网络容错能力强等优点,但其实现难度较大,因而目前仅在少数ＭＰＰ系统中得以实现。文中在ｍｅｓｈ结构上提出了一个低代价无死锁的安全自适应最短虫孔路由算法ＬＣＦＡＡ,该算法所需虚通道数少,具有代价低、自适应性强的特点。文中证明了算法的无死锁、无活锁性和完全自适应性,并模拟验证     

覆盖表生成的粒子群算法:参数优化和自适应算法

组合测试是一种能有效检测由参数间相互作用所引发错误的软件测试方法,覆盖表的生成是该研究领域的一个重要问题.目前,很多方法已被应用于覆盖表生成,基于演化搜索的粒子群算法尽管能得到较优的解,但其性能容易受到配置参数的影响.本文首先使用试验设计的方法,对不同覆盖表生成的算法参数进行优化,系统分析了参数对算法性能的影响.同时,考虑到对不同的覆盖表,最优的算法参数往往不同,因此进一步提出了一种适用于覆盖表生成的自适应粒子群算法.实验结果表明,在一定的参数取值范围内粒子群算法都能获得较好的结果,且不存在一组对任意覆盖表都能有最优性能的算法参数.通过参数调优,能使粒子群算法获得比已有结果规模更小的覆盖表,同时,与经过参数调优后的算法相比,自适应粒子群算法在大部分情况下有更好的性能.     

Online set-point optimisation cooperating with predictive control of a yeast fermentation process: A neural network approach

Online set-point optimisation which cooperates with model predictive control (MPC) and its application to a yeast fermentation process are described. A computationally efficient multilayer control system structure with adaptive steady-state target optimisation (ASSTO) and a suboptimal MPC algorithm are presented in which two neural models of the process are used. For set-point optimisation, a steady-state neural model is linearised online and the set-point is calculated from a linear programming problem. For MPC, a dynamic neural model is linearised online and the control policy is calculated from a quadratic programming problem. In consequence of linearisation of neural models, the necessity of online nonlinear optimisation is eliminated. Results obtained in the proposed structure are comparable with those achieved in a computationally demanding structure with nonlinear optimisation used for set-point optimisation and MPC.