Admission Control Methods in IMS Networks
Directory of Open Access Journals (Sweden)
Michal Cuba
2016-01-01
Full Text Available The article deals with solving the problem of ensuring Quality of Service (QoS in IP Multimedia Subsystem (IMS networks. Admission Control methods (AC are used to prevent network congestion and the decrease of QoS. The main function of AC is to maximize utilization of network resources and to ensure the level of QoS. Four methods were chosen for comparison. These methods are described in the main part of the article. The last part deals with simulations of these methods in the software MATLAB.
Admission Control Methods in IMS Networks
Directory of Open Access Journals (Sweden)
Erik Chromy
2016-09-01
Full Text Available In this paper we present admission control methods for IMS network. The task of RACS block is to accept or reject new connection into the network. The main goal of the admission control method is to ensure the Quality of Service not only for new connection but also for already accepted connections. We discuss and compare three admission control algorithms in the paper from the qualitative parameters point of view.
Control and estimation methods over communication networks
Mahmoud, Magdi S
2014-01-01
This book provides a rigorous framework in which to study problems in the analysis, stability and design of networked control systems. Four dominant sources of difficulty are considered: packet dropouts, communication bandwidth constraints, parametric uncertainty, and time delays. Past methods and results are reviewed from a contemporary perspective, present trends are examined, and future possibilities proposed. Emphasis is placed on robust and reliable design methods. New control strategies for improving the efficiency of sensor data processing and reducing associated time delay are presented. The coverage provided features: · an overall assessment of recent and current fault-tolerant control algorithms; · treatment of several issues arising at the junction of control and communications; · key concepts followed by their proofs and efficient computational methods for their implementation; and · simulation examples (including TrueTime simulations) to...
Control of beam halo-chaos using neural network self-adaptation method
International Nuclear Information System (INIS)
Fang Jinqing; Huang Guoxian; Luo Xiaoshu
2004-11-01
Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)
Novel methods of utilizing Jitter for Network Congestion Control
Directory of Open Access Journals (Sweden)
Ivan
2013-12-01
Full Text Available This paper proposes a novel paradigm for network congestion control. Instead of perpetual conflict as in TCP, a proof-of-concept first-ever protocol enabling inter-flow communication without infrastructure support thru a side channel constructed on generic FIFO queue behaviour is presented. This enables independent flows passing thru the same bottleneck queue to communicate and achieve fair capacity sharing and a stable equilibrium state in a rapid fashion.
Kim, Nakwan
Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.
Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.
Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia
2016-01-01
The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.
Directory of Open Access Journals (Sweden)
Ling Yongfa
2016-01-01
Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.
Method for neural network control of motion using real-time environmental feedback
Buckley, Theresa M. (Inventor)
1997-01-01
A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.
On control of singleton attractors in multiple Boolean networks: integer programming-based method.
Qiu, Yushan; Tamura, Takeyuki; Ching, Wai-Ki; Akutsu, Tatsuya
2014-01-01
Boolean network (BN) is a mathematical model for genetic network and control of genetic networks has become an important issue owing to their potential application in the field of drug discovery and treatment of intractable diseases. Early researches have focused primarily on the analysis of attractor control for a randomly generated BN. However, one may also consider how anti-cancer drugs act in both normal and cancer cells. Thus, the development of controls for multiple BNs is an important and interesting challenge. In this article, we formulate three novel problems about attractor control for two BNs (i.e., normal cell and cancer cell). The first is about finding a control that can significantly damage cancer cells but has a limited damage to normal cells. The second is about finding a control for normal cells with a guaranteed damaging effect on cancer cells. Finally, we formulate a definition for finding a control for cancer cells with limited damaging effect on normal cells. We propose integer programming-based methods for solving these problems in a unified manner, and we conduct computational experiments to illustrate the efficiency and the effectiveness of our method for our multiple-BN control problems. We present three novel control problems for multiple BNs that are realistic control models for gene regulation networks and adopt an integer programming approach to address these problems. Experimental results indicate that our proposed method is useful and effective for moderate size BNs.
Networked and Distributed Control Method with Optimal Power Dispatch for Islanded Microgrids
DEFF Research Database (Denmark)
Li, Qiang; Peng, Congbo; Chen, Minyou
2017-01-01
In this paper, a two-layer network and distributed control method is proposed, where there is a top layer communication network over a bottom layer microgrid. The communication network consists of two subgraphs, in which the first is composed of all agents, while the second is only composed...... of controllable agents. The distributed control laws derived from the first subgraph guarantee the supply-demand balance, while further control laws from the second subgraph reassign the outputs of controllable distributed generators, which ensure active and reactive power are dispatched optimally. However......, for reducing the number of edges in the second subgraph, generally a simpler graph instead of a fully connected graph is adopted. In this case, a near-optimal dispatch of active and reactive power can be obtained gradually, only if controllable agents on the second subgraph calculate set points iteratively...
Traffic Management by Using Admission Control Methods in Multiple Node IMS Network
Directory of Open Access Journals (Sweden)
Filip Chamraz
2016-01-01
Full Text Available The paper deals with Admission Control methods (AC as a possible solution for traffic management in IMS networks (IP Multimedia Subsystem - from the point of view of an efficient redistribution of the available network resources and keeping the parameters of Quality of Service (QoS. The paper specifically aims at the selection of the most appropriate method for the specific type of traffic and traffic management concept using AC methods on multiple nodes. The potential benefit and disadvantage of the used solution is evaluated.
A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network
Directory of Open Access Journals (Sweden)
Linhui Li
2015-01-01
Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.
A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network
Directory of Open Access Journals (Sweden)
Chao Tan
2015-01-01
Full Text Available In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.
A new method for the control of discrete nonlinear dynamic systems using neural networks.
Adetona, O; Garcia, E; Keel, L H
2000-01-01
A new controller design method for nonaffine nonlinear dynamic systems is presented in this paper. An identified neural network model of the nonlinear plant is used in the proposed method. The method is based on a new control law that is developed for any discrete deterministic time-invariant nonlinear dynamic system in a subregion Phi(x) of an asymptotically stable equilibrium point of the plant. The performance of the control law is not necessarily dependent on the distance between the current state of the plant and the equilibrium state if the nonlinear dynamic system satisfies some mild requirements in Phi(x). The control law is simple to implement and is based on a novel linearization of the input-output model of the plant at each instant in time. It can be used to control both minimum phase and nonminimum phase nonaffine nonlinear plants. Extensive empirical studies have confirmed that the control law can be used to control a relatively general class of highly nonlinear multiinput-multioutput (MIMO) plants.
Advances in complexity of beam halo-chaos and its control methods for beam transport networks
International Nuclear Information System (INIS)
Fang Jinqing
2004-11-01
The complexity theory of beam halo-chaos in beam transport networks and its control methods for a new subject of high-tech field is discussed. It is pointed that in recent years, there has been growing interest in proton beams of high power linear accelerator due to its attractive features in possible breakthrough applications in national defense and industry. In particular, high-current accelerator driven clean activity nuclear power systems for various applications as energy resources has been one of the most focusing issues in the current research, because it provides a safer, cleaner and cheaper nuclear energy resource. However, halo-chaos in high-current beam transport networks become a key concerned issue because it can generate excessive radioactivity therefore significantly limits its applications. It is very important to study the complexity properties of beam halo-chaos and to understand the basic physical mechanisms for halo chaos formation as well as to develop effective control methods for its suppression. These are very challenging subjects for the current research. The main research advances in the subjects, including experimental investigation and the oretical research, especially some very efficient control methods developed through many years of efforts of authors are reviewed and summarized. Finally, some research outlooks are given. (author)
Joint power and multiple access control for wireless mesh network with Rose projection method.
Tang, Meiqin; Shang, Lili; Xin, Yalin; Liu, Xiaohua; Wei, Xinjiang
2014-01-01
This paper investigates the utility maximization problem for the downlink of the multi-interface multichannel wireless mesh network with orthogonal frequency division multiple access. A cross-layer joint power and multiple access control algorithm are proposed. Rosen projection matrix is combined with Solodov projection techniques to build a three-memory gradient Rosen projection method, which is applied to solve this optimization problem. The convergence analysis is given and simulations show that the proposed solution achieves significant throughput compared with existing approaches.
A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence
International Nuclear Information System (INIS)
Yang Lixing; Li Xiang; Li Keping
2011-01-01
Based on the discrete time method, an effective movement control model is designed for a group of highspeed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains' movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and rescheduling trains on the rail network. (general)
Framework and Method for Controlling a Robotic System Using a Distributed Computer Network
Sanders, Adam M. (Inventor); Barajas, Leandro G. (Inventor); Permenter, Frank Noble (Inventor); Strawser, Philip A. (Inventor)
2015-01-01
A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks.
Li, Xiaodi; Song, Shiji
2014-10-01
In this paper, an impulsive controller is designed to achieve the exponential synchronization of chaotic delayed neural networks with stochastic perturbation. By using the impulsive delay differential inequality technique that was established in recent publications, several sufficient conditions ensuring the exponential synchronization of chaotic delayed networks are derived, which can be easily checked by LMI Control Toolbox in Matlab. A numerical example and its simulation is given to demonstrate the effectiveness and advantage of the theory results.
Directory of Open Access Journals (Sweden)
Noriki Uchida
2017-02-01
Full Text Available It is considered that the road condition in the winter is one of the significant issues for the safety driving by tourists or residents. However, there are many difficulties of the V2V networks such as the transmission range of wireless networks and the noises from the automobilefs bodies. Thus, this paper introduces the Adaptive Array Antenna (AAA controls for the vehicle-to-vehicle (V2V networks based the Delay Tolerant Networking (DTN in the road surveillance system. In the proposed system, the vehicles equip the AAA control systems with IEEE802.11a/b/g based the DTN, and the wireless directions are controlled by the visual recognitions with Kalman filter algorithm to make the longer and stable wireless connections for the efficiency of the DTN. The porotype system is introduced in this paper, and the results are discussed for the future studies.
A new cognitive radio based admission control method for wireless mesh networks
Wang, Jing; Li, Fangfang
2011-10-01
In this paper, we address the problem of supporting applications with high bandwidth requirements in Wireless Mesh Networks (WMN) and develop a Markov analytic framework to study the important performance measures experienced by SUs in a cognitive radio (CR) based wireless mesh network. Specifically, we study the blocking and forced termination probabilities and throughput of secondary users under systems with/without spectrum handoff and channel reservation. Based on this framework, a novel dynamic cognitive channel access control algorithm for wireless mesh networks is proposed in order to maintain given quality of service (QoS) requirements. Simulation and analysis results show that our proposed dynamic cognitive channel access control algorithm can maximize the throughput while keeping forced termination and blocking probabilities of SUs' requests under the desired constraints, which providing a solution to improve load balance among multipath in wireless mesh networks.
International Nuclear Information System (INIS)
Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen
2015-01-01
We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)
Eiriksson, D.; Jones, A. S.; Horsburgh, J. S.; Cox, C.; Dastrup, D.
2017-12-01
Over the past few decades, advances in electronic dataloggers and in situ sensor technology have revolutionized our ability to monitor air, soil, and water to address questions in the environmental sciences. The increased spatial and temporal resolution of in situ data is alluring. However, an often overlooked aspect of these advances are the challenges data managers and technicians face in performing quality control on millions of data points collected every year. While there is general agreement that high quantities of data offer little value unless the data are of high quality, it is commonly understood that despite efforts toward quality assurance, environmental data collection occasionally goes wrong. After identifying erroneous data, data managers and technicians must determine whether to flag, delete, leave unaltered, or retroactively correct suspect data. While individual instrumentation networks often develop their own QA/QC procedures, there is a scarcity of consensus and literature regarding specific solutions and methods for correcting data. This may be because back correction efforts are time consuming, so suspect data are often simply abandoned. Correction techniques are also rarely reported in the literature, likely because corrections are often performed by technicians rather than the researchers who write the scientific papers. Details of correction procedures are often glossed over as a minor component of data collection and processing. To help address this disconnect, we present case studies of quality control challenges, solutions, and lessons learned from a large scale, multi-watershed environmental observatory in Northern Utah that monitors Gradients Along Mountain to Urban Transitions (GAMUT). The GAMUT network consists of over 40 individual climate, water quality, and storm drain monitoring stations that have collected more than 200 million unique data points in four years of operation. In all of our examples, we emphasize that scientists
Intelligent networked teleoperation control
Li, Zhijun; Su, Chun-Yi
2015-01-01
This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.
Directory of Open Access Journals (Sweden)
Vrcelj Nada
2013-01-01
Full Text Available The method is based on data obtained from the so-called. hand-held measuring current at 10 kV voltage level and from reports of outages at reclosers that are installed in a part of network that is observed. At first, is calculates the electrical load of the main distribution power lines, and then simulates the corresponding power flow and calculates the undelivered electricity. The method was applied to parts of the network PD ED Belgrade that are not in the remote control system and is developed for the purpose of considering the effects of automation in the 10 kV PD ED Belgrade.
Admission Control in IMS Networks
Directory of Open Access Journals (Sweden)
Erik Chromy
2013-01-01
Full Text Available In our paper there is an emphasis on simulations of admission control methods in MATLAB environment. The main task of admission control method is to make a decision if the connection requiring network access should be accepted to the network or the access should be rejected. If the connection is accepted to the network, the admission control has to ensure that Quality of Service of this connection will be satisfied, as well as Quality of Service of all other existing connections. We have observed several Measurement based admission control algorithms and the result is the identification of the suitable algorithm which can estimate the required bandwidth.
Virtualized Network Control (VNC)
Energy Technology Data Exchange (ETDEWEB)
Lehman, Thomas [Univ. of Southern California, Los Angeles, CA (United States); Guok, Chin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)
2013-01-31
The focus of this project was on the development of a "Network Service Plane" as an abstraction model for the control and provisioning of multi-layer networks. The primary motivation for this work were the requirements of next generation networked applications which will need to access advanced networking as a first class resource at the same level as compute and storage resources. A new class of "Intelligent Network Services" were defined in order to facilitate the integration of advanced network services into application specific workflows. This new class of network services are intended to enable real-time interaction between the application co-scheduling algorithms and the network for the purposes of workflow planning, real-time resource availability identification, scheduling, and provisioning actions.
DEFF Research Database (Denmark)
Hasheminamin, Maryam; Agelidis, Vassilios; Ahmadi, Abdollah
2018-01-01
Voltage rise (VR) due to reverse power flow is an important obstacle for high integration of Photovoltaic (PV) into residential networks. This paper introduces and elaborates a novel methodology of an index-based single-point-reactive power-control (SPRPC) methodology to mitigate voltage rise by ...... system with high r/x ratio. Efficacy, effectiveness and cost study of SPRPC is compared to droop control to evaluate its advantages....... by absorbing adequate reactive power from one selected point. The proposed index utilizes short circuit analysis to select the best point to apply this Volt/Var control method. SPRPC is supported technically and financially by distribution network operator that makes it cost effective, simple and efficient...
International Nuclear Information System (INIS)
Dai, Hao; Si, Gangquan; Jia, Lixin; Zhang, Yanbin
2014-01-01
This paper investigates the problem of finite-time generalized function matrix projective lag synchronization between two different coupled dynamical networks with different dimensions of network nodes. The double power function nonlinear feedback control method is proposed in this paper to guarantee that the state trajectories of the response network converge to the state trajectories of the drive network according to a function matrix in a given finite time. Furthermore, in comparison with the traditional nonlinear feedback control method, the new method improves the synchronization efficiency, and shortens the finite synchronization time. Numerical simulation results are presented to illustrate the effectiveness of this method. (papers)
Delays and networked control systems
Hetel, Laurentiu; Daafouz, Jamal; Johansson, Karl
2016-01-01
This edited monograph includes state-of-the-art contributions on continuous time dynamical networks with delays. The book is divided into four parts. The first part presents tools and methods for the analysis of time-delay systems with a particular attention on control problems of large scale or infinite-dimensional systems with delays. The second part of the book is dedicated to the use of time-delay models for the analysis and design of Networked Control Systems. The third part of the book focuses on the analysis and design of systems with asynchronous sampling intervals which occur in Networked Control Systems. The last part of the book exposes several contributions dealing with the design of cooperative control and observation laws for networked control systems. The target audience primarily comprises researchers and experts in the field of control theory, but the book may also be beneficial for graduate students. .
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...
International Nuclear Information System (INIS)
Jiang Zheng-Xian; Cui Bao-Tong; Lou Xu-Yang; Zhuang Bo
2017-01-01
In this paper, the control problem of distributed parameter systems is investigated by using wireless sensor and actuator networks with the observer-based method. Firstly, a centralized observer which makes use of the measurement information provided by the fixed sensors is designed to estimate the distributed parameter systems. The mobile agents, each of which is affixed with a controller and an actuator, can provide the observer-based control for the target systems. By using Lyapunov stability arguments, the stability for the estimation error system and distributed parameter control system is proved, meanwhile a guidance scheme for each mobile actuator is provided to improve the control performance. A numerical example is finally used to demonstrate the effectiveness and the advantages of the proposed approaches. (paper)
Maximum entropy networks are more controllable than preferential attachment networks
International Nuclear Information System (INIS)
Hou, Lvlin; Small, Michael; Lao, Songyang
2014-01-01
A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution. - Highlights: • The controllability of maximum entropy (ME) and Barabási–Albert (BA) networks is investigated. • ME networks are significantly more easily controlled than BA networks of the same degree distribution. • The properties of the neighbors of driver node sensitively affect the network controllability. • Subtle and important structural differences exist between BA networks and typical scale-free networks
DEFF Research Database (Denmark)
Chen, Shuheng; Hu, Weihao; Su, Chi
2015-01-01
A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33-node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove...... that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods....
Launch Control Network Engineer
Medeiros, Samantha
2017-01-01
The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.
Control Method of Single-phase Inverter Based Grounding System in Distribution Networks
DEFF Research Database (Denmark)
Wang, Wen; Yan, L.; Zeng, X.
2016-01-01
of neutral-to-ground voltage is critical for the safety of distribution networks. An active grounding system based on single-phase inverter is proposed to achieve this objective. Relationship between output current of the system and neutral-to-ground voltage is derived to explain the principle of neutral...
Rusu, Teodora; Gogan, Oana Marilena
2016-05-01
This paper describes the use of artificial intelligence method in copolymer networks design. In the present study, we pursue a hybrid algorithm composed from two research themes in the genetic design framework: a Kohonen neural network (KNN), path (forward problem) combined with a genetic algorithm path (backward problem). The Tabu Search Method is used to improve the performance of the genetic algorithm path.
Directory of Open Access Journals (Sweden)
Yu Li
2017-05-01
Full Text Available With the incremental introduction of solar photovoltaic (PV generators into existing power systems, and their fast-growing share in the gross electricity generation, system voltage stability has become a critical issue. One of the major concerns is voltage fluctuation, due to large and random penetration of solar PV generators. To suppress severe system voltage deviation, reactive power control of the photovoltaic system inverter has been widely proposed in recent works; however, excessive use of reactive power control would increase both initial and operating costs. In this paper, a method for efficient allocation and control of reactive power injection using the sparse optimization technique is proposed. Based on a constrained linearized model describing the influence of reactive power injection on voltage magnitude change, the objective of this study is formulated as an optimization problem, which aims to find the best reactive power injection that minimizes the whole system voltage variation. Two types of formulations are compared: the first one is the conventional least-square optimization, while the second one is adopted from a sparse optimization technique, called the constrained least absolute shrinkage and selection operator (LASSO method. The constrained LASSO method adds ℓ 1 -norm penalty to the total reactive power injection, which contributes to the suppression of the number of control nodes with non-zero reactive power injection. The authors analyzed the effectiveness of the constrained LASSO method using the IEEE 39-bus and 57-bus power network as benchmark examples, under various PV power generation and allocation patterns. The simulation results show that the constrained LASSO method automatically selects the minimum number of inverters required for voltage regulation at the current operating point.
Neural Networks Applied to Optimal Flight Control
McKelvey, Tomas
1992-01-01
This paper presents a method for developing control laws for nonlinear systems based on an optimal control formulation. Due to the nonlinearities of the system, no analytical solution exists. The method proposed here uses the 'black box' structure of a neural network to model a feedback control law. The network is trained with the back-propagation learning method by using examples of optimal control produced with a differential dynamic programming technique. Two different optimal control prob...
Constrained target controllability of complex networks
Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan
2017-06-01
It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.
Pereira, Guillermo; Palfner, Götz; Chávez, Daniel; Suz, Laura M; Machuca, Angela; Honrubia, Mario
2013-07-01
The high cost and restricted availability of black truffle spore inoculum for controlled mycorrhiza formation of host trees produced for truffle orchards worldwide encourage the search for more efficient and sustainable inoculation methods that can be applied globally. In this study, we evaluated the potential of the nurse plant method for the controlled inoculation of Quercus cerris and Quercus robur with Tuber melanosporum by mycorrhizal networks in pot cultures. Pine bark compost, adjusted to pH 7.8 by liming, was used as substrate for all assays. Initially, Q. robur seedlings were inoculated with truffle spores and cultured for 12 months. After this period, the plants presenting 74 % mycorrhizal fine roots were transferred to larger containers. Nurse plants were used for two treatments of two different nursling species: five sterilized acorns or five 45-day-old, axenically grown Q. robur or Q. cerris seedlings, planted in containers around the nurse plant. After 6 months, colonized nursling plant root tips showed that mycorrhiza formation by T. melanosporum was higher than 45 % in the seedlings tested, with the most successful nursling combination being Q. cerris seedlings, reaching 81 % colonization. Bulk identification of T. melanosporum mycorrhizae was based on morphological and anatomical features and confirmed by sequencing of the internal transcribed spacer region of the ribosomal DNA of selected root tips. Our results show that the nurse plant method yields attractive rates of mycorrhiza formation by the Périgord black truffle and suggest that establishing and maintaining common mycorrhizal networks in pot cultures enables sustained use of the initial spore inoculum.
Control theory of digitally networked dynamic systems
Lunze, Jan
2013-01-01
The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic
Neural Networks for Flight Control
Jorgensen, Charles C.
1996-01-01
Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.
Neural networks for aircraft control
Linse, Dennis
1990-01-01
Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.
Communication and control for networked complex systems
Peng, Chen; Han, Qing-Long
2015-01-01
This book reports on the latest advances in the study of Networked Control Systems (NCSs). It highlights novel research concepts on NCSs; the analysis and synthesis of NCSs with special attention to their networked character; self- and event-triggered communication schemes for conserving limited network resources; and communication and control co-design for improving the efficiency of NCSs. The book will be of interest to university researchers, control and network engineers, and graduate students in the control engineering, communication and network sciences interested in learning the core principles, methods, algorithms and applications of NCSs.
Controllability of Surface Water Networks
Riasi, M. Sadegh; Yeghiazarian, Lilit
2017-12-01
To sustainably manage water resources, we must understand how to control complex networked systems. In this paper, we study surface water networks from the perspective of structural controllability, a concept that integrates classical control theory with graph-theoretic formalism. We present structural controllability theory and compute four metrics: full and target controllability, control centrality and control profile (FTCP) that collectively determine the structural boundaries of the system's control space. We use these metrics to answer the following questions: How does the structure of a surface water network affect its controllability? How to efficiently control a preselected subset of the network? Which nodes have the highest control power? What types of topological structures dominate controllability? Finally, we demonstrate the structural controllability theory in the analysis of a wide range of surface water networks, such as tributary, deltaic, and braided river systems.
Broadband accelerator control network
International Nuclear Information System (INIS)
Skelly, J.; Clifford, T.; Frankel, R.
1983-01-01
A broadband data communications network has been implemented at BNL for control of the Alternating Gradient Synchrotron (AG) proton accelerator, using commercial CATV hardware, dual coaxial cables as the communications medium, and spanning 2.0 km. A 4 MHz bandwidth Digital Control channel using CSMA-CA protocol is provided for digital data transmission, with 8 access nodes available over the length of the RELWAY. Each node consists of an rf modem and a microprocessor-based store-and-forward message handler which interfaces the RELWAY to a branch line implemented in GPIB. A gateway to the RELWAY control channel for the (preexisting) AGS Computerized Accelerator Operating system has been constructed using an LSI-11/23 microprocessor as a device in a GPIB branch line. A multilayer communications protocol has been defined for the Digital Control Channel, based on the ISO Open Systems Interconnect layered model, and a RELWAY Device Language defined as the required universal language for device control on this channel
Control patterns in an healthcare network
Kartseva, V.; Hulstijn, J.; Gordijn, J.; Tan, Y.H.
2010-01-01
To keep a network of enterprises sustainable, inter-organizational control measures are needed to detect or prevent opportunistic behaviour of network participants. We present a requirements engineering method for understanding control problems and designing solutions, based on an economic value
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Kullgren, Jeffrey T.; Harkins, Kristin A.; Bellamy, Scarlett L.; Gonzales, Amy; Tao, Yuanyuan; Zhu, Jingsan; Volpp, Kevin G.; Asch, David A.; Heisler, Michele; Karlawish, Jason
2014-01-01
Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants…
Congestion control in satellite networks
Byun, Do Jun
Due to exponential increases in internet traffic, Active Queue Management (AQM) has been heavily studied by numerous researchers. However, little is known about AQM in satellite networks. A microscopic examination of queueing behavior in satellite networks is conducted to identify problems with applying existing AQM methods. A new AQM method is proposed to overcome the problems and it is validated using a realistic emulation environment and a mathematical model. Three problems that were discovered during the research are discussed in this dissertation. The first problem is oscillatory queueing, which is caused by high buffering due to Performance Enhancing Proxy (PEP) in satellite networks where congestion control after the PEP buffering does not effectively control traffic senders. Existing AQMs that can solve this problem have tail drop queueing that results in consecutive packet drops (global synchronization). A new AQM method called Adaptive Virtual Queue Random Early Detection (AVQRED) is proposed to solve this problem. The second problem is unfair bandwidth sharing caused by inaccurate measurements of per-flow bandwidth usage. AVQRED is enhanced to accurately measure per-flow bandwidth usage to solve this problem without adding much complexity to the algorithm. The third problem is queueing instability caused by buffer flow control where TCP receive windows are adjusted to flow control traffic senders instead of dropping received packets during congestion. Although buffer flow control is quite attractive to satellite networks, queueing becomes unstable because accepting packets instead of dropping them aggravates the congestion level. Furthermore, buffer flow control has abrupt reductions in the TCP receive window size due to high PEP buffering causing more instability. AVQRED with packet drop is proposed to solve this problem. Networks with scarce bandwidth and high propagation delays can not afford to have an unstable AQM. In this research, three problems
Control Augmentation Using Adaptive Fuzzy Neural Networks
Kato, Akio; Wada, Yoshihisa
Control to improve control characteristics of aircraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. When the control laws using fuzzy inference were designed, trial and error was repeated for optimization of the parameter. Here, in designing control laws using fuzzy neural networks, the systematic optimization of the parameter was possible using the learning algorithm usually used in neural networks, by expressing the fuzzy inference in the form of neural networks. Here, the control laws, which learned the characteristics of the aircraft for one flight condition only, were used in all flight conditions without changing any parameter. Evaluation of the designed control laws showed good performance in all flight conditions. This proves that fuzzy neural networks are an effective and flexible method when applied to control laws for control augmentation of aircraft.
Asynchronous control for networked systems
Rubio, Francisco; Bencomo, Sebastián
2015-01-01
This book sheds light on networked control systems; it describes different techniques for asynchronous control, moving away from the periodic actions of classical control, replacing them with state-based decisions and reducing the frequency with which communication between subsystems is required. The text focuses specially on event-based control. Split into two parts, Asynchronous Control for Networked Systems begins by addressing the problems of single-loop networked control systems, laying out various solutions which include two alternative model-based control schemes (anticipatory and predictive) and the use of H2/H∞ robust control to deal with network delays and packet losses. Results on self-triggering and send-on-delta sampling are presented to reduce the need for feedback in the loop. In Part II, the authors present solutions for distributed estimation and control. They deal first with reliable networks and then extend their results to scenarios in which delays and packet losses may occur. The novel ...
Directory of Open Access Journals (Sweden)
D.V. Voytikov
2012-04-01
Full Text Available It is considered the methods of expert systems for automation of the operative control of a condition of the scheme of a contact network of traction electrosupply of the railway. It is designated the directions of researches on formation of structure of the knowledge base.
Controllability of structural brain networks
Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.
2015-10-01
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
Opinion control in complex networks
Masuda, Naoki
2015-03-01
In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.
Neural Networks For Robot Control
National Research Council Canada - National Science Library
Nasr, Chaiban
2001-01-01
...; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed-loop 2D planar robot arm and comparison with the use of proportional-integral-differential (PID) controllers...
Positive train control shared network.
2015-05-01
The Interoperable Train Control (ITC) Positive : Train Control (PTC) Shared Network (IPSN) : project investigated anticipated industry benefits : and the level of support for the development of : a hosted technological platform for PTC : messaging ac...
Neural networks and orbit control in accelerators
International Nuclear Information System (INIS)
Bozoki, E.; Friedman, A.
1994-01-01
An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to 'kicks' and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given
HSUPA Transport Network Congestion Control
Directory of Open Access Journals (Sweden)
Szilveszter Nádas
2009-01-01
Full Text Available The introduction of High Speed Uplink Packet Access (HSUPA greatly improves achievable uplink bitrate but it presents new challenges to be solved in the WCDMA radio access network. In the transport network, bandwidth reservation for HSUPA is not efficient and TCP cannot efficiently resolve congestion because of lower layer retransmissions. This paper proposes an HSUPA transport network flow control algorithm that handles congestion situations efficiently and supports Quality of Service differentiation. In the Radio Network Controller (RNC, transport network congestion is detected. Relying on the standardized control frame, the RNC notifies the Node B about transport network congestion. In case of transport network congestion, the Node B part of the HSUPA flow control instructs the air interface scheduler to reduce the bitrate of the flow to eliminate congestion. The performance analysis concentrates on transport network limited scenarios. It is shown that TCP cannot provide efficient congestion control. The proposed algorithm can achieve high end-user perceived throughput, while maintaining low delay, loss, and good fairness in the transport network.
Distributed controller clustering in software defined networks.
Directory of Open Access Journals (Sweden)
Ahmed Abdelaziz
Full Text Available Software Defined Networking (SDN is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN SDN and Open Network Operating System (ONOS controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.
Distributed controller clustering in software defined networks
Gani, Abdullah; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond
2017-01-01
Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability. PMID:28384312
Understanding control of network spreading from network controllability
Sun, Peng Gang; Ma, Xiaoke
2017-09-01
How to control the spread of an epidemic or information is a great challenge for us. A dynamic network-based system’s structural controllability provides a new way to control spreading with the minimum input of external signals, and the dynamic system is controllable if the signals can drive it from any initial state to any desired final state in finite time. Therefore, we are motivated to develop a new framework by introducing spreading networks (SNs) to describe the spreading pathways from a global view, and we try to understand the control of the spreading by the structural controllability of the SNs. The SNs are transformed from original networks, in which each node is considered as a single spreading origin. The weights of directed links pointing at its direct contacts in the SNs denote the spreading abilities, which can be determined by a new probability function. Furthermore, we also investigate the impact of the dynamics of network structures on the framework. The results show that sparse homogeneous networks with a higher transmission probability tend to trigger a larger scale of diffusion, which is easier to control. We can also see that an epidemic or information is inclined to diffuse easily on the networks with strong community strengths and heterogeneous community sizes. From the structural controllability of the SNs, we observe that driver nodes for the control of the spread tend not to be the nodes located within the core of original networks or those with high-degree. In addition, the scale of diffusion, the number of driver nodes and positions of nodes are highly associated with the degree distribution of the original networks.
Secure network for beamline control
Ohata, T.; Fukui, T.; Ishii, M.; Furukawa, Y.; Nakatani, T.; Matsushita, T.; Takeuchi, M.; Tanaka, R.; Ishikawa, T.
2001-07-01
In SPring-8, beamline control system is constructed with a highly available distributed network system. The socket based communication protocol is used for the beamline control mainly. Beamline users can control the equipment by sending simple control commands to a server process, which is running on a beamline-managing computer (Ohata et al., SPring-8 beamline control system, ICALEPCS'99, Trieste, Italy, 1999). At the beginning the network was based on the shared topology at all beamlines. Consequently, it has a risk for misapplication of the user's program to access different machines on the network system cross over beamlines. It is serious problem for the SPring-8 beamline control system, because all beamlines controlled with unified software interfaces. We introduced the switching technology and the firewalls to support network access control. Also the virtual networking (VLAN: IEEE 802.1Q) and the gigabit Ethernet technology (IEEE 802.3ab) are introduced. Thus the network security and the reliability are guaranteed at the higher level in SPring-8 beamline.
Secure network for beamline control
International Nuclear Information System (INIS)
Ohata, T.; Fukui, T.; Ishii, M.; Furukawa, Y.; Nakatani, T.; Matsushita, T.; Takeuchi, M.; Tanaka, R.; Ishikawa, T.
2001-01-01
In SPring-8, beamline control system is constructed with a highly available distributed network system. The socket based communication protocol is used for the beamline control mainly. Beamline users can control the equipment by sending simple control commands to a server process, which is running on a beamline-managing computer (Ohata et al., SPring-8 beamline control system, ICALEPCS'99, Trieste, Italy, 1999). At the beginning the network was based on the shared topology at all beamlines. Consequently, it has a risk for misapplication of the user's program to access different machines on the network system cross over beamlines. It is serious problem for the SPring-8 beamline control system, because all beamlines controlled with unified software interfaces. We introduced the switching technology and the firewalls to support network access control. Also the virtual networking (VLAN: IEEE 802.1Q) and the gigabit Ethernet technology (IEEE 802.3ab) are introduced. Thus the network security and the reliability are guaranteed at the higher level in SPring-8 beamline
Control of autonomous robot using neural networks
Barton, Adam; Volna, Eva
2017-07-01
The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.
Reduction Method for Active Distribution Networks
DEFF Research Database (Denmark)
Raboni, Pietro; Chen, Zhe
2013-01-01
On-line security assessment is traditionally performed by Transmission System Operators at the transmission level, ignoring the effective response of distributed generators and small loads. On the other hand the required computation time and amount of real time data for including Distribution...... Networks also would be too large. In this paper an adaptive aggregation method for subsystems with power electronic interfaced generators and voltage dependant loads is proposed. With this tool may be relatively easier including distribution networks into security assessment. The method is validated...... by comparing the results obtained in PSCAD® with the detailed network model and with the reduced one. Moreover the control schemes of a wind turbine and a photovoltaic plant included in the detailed network model are described....
Patkin, M. L.; Rogachev, G. N.
2018-02-01
A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.
Advanced mobile networking, sensing, and controls.
Energy Technology Data Exchange (ETDEWEB)
Feddema, John Todd; Kilman, Dominique Marie; Byrne, Raymond Harry; Young, Joseph G.; Lewis, Christopher L.; Van Leeuwen, Brian P.; Robinett, Rush D. III; Harrington, John J.
2005-03-01
This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition, queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.
Robust Multiobjective Controllability of Complex Neuronal Networks.
Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen
2016-01-01
This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.
Optimal control learning with artificial neural networks
International Nuclear Information System (INIS)
Martinez, J.M.; Parey, C.; Houkari, M.
1993-01-01
This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)
Quantitative Efficiency Evaluation Method for Transportation Networks
Directory of Open Access Journals (Sweden)
Jin Qin
2014-11-01
Full Text Available An effective evaluation of transportation network efficiency/performance is essential to the establishment of sustainable development in any transportation system. Based on a redefinition of transportation network efficiency, a quantitative efficiency evaluation method for transportation network is proposed, which could reflect the effects of network structure, traffic demands, travel choice, and travel costs on network efficiency. Furthermore, the efficiency-oriented importance measure for network components is presented, which can be used to help engineers identify the critical nodes and links in the network. The numerical examples show that, compared with existing efficiency evaluation methods, the network efficiency value calculated by the method proposed in this paper can portray the real operation situation of the transportation network as well as the effects of main factors on network efficiency. We also find that the network efficiency and the importance values of the network components both are functions of demands and network structure in the transportation network.
Flight control with adaptive critic neural network
Han, Dongchen
2001-10-01
In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.
Congestion control and routing over satellite networks
Cao, Jinhua
Satellite networks and transmissions find their application in fields of computer communications, telephone communications, television broadcasting, transportation, space situational awareness systems and so on. This thesis mainly focuses on two networking issues affecting satellite networking: network congestion control and network routing optimization. Congestion, which leads to long queueing delays, packet losses or both, is a networking problem that has drawn the attention of many researchers. The goal of congestion control mechanisms is to ensure high bandwidth utilization while avoiding network congestion by regulating the rate at which traffic sources inject packets into a network. In this thesis, we propose a stable congestion controller using data-driven, safe switching control theory to improve the dynamic performance of satellite Transmission Control Protocol/Active Queue Management (TCP/AQM) networks. First, the stable region of the Proportional-Integral (PI) parameters for a nominal model is explored. Then, a PI controller, whose parameters are adaptively tuned by switching among members of a given candidate set, using observed plant data, is presented and compared with some classical AQM policy examples, such as Random Early Detection (RED) and fixed PI control. A new cost detectable switching law with an interval cost function switching algorithm, which improves the performance and also saves the computational cost, is developed and compared with a law commonly used in the switching control literature. Finite-gain stability of the system is proved. A fuzzy logic PI controller is incorporated as a special candidate to achieve good performance at all nominal points with the available set of candidate controllers. Simulations are presented to validate the theory. An effocient routing algorithm plays a key role in optimizing network resources. In this thesis, we briefly analyze Low Earth Orbit (LEO) satellite networks, review the Cross Entropy (CE
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
System and method for networking electrochemical devices
Williams, Mark C.; Wimer, John G.; Archer, David H.
1995-01-01
An improved electrochemically active system and method including a plurality of electrochemical devices, such as fuel cells and fluid separation devices, in which the anode and cathode process-fluid flow chambers are connected in fluid-flow arrangements so that the operating parameters of each of said plurality of electrochemical devices which are dependent upon process-fluid parameters may be individually controlled to provide improved operating efficiency. The improvements in operation include improved power efficiency and improved fuel utilization in fuel cell power generating systems and reduced power consumption in fluid separation devices and the like through interstage process fluid parameter control for series networked electrochemical devices. The improved networking method includes recycling of various process flows to enhance the overall control scheme.
A Network Traffic Control Enhancement Approach over Bluetooth Networks
DEFF Research Database (Denmark)
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
2003-01-01
This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...
Information and control in networks
Bernhardsson, Bo; Rantzer, Anders
2014-01-01
Information and Control in Networks demonstrates the way in which system dynamics and information flows intertwine as they evolve, and the central role played by information in the control of complex networked systems. It is a milestone on the road to that convergence from traditionally independent development of control theory and information theory which has emerged strongly in the last fifteen years, and is now a very active research field. In addition to efforts in control and information theory, the text is witness to strong research in such diverse fields as computer science, mathematics, and statistics. Aspects that are given specialist treatment include: · data-rate theorems; · computation and control over communication networks; · decentralized stochastic control; · Gaussian networks and Gaussian–Markov random fields; and · routability ...
Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network
Directory of Open Access Journals (Sweden)
Hui ZHU
2008-02-01
Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.
Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac
2017-11-01
This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
An Optimal Method to Design Wireless Sensor Network Structures
Directory of Open Access Journals (Sweden)
Yang Ling
2018-01-01
Full Text Available In order to optimize the structure of wireless sensor network, an improved wireless sensor network sleep mechanism is proposed. First, some nodes in the area with too high redundancy are dormant by density control, so that the active nodes are even more distributed. Then, the active node is subjected to circular coverage redundancy decision. Different circumferential coverage decision methods are used for network boundary nodes and non-boundary nodes. As a result, the boundary nodes and non-boundary nodes are well dormant, and the network redundancy is reduced. The simulation results show that the improved dormancy mechanism makes the number of active nodes in the network smaller and more evenly, and the network lifetime is extended on the basis of maintaining the original coverage of the network. Therefore, the proposed method can achieve optimal coverage in wireless sensor networks. The network prolongs network lifetime while ensuring reliable monitoring performance.
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...... concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction...
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Coordinated Voltage Control of Active Distribution Network
Directory of Open Access Journals (Sweden)
Xie Jiang
2016-01-01
Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.
Li, Y.
2010-01-01
Food quality problems in Food Supply Chain Networks (FSCN) have not only brought losses to the food industry, but also risks to the health of consumers. In current FSCN, Information Systems are widely used. Those information systems contain the data about various aspects of food production (e.g.
Ge, Long; Tian, Jin-hui; Li, Lun; Wang, Quan; Yang, Ke-hu
2015-11-19
Randomised clinical trials (RCTs) have been used to compare and evaluate different types of mesh fixation usually employed to repair open inguinal hernia. However, there is no consensus among surgeons on the best type of mesh fixation method to obtain optimal results. The choice often depends on surgeons' personal preference. This study aims to compare different types of mesh fixation methods to repair open inguinal hernias and their role in the incidences of chronic groin pain, risk of hernia recurrence, complications, operative time, length of hospital stay and postoperative pain, using Bayesian network meta-analysis and trial sequential analysis of RCTs. A systematic search will be performed using PubMed, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Chinese Biomedical Literature Database (CBM) and Chinese Journal Full-text Database, to include RCTs of different mesh fixation methods (or fixation vs no fixation) during open inguinal hernia repair. The risk of bias in included RCTs will be evaluated according to the Cochrane Handbook V.5.1.0. Standard pairwise meta-analysis, trial sequential analysis and Bayesian network meta-analysis will be performed to compare the efficacy of different mesh fixation methods. Ethical approval and patient consent are not required since this study is a meta-analysis based on published studies. The results of this network meta-analysis and trial sequential analysis will be submitted to a peer-reviewed journal for publication. PROSPERO CRD42015023758. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Virtualized Network Control. Final Report
Energy Technology Data Exchange (ETDEWEB)
Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)
2013-02-01
This document is the final report for the Virtualized Network Control (VNC) project, which was funded by the United States Department of Energy (DOE) Office of Science. This project was also informally referred to as Advanced Resource Computation for Hybrid Service and TOpology NEtworks (ARCHSTONE). This report provides a summary of the project's activities, tasks, deliverable, and accomplishments. It also provides a summary of the documents, software, and presentations generated as part of this projects activities. Namely, the Appendix contains an archive of the deliverables, documents, and presentations generated a part of this project.
Fusion Control of Flexible Logic Control and Neural Network
Directory of Open Access Journals (Sweden)
Lihua Fu
2014-01-01
Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.
SCM: A method to improve network service layout efficiency with network evolution.
Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.
SCM: A method to improve network service layout efficiency with network evolution
Zhao, Qi; Zhang, Chuanhao
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299
A Short-Circuit Method for Networks.
Ong, P. P.
1983-01-01
Describes a method of network analysis that allows avoidance of Kirchoff's Laws (providing the network is symmetrical) by reduction to simple series/parallel resistances. The method can be extended to symmetrical alternating current, capacitance or inductance if corresponding theorems are used. Symmetric cubic network serves as an example. (JM)
Predicting and Controlling Complex Networks
2015-06-22
ubiquitous in nature and fundamental to evolution in ecosystems. However, a significant chal- lenge remains in understanding biodiversity since, by the...networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83
Proxy SDN Controller for Wireless Networks
Directory of Open Access Journals (Sweden)
Won-Suk Kim
2016-01-01
Full Text Available Management of wireless networks as well as wired networks by using software-defined networking (SDN has been highlighted continually. However, control features of a wireless network differ from those of a wired network in several aspects. In this study, we identify the various inefficient points when controlling and managing wireless networks by using SDN and propose SDN-based control architecture called Proxcon to resolve these problems. Proxcon introduces the concept of a proxy SDN controller (PSC for the wireless network control, and the PSC entrusted with the role of a main controller performs control operations and provides the latest network state for a network administrator. To address the control inefficiency, Proxcon supports offloaded SDN operations for controlling wireless networks by utilizing the PSC, such as local control by each PSC, hybrid control utilizing the PSC and the main controller, and locally cooperative control utilizing the PSCs. The proposed architecture and the newly supported control operations can enhance scalability and response time when the logically centralized control plane responds to the various wireless network events. Through actual experiments, we verified that the proposed architecture could address the various control issues such as scalability, response time, and control overhead.
Directory of Open Access Journals (Sweden)
Yousef Bazargan-Lari
Full Text Available AbstractIn order to achieve the practical characteristics of natural bipedal walking, a key feature is to realize "the straight knee state of walking" during stance and swing motions. Considering a straight knee necessitates that the shank link of each leg not to undergo the rotation angles which are greater than that of the thigh link. For this purpose, various methods have been proposed; the joint self-impact constraint has been suggested for energy-efficient (natural bipedal walking while realizing the straight knee constraint.The prominent objective of this research is to present a model based control method for trajectory tracking of a normal human-like bipedal walking, by considering the joint self-impact constraint. To achieve this objective, the dynamical equations of motion of an unconstrained biped are taken, developed and then modified to consider the joint self-impact constraint at the knee joint.To control this complicated dynamical system, the available anthropometric normal gait cycle data are taken to generate the desired trajectories of the thigh and knee joints of the self-impact biped. Due to the existence of complex nonlinear terms in the dynamical governing equations of self-impact biped, the authors propose to design a nonlinear intelligent controller by taking advantage of the adaptive neural network control method, which neither requires the evaluation of inverse dynamical model nor the time consuming training process. According to the simulation results, the tracking control of the biped robot is accomplished well and the biped walking seems naturally, despite of involving complex nonlinear terms in the dynamical governing equations of the self-impact biped.
Wilson, David G [Tijeras, NM; Robinett, III, Rush D.
2012-02-21
A control system design method and concomitant control system comprising representing a physical apparatus to be controlled as a Hamiltonian system, determining elements of the Hamiltonian system representation which are power generators, power dissipators, and power storage devices, analyzing stability and performance of the Hamiltonian system based on the results of the determining step and determining necessary and sufficient conditions for stability of the Hamiltonian system, creating a stable control system based on the results of the analyzing step, and employing the resulting control system to control the physical apparatus.
Hexacopter trajectory control using a neural network
Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.
2013-10-01
The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.
... Women can choose from many different types of birth control methods. These include, in order of most effective to least effective at preventing pregnancy: Female and male sterilization (female tubal ligation or occlusion, male vasectomy) — Birth control that prevents pregnancy for the rest of ...
Buttles, John W
2013-04-23
Wireless communication devices include a software-defined radio coupled to processing circuitry. The system controller is configured to execute computer programming code. Storage media is coupled to the system controller and includes computer programming code configured to cause the system controller to configure and reconfigure the software-defined radio to operate on each of a plurality of communication networks according to a selected sequence. Methods for communicating with a wireless device and methods of wireless network-hopping are also disclosed.
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction......, simulation and filtering of dynamic, non-linear, multi-variable and noisy processes in a very satisfactory manner. The further examinations mainly concentrate on two models, the Non-linear ARMAX (NARMAX) model representing input/output description, and the Non-linear Innovation state Space (NISS) model (a...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...
Complex systems and networks dynamics, controls and applications
Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu
2016-01-01
This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...
Structural Controllability of Temporal Networks with a Single Switching Controller
Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang
2017-01-01
Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
A broadband accelerator control network
International Nuclear Information System (INIS)
Skelly, J.; Clifford, T.; Frankel, R.
1983-01-01
A broadband data communications network has been implemented at BNL for control of the Alternating Gradient Synchrotron (AGS) proton accelerator, using commercial CATV hardware, dual coaxial cables as the communications medium, and spanning 2.0 km. A 4 MHz bandwidth Digital Control Channel using CSMA-CA protocol is provided for digital data transmission, with 8 access nodes available over the length of the RELWAY. Each node consists of an rf modem and a microprocessor-based store-and-forward message handler which interfaces the RELWAY to a branch line implemented in GPIB. A gateway to the RELWAY control channel for the (preexisting) AGS Computerized Accelerator Operating System has been constructed using an LSI-11/23 microprocessor as a device in a GPIB branch line. A multilayer communications protocol has been defined for the Digital Control Channel, based on the ISO Open Systems Interconnect layered model, and a RELWAY Device Language defined as the required universal language for device control on this channel
Simplified LQG Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...
Neural Networks in Nonlinear Aircraft Control
Linse, Dennis J.
1990-01-01
Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.
Attractor Transformation by Impulsive Control in Boolean Control Network
Directory of Open Access Journals (Sweden)
Bo Gao
2013-01-01
Full Text Available Boolean control networks have recently been attracting considerable interests as computational models for genetic regulatory networks. In this paper, we present an approach of impulsive control for attractor transitions in Boolean control networks based on the recent developed matrix semitensor product theory. The reachability of attractors is estimated, and the controller is also obtained. The general derivation proposed here is exemplified with a kind of gene model, which is the protein-nucleic acid interactions network, on numerical simulations.
Neural networks as a control methodology
Mccullough, Claire L.
1990-01-01
While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.
Cloud-based Networked Visual Servo Control
DEFF Research Database (Denmark)
Wu, Haiyan; Lu, Lei; Chen, Chih-Chung
2013-01-01
The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control...... feedback, ii) a stabilizing control law for the networked visual servo control system with time-varying feedback time delay, and iii) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the networked visual servo control system with sending rate...
Connected Dominating Set Based Topology Control in Wireless Sensor Networks
He, Jing
2012-01-01
Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…
A random network based, node attraction facilitated network evolution method
Directory of Open Access Journals (Sweden)
WenJun Zhang
2016-03-01
Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.
Logistic control in automated transportation networks
Ebben, Mark
2001-01-01
Increasing congestion problems lead to a search for alternative transportation systems. Automated transportation networks, possibly underground, are an option. Logistic control systems are essential for future implementations of such automated transportation networks. This book contributes to the
A Systematic, Automated Network Planning Method
DEFF Research Database (Denmark)
Holm, Jens Åge; Pedersen, Jens Myrup
2006-01-01
This paper describes a case study conducted to evaluate the viability of a systematic, automated network planning method. The motivation for developing the network planning method was that many data networks are planned in an adhoc manner with no assurance of quality of the solution with respect...... to consistency and long-term characteristics. The developed method gives significant improvements on these parameters. The case study was conducted as a comparison between an existing network where the traffic was known and a proposed network designed by the developed method. It turned out that the proposed...... structures, that are ready to implement in a real world scenario, are discussed in the end of the paper. These are in the area of ensuring line independence and complexity of the design rules for the planning method....
Neural Network Controller for the Pressurized Water Reactor Power Control
International Nuclear Information System (INIS)
Haggag, S.S.; Kotb, S.A.
2017-01-01
Although there have been some severe nuclear accidents such as Three Mile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan), nuclear fission energy is still a source of clean energy that can substitute fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR) is the most widely used nuclear fission reactor, it is safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs (Pressurized Water Reactors ). This paper presents the effect of utilizing the Neural Network controller methodology in the power control model of the PWR. The Neural Network Controller was tested on a PWR model using the Matlab Simulink Interface. Two case studies were performed on the model using both the Neural Network method and the traditional rod speed program for controlling the nuclear power plant variables. The proposed controller presents a higher performance than that of the traditional rod speed program controller.
Adaptive Dynamics, Control, and Extinction in Networked Populations
2015-07-09
extinction . VI. CONCLUSIONS We have presented a method for predicting extinction in stochastic network systems by analyzing a pair-based proxy model...including games on networks (e.g., [40], [41]). Further, we expect that our method of continuously varying a parameter while tracking the path to extinction ...Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Neural networks for function approximation in nonlinear control
Linse, Dennis J.; Stengel, Robert F.
1990-01-01
Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.
Filtering and control of wireless networked systems
Zhang, Dan; Yu, Li
2017-01-01
This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems.
Method and system for mesh network embedded devices
Wang, Ray (Inventor)
2009-01-01
A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.
Network device interface for digitally interfacing data channels to a controller a via network
Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)
2006-01-01
The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.
Plasma position control method
International Nuclear Information System (INIS)
Shirahama, Hidefumi; Sakurai, Yoshimi.
1988-01-01
Purpose: To suppress the non-linear relationship between the horizontal position of plasmas and the intensity of the vertical magnetic fields, thereby control the horizontal position of the plasmas stably and at a high speed in a closed loop in a tokamak type thermonuclear device. Method: In a control method for approaching the plasma position to a target position in the tokamak type thermonuclear device, a function circuit having two inputs and one output is disposed in a loop constituting the plasma position control system. The characteristics of the function circuit are so designed as to provide a proportional relationship between the first input and the output, while an inverse proportional relationship between the second input and the output. A detected value for the plasma current is used as a signal for the first input, while the detected value for the plasma position or a operation amount based on the difference with the detected value is used as a signal for the second input. By using the function circuit, it is possible to suppress the non-linear characteristics in the dynamic properties of the plasmas, by which the remarkable change of gains in the plasma position control circuit due to the fluctuations of the plasma current and the position can be prevented. (Ikeda, J.)
Directory of Open Access Journals (Sweden)
Jing Sun
2016-07-01
Full Text Available Abstract Background Families with children under age six participating in the Temporary Assistance for Needy Families Program (TANF must participate in work-related activities for 20 h per week. However, due to financial hardship, poor health, and exposure to violence and adversity, families may experience great difficulty in reaching self-sufficiency. The purpose of this report is to describe study design and baseline findings of a trauma-informed financial empowerment and peer support intervention meant to mitigate these hardships. Methods We conducted a randomized controlled trial of a 28-week intervention called Building Wealth and Health Network to improve financial security and maternal and child health among caregivers participating in TANF. Participants, recruited from County Assistance offices in Philadelphia, PA, were randomized into two intervention groups (partial and full and one control group. Participants completed questionnaires at baseline to assess career readiness, economic hardship, health and wellbeing, exposure to adversity and violence, and interaction with criminal justice systems. Results Baseline characteristics demonstrate that among 103 participants, there were no significant differences by group. Mean age of participants was 25 years, and youngest child was 30 months. The majority of participants were women (94.2 %, never married (83.5 %, unemployed (94.2 %, and without a bank account (66.0 %. Many reported economic hardship (32.0 % very low household food secure, 65.0 % housing insecure, and 31.1 % severe energy insecure, and depression (57.3 %. Exposure to adversity was prevalent, where 38.8 % reported four or more Adverse Childhood Experiences including abuse, neglect and household dysfunction. In terms of community violence, 64.7 % saw a seriously wounded person after an incident of violence, and 27.2 % had seen someone killed. Finally, 14.6 % spent time in an adult correctional institution, and 48
Cell fate reprogramming by control of intracellular network dynamics
Zanudo, Jorge G. T.; Albert, Reka
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.
Methodically Modeling the Tor Network
2012-08-01
such well- intentioned research might have a negative impact on real Tor users’ quality of service or privacy [25].1 In an effort to enhance the...software within the virtual network. Also unlike Shadow, ExperimenTor does not endeavor to account for the effects of unrelated back- ground Internet...and down D 1 for i← 0 to getRelayCount()−1 do 2 if B[i]> 0 then 3 ifR [i]> 0 andW[i]> 0 then 4 ratio← R[i]W[i] ; 5 if ratio > 1 then 6 U [i]←B[i]; 7 D[i
Constructing an Intelligent Patent Network Analysis Method
Directory of Open Access Journals (Sweden)
Chao-Chan Wu
2012-11-01
Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.
The analysis of network transmission method for welding robot information
Cheng, Weide; Zhang, Hua; Liu, Donghua; Wang, Hongbo
2012-01-01
On the asis of User Datagram Protocol (UserDatagram Protocol, UDP), to do some improvement and design a welding robot network communication protocol (welding robot network communicate protocol: WRNCP), working on the fields of the transport layer and application layer of TCP / IP protocol. According to the characteristics of video data, to design the radio push-type (Broadcast Push Model, BPM) transmission method, improving the efficiency and stability of video transmission.and to designed the network information transmission system, used for real-time control of welding robot network.
Congestion control of high-speed networks
1993-06-01
We report on four areas of activity in the past six months. These areas include the following: (1) work on the control of integrated video and image traffic, both at the access to a network and within a high-speed network; (2) more general/game theoretic models for flow control in networks; (3) work on fault management for high-speed heterogeneous networks to improve survivability; and (4) work on all-optical (lightwave) networks of the future, designed to take advantage of the enormous bandwidth capability available at optical frequencies.
Distributed medium access control in wireless networks
Wang, Ping
2013-01-01
This brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network servi
Complex networks principles, methods and applications
Latora, Vito; Russo, Giovanni
2017-01-01
Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, ...
Binary Classification Method of Social Network Users
Directory of Open Access Journals (Sweden)
I. A. Poryadin
2017-01-01
Full Text Available The subject of research is a binary classification method of social network users based on the data analysis they have placed. Relevance of the task to gain information about a person by examining the content of his/her pages in social networks is exemplified. The most common approach to its solution is a visual browsing. The order of the regional authority in our country illustrates that its using in school education is needed. The article shows restrictions on the visual browsing of pupil’s pages in social networks as a tool for the teacher and the school psychologist and justifies that a process of social network users’ data analysis should be automated. Explores publications, which describe such data acquisition, processing, and analysis methods and considers their advantages and disadvantages. The article also gives arguments to support a proposal to study the classification method of social network users. One such method is credit scoring, which is used in banks and credit institutions to assess the solvency of clients. Based on the high efficiency of the method there is a proposal for significant expansion of its using in other areas of society. The possibility to use logistic regression as the mathematical apparatus of the proposed method of binary classification has been justified. Such an approach enables taking into account the different types of data extracted from social networks. Among them: the personal user data, information about hobbies, friends, graphic and text information, behaviour characteristics. The article describes a number of existing methods of data transformation that can be applied to solve the problem. An experiment of binary gender-based classification of social network users is described. A logistic model obtained for this example includes multiple logical variables obtained by transforming the user surnames. This experiment confirms the feasibility of the proposed method. Further work is to define a system
Advanced fault diagnosis methods in molecular networks.
Habibi, Iman; Emamian, Effat S; Abdi, Ali
2014-01-01
Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.
Index and query methods in road networks
Feng, Jun
2015-01-01
This book presents the index and query techniques on road network and moving objects which are limited to road network. Here, the road network of non-Euclidean space has its unique characteristics such that two moving objects may be very close in a straight line distance. The index used in two-dimensional Euclidean space is not always appropriate for moving objects on road network. Therefore, the index structure needs to be improved in order to obtain suitable indexing methods, explore the shortest path and acquire nearest neighbor query and aggregation query methods under the new index structures. Chapter 1 of this book introduces the present situation of intelligent traffic and index in road network, Chapter 2 introduces the relevant existing spatial indexing methods. Chapter 3-5 focus on several issues of road network and query, they involves: traffic road network models (see Chapter 3), index structures (see Chapter 4) and aggregate query methods (see Chapter 5). Finally, in Chapter 6, the book briefly de...
Trends in Integrated Ship Control Networking
DEFF Research Database (Denmark)
Jørgensen, N.; Nielsen, Jens Frederik Dalsgaard
1997-01-01
Integrated Ship Control systems can be designed as robust, distributed, autonomous control systems. The EU funded ATOMOS and ATOMOS II projects involves both technical and non technical aspects of this process. A reference modelling concept giving an outline of a generic ISC system covering...... the network and the equipment connected to it, a framework for verification of network functionality and performance by simulation and a general distribution platform for ISC systems, The ATOMOS Network, are results of this work....
Centralized surveillance and control of satellite networks
Rzewnicki, S. E.; McBeath, J. W.; Brostrup-Jensen, P.
Satellite based services and networks are increasing in number. This paper describes how such networks can be operated efficiently using software based systems to do satellite transmission surveillance and remote earth station status, alarm and control monitoring at a centralized operations control center. Arrangements are available to accomplish real time, customer controlled configuration of space segments and earth station equipment. Application of the system elements satellite transmission surveillance, alarm and control central, earth station remote, and customer control terminals - to a number of typical networks is described.
Implementation of neural network based non-linear predictive control
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1999-01-01
This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Additive Feed Forward Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1999-01-01
. A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...
Directory of Open Access Journals (Sweden)
Wei Yu
2016-01-01
Full Text Available Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure. According to the hierarchical control principle, this research puts forward the integrated control strategy of the chassis based on supervision mechanism. The purpose is to consider how the integrated control architecture affects the control performance of the system after the intervention of CAN network. Based on the principle of hierarchical control and fuzzy control, a fuzzy controller is designed, which is used to monitor and coordinate the ESP, AFS, and ARS. And the IVC system is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform. The network topology structure of IVC is proposed, and the IVC communication matrix based on CAN network communication is designed. With the common sensors and the subcontrollers as the CAN network independent nodes, the network induced delay and packet loss rate on the system control performance are studied by simulation. The results show that the simulation method can be used for designing the communication network of the vehicle.
Distributed intelligent control and status networking
Fortin, Andre; Patel, Manoj
1993-01-01
Over the past two years, the Network Control Systems Branch (Code 532) has been investigating control and status networking technologies. These emerging technologies use distributed processing over a network to accomplish a particular custom task. These networks consist of small intelligent 'nodes' that perform simple tasks. Containing simple, inexpensive hardware and software, these nodes can be easily developed and maintained. Once networked, the nodes can perform a complex operation without a central host. This type of system provides an alternative to more complex control and status systems which require a central computer. This paper will provide some background and discuss some applications of this technology. It will also demonstrate the suitability of one particular technology for the Space Network (SN) and discuss the prototyping activities of Code 532 utilizing this technology.
The application of neural network PID controller to control the light gasoline etherification
Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong
2017-06-01
Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.
Artificial neural network intelligent method for prediction
Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi
2017-09-01
Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.
Network aspects of the Fermilab control system
International Nuclear Information System (INIS)
Barton, H.R. Jr.
1977-01-01
The control system of the Fermi National Accelerator is a heavily computerized network of distributed processors. One part of the control system includes a multidrop network of eleven Lockheed MAC-16 processors, a Digital Equipment Corporation PDP-11 computer, a Xerox 530, and a Control Data 6600 system. These computers exchange information using serial hardware and dedicated cable buses. The individual functions of the central processing units in this network, the message protocols for computer communications, and design guidelines for future distributed processing control systems are discussed
Control of coupled oscillator networks with application to microgrid technologies
Skardal, Per Sebastian; Arenas, Alex
2015-01-01
The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231
Control of coupled oscillator networks with application to microgrid technologies
Arenas, Alex
The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable syn- chronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.
An overview on network cost allocation methods
Energy Technology Data Exchange (ETDEWEB)
Lima, Delberis A. [PUC - Pontificia University Catholic, Department of Electrical Engineering, Rua Marques de Sao Vicente, 225, 22453-900 Gavea - Rio de Janeiro (Brazil); Padilha-Feltrin, Antonio [UNESP - State University of Sao Paulo, Department of Electrical Engineering, Campus of Ilha Solteira, 15385-000, Ilha Solteira, Sao Paulo (Brazil); Contreras, Javier [UCLM - Universidad de Castilla-La Mancha, E.T.S. de Ingenieros Industriales, Campus Universitario s/n, 13071 Ciudad Real (Spain)
2009-05-15
This work is devoted to study and discuss the main methods to solve the network cost allocation problem both for generators and demands. From the presented, compared and discussed methods, the first one is based on power injections, the second deals with proportional sharing factors, the third is based upon Equivalent Bilateral Exchanges, the fourth analyzes the power flow sensitivity in relation to the power injected, and the last one is based on Z{sub bus} network matrix. All the methods are initially illustrated using a 4-bus system. In addition, the IEEE 24-bus RTS system is presented for further comparisons and analysis. Appropriate conclusions are finally drawn. (author)
An overview on network cost allocation methods
International Nuclear Information System (INIS)
Lima, Delberis A.; Padilha-Feltrin, Antonio; Contreras, Javier
2009-01-01
This work is devoted to study and discuss the main methods to solve the network cost allocation problem both for generators and demands. From the presented, compared and discussed methods, the first one is based on power injections, the second deals with proportional sharing factors, the third is based upon Equivalent Bilateral Exchanges, the fourth analyzes the power flow sensitivity in relation to the power injected, and the last one is based on Z bus network matrix. All the methods are initially illustrated using a 4-bus system. In addition, the IEEE 24-bus RTS system is presented for further comparisons and analysis. Appropriate conclusions are finally drawn. (author)
MPC control of water supply networks
DEFF Research Database (Denmark)
Baunsgaard, Kenneth Marx Hoe; Ravn, Ole; Kallesoe, Carsten Skovmose
2016-01-01
This paper investigates the modelling and predictive control of a drinking water supply network with the aim of minimising the energy and economic cost. A model predictive controller, MPC, is applied to a nonlinear model of a drinking water network that follows certain constraints to maintain...... consumer pressure desire. A model predictive controller, MPC, is based on a simple model that models the main characteristics of a water distribution network, optimizes a desired cost minimisation, and keeps the system inside specified constraints. In comparison to a logic (on/off) control design......, controlling the drinking water supply network with the MPC showed reduction of the energy and the economic cost of running the system. This has been achieved by minimising actuator control effort and by shifting the actuator use towards the night time, where energy prices are lower. Along with energy cost...
Distributed synchronization of coupled neural networks via randomly occurring control.
Tang, Yang; Wong, Wai Keung
2013-03-01
In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertex's neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.
Stability and synchronization control of stochastic neural networks
Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing
2016-01-01
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
Fault Detection for Quantized Networked Control Systems
Directory of Open Access Journals (Sweden)
Wei-Wei Che
2013-01-01
Full Text Available The fault detection problem in the finite frequency domain for networked control systems with signal quantization is considered. With the logarithmic quantizer consideration, a quantized fault detection observer is designed by employing a performance index which is used to increase the fault sensitivity in finite frequency domain. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. By using the Kalman-Yakubovich-Popov (GKYP Lemma, an iterative LMI-based optimization algorithm is developed for designing the quantized fault detection observer. And a numerical example is given to illustrate the effectiveness of the proposed method.
Boundary Constraints for Minimum Cost Control of Directed Networks.
Li, Guoqi; Tang, Pei; Wen, Changyun; Meng, Ziyang
2017-12-01
Controlling directed networks with minimum cost has become an emerging branch in the areas of complex networks and control recently. In this paper, we focus on this minimum cost control problem subject to two types of boundary constraints, namely, trace boundary constraint and orthonormal boundary constraint on the input matrices. First, the minimum cost control problem is formulated as an optimization model for each type of boundary constraint. Next, two iterative algorithms, named as trace-constraint-based projected gradient method and orthonormal-constraint-based projected gradient method, are proposed to solve the optimal problem, respectively. Then, convergence properties of both algorithms are established. Finally, extensive simulation results show the effectiveness of our methods based on detailed comparisons between the two boundary conditions. We believe the results reveal some interesting physical insights for the optimal control of directed networks.
Network performance for graphical control systems
International Nuclear Information System (INIS)
Clout, P.; Geib, M.; Westervelt, R.
1992-01-01
Vsystem is a toolbox for building graphically-based control systems. The real-tiem database component, Vaccess, includes all the networking support necessary to build multi-computer control systems. Vaccess has two modes of database access, synchronous and asynchronous. Vdraw is another component of Vsystem that allows developers and users to develop control screens and windows by drawing rather than programming. Based on X-windows, Vsystem provides the possibility of running Vdraw either on the workstation with the graphics or on the computer with the database. We have made some measurements on the cpu loading, elapsed time and the network loading to give some guidance in system configuration performance. It will be seen that asynchronous network access gives large performance increases and that the network database change notification protocol can be either more or less efficient than the X-window network protocol, depending on the graphical representation of the data. (author)
Plug & Play Control of Hydraulic Networks
DEFF Research Database (Denmark)
Jensen, Tom Nørgaard
2012-01-01
Process Control research program, which the work presented here is a part of. An industrial case study involving a large-scale hydraulic network with non-linear dynamics is studied. The hydraulic network underlies a district heating system, which provides heating water to a number of end-users in a city...
Filippov systems and quasi-synchronization control for switched networks.
Liu, Xiaoyang; Cao, Jinde; Yu, Wenwu
2012-09-01
This paper is concerned with the quasi-synchronization issue of linearly coupled networks with discontinuous nonlinear functions in each isolated node. Under the framework of Filippov systems, the existence and boundedness of solutions for such complex networks can be guaranteed by the matrix measure approach. A design method is presented for the synchronization controllers of coupled networks with non-identical discontinuous systems. Moreover, a sufficient condition is derived to ensure the quasi-synchronization of switched coupled complex networks with discontinuous isolated nodes, which could be controlled by some designed linear controllers. The obtained results extend the previous work on the synchronization issue of coupled complex networks with Lipschitz continuous conditions. Numerical simulations on the coupled chaotic systems are given to demonstrate the effectiveness of the theoretical results.
Protocol independent transmission method in software defined optical network
Liu, Yuze; Li, Hui; Hou, Yanfang; Qiu, Yajun; Ji, Yuefeng
2016-10-01
With the development of big data and cloud computing technology, the traditional software-defined network is facing new challenges (e.i., ubiquitous accessibility, higher bandwidth, more flexible management and greater security). Using a proprietary protocol or encoding format is a way to improve information security. However, the flow, which carried by proprietary protocol or code, cannot go through the traditional IP network. In addition, ultra- high-definition video transmission service once again become a hot spot. Traditionally, in the IP network, the Serial Digital Interface (SDI) signal must be compressed. This approach offers additional advantages but also bring some disadvantages such as signal degradation and high latency. To some extent, HD-SDI can also be regard as a proprietary protocol, which need transparent transmission such as optical channel. However, traditional optical networks cannot support flexible traffics . In response to aforementioned challenges for future network, one immediate solution would be to use NFV technology to abstract the network infrastructure and provide an all-optical switching topology graph for the SDN control plane. This paper proposes a new service-based software defined optical network architecture, including an infrastructure layer, a virtualization layer, a service abstract layer and an application layer. We then dwell on the corresponding service providing method in order to implement the protocol-independent transport. Finally, we experimentally evaluate that proposed service providing method can be applied to transmit the HD-SDI signal in the software-defined optical network.
Structurally robust control of complex networks
Nacher, Jose C.; Akutsu, Tatsuya
2015-01-01
Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role.
Synchronizability on complex networks via pinning control
Indian Academy of Sciences (India)
article/fulltext/pram/080/04/0593-0606 ... Numerical simulations show that different pinning strategies have different pinning synchronizability on the same complex network, and the synchronizability with pinning control is consistent with one ...
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei; Liu, Zhaoxi
2014-01-01
This paper reviews the existing congestion management methods for distribution networks with high penetration of DERs documented in the recent research literatures. The congestion management methods for distribution networks reviewed can be grouped into two categories – market methods and direct...... control methods. The market methods consist of dynamic tariff, distribution capacity market, shadow price and flexible service market. The direct control methods are comprised of network reconfiguration, reactive power control and active power control. Based on the review of the existing methods......, the authors suggest a priority list of the existing methods....
Network device interface for digitally interfacing data channels to a controller via a network
Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)
2009-01-01
A communications system and method are provided for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. Network device interfaces associated with different data channels can coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.
Topology control with IPD network creation games
International Nuclear Information System (INIS)
Scholz, Jan C; Greiner, Martin O W
2007-01-01
Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems
Topology control with IPD network creation games
Energy Technology Data Exchange (ETDEWEB)
Scholz, Jan C [Frankfurt Institute for Advanced Studies and Frankfurt International Graduate School for Science, Johann Wolfgang Goethe Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt (Germany); Greiner, Martin O W [Corporate Technology, Information and Communications, Siemens AG, D-81730 Munich (Germany)
2007-06-15
Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems.
Decentralized Networked Control of Building Structures
Czech Academy of Sciences Publication Activity Database
Bakule, Lubomír; Rehák, Branislav; Papík, Martin
2016-01-01
Roč. 31, č. 11 (2016), s. 871-886 ISSN 1093-9687 R&D Projects: GA ČR GA13-02149S Institutional support: RVO:67985556 Keywords : decentralized control * networked control * building structures Subject RIV: BC - Control Systems Theory Impact factor: 5.786, year: 2016
Data acquisition and control network
International Nuclear Information System (INIS)
Hajjar, Victor.
1983-02-01
We have participated in the construction of the CELLO detector on the PETRA e + e - Collider in Hamburg in order to test some of the current high energy physics theories. Some 60.000 channels collecting the detector informations are connected to the main computer through the CAMAC acquisition system and specialized ROMULUS subsystems. Each of these subsystems is monitored by its dedicated microprocessor using a CAMAC dataway spy module. All these microprocessors are connected to the main computer through a ''STAR'' type network. Data are read out by the main computer (PDP11-45) and concentrated in a circular type buffer. They are then filtered and transfered to a PDP11-55, also in the network, for storing [fr
Birth control - slow release methods
... this page: //medlineplus.gov/ency/article/007555.htm Birth control - slow release methods To use the sharing features on this page, please enable JavaScript. Certain birth control methods contain man-made forms of hormones. ...
Network Communication for Low Level RF Control
International Nuclear Information System (INIS)
Liu Weiqing; Yin Chengke; Zhang Tongxuan; Fu Zechuan; Liu Jianfei
2009-01-01
Low Level RF (LLRF) control system for storage ring of Shanghai Synchrotron Radiation Facility (SSRF) has been built by digital technology. The settings of parameters and the feedback loop status are carried out through the network communication interface, and the local oscillation and clock, which is the important component of the digital LLRF control system, are also configured through network communication. NIOS II processor was employed as a core to build the embedded system with a real-time operating system MicroC/OS-II, finally Lightweight TCP/IP (LwIP) was used to achieve the communication interface. The communication network is stable after a long-term operation. (authors)
Method Accelerates Training Of Some Neural Networks
Shelton, Robert O.
1992-01-01
Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.
Control systems with network delay
Şabanoviç, Asif; Sabanovic, Asif; Ohnishi, Kouhei; Yashiro, Daisuke; Acer, Merve; Ş.-Behliloviç, Nadira; S.-Behlilovic, Nadira
2009-01-01
In this paper motion control systems with delay in measurement and control channels are discussed and a new structure of the observer-predictor is proposed. The feature of the proposed system is enforcement of the convergence in both the estimation and the prediction of the plant output in the presence of the variable, unknown delay in both measurement and in the control channels. The estimation is based on the available data – undelayed control input, the delayed measurement of position o...
Controllability and observability of Boolean networks arising from biology
Li, Rui; Yang, Meng; Chu, Tianguang
2015-02-01
Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train......In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system...
The network of global corporate control.
Vitali, Stefania; Glattfelder, James B; Battiston, Stefano
2011-01-01
The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic "super-entity" that raises new important issues both for researchers and policy makers.
Urgent epidemic control mechanism for aviation networks
Peng, Chengbin
2011-01-01
In the current century, the highly developed transportation system can not only boost the economy, but also greatly accelerate the spreading of epidemics. While some epidemic diseases may infect quite a number of people ahead of our awareness, the health care resources such as vaccines and the medical staff are usually locally or even globally insufficient. In this research, with the network of major aviation routes as an example, we present a method to determine the optimal locations to allocate the medical service in order to minimize the impact of the infectious disease with limited resources. Specifically, we demonstrate that when the medical resources are insufficient, we should concentrate our efforts on the travelers with the objective of effectively controlling the spreading rate of the epidemic diseases. © 2011 Springer-Verlag Berlin Heidelberg.
Methods in Logic Based Control
DEFF Research Database (Denmark)
Christensen, Georg Kronborg
1999-01-01
Desing and theory of Logic Based Control systems.Boolean Algebra, Karnaugh Map, Quine McClusky's algorithm. Sequential control design. Logic Based Control Method, Cascade Control Method. Implementation techniques: relay, pneumatic, TTL/CMOS,PAL and PLC- and Soft_PLC implementation. PLC...
Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.
Zhang, Yanjun; Tao, Gang; Chen, Mou
2016-09-01
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Adaptive Control Using a Neural Network Estimator and Dynamic Inversion
Ninomiya, Tetsujiro; Miyazawa, Yoshikazu
More and more UAVs are developed for various purposes and their flight controllers are required to have sufficient robustness and performance to realize their versatile missions. To design these sophisticated controller is pretty much time-consuming task by traditional design method. Neural network based adaptive control with dynamic inversion is applied to solve this problem. This method has two advantages. One is that the gain scheduling is not necessary because nonlinear dynamic inversion is applied to control nonlinear systems. The other is that neural network improves the controller performance by estimating parameters of the actual plant. Numerical examples show its effectiveness and its ability to adapt to modeling errors. This paper concludes that proposed method reduces the workload of controller design task and it has ability to adapt various errors of nonlinear systems.
NOVANET: communications network for a control system
International Nuclear Information System (INIS)
Hill, J.R.; Severyn, J.R.; VanArsdall, P.J.
1983-01-01
NOVANET is a control system oriented fiber optic local area network that was designed to meet the unique and often conflicting requirements of the Nova laser control system which will begin operation in 1984. The computers and data acquisition devices that form the distributed control system for a large laser fusion research facility need reliable, high speed communications. Both control/status messages and experimental data must be handled. A subset of NOVANET is currently operating on the two beam Novette laser system
Four Degree Freedom Robot Arm with Fuzzy Neural Network Control
Directory of Open Access Journals (Sweden)
Şinasi Arslan
2013-01-01
Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.
Exploiting traffic periodicity in industrial control networks
Barbosa, R.R.R.; Sadre, R.; Pras, Aiko
Industrial control systems play a major role in the operation of critical infrastructure assets. Due to the polling mechanisms typically used to retrieve data from field devices, industrial control network traffic exhibits strong periodic patterns. This paper presents a novel approach that uses
DEFF Research Database (Denmark)
Wang, Jiayuan; Yan, Ying; Dittmann, Lars
2013-01-01
This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...
Decentralized control of ecological and biological networks through Evolutionary Network Control
Directory of Open Access Journals (Sweden)
Alessandro Ferrarini
2016-09-01
Full Text Available Evolutionary Network Control (ENC has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data. In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets.
Optimization of stochastic discrete systems and control on complex networks computational networks
Lozovanu, Dmitrii
2014-01-01
This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...
Toward controlling perturbations in robotic sensor networks
Banerjee, Ashis G.; Majumder, Saikat R.
2014-06-01
Robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation1. The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation2. In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.
Chemical control methods and tools
Steven Manning; James. Miller
2011-01-01
After determining the best course of action for control of an invasive plant population, it is important to understand the variety of methods available to the integrated pest management professional. A variety of methods are now widely used in managing invasive plants in natural areas, including chemical, mechanical, and cultural control methods. Once the preferred...
Geometrical Methods for Power Network Analysis
Bellucci, Stefano; Gupta, Neeraj
2013-01-01
This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.
An architecture for designing fuzzy logic controllers using neural networks
Berenji, Hamid R.
1991-01-01
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.
Web Page Classification Method Using Neural Networks
Selamat, Ali; Omatu, Sigeru; Yanagimoto, Hidekazu; Fujinaka, Toru; Yoshioka, Michifumi
Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.
Spectral Analysis Methods of Social Networks
Directory of Open Access Journals (Sweden)
P. G. Klyucharev
2017-01-01
Full Text Available Online social networks (such as Facebook, Twitter, VKontakte, etc. being an important channel for disseminating information are often used to arrange an impact on the social consciousness for various purposes - from advertising products or services to the full-scale information war thereby making them to be a very relevant object of research. The paper reviewed the analysis methods of social networks (primarily, online, based on the spectral theory of graphs. Such methods use the spectrum of the social graph, i.e. a set of eigenvalues of its adjacency matrix, and also the eigenvectors of the adjacency matrix.Described measures of centrality (in particular, centrality based on the eigenvector and PageRank, which reflect a degree of impact one or another user of the social network has. A very popular PageRank measure uses, as a measure of centrality, the graph vertices, the final probabilities of the Markov chain, whose matrix of transition probabilities is calculated on the basis of the adjacency matrix of the social graph. The vector of final probabilities is an eigenvector of the matrix of transition probabilities.Presented a method of dividing the graph vertices into two groups. It is based on maximizing the network modularity by computing the eigenvector of the modularity matrix.Considered a method for detecting bots based on the non-randomness measure of a graph to be computed using the spectral coordinates of vertices - sets of eigenvector components of the adjacency matrix of a social graph.In general, there are a number of algorithms to analyse social networks based on the spectral theory of graphs. These algorithms show very good results, but their disadvantage is the relatively high (albeit polynomial computational complexity for large graphs.At the same time it is obvious that the practical application capacity of the spectral graph theory methods is still underestimated, and it may be used as a basis to develop new methods.The work
Active Noise Feedback Control Using a Neural Network
Directory of Open Access Journals (Sweden)
Zhang Qizhi
2001-01-01
Full Text Available The active noise control (ANC is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.
Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft
Burken, John J.
2007-01-01
The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Next Generation Network Routing and Control Plane
DEFF Research Database (Denmark)
Fu, Rong
proved, the dominating Border Gateway Protocol (BGP) cannot address all the issues that in inter-domain QoS routing. Thus a new protocol or network architecture has to be developed to be able to carry the inter-domain traffic with the QoS and TE consideration. Moreover, the current network control also...... (RACF) provides the platform that enables cooperation and ubiquitous integration between networks. In this paper, we investigate in the network architecture, protocols and algorithms for inter-domain QoS routing and traffic engineering. The PCE based inter-domain routing architecture is enhanced...... with Domain Path Vector based protocol that compute the domain level path dynamically for the further inter-domain path routing mechanism Backward Recursive Path Computation (BRPC). Furthermore, several algorithms are proposed to compute the domain-level path under more than one constrains (multi...
Tensor network methods for invariant theory
Biamonte, Jacob; Bergholm, Ville; Lanzagorta, Marco
2013-11-01
Invariant theory is concerned with functions that do not change under the action of a given group. Here we communicate an approach based on tensor networks to represent polynomial local unitary invariants of quantum states. This graphical approach provides an alternative to the polynomial equations that describe invariants, which often contain a large number of terms with coefficients raised to high powers. This approach also enables one to use known methods from tensor network theory (such as the matrix product state (MPS) factorization) when studying polynomial invariants. As our main example, we consider invariants of MPSs. We generate a family of tensor contractions resulting in a complete set of local unitary invariants that can be used to express the Rényi entropies. We find that the graphical approach to representing invariants can provide structural insight into the invariants being contracted, as well as an alternative, and sometimes much simpler, means to study polynomial invariants of quantum states. In addition, many tensor network methods, such as MPSs, contain excellent tools that can be applied in the study of invariants.
REAL TIME ANALYSIS OF WIRELESS CONTROLLER AREA NETWORK
Directory of Open Access Journals (Sweden)
Gerardine Immaculate Mary
2014-09-01
Full Text Available It is widely known that Control Area Networks (CAN are used in real-time, distributed and parallel processing which cover manufacture plants, humanoid robots, networking fields, etc., In applications where wireless conditions are encountered it is convenient to continue the exchange of CAN frames within the Wireless CAN (WCAN. The WCAN considered in this research is based on wireless token ring protocol (WTRP; a MAC protocol for wireless networks to reduce the number of retransmissions due to collision and the wired counterpart CAN attribute on message based communication. WCAN uses token frame method to provide channel access to the nodes in the system. This method allow all the nodes to share common broadcast channel by taken turns in transmitting upon receiving the token frame which is circulating within the network for specified amount of time. This method provides high throughput in bounded latency environment, consistent and predictable delays and good packet delivery ratio. The most important factor to consider when evaluating a control network is the end-to-end time delay between sensors, controllers, and actuators. The correct operation of a control system depends on the timeliness of the data coming over the network, and thus, a control network should be able to guarantee message delivery within a bounded transmission time. The proposed WCAN is modeled and simulated using QualNet, and its average end to end delay and packet delivery ratio (PDR are calculated. The parameters boundaries of WCAN are evaluated to guarantee a maximum throughput and a minimum latency time, in the case of wireless communications, precisely WCAN.
A microcomputer network for the control of digitising machines
International Nuclear Information System (INIS)
Seller, P.
1981-01-01
A distributed microcomputing network operates in the Bubble Chamber Research Group Scanning Laboratory at the Rutherford and Appleton Laboratories. A microcomputer at each digitising table buffers information, controls the functioning of the table and enhances the machine/operator interface. The system consists of fourteen microcomputers together with a VAX 11/780 computer used for data analysis. These are inter-connected via a packet switched network. This paper will describe the features of the combined system, including the distributed computing architecture and the packet switched method of communication. This paper will also describe in detail a high speed packet switching controller used as a central node of the network. This controller is a multiprocessor microcomputer system with eighteen central processor units, thirty-four direct memory access channels and thirty-four prioritorised and vectored interrupt channels. This microcomputer is of general interest as a communications controller due to its totally programmable nature. (orig.)
Tensor network method for reversible classical computation
Yang, Zhi-Cheng; Kourtis, Stefanos; Chamon, Claudio; Mucciolo, Eduardo R.; Ruckenstein, Andrei E.
2018-03-01
We develop a tensor network technique that can solve universal reversible classical computational problems, formulated as vertex models on a square lattice [Nat. Commun. 8, 15303 (2017), 10.1038/ncomms15303]. By encoding the truth table of each vertex constraint in a tensor, the total number of solutions compatible with partial inputs and outputs at the boundary can be represented as the full contraction of a tensor network. We introduce an iterative compression-decimation (ICD) scheme that performs this contraction efficiently. The ICD algorithm first propagates local constraints to longer ranges via repeated contraction-decomposition sweeps over all lattice bonds, thus achieving compression on a given length scale. It then decimates the lattice via coarse-graining tensor contractions. Repeated iterations of these two steps gradually collapse the tensor network and ultimately yield the exact tensor trace for large systems, without the need for manual control of tensor dimensions. Our protocol allows us to obtain the exact number of solutions for computations where a naive enumeration would take astronomically long times.
Neural Network Analysis of Pilot Landing Control in Real Flight
Mori, Ryota; Suzuki, Shinji; Masui, Kazuya; Tomita, Hiroshi
A methodology for analysis of a pilots' landing control at the visual approach has been developed using a neural network modeling. While our previous study analyzed flight simulator operations, this paper describes the analysis of a real flight landing case. An experimental method which utilizes image processing of recorded video data is developed to obtain necessary data such as time histories of visual cues and control inputs. The effectiveness of this proposed method is confirmed by comparing values and analysis results from the video data with results obtained using GPS/INS data. It is expected that these methods can be used to reveal the characteristic of pilot control in real flight operation.
Controls from remote through Social networks
Directory of Open Access Journals (Sweden)
Alessandra Ingrao
2016-03-01
Full Text Available The Author focuses on the recently reformed provisions regulating the employer’s power to control from remote the employees’ activities (art. 4 of the Workers Statute, with particular regard to controls performed by means of Social networks.Such controls are in fact extremely powerful due to the versatile and multi-purpose character of Social networks, which may also be used as a working device. A widespread case law shows indeed that employer’s controls may cost a worker his job.Therefore, after the reform, all employees will have to read carefully the employer’s Privacy policies, before accessing socials during the worktime to express opinions and/or frustrations.
Network Operations Control Center Block 3 modifications
Garcia, E. A.
1978-01-01
The Network Operations Control Center (NOCC) Block 3 configuration is in the process of being upgraded to provide the capabilities required to support Voyager and Pioneer Venus 1978 Project commitments as well as to support Deep Space Station changes arising from the Mark 3 DSN Data Subsystem implementation. Information is given on the hardware and software changes necessary to implement such capabilities.
Deep networks for motor control functions
Directory of Open Access Journals (Sweden)
Max eBerniker
2015-03-01
Full Text Available The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body’s state (forward and inverse models, and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a nonlinear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control.
Synchronizability on complex networks via pinning control
Indian Academy of Sciences (India)
Synchronizability on complex networks via pinning control. YI LIANG1,2 and XINGYUAN WANG1,∗. 1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology,. Dalian 116024, China. 2Department of Electronics and Information Engineering, Yili Normal College,. Yining 835000, China. ∗.
Development of Active External Network Topology Module for Floodlight SDN Controller
Directory of Open Access Journals (Sweden)
A. A. Noskov
2015-01-01
Full Text Available Traditional network architecture is inflexible and complicated. This observation has led to a paradigm shift towards software-defined networking (SDN, where network management level is separated from data forwarding level. This change was made possible by control plane transfer from the switching equipment to software modules that run on a dedicated server, called the controller (or network operating system, or network applications, that work with this controller. Methods of representation, storage and communication interfaces with network topology elements are the most important aspects of network operating systems available to SDN user because performance of some key controller modules is heavily dependent on internal representation of the network topology. Notably, firewall and routing modules are examples of such modules. This article describes the methods used for presentation and storage of network topologies, as well as interface to the corresponding Floodlight modules. An alternative algorithm has been suggested and developed for message exchange conveying network topology alterations between the controller and network applications. Proposed algorithm makes implementation of module alerting based on subscription to the relevant events. API for interaction between controller and network applications has been developed. This algorithm and API formed the base for Topology Tracker module capable to inform network applications about the changes that had occurred in the network topology and also stores compact representation of the network to speed up the interaction process.
Mixed Methods Analysis of Enterprise Social Networks
DEFF Research Database (Denmark)
Behrendt, Sebastian; Richter, Alexander; Trier, Matthias
2014-01-01
The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...... insights into the multi-facetted phenomenon of ESN use. In order to address this issue, we first conducted a systematic literature review to identify available data dimensions and integrated them into a conceptual framework. We then adopted this framework as part of a mixed methods research approach...
Towards Controlling Latency in Wireless Networks
Bouacida, Nader
2017-04-24
Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications in the Internet (i.e., online gaming and VoIP), latency becomes a major issue for the development of wireless technology. Taking advantage of the significant decline in memory prices, industrialists equip the network devices with larger buffering capacities to improve the network throughput by limiting packets drops. Over-buffering results in increasing the time that packets spend in the queues and, thus, introducing more latency in networks. This phenomenon is known as “bufferbloat”. While throughput is the dominant performance metric, latency also has a huge impact on user experience not only for real-time applications but also for common applications like web browsing, which is sensitive to latencies in order of hundreds of milliseconds. Concerns have arisen about designing sophisticated queue management schemes to mitigate the effects of such phenomenon. My thesis research aims to solve bufferbloat problem in both traditional half-duplex and cutting-edge full-duplex wireless systems by reducing delay while maximizing wireless links utilization and fairness. Our work shed lights on buffer management algorithms behavior in wireless networks and their ability to reduce latency resulting from excessive queuing delays inside oversized static network buffers without a significant loss in other network metrics. First of all, we address the problem of buffer management in wireless full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. Compared to the default case, our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases. In the second part of this thesis
Wireless Sensor/Actuator Network Design for Mobile Control Applications
Directory of Open Access Journals (Sweden)
Youxian Sung
2007-10-01
Full Text Available Wireless sensor/actuator networks (WSANs are emerging as a new generationof sensor networks. Serving as the backbone of control applications, WSANs will enablean unprecedented degree of distributed and mobile control. However, the unreliability ofwireless communications and the real-time requirements of control applications raise greatchallenges for WSAN design. With emphasis on the reliability issue, this paper presents anapplication-level design methodology for WSANs in mobile control applications. Thesolution is generic in that it is independent of the underlying platforms, environment,control system models, and controller design. To capture the link quality characteristics interms of packet loss rate, experiments are conducted on a real WSAN system. From theexperimental observations, a simple yet efficient method is proposed to deal withunpredictable packet loss on actuator nodes. Trace-based simulations give promisingresults, which demonstrate the effectiveness of the proposed approach.
Adaptive model predictive process control using neural networks
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
1997-01-01
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
The Adaptive Neural Network Control of Quadrotor Helicopter
Directory of Open Access Journals (Sweden)
A. S. Yushenko
2017-01-01
Full Text Available The current steady-rising interest in using the unmanned multi-rotor aerial vehicles (UMAV designed to solve a wide range of tasks is, mainly, due to their simple design and high weight-carrying capacity as compared to classical helicopter options. Unfortunately, to solve a problem of multi-copter control is complicated because of essential nonlinearity and environmental perturbations. The most widely spread PID controllers and linear-quadratic regulators do not quite well cope with this task. The need arises for the prompt adjustment of PID controller coefficients in the course of operation or their complete re-tuning in cases of changing parameters of the control object.One of the control methods under changing conditions is the use of the sliding mode. This technology enables us to reach the stabilization and proper operation of the controlled system even under accidental external exposures and when there is a lack of the reasonably accurate mathematical model of the control object. The sliding principle is to ensure the system motion in the immediate vicinity of the sliding surface in the phase space. On the other hand, the sliding mode has some essential disadvantages. The most significant one is the high-frequency jitter of the system near the sliding surface. The sliding mode also implies the complete knowledge of the system dynamics. Various methods have been proposed to eliminate these drawbacks. For example, A.G. Aissaoui’s, H. Abid’s and M. Abid’s paper describes the application of fuzzy logic to control a drive and in Lon-Chen Hung’s and Hung-Yuan Chung’s paper an artificial neural network is used for the manipulator control.This paper presents a method of the quad-copter control with the aid of a neural network controller. This method enables us to control the system without a priori information on parameters of the dynamic model of the controlled object. The main neural network is a MIMO (“Multiple Input Multiple
A hyperstable neural network for the modelling and control of ...
Indian Academy of Sciences (India)
A hyperstable neural network for the modelling and control of nonlinear systems ... Computer control; neural networks; nonlinear systems; adaptive control. ... control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure.
Flexible body control using neural networks
Mccullough, Claire L.
1992-01-01
Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.
Parameter estimation methods for chaotic intercellular networks.
Directory of Open Access Journals (Sweden)
Inés P Mariño
Full Text Available We have investigated simulation-based techniques for parameter estimation in chaotic intercellular networks. The proposed methodology combines a synchronization-based framework for parameter estimation in coupled chaotic systems with some state-of-the-art computational inference methods borrowed from the field of computational statistics. The first method is a stochastic optimization algorithm, known as accelerated random search method, and the other two techniques are based on approximate Bayesian computation. The latter is a general methodology for non-parametric inference that can be applied to practically any system of interest. The first method based on approximate Bayesian computation is a Markov Chain Monte Carlo scheme that generates a series of random parameter realizations for which a low synchronization error is guaranteed. We show that accurate parameter estimates can be obtained by averaging over these realizations. The second ABC-based technique is a Sequential Monte Carlo scheme. The algorithm generates a sequence of "populations", i.e., sets of randomly generated parameter values, where the members of a certain population attain a synchronization error that is lesser than the error attained by members of the previous population. Again, we show that accurate estimates can be obtained by averaging over the parameter values in the last population of the sequence. We have analysed how effective these methods are from a computational perspective. For the numerical simulations we have considered a network that consists of two modified repressilators with identical parameters, coupled by the fast diffusion of the autoinducer across the cell membranes.
Control Plane Strategies for Elastic Optical Networks
DEFF Research Database (Denmark)
Turus, Ioan
(Generalized Multi-Protocol Label Switching)-based control framework in accordance with existing IETF standards and recommendations. The usual approach of extending capacity in transport networks by incrementally adding more optical resources results in a very inefficient usage and determines a high power...... Networks (EONs) concept is proposed as a solution to enable a more flexible handling of the optical capacity and allows an increase of available capacity over the existing optical infrastructure. One main requirement for enabling EONs is to have a flexible spectrum structure (i.e.Flex-Grid) which allows...... the spectrum to be used as an on-demand resource. Flex-Grid raises new challenges for controlling the dynamic spectrum slots environment. This thesis addresses, as part of the Celtic project “Elastic Optical Networks” (EONet), the control of Flex-Grid architectures by extending the capabilities of a GMPLS...
Optical network control plane for multi-domain networking
DEFF Research Database (Denmark)
Manolova, Anna Vasileva
process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... that the applied routing protocol and the topology of the multi-domain nework have very strong influence on the efficiency of the applied restoration techniques. Finally, different challenges of the integration of the GMPLS control framework with the novel Optical Burst Switching technology are analyzed. Existing...
Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System
Wang, Shin-Ywan
2012-01-01
The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.
Evolution of Controllability in Interbank Networks
Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido
2013-04-01
The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected ``hub'' institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.
Directory of Open Access Journals (Sweden)
Xiao-Hong Li
2014-01-01
Full Text Available Cooperative communication (CC is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime.
Networked control of microgrid system of systems
Mahmoud, Magdi S.; Rahman, Mohamed Saif Ur; AL-Sunni, Fouad M.
2016-08-01
The microgrid has made its mark in distributed generation and has attracted widespread research. However, microgrid is a complex system which needs to be viewed from an intelligent system of systems perspective. In this paper, a network control system of systems is designed for the islanded microgrid system consisting of three distributed generation units as three subsystems supplying a load. The controller stabilises the microgrid system in the presence of communication infractions such as packet dropouts and delays. Simulation results are included to elucidate the effectiveness of the proposed control strategy.
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav; Herzallah, R.
2017-01-01
Roč. 47, č. 3 (2017), s. 394-404 ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 2.350, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Resilient distributed control in the presence of misbehaving agents in networked control systems.
Zeng, Wente; Chow, Mo-Yuen
2014-11-01
In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks.
DEFF Research Database (Denmark)
Wang, Jiayuan; Yan, Ying; Dittmann, Lars
2013-01-01
This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...... and enable wider areas' energy efficiency networking. The energy related information of the networks and the types of the traffic flows are collected and utilized for the end-to-end QoS provision. Dynamic network simulation results show that by applying different routing algorithms according to the type...
Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network
Directory of Open Access Journals (Sweden)
Kazuhiko Hiramoto
2018-01-01
Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.
Egerstedt, Magnus; Martini, Simone; Cao, Ming; Camlibel, Kanat; Bicchi, Antonio
As networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multiagent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to
Control strategies for power distribution networks with electric vehicles integration
DEFF Research Database (Denmark)
Hu, Junjie
control, market based control, and price control. The thesis investigates new approaches for distribution networks congestion management. It suggests and develops a market based control for distribution grid congestion management. The general equilibrium market mechanism is utilized in the operation...... of the ii market. To build a complete solution for integration of EVs into the distribution network, a price coordinated hierarchical scheduling system is proposed which can well characterize the involved actors in the smart grid. With this system, we demonstrate that it is possible to schedule the charging...... scheme of EVs according to the users' energy driving requirements and the forecasted day-ahead electricity market price. Several electric vehicle eet operators are specied to manage the electric vehicle eets. The method of market based control can then be used by the DSO to interact with the electric...
Adaptive control of call acceptance in WCDMA network
Directory of Open Access Journals (Sweden)
Milan Manojle Šunjevarić
2013-10-01
Full Text Available In this paper, an overview of the algorithms for access control in mobile wireless networks is presented. A review of adaptive control methods of accepting a call in WCDMA networks is discussed, based on the overview of the algorithms used for this purpose, and their comparison. Appropriate comments and conculsions in comparison with the basic characteristics of these algorithms are given. The OVSF codes are explained as well as how the allocation method influences the capacity and probability of blocking.. Introduction We are witnessing a steady increase in the number of demands placed upon modern wireless networks. New applications and an increasing number of users as well as user activities growth in recent years reinforce the need for an efficient use of the spectrum and its proper distribution among different applications and classes of services. Besides humans, the last few years saw different computers, machines, applications, and, in the future, many other devices, RFID applications, and finally networked objects, as a new kind of wireless networks "users". Because of the exceptional rise in the number of users, the demands placed upon modern wireless networks are becoming larger, and spectrum management plays an important role. For these reasons, choosing an appropriate call admission control algorithm is of great importance. Multiple access and resource management in wireless networks Radio resource management of mobile networks is a set of algorithms to manage the use of radio resources with the aim is to maximize the total capacity of wireless systems with equal distribution of resources to users. Management of radio resources in cellular networks is usually located in the base station controller, the base station and the mobile terminal, and is based on decisions made on appropriate measurement and feedback. It is often defined as the maximum volume of traffic load that the system can provide for some of the requirements for the
Call Admission Control in Mobile Cellular Networks
Ghosh, Sanchita
2013-01-01
Call Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently. CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators. The second approach is concerned with formulation of CAC ...
Neural node network and model, and method of teaching same
Parlos, Alexander G. (Inventor); Atiya, Amir F. (Inventor); Fernandez, Benito (Inventor); Tsai, Wei K. (Inventor); Chong, Kil T. (Inventor)
1995-01-01
The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.
Application of neural networks to seismic active control
International Nuclear Information System (INIS)
Tang, Yu.
1995-01-01
An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads
A comprehensive Network Security Risk Model for process control networks.
Henry, Matthew H; Haimes, Yacov Y
2009-02-01
The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.
A new hierarchical method to find community structure in networks
Saoud, Bilal; Moussaoui, Abdelouahab
2018-04-01
Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.
Flexible Tube-Based Network Control, Phase I
National Aeronautics and Space Administration — The Innovation Laboratory, Inc. builds a control system which controls the topology of an air traffic flow network and the network flow properties which enables Air...
An Artificial Neural Network Controller for Intelligent Transportation Systems Applications
1996-01-01
An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...
Crosstalk between pathways enhances the controllability of signalling networks.
Wang, Dingjie; Jin, Suoqin; Zou, Xiufen
2016-02-01
The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability.
Towards structural controllability of local-world networks
International Nuclear Information System (INIS)
Sun, Shiwen; Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi
2016-01-01
Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.
Towards structural controllability of local-world networks
Energy Technology Data Exchange (ETDEWEB)
Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)
2016-05-20
Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.
Analysis and design of networked control systems
You, Keyou; Xie, Lihua
2015-01-01
This monograph focuses on characterizing the stability and performance consequences of inserting limited-capacity communication networks within a control loop. The text shows how integration of the ideas of control and estimation with those of communication and information theory can be used to provide important insights concerning several fundamental problems such as: · minimum data rate for stabilization of linear systems over noisy channels; · minimum network requirement for stabilization of linear systems over fading channels; and · stability of Kalman filtering with intermittent observations. A fundamental link is revealed between the topological entropy of linear dynamical systems and the capacities of communication channels. The design of a logarithmic quantizer for the stabilization of linear systems under various network environments is also extensively discussed and solutions to many problems of Kalman filtering with intermittent observations are de...
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
neural network based load frequency control for restructuring power
African Journals Online (AJOL)
2012-03-01
Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...
Efficient Access Control in Multimedia Social Networks
Sachan, Amit; Emmanuel, Sabu
Multimedia social networks (MMSNs) have provided a convenient way to share multimedia contents such as images, videos, blogs, etc. Contents shared by a person can be easily accessed by anybody else over the Internet. However, due to various privacy, security, and legal concerns people often want to selectively share the contents only with their friends, family, colleagues, etc. Access control mechanisms play an important role in this situation. With access control mechanisms one can decide the persons who can access a shared content and who cannot. But continuously growing content uploads and accesses, fine grained access control requirements (e.g. different access control parameters for different parts in a picture), and specific access control requirements for multimedia contents can make the time complexity of access control to be very large. So, it is important to study an efficient access control mechanism suitable for MMSNs. In this chapter we present an efficient bit-vector transform based access control mechanism for MMSNs. The proposed approach is also compatible with other requirements of MMSNs, such as access rights modification, content deletion, etc. Mathematical analysis and experimental results show the effectiveness and efficiency of our proposed approach.
Bilingual Contexts Modulate the Inhibitory Control Network
Directory of Open Access Journals (Sweden)
Jing Yang
2018-03-01
Full Text Available The present functional magnetic resonance imaging (fMRI study investigated influences of language contexts on inhibitory control and the underlying neural processes. Thirty Cantonese–Mandarin–English trilingual speakers, who were highly proficient in Cantonese (L1 and Mandarin (L2, and moderately proficient in English (L3, performed a picture-naming task in three dual-language contexts (L1-L2, L2-L3, and L1-L3. After each of the three naming tasks, participants performed a flanker task, measuring contextual effects on the inhibitory control system. Behavioral results showed a typical flanker effect in the L2-L3 and L1-L3 condition, but not in the L1-L2 condition, which indicates contextual facilitation on inhibitory control performance by the L1-L2 context. Whole brain analysis of the fMRI data acquired during the flanker tasks showed more neural activations in the right prefrontal cortex and subcortical areas in the L2-L3 and L1-L3 condition on one hand as compared to the L1-L2 condition on the other hand, suggesting greater involvement of the cognitive control areas when participants were performing the flanker task in L2-L3 and L1-L3 contexts. Effective connectivity analyses displayed a cortical-subcortical-cerebellar circuitry for inhibitory control in the trilinguals. However, contrary to the right-lateralized network in the L1-L2 condition, functional networks for inhibitory control in the L2-L3 and L1-L3 condition are less integrated and more left-lateralized. These findings provide a novel perspective for investigating the interaction between bilingualism (multilingualism and inhibitory control by demonstrating instant behavioral effects and neural plasticity as a function of changes in global language contexts.
Controller area network for monitor and control in ALMA
Brooks, Michael J.
2000-06-01
The Controller Area Network (CAN), initially developed for the automotive industry, is becoming increasingly popular in industrial process control applications. The need for distributed low data rate monitor and control networking in industry is similar to the needs of the various instrumentation and support equipment in a modern radio telescope. In particular, immunity to noise and low radio frequency emission characteristics are common to both domains. The Atacama Large Millimeter Array project has adopted CAN technology for use in local monitor and control applications at each of its 64 antennas. A standard interface slave node providing flexible I/O options is under development and a simple application-level protocol making use of CAN to access these nodes in a master/slave fashion has been implemented. This paper will present the work which has been completed to date including experiences in the use of CAN in an astronomical environment. In addition, analysis and simulation of CAN networks is compared with the performance of our implementation in the lab.
Toomarian, N.; Kirkham, Harold
1994-01-01
This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.
The network control system of high-bay warehouse
Directory of Open Access Journals (Sweden)
Malaka Julian
2017-01-01
Full Text Available Presentation of developing a method of automation of the storage process using electric drives with frequency converters, logic control and communication in industrial networks was the main purpose of this article. A connection structure was proposed to exchange information between devices that are part of a high-storage warehouse. It was assumed that modern communication protocols are used to synchronize the drives and to create a central control and information center in the PLC. The results of theoretical considerations were applied in practice by performing a laboratory model of a high storage warehouse with a developed automatic control system. Benefits of the proposed solutions was shown in the conclusions.
Event-triggered output feedback control for distributed networked systems.
Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa
2016-01-01
This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Dynamic power control for wireless backbone mesh networks: a survey
CSIR Research Space (South Africa)
Olwal, TO
2010-01-01
Full Text Available that there is a limited battery power available at each node but each user demands unlimited utility satisfaction, effective and efficient power control strategies ought to be in place [55]. These strategies may be designed to achieve user oriented quality... (i.e., throughput and delay) [69]. Methods from control theory have been used to analyse the dynamical effects and to design appropriate control strategies (e.g., [8]). The basic block Network Protocols and Algorithms ISSN 1943-3581 2010, Vol. 2...
Sensor Network Information Analytical Methods: Analysis of Similarities and Differences
Directory of Open Access Journals (Sweden)
Chen Jian
2014-04-01
Full Text Available In the Sensor Network information engineering literature, few references focus on the definition and design of Sensor Network information analytical methods. Among those that do are Munson, et al. and the ISO standards on functional size analysis. To avoid inconsistent vocabulary and potentially incorrect interpretation of data, Sensor Network information analytical methods must be better designed, including definitions, analysis principles, analysis rules, and base units. This paper analyzes the similarities and differences across three different views of analytical methods, and uses a process proposed for the design of Sensor Network information analytical methods to analyze two examples of such methods selected from the literature.
Dynamic analysis of biochemical network using complex network method
Directory of Open Access Journals (Sweden)
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
A normalized PID controller in networked control systems with varying time delays.
Tran, Hoang-Dung; Guan, Zhi-Hong; Dang, Xuan-Kien; Cheng, Xin-Ming; Yuan, Fu-Shun
2013-09-01
It requires not only simplicity and flexibility but also high specified stability and robustness of system to design a PI/PID controller in such complicated networked control systems (NCSs) with delays. By gain and phase margins approach, this paper proposes a novel normalized PI/PID controller for NCSs based on analyzing the stability and robustness of system under the effect of network-induced delays. Specifically, We take into account the total measured network delays to formulate the gain and phase margins of the closed-loop system in the form of a set of equations. With pre-specified values of gain and phase margins, this set of equations is then solved for calculating the closed forms of control parameters which enable us to propose the normalized PI/PID controller simultaneously satisfying the following two requirements: (1) simplicity without re-solving the optimization problem for a new process, (2) high flexibility to cope with large scale of random delays and deal with many different processes in different conditions of network. Furthermore, in our method, the upper bound of random delay can be estimated to indicate the operating domain of proposed PI/PID controller. Finally, simulation results are shown to demonstrate the advantages of our proposed controller in many situations of network-induced delays. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias
2013-04-24
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
Social network extraction based on Web: 1. Related superficial methods
Khairuddin Matyuso Nasution, Mahyuddin
2018-01-01
Often the nature of something affects methods to resolve the related issues about it. Likewise, methods to extract social networks from the Web, but involve the structured data types differently. This paper reveals several methods of social network extraction from the same sources that is Web: the basic superficial method, the underlying superficial method, the description superficial method, and the related superficial methods. In complexity we derive the inequalities between methods and so are their computations. In this case, we find that different results from the same tools make the difference from the more complex to the simpler: Extraction of social network by involving co-occurrence is more complex than using occurrences.
Synchronizability on complex networks via pinning control
Indian Academy of Sciences (India)
logical neural networks, electric power grids, social networks, etc., can be described by models of complex networks. So far, complex networks have been intensively investi- gated across many fields of science and engineering [1–5]. The synchronization of all dynamical nodes in a network is one of the most interesting and ...
Design of energy efficient optical networks with software enabled integrated control plane
DEFF Research Database (Denmark)
Wang, Jiayuan; Yan, Ying; Dittmann, Lars
2015-01-01
methods and the control over quality of service (QoS). The structure is defined as an overlay generalised multi-protocol label switching (GMPLS) control model. With the defined structure, the integrated control plane is able to gather information from different domains (i.e. optical core network...... energy consumption by proposing a new integrated control plane structure utilising Software Defined Networking technologies. The integrated control plane increases the efficiencies of exchanging control information across different network domains, while introducing new possibilities to the routing...... and the access networks), and enable energy efficiency networking over a wider area. In the case presented, the integrated control plane collects the network energy related information and the QoS requirements of different types of traffic. This information is used to define the specific group of traffic's (flow...
Atta Yaseen, Amer; Bayart, Mireille
2017-01-01
In this work, a new approach will be introduced as a development for the attack-tolerant scheme in the Networked Control System (NCS). The objective is to be able to detect an attack such as the Stuxnet case where the controller is reprogrammed and hijacked. Besides the ability to detect the stealthy controller hijacking attack, the advantage of this approach is that there is no need for a priori mathematical model of the controller. In order to implement the proposed scheme, a specific detector for the controller hijacking attack is designed. The performance of this scheme is evaluated be connected the detector to NCS with basic security elements such as Data Encryption Standard (DES), Message Digest (MD5), and timestamp. The detector is tested along with networked PI controller under stealthy hijacking attack. The test results of the proposed method show that the hijacked controller can be significantly detected and recovered.
Blockwise Frequency Domain Active Noise Controller Over Distributed Networks
Directory of Open Access Journals (Sweden)
Christian Antoñanzas
2016-04-01
Full Text Available This work presents a practical active noise control system composed of distributed and collaborative acoustic nodes. To this end, experimental tests have been carried out in a listening room with acoustic nodes equipped with loudspeakers and microphones. The communication among the nodes is simulated by software. We have considered a distributed algorithm based on the Filtered-x Least Mean Square (FxLMS method that introduces collaboration between nodes following an incremental strategy. For improving the processing efficiency in practical scenarios where data acquisition systems work by blocks of samples, the frequency-domain partitioned block technique has been used. Implementation aspects such as computational complexity, processing time of the network and convergence of the algorithm have been analyzed. Experimental results show that, without constraints in the network communications, the proposed distributed algorithm achieves the same performance as the centralized version. The performance of the proposed algorithm over a network with a given communication delay is also included.
Prediction-based association control scheme in dense femtocell networks
Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond
2017-01-01
The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.
2016-01-01
We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Predictive Control of Networked Multiagent Systems via Cloud Computing.
Liu, Guo-Ping
2017-01-18
This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.
Impact of the Cancer Prevention and Control Research Network
Ribisl, Kurt M.; Fernandez, Maria E.; Friedman, Daniela B.; Hannon, Peggy; Leeman, Jennifer; Moore, Alexis; Olson, Lindsay; Ory, Marcia; Risendal, Betsy; Sheble, Laura; Taylor, Vicky; Williams, Rebecca; Weiner, Bryan J.
2018-01-01
The Cancer Prevention and Control Research Network (CPCRN) is a thematic network dedicated to accelerating the adoption of evidence-based cancer prevention and control practices in communities by advancing dissemination and implementation science. Funded by the Centers for Disease Control and Prevention and National Cancer Institute, CPCRN has operated at two levels: Each participating Network Center conducts research projects with primarily local partners as well as multicenter collaborative research projects with state and national partners. Through multicenter collaboration, thematic networks leverage the expertise, resources, and partnerships of participating centers to conduct research projects collectively that might not be feasible individually. Although multicenter collaboration often is advocated, it is challenging to promote and assess. Using bibliometric network analysis and other graphical methods, this paper describes CPCRN’s multicenter publication progression from 2004 to 2014. Searching PubMed, Scopus, and Web of Science in 2014 identified 249 peer-reviewed CPCRN publications involving two or more centers out of 6,534 total. The research and public health impact of these multicenter collaborative projects initiated by CPCRN during that 10-year period were then examined. CPCRN established numerous workgroups around topics such as: 2-1-1, training and technical assistance, colorectal cancer control, federally qualified health centers, cancer survivorship, and human papillomavirus. The paper discusses the challenges that arise in promoting multicenter collaboration and the strategies that CPCRN uses to address those challenges. The lessons learned should broadly interest those seeking to promote multisite collaboration to address public health problems, such as cancer prevention and control. PMID:28215371
Method for controlling powertrain pumps
Sime, Karl Andrew; Spohn, Brian L; Demirovic, Besim; Martini, Ryan D; Miller, Jean Marie
2013-10-22
A method of controlling a pump supplying a fluid to a transmission includes sensing a requested power and an excess power for a powertrain. The requested power substantially meets the needs of the powertrain, while the excess power is not part of the requested power. The method includes sensing a triggering condition in response to the ability to convert the excess power into heat in the transmission, and determining that an operating temperature of the transmission is below a maximum. The method also includes determining a calibrated baseline and a dissipation command for the pump. The calibrated baseline command is configured to supply the fluid based upon the requested power, and the dissipation command is configured to supply additional fluid and consume the excess power with the pump. The method operates the pump at a combined command, which is equal to the calibrated baseline command plus the dissipation command.
Optimal control linear quadratic methods
Anderson, Brian D O
2007-01-01
This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the
Aslani, Mohammad; Mesgari, Mohammad Saadi; Wiering, Marco
2017-01-01
The transportation demand is rapidly growing in metropolises, resulting in chronic traffic con-gestions in dense downtown areas. Adaptive traffic signal control as the principle part of in-telligent transportation systems has a primary role to effectively reduce traffic congestion by making a
Specificity and robustness in transcription control networks.
Sengupta, Anirvan M; Djordjevic, Marko; Shraiman, Boris I
2002-02-19
Recognition by transcription factors of the regulatory DNA elements upstream of genes is the fundamental step in controlling gene expression. How does the necessity to provide stability with respect to mutation constrain the organization of transcription control networks? We examine the mutation load of a transcription factor interacting with a set of n regulatory response elements as a function of the factor/DNA binding specificity and conclude on theoretical grounds that the optimal specificity decreases with n. The predicted correlation between variability of binding sites (for a given transcription factor) and their number is supported by the genomic data for Escherichia coli. The analysis of E. coli genomic data was carried out using an algorithm suggested by the biophysical model of transcription factor/DNA binding. Complete results of the search for candidate transcription factor binding sites are available at http://www.physics.rockefeller.edu/~boris/public/search_ecoli.
A divisive spectral method for network community detection
International Nuclear Information System (INIS)
Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo
2016-01-01
Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)
A novel community detection method in bipartite networks
Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan
2018-02-01
Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.
Computation and control with neural networks
Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.
1990-08-01
As energies have increased exponentially with time, so have the size and complexity of accelerators and control systems. Neural networks (NNs) may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control, their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least-squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, a printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal-processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways, e.g. via token, slotted or parallel rings (or Steiner trees), for compatibility with existing systems. Problems and advantages of this approach, such as an optimal, real-time Turing machine, are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future "microprocessors" are predicted and requested on this basis.
Network-based production quality control
Kwon, Yongjin; Tseng, Bill; Chiou, Richard
2007-09-01
This study investigates the feasibility of remote quality control using a host of advanced automation equipment with Internet accessibility. Recent emphasis on product quality and reduction of waste stems from the dynamic, globalized and customer-driven market, which brings opportunities and threats to companies, depending on the response speed and production strategies. The current trends in industry also include a wide spread of distributed manufacturing systems, where design, production, and management facilities are geographically dispersed. This situation mandates not only the accessibility to remotely located production equipment for monitoring and control, but efficient means of responding to changing environment to counter process variations and diverse customer demands. To compete under such an environment, companies are striving to achieve 100%, sensor-based, automated inspection for zero-defect manufacturing. In this study, the Internet-based quality control scheme is referred to as "E-Quality for Manufacturing" or "EQM" for short. By its definition, EQM refers to a holistic approach to design and to embed efficient quality control functions in the context of network integrated manufacturing systems. Such system let designers located far away from the production facility to monitor, control and adjust the quality inspection processes as production design evolves.
Energy efficient topology control algorithm for wireless mesh networks
CSIR Research Space (South Africa)
Aron, FO
2008-08-01
Full Text Available The control of the topology of a network makes it possible for the network nodes to reduce their power of transmission while ensuring that network connectivity is preserved. This paper explains the need for energy consumption control in Wireless...
Modern computer networks and distributed intelligence in accelerator controls
International Nuclear Information System (INIS)
Briegel, C.
1991-01-01
Appropriate hardware and software network protocols are surveyed for accelerator control environments. Accelerator controls network topologies are discussed with respect to the following criteria: vertical versus horizontal and distributed versus centralized. Decision-making considerations are provided for accelerator network architecture specification. Current trends and implementations at Fermilab are discussed
Method of controlling reactor operation
International Nuclear Information System (INIS)
Masuda, Hiroyuki.
1982-01-01
Purpose: To prevent fuel failures, as well as enable easy control of power fluctuation due to transient changes in the xenon density. Method: Upon actuation of a control valve, heavy water containing poisons flows through a poison removing tower and the poisons are removed by chemical resins charged in the removing tower. The heavy water flows passing through the heavy water inlet pipe into a reactor core. As the result, neutron absorption in the reactor core is decreased to increase the reactor power. Then, neutron fluxes in the reactor core are detected and the reactor power from a power converter is compared with the output from a power-up ratio setter in a power judging device to control a helium control valve to thereby decrease or increase the heavy water level. While on the other hand, the output from an operation signal generator is sent to a memory unit for the heavy water level operation time and the control time for the liquid poison density is corrected based on the control time for the moderator liquid level, whereby fuel soundness can be maintained. (Yoshino, Y.)
Vulnerability analysis methods for road networks
Bíl, Michal; Vodák, Rostislav; Kubeček, Jan; Rebok, Tomáš; Svoboda, Tomáš
2014-05-01
Road networks rank among the most important lifelines of modern society. They can be damaged by either random or intentional events. Roads are also often affected by natural hazards, the impacts of which are both direct and indirect. Whereas direct impacts (e.g. roads damaged by a landslide or due to flooding) are localized in close proximity to the natural hazard occurrence, the indirect impacts can entail widespread service disabilities and considerable travel delays. The change in flows in the network may affect the population living far from the places originally impacted by the natural disaster. These effects are primarily possible due to the intrinsic nature of this system. The consequences and extent of the indirect costs also depend on the set of road links which were damaged, because the road links differ in terms of their importance. The more robust (interconnected) the road network is, the less time is usually needed to secure the serviceability of an area hit by a disaster. These kinds of networks also demonstrate a higher degree of resilience. Evaluating road network structures is therefore essential in any type of vulnerability and resilience analysis. There are a range of approaches used for evaluation of the vulnerability of a network and for identification of the weakest road links. Only few of them are, however, capable of simulating the impacts of the simultaneous closure of numerous links, which often occurs during a disaster. The primary problem is that in the case of a disaster, which usually has a large regional extent, the road network may remain disconnected. The majority of the commonly used indices use direct computation of the shortest paths or time between OD (origin - destination) pairs and therefore cannot be applied when the network breaks up into two or more components. Since extensive break-ups often occur in cases of major disasters, it is important to study the network vulnerability in these cases as well, so that appropriate
Su, Housheng
2013-01-01
Synchronization, consensus and flocking are ubiquitous requirements in networked systems. Pinning Control of Complex Networked Systems investigates these requirements by using the pinning control strategy, which aims to control the whole dynamical network with huge numbers of nodes by imposing controllers for only a fraction of the nodes. As the direct control of every node in a dynamical network with huge numbers of nodes might be impossible or unnecessary, it’s then very important to use the pinning control strategy for the synchronization of complex dynamical networks. The research on pinning control strategy in consensus and flocking of multi-agent systems can not only help us to better understand the mechanisms of natural collective phenomena, but also benefit applications in mobile sensor/robot networks. This book offers a valuable resource for researchers and engineers working in the fields of control theory and control engineering. Housheng Su is an Associate Professor at the Department of Contro...
Costs evaluation methodic of energy efficient computer network reengineering
Directory of Open Access Journals (Sweden)
S.A. Nesterenko
2016-09-01
Full Text Available A key direction of modern computer networks reengineering is their transfer to a new energy-saving technology IEEE 802.3az. To make a reasoned decision about the transition to the new technology is needed a technique that allows network engineers to answer the question about the economic feasibility of a network upgrade. Aim: The aim of this research is development of methodic for calculating the cost-effectiveness of energy-efficient computer network reengineering. Materials and Methods: The methodic uses analytical models for calculating power consumption of a computer network port operating in IEEE 802.3 standard and energy-efficient mode of IEEE 802.3az standard. For frame transmission time calculation in the communication channel used the queuing model. To determine the values of the network operation parameters proposed to use multiagent network monitoring method. Results: The methodic allows calculating the economic impact of a computer network transfer to energy-saving technology IEEE 802.3az. To determine the network performance parameters proposed to use network SNMP monitoring systems based on RMON MIB agents.
Network resource control for grid workflow management systems
Strijkers, R.J.; Cristea, M.; Korkhov, V.; Marchal, D.; Belloum, A.; Laat, C.de; Meijer, R.J.
2010-01-01
Grid workflow management systems automate the orchestration of scientific applications with large computational and data processing needs, but lack control over network resources. Consequently, the management system cannot prevent multiple communication intensive applications to compete for network
Study on the evolutionary optimization of the topology of network control systems
DEFF Research Database (Denmark)
Zhou, Z.; Chen, B.; Wang, H.
2010-01-01
Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology...... optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition...
Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen
2017-10-01
This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.
Modeling of Random Delays in Networked Control Systems
Directory of Open Access Journals (Sweden)
Yuan Ge
2013-01-01
Full Text Available In networked control systems (NCSs, the presence of communication networks in control loops causes many imperfections such as random delays, packet losses, multipacket transmission, and packet disordering. In fact, random delays are usually the most important problems and challenges in NCSs because, to some extent, other problems are often caused by random delays. In order to compensate for random delays which may lead to performance degradation and instability of NCSs, it is necessary to establish the mathematical model of random delays before compensation. In this paper, four major delay models are surveyed including constant delay model, mutually independent stochastic delay model, Markov chain model, and hidden Markov model. In each delay model, some promising compensation methods of delays are also addressed.
Equipment to Support Development of Neuronal Network Controlled Robots
2016-06-25
growth and training of neuronal neural networks to control robot arms. This work was done to learn the properties of the neurons and neuronal network , by...Equipment to Support Development of Neuronal Network Controlled Robots With this award, our team purchased an ALA 2-channel stimulus generator, an...peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Equipment to Support Development of Neuronal Network
Multilevel method for modeling large-scale networks.
Energy Technology Data Exchange (ETDEWEB)
Safro, I. M. (Mathematics and Computer Science)
2012-02-24
Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from
Do-it-yourself networks: a novel method of generating weighted networks.
Shanafelt, D W; Salau, K R; Baggio, J A
2017-11-01
Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.
Performance of the TRISTAN computer control network
International Nuclear Information System (INIS)
Koiso, H.; Abe, K.; Akiyama, A.; Katoh, T.; Kikutani, E.; Kurihara, N.; Kurokawa, S.; Oide, K.; Shinomoto, M.
1985-01-01
An N-to-N token ring network of twenty-four minicomputers controls the TRISTAN accelerator complex. The computers are linked by optical fiber cables with 10 Mbps transmission speed. The software system is based on the NODAL, a multi-computer interpreter language developed at CERN SPS. Typical messages exchanged between computers are NODAL programs and NODAL variables transmitted by the EXEC and the REMIT commands. These messages are exchanged as a cluster of packets whose maximum size is 512 bytes. At present, eleven minicomputers are connected to the network and the total length of the ring is 1.5 km. In this condition, the maximum attainable throughput is 980 kbytes/s. The response of a pair of an EXEC and a REMIT transactions which transmit a NODAL array A and one line of program 'REMIT A' and immediately remit the A is measured to be 95+0.039/chi/ ms, where /chi/ is the array size in byte. In ordinary accelerator operations, the maximum channel utilization is 2%, the average packet length is 96 bytes and the transmission rate is 10 kbytes/s
Design and Performance Analysis of Incremental Networked Predictive Control Systems.
Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua
2016-06-01
This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.
A Method for Automated Planning of FTTH Access Network Infrastructures
DEFF Research Database (Denmark)
Riaz, Muhammad Tahir; Pedersen, Jens Myrup; Madsen, Ole Brun
2005-01-01
In this paper a method for automated planning of Fiber to the Home (FTTH) access networks is proposed. We introduced a systematic approach for planning access network infrastructure. The GIS data and a set of algorithms were employed to make the planning process more automatic. The method explains...
Coordinator Role Mobility Method for Increasing the Life Expectancy of Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jurenoks Aleksejs
2017-05-01
Full Text Available The general problem of wireless sensor network nodes is the low-power batteries that significantly limit the life expectancy of a network. Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. The present paper proposes using a coordinator role mobility method for controlling the routing processes for energy balancing in nodes, which provides dynamic network reconfiguration possibilities. The method is designed to operate fully in the background and can be integrated into any exiting working system.
Burken, John J.
2005-01-01
This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.
Distributed Energy-Efficient Topology Control Algorithm in Home M2M Networks
Lee, Chao-Yang; Yang, Chu-Sing
2012-01-01
Because machine-to-machine (M2M) technology enables machines to communicate with each other without human intervention, it could play a big role in sensor network systems. Through wireless sensor network (WSN) gateways, various information can be collected by sensors for M2M systems. For home M2M networks, this study proposes a distributed energy-efficient topology control algorithm for both topology construction and topology maintenance. Topology control is an effective method of enhancing e...
Controllability of giant connected components in a directed network.
Liu, Xueming; Pan, Linqiang; Stanley, H Eugene; Gao, Jianxi
2017-04-01
When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=p_{m}. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by p_{m}〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.
Control method for prosthetic devices
Bozeman, Richard J., Jr. (Inventor)
1995-01-01
A control system and method for prosthetic devices is provided. The control system comprises a transducer for receiving movement from a body part for generating a sensing signal associated with that movement. The sensing signal is processed by a linearizer for linearizing the sensing signal to be a linear function of the magnitude of the distance moved by the body part. The linearized sensing signal is normalized to be a function of the entire range of body part movement from the no-shrug position of the moveable body part. The normalized signal is divided into a plurality of discrete command signals. The discrete command signals are used by typical converter devices which are in operational association with the prosthetic device. The converter device uses the discrete command signals for driving the moveable portions of the prosthetic device and its sub-prosthesis. The method for controlling a prosthetic device associated with the present invention comprises the steps of receiving the movement from the body part, generating a sensing signal in association with the movement of the body part, linearizing the sensing signal to be a linear function of the magnitude of the distance moved by the body part, normalizing the linear signal to be a function of the entire range of the body part movement, dividing the normalized signal into a plurality of discrete command signals, and implementing the plurality of discrete command signals for driving the respective moveable prosthesis device and its sub-prosthesis.
Anomaly-based Network Intrusion Detection Methods
Directory of Open Access Journals (Sweden)
Pavel Nevlud
2013-01-01
Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.
Method of controlling reactor powers
International Nuclear Information System (INIS)
Takayama, Yoshito.
1980-01-01
Purpose: To compensate power fluctuations with no distortions in the power distribution by varying the temperature of heavy water as moderators to thereby vary the density of boron therein. Method: A value set for the expected power is compared with a detected value for the power and the power deviation is inputted into a judging circuit. If the deviation exceeds a predetermined value, a power fluctuation amount signal from the judging circuit is inputted into a function generator, where the heavy water temperature fluctuation amount corresponding to the reactivity fluctuation amount is computed and outputted. The heavy water temperature fluctuation amount signal is compared with the detected heavy water temperature and a temperature deviation signal is inputted into a function generator, where the opening degree for a control valve is computed and an opening degree signal is supplied to a temperature control valve. The control valve, upon receiving the signal, regulates the amount of coolants to control the temperature of heavy water. (Sekiya, K.)
Accelerator and feedback control simulation using neural networks
International Nuclear Information System (INIS)
Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.
1991-05-01
Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs
Mean field methods for cortical network dynamics
DEFF Research Database (Denmark)
Hertz, J.; Lerchner, Alexander; Ahmadi, M.
2004-01-01
We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases with the stren......We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...
[Fleas and methods of control].
Franc, M
1994-12-01
Over 2,000 species of fleas parasitize mammals and birds. A simplified study of their morphology indicates for the main identification criteria. After listing the main families of fleas, the author outlines the identification of species most often encountered by veterinarians. Knowledge of the different types of flea parasitism and their life cycles is essential for effective control measures. Control is justified by the direct and indirect pathogenic roles of fleas (transmission of plague, tularaemia, myxomatosis, Dipylidium caninum). Effective agents are organochlorine compounds, organophosphorus compounds, pyrethroids and insect growth regulators, available in various formulations to destroy parasitic fleas on animals or in the environment. A novel method is to administer a systemic growth regulator to dogs and cats, which persists in the bloodstream and inhibits the reproduction of fleas which feed on a treated animal. Advantages and disadvantages of each formulation are presented.
Neural Network Based Montioring and Control of Fluidized Bed.
Energy Technology Data Exchange (ETDEWEB)
Bodruzzaman, M.; Essawy, M.A.
1996-04-01
The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to
Controllability of Weighted and Directed Networks with Nonidentical Node Dynamics
Directory of Open Access Journals (Sweden)
Linying Xiang
2013-01-01
Full Text Available The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are grouped into leaders and followers. Under this framework, the controllability of the controlled network can be decomposed into two independent problems: the controllability of the isolated leader subsystem and the controllability of the extended follower subsystem. Some necessary and/or sufficient conditions for the controllability of the leader-follower network are derived based on matrix theory and graph theory. In particular, it is shown that a single-leader network is controllable if it is a directed path or cycle, but it is uncontrollable for a complete digraph or a star digraph in general. Furthermore, some approaches to improving the controllability of a heterogenous network are presented. Some simulation examples are given for illustration and verification.
Dynamic Network Security Control Using Software Defined Networking
2016-03-24
rapidly respond to host level security events using SDN flow table updates, role-based flow classes , and Advanced Messaging Queuing Protocol to auto...the success of most organizations. One approach is to apply host and network-based security systems, which typically come in the form of antivirus or...intrusion detection/prevention products to man- age these threats. However, since traditional networks require manual configuration, an antivirus alert
Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity
Zhang, Jihui; Xu, Junqin
Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.
Self-generation of controller of an underwater robot with neural network
International Nuclear Information System (INIS)
Suto, T.; Ura, T.
1994-01-01
A self-organizing controller system is constructed based on artificial neural networks and applied to constant altitude swimming of the autonomous underwater robot PTEROA 150. The system consists of a controller and a forward model which calculates the values for evaluation as a result of control. Some methods are introduced for quick and appropriate adjustment of the controller network. Modification of the controller network is executed based on error-back-propagation method utilizing the forward model network. The forward model is divided into three sub-networks which represent dynamics of the vehicle, estimation of relative position to the seabed and calculation of the altitude. The proposed adaptive system is demonstrated in computer simulations where objective of a vehicle is keeping a constant altitude from seabed which is constituted of triangular ridges
An image segmentation method based on network clustering model
Jiao, Yang; Wu, Jianshe; Jiao, Licheng
2018-01-01
Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.
Robust backstepping control of induction motors using neural networks.
Kwan, C M; Lewis, F L
2000-01-01
In this paper, we present a new robust control technique for induction motors using neural networks (NNs). The method is systematic and robust to parameter variations. Motivated by the well-known backstepping design technique, we first treat certain signals in the system as fictitious control inputs to a simpler subsystem. A two-layer NN is used in this stage to design the fictitious controller. Then we apply a second two-layer NN to robustly realize the fictitious NN signals designed in the previous step. A new tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. A main advantage of our method is that we do not require regression matrices, so that no preliminary dynamical analysis is needed. Another salient feature of our NN approach is that the off-line learning phase is not needed. Full state feedback is needed for implementation. Load torque and rotor resistance can be unknown but bounded.
Optimization and control of a small angle ion source using an adaptive neural network controller
Energy Technology Data Exchange (ETDEWEB)
Brown, S.K.; Mead, W.C.; Bowling, P.S.; Jones, R.D.; Barnes, C.W.
1993-09-01
This project developed an automated controller based on an artificial neural network and evaluated its applicability in a real-time environment. This capability was developed within the context of a small angle negative ion source on the Discharge Test Stand at Los Alamos. The controller processes information obtained from the beam current waveform, developing a figure of merit (fom) to determine the ion source operating conditions. The fom is composed of the magnitude of the beam current, the stability of operation, and the quietness of the beam. Using no knowledge of operating conditions, the controller begins by making of rough scan of the four-dimensional operating surface. This surface uses as independent variables the anode and cathode temperatures, the hydrogen flow rate, and the arc voltage. `Me dependent variable is the fom described above. Once the rough approximation of the surface has been determined, the network formulates a model from which it determines the best operating point. The controller takes the ion source to that operating point for a reality check. As real data is fed in, the model of the operating surface is updated until the neural network`s model agrees with reality. The controller then uses a gradient ascent method to optimize the operation of the ion source. Initial tests of the controller indicate that it is remarkably capable. It has optimized the operation of the ion source on six different occasions bringing the beam to excellent quality and stability.
On the Control of Consensus Networks: Theory and Applications
Hudoba de Badyn, Mathias
Signed networks allow the study of positive and negative interactions between agents. In this thesis, three papers are presented that address controllability of networked dynamics. First, controllability of signed consensus networks is approached from a symmetry perspective, for both linear and nonlinear consensus protocols. It is shown that the graph-theoretic property of signed networks known as structural balance renders the consensus protocol uncontrollable when coupled with a certain type of symmetry. Stabilizability and output controllability of signed linear consensus is also examined, as well as a data-driven approach to finding bipartite consensus stemming from structural balance for signed nonlinear consensus. Second, an algorithm is constructed that allows one to grow a network while preserving controllability, and some generalizations of this algorithm are presented. Submodular optimization is used to analyze a second algorithm that adds nodes to a network to maximize the network connectivity.
Dynamic baseline detection method for power data network service
Chen, Wei
2017-08-01
This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
Application of reflective memory network in Tokamak fast controller
International Nuclear Information System (INIS)
Weng Chuqiao; Zhang Ming; Liu Rui; Zheng Wei; Zhuang Ge
2014-01-01
A specific application of reflective memory network in Tokamak fast controller was introduced in this paper. The PMC-5565 reflective memory card and ACC-5565 network hub were used to build a reflective memory real-time network to test its real- time function. The real-time, rapidity and determinacy of the time delay for fast controller controlling power device under the reflective memory network were tested in the LabVIEW RT real-time operation system. Depending on the reflective memory technology, the data in several fast controllers were synchronized, and multiple control tasks using a single control task were finished. The experiment results show that the reflective memory network can meet the real-time requirements for fast controller to perform the feedback control over devices. (authors)
Digital Signal Processing and Control for the Study of Gene Networks
Shin, Yong-Jun
2016-04-01
Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.
Digital Signal Processing and Control for the Study of Gene Networks.
Shin, Yong-Jun
2016-04-22
Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.
Event-triggered control design of linear networked systems with quantizations.
Hu, Songlin; Yue, Dong
2012-01-01
This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.
An introduction to neural network methods for differential equations
Yadav, Neha; Kumar, Manoj
2015-01-01
This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed...
Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics
Directory of Open Access Journals (Sweden)
Fei Gao
2015-01-01
Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.
Near-Minimal Node Control of Networked Evolutionary Games
Riehl, James Robert; Cao, Ming
2014-01-01
We investigate a problem related to the controllability of networked evolutionary games, first presenting an algorithm that computes a near-minimal set of nodes to drive all nodes in a tree network to a desired strategy, and then briefly discussing an algorithm that works for arbitrary networks
Training- and education system in network control rooms
Energy Technology Data Exchange (ETDEWEB)
Eichner, M.; Koentges, U.
1987-03-01
The training system for the project network control center of Duesseldorf city is described. Reasons for the implementation of the training system are presented, the main requirements are brought forward, and the envisaged solutions are described. The main training element is a network simulator including also the network protection systems. The software requirements are outlined.
Scalable Approaches to Control Network Dynamics: Prospects for City Networks
Motter, Adilson E.; Gray, Kimberly A.
2014-07-01
A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...
Learning Methods for Radial Basis Functions Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Classification Method in Integrated Information Network Using Vector Image Comparison
Directory of Open Access Journals (Sweden)
Zhou Yuan
2014-05-01
Full Text Available Wireless Integrated Information Network (WMN consists of integrated information that can get data from its surrounding, such as image, voice. To transmit information, large resource is required which decreases the service time of the network. In this paper we present a Classification Approach based on Vector Image Comparison (VIC for WMN that improve the service time of the network. The available methods for sub-region selection and conversion are also proposed.
IP2P K-means: an efficient method for data clustering on sensor networks
Directory of Open Access Journals (Sweden)
Peyman Mirhadi
2013-03-01
Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.
Spectral Methods for Immunization of Large Networks
Directory of Open Access Journals (Sweden)
Muhammad Ahmad
2017-11-01
Full Text Available Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbour nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint that are immunized (vaccinated, screened, filtered so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature. We test the performance of our algorithm on several real world graphs. Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic and efficiency (runtime and space complexity.
Semigroup methods for evolution equations on networks
Mugnolo, Delio
2014-01-01
This concise text is based on a series of lectures held only a few years ago and originally intended as an introduction to known results on linear hyperbolic and parabolic equations. Yet the topic of differential equations on graphs, ramified spaces, and more general network-like objects has recently gained significant momentum and, well beyond the confines of mathematics, there is a lively interdisciplinary discourse on all aspects of so-called complex networks. Such network-like structures can be found in virtually all branches of science, engineering and the humanities, and future research thus calls for solid theoretical foundations. This book is specifically devoted to the study of evolution equations – i.e., of time-dependent differential equations such as the heat equation, the wave equation, or the Schrödinger equation (quantum graphs) – bearing in mind that the majority of the literature in the last ten years on the subject of differential equations of graphs has been devoted to ellip...
Transcriptional control in the segmentation gene network of Drosophila.
Schroeder, Mark D; Pearce, Michael; Fak, John; Fan, HongQing; Unnerstall, Ulrich; Emberly, Eldon; Rajewsky, Nikolaus; Siggia, Eric D; Gaul, Ulrike
2004-09-01
The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of
Transcriptional control in the segmentation gene network of Drosophila.
Directory of Open Access Journals (Sweden)
Mark D Schroeder
2004-09-01
Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a
MoveSteg: A Method of Network Steganography Detection
Szczypiorski, Krzysztof; Tyl, Tomasz
2016-01-01
This article presents a new method for detecting a source point of time based network steganography - MoveSteg. A steganography carrier could be an example of multimedia stream made with packets. These packets are then delayed intentionally to send hidden information using time based steganography methods. The presented analysis describes a method that allows finding the source of steganography stream in network that is under our management.
A geometrical approach to control and controllability of nonlinear dynamical networks.
Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng
2016-04-14
In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.
Agent-Based Decentralized Control Method for Islanded Microgrids
DEFF Research Database (Denmark)
Li, Qiang; Chen, Feixiong; Chen, Minyou
2016-01-01
In this paper, an agent-based decentralized control model for islanded microgrids is proposed, which consists of a two-layer control structure. The bottom layer is the electrical distribution microgrid, while the top layer is the communication network composed of agents. An agent is regarded......) a systematic method is presented, which can be used to derive a set of control laws for agents from any given communication network, where only local information is needed. Furthermore, it has been seen that the output power supplied by distributed generators satisfies the load demand in the microgrid, when...
Impulse-induced localized control of chaos in starlike networks
Chacón, Ricardo; Palmero, Faustino; Cuevas-Maraver, Jesús
2016-06-01
Locally decreasing the impulse transmitted by periodic pulses is shown to be a reliable method of taming chaos in starlike networks of dissipative nonlinear oscillators, leading to both synchronous periodic states and equilibria (oscillation death). Specifically, the paradigmatic model of damped kicked rotators is studied in which it is assumed that when the rotators are driven synchronously, i.e., all driving pulses transmit the same impulse, the networks display chaotic dynamics. It is found that the taming effect of decreasing the impulse transmitted by the pulses acting on particular nodes strongly depends on their number and degree of connectivity. A theoretical analysis is given explaining the basic physical mechanism as well as the main features of the chaos-control scenario.
Analytic method for calculating properties of random walks on networks
Goldhirsch, I.; Gefen, Y.
1986-01-01
A method for calculating the properties of discrete random walks on networks is presented. The method divides complex networks into simpler units whose contribution to the mean first-passage time is calculated. The simplified network is then further iterated. The method is demonstrated by calculating mean first-passage times on a segment, a segment with a single dangling bond, a segment with many dangling bonds, and a looplike structure. The results are analyzed and related to the applicability of the Einstein relation between conductance and diffusion.
Method and system for localization in a wireless network
Havinga, Paul J.M.; Dil, B.J.
2011-01-01
The present invention relates to a method and system for localization in wireless networks. More in particular, the present invention is relates to Received Signal Strength (RSS) based localization in wireless networks, such as localization based on Radio Interferometric Positioning (RIP). Unlike
Diagrammatic perturbation methods in networks and sports ranking combinatorics
International Nuclear Information System (INIS)
Park, Juyong
2010-01-01
Analytic and computational tools developed in statistical physics are being increasingly applied to the study of complex networks. Here we present recent developments in the diagrammatic perturbation methods for the exponential random graph models, and apply them to the combinatoric problem of determining the ranking of nodes in directed networks that represent pairwise competitions
Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection
Directory of Open Access Journals (Sweden)
Kang Xie
2015-01-01
Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.
Method of optimization onboard communication network
Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.
2018-02-01
In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.
Robust Planning and Control Using Neural Networks
1990-06-30
hyperspace . We have been investigating CMAC neural networks with tapered, rather than rectangular, receptive fields. Such networks promise better (continuous...CMOS Logic Cell Arrays.’ UNH Intelligent Structures Group Report ECE.IS.90.01, Feb. 6,1990. Miller, W. T., Box, B. A., Whitney, E. C., and Glynn, J...M., ’Design and Implementation of a High Speed CMAC Neural Network Using Logic Programmable CMOS Logic Cell Arrays." To be presented at the Naval
DEFF Research Database (Denmark)
Hu, Junjie; Yang, Guangya; Bindner, Henrik W.
2016-01-01
This paper develops a network-constrained transactive control method to integrate distributed energy resources (DERs) into a power distribution system with the purpose of optimizing the operational cost of DERs and power losses of the distribution network as well as preventing grid problems...... including power transformer congestion and voltage violations. In this method, a price coordinator is introduced to facilitate the interaction between the distribution system operator (DSO) and aggregators in the smart grid. Electric vehicles are used to illustrate the proposed network......-constrained transactive control method. Mathematical models are presented to describe the operation of the control method. Finally, simulations are presented to show the effectiveness of the proposed method. To guarantee its optimality, we also checked the numerical results obtained with the network...
Connectivity Gradients Between the Default Mode and Attention Control Networks
Ferguson, Michael A.; Lopez-Larson, Melissa; Yurgelun-Todd, Deborah
2011-01-01
Abstract Functional imaging studies have shown reduced activity within the default mode network during attention-demanding tasks. The network circuitry underlying this suppression remains unclear. Proposed hypotheses include an attentional switch in the right anterior insula and reciprocal inhibition between the default mode and attention control networks. We analyzed resting state blood oxygen level dependent (BOLD) data from 1278 subjects from 26 sites and constructed whole-brain maps of functional connectivity between 7266 regions of interest (ROIs) covering the gray matter at ∼5 mm resolution. ROIs belonging to the default mode network and attention control network were identified based on correlation to six published seed locations. Spatial heterogeneity of correlation between the default mode and attention control networks was observed, with smoothly varying gradients in every hub of both networks that ranged smoothly from weakly but significantly anticorrelated to positively correlated. Such gradients were reproduced in 3 separate groups of subjects. Anticorrelated subregions were identified in major hubs of both networks. Between-network connectivity gradients strengthen with age during late adolescence and early adulthood, with associated sharpening of the boundaries of the default mode network, integration of the insula and cingulate with frontoparietal attentional regions, and decreasing correlation between the default mode and attention control networks with age. PMID:22076305
Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control
Gai, Wendong; Wang, Honglun; Zhang, Jing; Li, Yuxia
2013-01-01
An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed meth...
The consideration of dynamics and control in the design of heat exchanger networks
International Nuclear Information System (INIS)
Reimann, K.A.
1986-03-01
The heat exchanger network method is a way of abstracting the enthalpy and heat flows from the blueprints of a planned or existing processing plant. It enables a systematic design of a plant-wide heat recovery system which is optimal with regard to energy costs, capital costs and operational requirements. A heat exchanger network is a representation of all heat transfer relations between hot process streams and cold process streams within a plant. During the past ten years, the optimal design of heat exchanger networks (i.e. the optimal arrangement of heat transfer relations within a plant) has developed into a field of research of its own. Both, static methods ('interaction analysis') and dynamic methods ('process reaction curve analysis') from control theory have been used to explore the new field of heat exchanger network dynamics. As a major tool, an interactive, portable computer program for network simulation and controllability assessment has been developed (it is available as a design tool within the frame of the International Energy Agency). Based on the well-understood global parameters: effectiveness and NTU, which follow from the network design, some straightforward methods covering the following topics are presented: - 'paths' for control and disturbance signal transfer across the network, - locations of control bypasses around heat exchangers, and their capacity of emitting control signals or absorbing disturbances, - influence of the equipment besides the heat exchangers (which can be regarded as 'surrounding' the network, thus forming an 'associated' network). It has been found that networks which are designed according to the 'pinch-based' method have a potential for good controllability. It is shown how, using the freedoms given in the 'pinch-based' design and the above-mentioned methods, that potential is put into effect. (author)
Control and Reliability of Optical Networks in Multiprocessors
1993-12-22
Theory 43 6.3 Error Diagnosis 48 6.4 Summary 49 7. INTELLIGENT LASER DRIVE CONTROL 51 7.1 The Laser Drive Problem 51 7.2 Conventional Solutions 53 7.3...below. 2.1 Connection Density From a network theory perspective, the most interesting advantage of optical interconnect is its potential for...be Nt(tt + t,). For the prototype simulations, this report uses a different method: wormhole routing [36]. Rather than store the entirety of each
Adaptive Control for Robotic Manipulators Base on RBF Neural Network
Directory of Open Access Journals (Sweden)
MA Jing
2013-09-01
Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.
Siemaszko, Daniel
2015-06-15
The handling of weak networks with asymmetric loads and disturbances im- plies the accurate handling of the second-harmonic component that appears in an unbalanced network. This paper proposes a classic vector control approach using a PI-based controller with superior decoupling capabilities for operation in weak networks with unbalanced phase voltages. A synchronization method for weak unbalanced networks is detailed, with dedicated dimensioning rules. The use of a double-frame controller allows a current symmetry or controlled imbalance to be forced for compensation of power oscillations by controlling the negative current sequence. This paper also serves as a useful reminder of the proper way to cancel the inherent coupling effect due to the transformation to the synchronous rotating reference frame, and of basic considerations of the relationship between switching frequency and control bandwidth.
Gene networks controlling Arabidopsis thaliana flower development.
Ó'Maoiléidigh, Diarmuid Seosamh; Graciet, Emmanuelle; Wellmer, Frank
2014-01-01
The formation of flowers is one of the main models for studying the regulatory mechanisms that underlie plant development and evolution. Over the past three decades, extensive genetic and molecular analyses have led to the identification of a large number of key floral regulators and to detailed insights into how they control flower morphogenesis. In recent years, genome-wide approaches have been applied to obtaining a global view of the gene regulatory networks underlying flower formation. Furthermore, mathematical models have been developed that can simulate certain aspects of this process and drive further experimentation. Here, we review some of the main findings made in the field of Arabidopsis thaliana flower development, with an emphasis on recent advances. In particular, we discuss the activities of the floral organ identity factors, which are pivotal for the specification of the different types of floral organs, and explore the experimental avenues that may elucidate the molecular mechanisms and gene expression programs through which these master regulators of flower development act. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.
DEFF Research Database (Denmark)
Österlund, Tobias; Bordel, Sergio; Nielsen, Jens
2015-01-01
we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network......Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here...... as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes n(D) needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8...
Identifying overlapping communities in networks using evolutionary method
Zhan, Weihua; Guan, Jihong; Chen, Huahui; Niu, Jun; Jin, Guang
2016-01-01
Community structure is a typical property of real-world networks, and has been recognized as a key to understand the dynamics of the networked systems. In most of the networks overwhelming nodes apparently live in a community while there often exists a few nodes straddling several communities. Hence, an ideal algorithm for community detection is that which can identify the overlapping communities in these networks. We present an evolutionary method for detecting overlapping community structure in the network. To represent an overlapping division of a network, we develop an encoding scheme composed of two segments, the first one represents a disjoint partition and the second one represents an extension of the partition that allows of multiple memberships. We give two measures for the informativeness of a node, and present a coevolutionary scheme between two segments over the population for solving the overlapping partition of the network. Experimental results show this method can give a better solution to a network. It is also revealed that a best overlapping partition of the network might not be rooted from a best disjoint partition.
Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning
Directory of Open Access Journals (Sweden)
Jingsha He
2016-11-01
Full Text Available As the Internet becomes larger in scale, more complex in structure and more diversified in traffic, the number of crimes that utilize computer technologies is also increasing at a phenomenal rate. To react to the increasing number of computer crimes, the field of computer and network forensics has emerged. The general purpose of network forensics is to find malicious users or activities by gathering and dissecting firm evidences about computer crimes, e.g., hacking. However, due to the large volume of Internet traffic, not all the traffic captured and analyzed is valuable for investigation or confirmation. After analyzing some existing network forensics methods to identify common shortcomings, we propose in this paper a new network forensics method that uses a combination of network vulnerability and network evidence graph. In our proposed method, we use vulnerability evidence and reasoning algorithm to reconstruct attack scenarios and then backtrack the network packets to find the original evidences. Our proposed method can reconstruct attack scenarios effectively and then identify multi-staged attacks through evidential reasoning. Results of experiments show that the evidence graph constructed using our method is more complete and credible while possessing the reasoning capability.
Li, Xiaodi; Rakkiyappan, R.
2013-06-01
This paper considers the chaotic synchronization problem of neural networks with time-varying and distributed delays using impulsive control method. By utilizing the stability theory for impulsive functional differential equations, several impulsive control laws are derived to guarantee the exponential synchronization of neural networks with time-varying and distributed delays. It is shown that chaotic synchronization of the networks is heavily dependent on the designed impulsive controllers. Moreover, these conditions are expressed in terms of LMI and can be easily checked by MATLAB LMI toolbox. Finally, a numerical example and its simulation are given to show the effectiveness and advantage of the proposed control schemes.
Developing a dynamic control system for mine compressed air networks
Van Heerden, S.W.; Pelzer, R.; Marais, J.H.
2014-01-01
Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed via compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of a number of compressors feeding the ring from various locations in the network. While these mines have sophisticated control systems to control these compressors, they are not dynamic systems. Compresso...
TCP Congestion Control for the Networks with Markovian Jump Parameters
Directory of Open Access Journals (Sweden)
MOMENI, H. R.
2011-05-01
Full Text Available This paper is concerned with the problem of TCP congestion control for the class of communication networks with random parameters. The linear dynamic model of TCP New Reno in congestion avoidance mode is considered which contains round trip delays in both state and input. The randomness of link capacity, round trip time delay and the number of TCP sessions is modeled with a continuous-time finite state Markov process. An Active Queue Management (AQM technique is then used to adjust the queue level of the congested link to a predefined value. For this purpose, a dynamic output feedback controller with mode dependent parameters is synthesized to stochastically stabilize the TCP/AQM dynamics. The procedure of the control synthesis is implemented by solving a linear matrix inequality (LMI. The results are tested within a simulation example and the effectiveness of the proposed design method is verified.
Neural network output feedback control of robot formations.
Dierks, Travis; Jagannathan, Sarangapani
2010-04-01
In this paper, a combined kinematic/torque output feedback control law is developed for leader-follower-based formation control using backstepping to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers. A neural network (NN) is introduced to approximate the dynamics of the follower and its leader using online weight tuning. Furthermore, a novel NN observer is designed to estimate the linear and angular velocities of both the follower robot and its leader. It is shown, by using the Lyapunov theory, that the errors for the entire formation are uniformly ultimately bounded while relaxing the separation principle. In addition, the stability of the formation in the presence of obstacles, is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation are prevented. Numerical results are provided to verify the theoretical conjectures.
System Identification for Nonlinear Control Using Neural Networks
Stengel, Robert F.; Linse, Dennis J.
1990-01-01
An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.
Active Engine Mounting Control Algorithm Using Neural Network
Directory of Open Access Journals (Sweden)
Fadly Jashi Darsivan
2009-01-01
Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.
Directory of Open Access Journals (Sweden)
Tat-Bao-Thien Nguyen
2014-01-01
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Deterministic learning enhanced neutral network control of unmanned helicopter
Directory of Open Access Journals (Sweden)
Yiming Jiang
2016-11-01
Full Text Available In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.
Positive train control interoperability and networking research : final report.
2015-12-01
This document describes the initial development of an ITC PTC Shared Network (IPSN), a hosted : environment to support the distribution, configuration management, and IT governance of Interoperable : Train Control (ITC) Positive Train Control (PTC) s...
Using a Control System Ethernet Network as a Field Bus
De Van, William R; Lawson, Gregory S; Wagner, William H; Wantland, David M; Williams, Ernest
2005-01-01
A major component of a typical accelerator distributed control system (DCS) is a dedicated, large-scale local area communications network (LAN). The SNS EPICS-based control system uses a LAN based on the popular IEEE-802.3 set of standards (Ethernet). Since the control system network infrastructure is available throughout the facility, and since Ethernet-based controllers are readily available, it is tempting to use the control system LAN for "fieldbus" communications to low-level control devices (e.g. vacuum controllers; remote I/O). These devices may or may not be compatible with the high-level DCS protocols. This paper presents some of the benefits and risks of combining high-level DCS communications with low-level "field bus" communications on the same network, and describes measures taken at SNS to promote compatibility between devices connected to the control system network.
High-resolution method for evolving complex interface networks
Pan, Shucheng; Hu, Xiangyu Y.; Adams, Nikolaus A.
2018-04-01
In this paper we describe a high-resolution transport formulation of the regional level-set approach for an improved prediction of the evolution of complex interface networks. The novelty of this method is twofold: (i) construction of local level sets and reconstruction of a global level set, (ii) local transport of the interface network by employing high-order spatial discretization schemes for improved representation of complex topologies. Various numerical test cases of multi-region flow problems, including triple-point advection, single vortex flow, mean curvature flow, normal driven flow, dry foam dynamics and shock-bubble interaction show that the method is accurate and suitable for a wide range of complex interface-network evolutions. Its overall computational cost is comparable to the Semi-Lagrangian regional level-set method while the prediction accuracy is significantly improved. The approach thus offers a viable alternative to previous interface-network level-set method.
Distributed Estimation and Control for Robotic Networks
Simonetto, A.
2012-01-01
Mobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks.
Local control of cognitive radio networks
Doerr, C.; Grunwald, D.; Sicker, D.C.
2009-01-01
In a network deployment, a cognitive radio will have to perform two fundamental tasks. First, each cognitive radio needs to optimize its internal operation, and second, it needs to derive a configuration that will enable and optimize communication with other nodes in the network. This latter
Large maneuverable flight control using neural networks dynamic inversion
Yang, Enquan; Gao, Jinyuan
2003-09-01
An adaptive dynamic-inversion-based neural network is applied to aircraft large maneuverable flight control. Neural network is used to cancel the inversion error which may arise from imperfect modeling or approximate inversion. Simulation results for an aircraft model are presented to illustrate the performance of the flight control system.
A control model for district heating networks with storage
Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro
2014-01-01
In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and
Controller Placement Algorithms in Software Defined Network - A Review of Trends and Challenges
Directory of Open Access Journals (Sweden)
Yoon Si-Kee
2017-01-01
Full Text Available Traditional network architectures are complex to manage, comparatively static, rigid and difficult to make changes for new innovation. The proprietary devices in such architectures are based on manual configuration which are unwieldy and error-prone. Software Defined Network (SDN which is described as a new network paradigm that decouple the control plane from data plane are capable to solve today's network issues and improve the network performance. Nevertheless, among so many challenges and research opportunity in SDN, Controller Placement Problem (CPP is said to be the most important issues which can directly affect the overall network performance. Thus far, the issue regarding the CPP and its challenge has not been completely reviewed and discussed properly in any other papers. In this paper, we provide a comprehensive review on several optimized controller placement problem algorithms in SDN. This paper also highlights some limitations of the reviewed methods and also emphasizes on suitable approach to address the aforementioned problems.
Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control
Directory of Open Access Journals (Sweden)
Wendong Gai
2013-01-01
Full Text Available An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.
Neural network predictive control of a heat exchanger
2011-01-01
Abstract The study attempts to show that using the neural network predictive control (NNPC) structure for control of thermal processes can lead to energy savings. The advantage of the NNPC is that it is not a linear-model-based strategy and the control input constraints are directly included into the synthesis. In the designed approach, the neural network is used as a nonlinear process model to predict the future behaviour of the controlled process with distributed parameters. The ...
Topology Control in Aerial Multi-Beam Directional Networks
2017-04-24
Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology ...underlying challenges to topology control in multi-beam direction networks. Two topology control algorithms are developed: a centralized algorithm
Establishment of 2000 National Geodetic Control Network of China and It’s Technological Progress
Directory of Open Access Journals (Sweden)
CHEN Junyong
2007-02-01
Full Text Available Objectives: 2000’ National Geodetic Control Network of China is an important fundamental scientific engineering project in China. It consists of three parts which are establishment of 2000 National GPS Geodetic Network, its combination adjustment with national astro-geodetic network and 2000 National Gravity Fundamental network. It provides the high precise coordinate reference and gravity reference for three dimensional geo-center national coordinates system and gravity system, respectively. Additionally, it provides precise unified geometric and physical geodesy information for the economic construction, the national defense and the scientific research. Methods: 1. The larger number of data are processed in triple networks adjustment of 2000 National GPS Geodetic Network, which are chosen from the GPS monitoring stations, such as grade A, B of national GPS network , grade 1st and 2nd of national GPS network, crustal movement observation network of China, and others crustal deformation monitoring stations. Finally, the data of 2666 GPS stations are used in the data processing of 2000 National GPS Geodetic Network, including 124 external stations and 2542 internal stations. In order to the results of triple networks adjustment are corresponding to that of three dimensional geo-center coordinates system, ITRF 97 and epoch 2000.0 are chosen as the coordinate reference frame and epoch reference, respectively. The methods of “strong reference” and “weak reference” are combined used in the control data selection of triple networks adjustment. The scale and rotation scales are adopted for each sub network. The least square adjustment is firstly adopted in each sub network adjustment. The data of obvious abnormal baselines are found and rejected firstly. And the method of double factor robust estimation is adopted in the data processing. 2. The combined adjustment of 2000 National GPS Geodetic Network and national astro-geodetic network is
Robust Neural Network Control of Electrically Driven Robot Manipulator using Backstepping Approach
Directory of Open Access Journals (Sweden)
Seyed Ehsan Shafiei
2010-02-01
Full Text Available A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method. A simple two-step backstepping is considered for an nlink robotic system, and a feedforward neural controller is designed at second step where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by this neural controller. Bounds of network reconstruction error and other imprecisions are estimated adaptively and for compensating them, a robust control signal is added and modified. Stability analysis is performed by the Lyapunov direct method and performance efficiency of the proposed controller is justified by the simulations.
PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network
Pan, Yue; Song, Ping; Li, Kejie
The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.
A CoD-based stationary control policy for intervening in large gene regulatory networks.
Ghaffari, Noushin; Ivanov, Ivan; Qian, Xiaoning; Dougherty, Edward R
2011-10-18
One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network. In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model. The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Systems and methods for modeling and analyzing networks
Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W
2013-10-29
The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.
Global efficiency of structural networks mediates cognitive control in Mild Cognitive Impairment
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Rok Berlot
2016-12-01
Full Text Available Background: Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. Objective: To determine the contribution of both localised white matter tract damage and disruption of global network architecture to cognitive control, in older age and Mild Cognitive Impairment (MCI.Methods: 25 patients with MCI and 20 age, sex and intelligence-matched healthy volunteers were investigated with 3 Tesla structural magnetic resonance imaging (MRI. Cognitive control and episodic memory were evaluated with established tests. Structural network graphs were constructed from diffusion MRI-based whole-brain tractography. Their global measures were calculated using graph theory. Regression models utilized both global network metrics and microstructure of specific connections, known to be critical for each domain, to predict cognitive scores. Results: Global efficiency and the mean clustering coefficient of networks were reduced in MCI. Cognitive control was associated with global network topology. Episodic memory, in contrast, correlated with individual temporal tracts only. Relationships between cognitive control and network topology were attenuated by addition of single tract measures to regression models, consistent with a partial mediation effect. The mediation effect was stronger in MCI than healthy volunteers, explaining 23-36% of the effect of cingulum microstructure on cognitive control performance. Network clustering was a significant mediator in the relationship between tract microstructure and cognitive control in both groups. Conclusions: The status of critical connections and large-scale network topology are both important for maintenance of cognitive control in MCI. Mediation via large-scale networks is more important in patients with MCI than healthy volunteers. This effect is domain-specific, and true for cognitive
Designing communication and remote controlling of virtual instrument network system
International Nuclear Information System (INIS)
Lei Lin; Wang Houjun; Zhou Xue; Zhou Wenjian
2005-01-01
In this paper, a virtual instrument network through the LAN and finally remote control of virtual instruments is realized based on virtual instrument and LabWindows/CVI software platform. The virtual instrument network system is made up of three subsystems. There are server subsystem, telnet client subsystem and local instrument control subsystem. This paper introduced virtual instrument network structure in detail based on LabWindows. Application procedure design of virtual instrument network communication, the Client/the programming mode of the server, remote PC and server communication far realizing, the control power of the workstation is transmitted, server program and so on essential technical were introduced. And virtual instruments network may connect to entire Internet on. Above-mentioned technology, through measuring the application in the electronic measurement virtual instrument network that is already built up, has verified the actual using value of the technology. Experiment and application validate that this design is resultful
Epigenetics and Why Biological Networks are More Controllable than Expected
Motter, Adilson
2013-03-01
A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.
International Nuclear Information System (INIS)
Balasubramaniam, P.; Kalpana, M.; Rakkiyappan, R.
2012-01-01
Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov—Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method. (interdisciplinary physics and related areas of science and technology)
Study of complex networks using statistical physics methods
Chen, Yiping
The goal of this thesis is to study the behaviors of complex networks in several aspects using methods from statistical physics. Networks are structures that consist of nodes and links. By changing the way links connect to nodes, different complex network structures can be constructed such as Erdḧs-Renyi (ER) networks and scale-free (SF) networks. Complex networks have wide relevance to many real world problems, including the spread of disease in human society, message routing in the Internet, etc. In this thesis analytical and simulation results are obtained regarding optimal paths in disordered networks, fragmentation of social networks, and improved strategies for immunization against diseases. In the study of disordered systems, of particular current interest is the scaling behavior of the optimal path length ℓopt from strong disorder to weak disorder state for different weight distributions P(w). Here we derive analytically a new criterion. Using this criterion we find that for all P(w) that possess a strong-weak disorder crossover, the distributions p(ℓ) of the optimal path lengths display the same universal behavior. Fragmentation in social networks is also studied using methods from percolation theory. Recently, a new measure of fragmentation F has been developed in social network studies. For each removal of a subset of links or nodes, F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removal, and the total number of pairs in the original fully connected network. We study the statistical behavior of F using both analytical and numerical methods and relate it to the traditional measure of fragmentation, the relative size of the largest cluster, Pinfinity, used in percolation theory. Finally, we tried to find a better immunization strategy. It is widely accepted that the most efficient immunization strategies are based on "targeted" strategies. Here we propose a novel "equal graph
Electromagnetic field computation by network methods
Felsen, Leopold B; Russer, Peter
2009-01-01
This monograph proposes a systematic and rigorous treatment of electromagnetic field representations in complex structures. The book presents new strong models by combining important computational methods. This is the last book of the late Leopold Felsen.
An algebra-based method for inferring gene regulatory networks.
Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard
2014-03-26
The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the
Graph methods for the investigation of metabolic networks in parasitology.
Cottret, Ludovic; Jourdan, Fabien
2010-08-01
Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.
Luo, R.
2016-01-01
This thesis aims at developing efficient methods for control of large-scale systems by employing state-of-the-art control methods and optimization techniques. This thesis is divided into two parts. In the first part, we address dynamic traffic routing for urban transportation networks. In the second
Third-Order Leader-Following Consensus in a Nonlinear Multiagent Network via Impulsive Control
Directory of Open Access Journals (Sweden)
Xiaomei Li
2013-01-01
Full Text Available Many facts indicate that the impulsive control method is a finer method, which is simple, efficient, and low in cost, for achieving consensus. In this paper, based on graph theory, Lyapunov stability theory, and matrix theory, a novel impulsive control protocol is given to realize the consensus of the multiagent network. Numerical simulations are performed to verify the theoretical results.
Momentum integral network method for thermal-hydraulic transient analysis
International Nuclear Information System (INIS)
Van Tuyle, G.J.
1983-01-01
A new momentum integral network method has been developed, and tested in the MINET computer code. The method was developed in order to facilitate the transient analysis of complex fluid flow and heat transfer networks, such as those found in the balance of plant of power generating facilities. The method employed in the MINET code is a major extension of a momentum integral method reported by Meyer. Meyer integrated the momentum equation over several linked nodes, called a segment, and used a segment average pressure, evaluated from the pressures at both ends. Nodal mass and energy conservation determined nodal flows and enthalpies, accounting for fluid compression and thermal expansion
A link prediction method for heterogeneous networks based on BP neural network
Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu
2018-04-01
Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.
Artificial neural networks in variable process control: application in particleboard manufacture
Energy Technology Data Exchange (ETDEWEB)
Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.
2009-07-01
Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.
Reliable and Congestion Control Protocols for Wireless Sensor Networks
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Kirti Kharb
2016-01-01
Full Text Available The objective of this paper is to analyze review and different congestion control protocols that are employed at the transport layer and some of them working at the medium access control layer in wireless sensor networks. Firstly, a brief introduction is given about wireless sensor networks and how congestion occurs in such networks. Secondly, the concept of congestion is discussed. Thirdly, the reason of occurrence of congestion in wireless sensor networks is analyzed. After that, congestion control and why it is needed in the wireless sensor networks is discussed. Then, a brief review of different congestion control and reliable data transport mechanisms are discussed. Finally, a comparative analysis of different protocols is made depending on their performance on various parameters such as - traffic direction, energy conservation characteristic, efficiency etc. and the paper is concluded.
Robust receding horizon control for networked and distributed nonlinear systems
Li, Huiping
2017-01-01
This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel ...
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...
Integrated control system and method
Wang, Paul Sai Keat; Baldwin, Darryl; Kim, Myoungjin
2013-10-29
An integrated control system for use with an engine connected to a generator providing electrical power to a switchgear is disclosed. The engine receives gas produced by a gasifier. The control system includes an electronic controller associated with the gasifier, engine, generator, and switchgear. A gas flow sensor monitors a gas flow from the gasifier to the engine through an engine gas control valve and provides a gas flow signal to the electronic controller. A gas oversupply sensor monitors a gas oversupply from the gasifier and provides an oversupply signal indicative of gas not provided to the engine. A power output sensor monitors a power output of the switchgear and provide a power output signal. The electronic controller changes gas production of the gasifier and the power output rating of the switchgear based on the gas flow signal, the oversupply signal, and the power output signal.
A new metric method-improved structural holes researches on software networks
Li, Bo; Zhao, Hai; Cai, Wei; Li, Dazhou; Li, Hui
2013-03-01
The scale software systems quickly increase with the rapid development of software technologies. Hence, how to understand, measure, manage and control software structure is a great challenge for software engineering. there are also many researches on software networks metrics: C&K, MOOD, McCabe and etc, the aim of this paper is to propose a new and better method to metric software networks. The metric method structural holes are firstly introduced to in this paper, which can not directly be applied as a result of modular characteristics on software network. Hence, structural holes is redefined in this paper and improved, calculation process and results are described in detail. The results shows that the new method can better reflect bridge role of vertexes on software network and there is a significant correlation between degree and improved structural holes. At last, a hydropower simulation system is taken as an example to show validity of the new metric method.
Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network
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Yundi Chu
2015-01-01
Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
A systems approach to mapping transcriptional networks controlling surfactant homeostasis
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Dave Vrushank
2010-07-01
Full Text Available Abstract Background Pulmonary surfactant is required for lung function at birth and throughout life. Lung lipid and surfactant homeostasis requires regulation among multi-tiered processes, coordinating the synthesis of surfactant proteins and lipids, their assembly, trafficking, and storage in type II cells of the lung. The mechanisms regulating these interrelated processes are largely unknown. Results We integrated mRNA microarray data with array independent knowledge using Gene Ontology (GO similarity analysis, promoter motif searching, protein interaction and literature mining to elucidate genetic networks regulating lipid related biological processes in lung. A Transcription factor (TF - target gene (TG similarity matrix was generated by integrating data from different analytic methods. A scoring function was built to rank the likely TF-TG pairs. Using this strategy, we identified and verified critical components of a transcriptional network directing lipogenesis, lipid trafficking and surfactant homeostasis in the mouse lung. Conclusions Within the transcriptional network, SREBP, CEBPA, FOXA2, ETSF, GATA6 and IRF1 were identified as regulatory hubs displaying high connectivity. SREBP, FOXA2 and CEBPA together form a common core regulatory module that controls surfactant lipid homeostasis. The core module cooperates with other factors to regulate lipid metabolism and transport, cell growth and development, cell death and cell mediated immune response. Coordinated interactions of the TFs influence surfactant homeostasis and regulate lung function at birth.
Neural networks in front-end processing and control
International Nuclear Information System (INIS)
Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.
1991-07-01
Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper we illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. We also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. We outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. We also present some of the difficulties encountered in applying these networks. (author) 13 figs., 9 refs
Neural networks in front-end processing and control
International Nuclear Information System (INIS)
Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.
1992-01-01
Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper the authors illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. The authors also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. The authors outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. The authors also present some of the difficulties encountered in applying these networks
Control of 12-Cylinder Camless Engine with Neural Networks
Directory of Open Access Journals (Sweden)
Ashhab Moh’d Sami
2017-01-01
Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.
Overflow control mechanism (OCM) for Ethernet passive optical networks (EPONs)
Hajduczenia, Marek; da Silva, Henrique J. A.; Monteiro, Paulo P.
2007-05-01
The nonfragmentable nature of Ethernet data frames, as well as operation of the priority oriented packet schedulers in the optical network units, in conjunction with heavy network load conditions and the lack of detailed knowledge about the queue's composition at the optical line terminal (OLT) level, result in the creation of upstream channel slot remainders. The existing methods, in the form of nonpreemptive packet schedulers and multithreshold reporting process defined vaguely by the IEEE 802.3-2005 standard, result in either increased packet delay or Ethernet passive optical network (EPON) system incompatibility, respectively, since threshold processing was never officially defined in the scope of the respective EPON standard. We propose an alternative approach, based on basic modifications of the standard and extended GATE multipoint control protocol data unit format and meaning, allowing for the OLT packet scheduling agent to grant always exactly the requested slot size, thus preventing creation of any upstream channel slot remainders. It is estimated that, on average, ˜3% of upstream channel bandwidth can be salvaged when slot remainders are absent in the upstream channel transmission.
Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill
International Nuclear Information System (INIS)
Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.
2008-01-01
The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved
Neural-Network Control Of Prosthetic And Robotic Hands
Buckley, Theresa M.
1991-01-01
Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.
IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
A ZERGAOUI
2000-06-01
Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics. We show that neural networks can be applied to control the stator currents of the induction motor. The results of the different simulations are presented to evaluate the performance of the neural controller proposed.
T-S Fuzzy Model Based Control Strategy for the Networked Suspension Control System of Maglev Train
Directory of Open Access Journals (Sweden)
Guang He
2015-01-01
Full Text Available The control problem for the networked suspension control system of maglev train with random induced time delay and packet dropouts is investigated. First, Takagi-Sugeno (T-S fuzzy models are utilized to represent the discrete-time nonlinear networked suspension control system, and the parameters uncertainties of the nonlinear model have also been taken into account. The controllers take the form of parallel distributed compensation. Then, a sufficient condition for the stability of the networked suspension control system is derived. Based on the criteria, the state feedback fuzzy controllers are obtained, and the controller gains can be computed by using MATLAB LMI Toolbox directly. Finally, both the numerical simulations and physical experiments on the full-scale single bogie of CMS-04 maglev train have been accomplished to demonstrate the effectiveness of this proposed method.
Nickray, Mohsen; Afzali-Kusha, Ali; Jäntti, Riku
2015-01-01
In this paper, we propose a topology control technique to reduce the energy consumption of wireless sensor networks (WSNs). The technique makes use of both power control and power management methods. The algorithm uses the power management technique to put as many idle nodes as possible into the sleep mode while invoking the power control method to adjust the transmission range of the active nodes. On the contrary to earlier works in which both of these methods were used separately, in this a...
Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks
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Cunwu Han
2014-01-01
Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.
Neural Network Based Load Frequency Control for Restructuring ...
African Journals Online (AJOL)
The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks controller is showed that the proposed controller can generate an improved ... The same technique is then applied to control a system compose of two single units tied together though a power line.
Optimization and control methods in industrial engineering and construction
Wang, Xiangyu
2014-01-01
This book presents recent advances in optimization and control methods with applications to industrial engineering and construction management. It consists of 15 chapters authored by recognized experts in a variety of fields including control and operation research, industrial engineering, and project management. Topics include numerical methods in unconstrained optimization, robust optimal control problems, set splitting problems, optimum confidence interval analysis, a monitoring networks optimization survey, distributed fault detection, nonferrous industrial optimization approaches, neural networks in traffic flows, economic scheduling of CCHP systems, a project scheduling optimization survey, lean and agile construction project management, practical construction projects in Hong Kong, dynamic project management, production control in PC4P, and target contracts optimization. The book offers a valuable reference work for scientists, engineers, researchers and practitioners in industrial engineering and c...
Modeling of the height control system using artificial neural networks
Directory of Open Access Journals (Sweden)
A. R Tahavvor
2016-09-01
Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of
A new method to construct co-author networks
Liu, Jie; Li, Yunpeng; Ruan, Zichan; Fu, Guangyuan; Chen, Xiaowu; Sadiq, Rehan; Deng, Yong
2015-02-01
In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.
Chen, Yinchao; Yang, Wei
2009-12-01
A dynamic inversion control method based on neural network compensation for UAV automatic landing is introduced. Aimed at the nonlinear characteristic of automatic landing procedure, the dynamic inversion method is used for feedback linearization. The on-line neural network is introduced to compensation dynamic inversion error caused by the disturbance factors during automatic landing and improves the controller performance. Numerical simulation presents that the control method can make the UAV follow the expected trace properly and have good dynamic performance and robust performance.
Predictive functional control for active queue management in congested TCP/IP networks.
Bigdeli, N; Haeri, M
2009-01-01
Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.
An Entropy-Based Network Anomaly Detection Method
Directory of Open Access Journals (Sweden)
Przemysław Bereziński
2015-04-01
Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.
Dynamic Subsidy Method for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei
2016-01-01
Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...
Submodularity in dynamics and control of networked systems
Clark, Andrew; Bushnell, Linda; Poovendran, Radha
2016-01-01
This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...
Structure-based control of complex networks with nonlinear dynamics
Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka
2017-01-01
What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances. PMID:28655847
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Integrated control platform for converged optical and wireless networks
DEFF Research Database (Denmark)
Yan, Ying
The next generation of broadband access networks is expected to be heterogeneous. Multiple wired and wireless systems can be integrated, in order to simultaneously provide seamless access with an appropriate Quality of Service (QoS). Wireless networks support ubiquitous connectivity yet low data...... control platform design. To achieve an integrated and unified control platform, enhanced signalling protocol plays an important role in gluing the two different technologies. Consequently, an integrated resource management system is developed. Furthermore, and admission control scheme for connections...... are distributed based on the network states, channel conditions, and QoS requirements. A new aspect in the design of future network is the energy efficiency. An energy management mechanism is proposed and evaluated for the optical network. With regard to power saving, a sleep mode operation is developed...
The harmonics detection method based on neural network applied ...
African Journals Online (AJOL)
The harmonics detection method based on neural network applied to harmonics compensation. R Dehini, A Bassou, B Ferdi. Abstract. Several different methods have been used to sense load currents and extract its harmonic component in order to produce a reference current in shunt active power filters (SAPF), and to ...
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Implementation of Neural Networks for Intelligent Sensors and Control Using MATLAB
NAW KHU SAY WAH
2015-01-01
This system is concerned with the design, sensing and intelligent control of robot that moves in synchronization with the movement of the natural eye. The system deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. Signal processing techniques used in sensor are studied using statistical methods and artificial neural network based techniques. Multilayer neural networks have been successfully applied as intel...
Dror, Shahar
1992-01-01
Approved for public release; distribution is unlimited Identification and control of non-linear dynamical systems is a very complex task which requires new methods of approaching. This research addresses the problem of emulation and control via the use of distributed parallel processing, namely artificial neural networks. Four models for describing non-linear MIMO dynamical systems are presented. Based on these models a combined feedforward and recurrent neural networks are structured t...
SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhi Chen
2014-01-01
Full Text Available Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs. Self-organization feature map (SOFM neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.
S-curve networks and an approximate method for estimating degree distributions of complex networks
Guo, Jin-Li
2010-12-01
In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research.
S-curve networks and an approximate method for estimating degree distributions of complex networks
International Nuclear Information System (INIS)
Guo Jin-Li
2010-01-01
In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research. (general)
Intrinsic dynamics induce global symmetry in network controllability
Zhao, Chen; Wang, Wen-Xu; Liu, Yang-Yu; Slotine, Jean-Jacques
2015-02-01
Controlling complex networked systems to desired states is a key research goal in contemporary science. Despite recent advances in studying the impact of network topology on controllability, a comprehensive understanding of the synergistic effect of network topology and individual dynamics on controllability is still lacking. Here we offer a theoretical study with particular interest in the diversity of dynamic units characterized by different types of individual dynamics. Interestingly, we find a global symmetry accounting for the invariance of controllability with respect to exchanging the densities of any two different types of dynamic units, irrespective of the network topology. The highest controllability arises at the global symmetry point, at which different types of dynamic units are of the same density. The lowest controllability occurs when all self-loops are either completely absent or present with identical weights. These findings further improve our understanding of network controllability and have implications for devising the optimal control of complex networked systems in a wide range of fields.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.
2016-04-01
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Guaranteed Cost Fault-Tolerant Control for Networked Control Systems with Sensor Faults
Directory of Open Access Journals (Sweden)
Qixin Zhu
2015-01-01
Full Text Available For the large scale and complicated structure of networked control systems, time-varying sensor faults could inevitably occur when the system works in a poor environment. Guaranteed cost fault-tolerant controller for the new networked control systems with time-varying sensor faults is designed in this paper. Based on time delay of the network transmission environment, the networked control systems with sensor faults are modeled as a discrete-time system with uncertain parameters. And the model of networked control systems is related to the boundary values of the sensor faults. Moreover, using Lyapunov stability theory and linear matrix inequalities (LMI approach, the guaranteed cost fault-tolerant controller is verified to render such networked control systems asymptotically stable. Finally, simulations are included to demonstrate the theoretical results.
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
Analysis of Basic Transmission Networks for Integrated Ship Control Systems
DEFF Research Database (Denmark)
Hansen, T.N.; Granum-Jensen, M.
1993-01-01
Description of a computer network for Integrated Ship Control Systems which is going to be developed as part of an EC-project. Today equipment of different make are not able to communicate with each other because most often each supplier of ISC systems has got their own proprietary network.....
A hyperstable neural network for the modelling and control of ...
Indian Academy of Sciences (India)
neural networks. IEEE Trans. Autom. Control 39: 1306±1310. Fortescue T, Kershenbaum L S, Ydstie B E 1981 Implementation of self-tuning regulators with variable forgetting factors. Automatica 17: 831±835. Garces F, Warwick K, Craddock C 1998 Multiple PID mapping using neural networks in a MIMO generator system.
Synchronization of general complex networks via adaptive control ...
Indian Academy of Sciences (India)
2014-03-07
Mar 7, 2014 ... networks with derivative coupling and time-delay coupling was investigated by adaptive control schemes [42]. However ... [41], the synchronization of complex dynamical networks with non-derivative coupling and derivative coupling .... For any symmetric positive definite matrix. M ∈ Rn×n and x,y ∈ Rn, ...
Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks
Directory of Open Access Journals (Sweden)
Jose P. Perez
2014-01-01
Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.
Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Yang, Shiju; Li, Chuandong; Huang, Tingwen
2016-03-01
The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Managing Recurrent Congestion of Subway Network in Peak Hours with Station Inflow Control
Qingru Zou; Xiangming Yao; Peng Zhao; Fei Dou; Taoyuan Yang
2018-01-01
Station inflow control (SIC) is an important and effective method for reducing recurrent congestion during peak hours in the Beijing, Shanghai, and Guangzhou subway systems. This work proposes a practical and efficient method for establishing a static SIC scheme in normal weekdays for large-scale subway networks. First, a traffic assignment model without capacity constraint is utilized to determine passenger flow distributions on the network. An internal relationship between station inflows a...
International Nuclear Information System (INIS)
Cui Baotong; Lou Xuyang
2009-01-01
In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme
A new method to measure mechanics and dynamic assembly of branched actin networks
DEFF Research Database (Denmark)
Bauër, Pierre; Tavacoli, Joe; Pujol, Thomas
2017-01-01
networks and controlled enough to access to their non linear mechanical responses. Deformations are measured with nanometer-resolution, well below the optical resolution. Self-assembly of the magnetic particles into chains simplifies experiments and allows for parallel measurements. The combination......We measured mechanical properties and dynamic assembly of actin networks with a new method based on magnetic microscopic cylinders. Dense actin networks are grown from the cylinders’ surfaces using the biochemical Arp2/3-machinery at play in the lamellipodium extension and other force...... of accuracy and good throughput of measurements results in a method with high potential for cell and cytoskeleton mechanics. Using this method, we observed in particular a strong non linear mechanical behavior of dense branched actin networks at low forces that has not been reported previously....
Public authority control strategy for opinion evolution in social networks
Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei
2016-08-01
This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.
Energy scaling and reduction in controlling complex networks
Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng
2016-01-01
Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220
PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller
Directory of Open Access Journals (Sweden)
MARABA, V. A.
2011-11-01
Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.
Control of industrial robot using neural network compensator
Directory of Open Access Journals (Sweden)
Ranković Vesna
2005-01-01
Full Text Available In the paper is considered synthesis of the controller with tachometric feedback with feed forward compensation of disturbance torque, velocity and acceleration errors. It is difficult to obtain the desired control performance when the control algorithm is only based on the robot dynamic model. We use the neural network to generate auxiliary joint control torque to compensate these uncertainties. The two-layer neural network is used as the compensator. The main task of control system here is to track the required trajectory. Simulations are done in MATLAB for RzRyRy robot minimal configuration.
Directory of Open Access Journals (Sweden)
Sapna Kumari
Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.
Critical controllability analysis of directed biological networks using efficient graph reduction.
Ishitsuka, Masayuki; Akutsu, Tatsuya; Nacher, Jose C
2017-10-30
Network science has recently integrated key concepts from control theory and has applied them to the analysis of the controllability of complex networks. One of the proposed frameworks uses the Minimum Dominating Set (MDS) approach, which has been successfully applied to the identification of cancer-related proteins and in analyses of large-scale undirected networks, such as proteome-wide protein interaction networks. However, many real systems are better represented by directed networks. Therefore, fast algorithms are required for the application of MDS to directed networks. Here, we propose an algorithm that utilises efficient graph reduction to identify critical control nodes in large-scale directed complex networks. The algorithm is 176-fold faster than existing methods and increases the computable network size to 65,000 nodes. We then applied the developed algorithm to metabolic pathways consisting of 70 plant species encompassing major plant lineages ranging from algae to angiosperms and to signalling pathways from C. elegans, D. melanogaster and H. sapiens. The analysis not only identified functional pathways enriched with critical control molecules but also showed that most control categories are largely conserved across evolutionary time, from green algae and early basal plants to modern angiosperm plant lineages.
Control and Optimization Methods for Electric Smart Grids
Ilić, Marija
2012-01-01
Control and Optimization Methods for Electric Smart Grids brings together leading experts in power, control and communication systems,and consolidates some of the most promising recent research in smart grid modeling,control and optimization in hopes of laying the foundation for future advances in this critical field of study. The contents comprise eighteen essays addressing wide varieties of control-theoretic problems for tomorrow’s power grid. Topics covered include: Control architectures for power system networks with large-scale penetration of renewable energy and plug-in vehicles Optimal demand response New modeling methods for electricity markets Control strategies for data centers Cyber-security Wide-area monitoring and control using synchronized phasor measurements. The authors present theoretical results supported by illustrative examples and practical case studies, making the material comprehensible to a wide audience. The results reflect the exponential transformation that today’s grid is going...
Adaptive artificial neural network for autonomous robot control
Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.
1992-01-01
The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.
Autonomous Congestion Control in Delay-Tolerant Networks
Burleigh, Scott; Jennings, Esther; Schoolcraft, Joshua
2006-01-01
This presentation highlights communication congestion control in delay-tolerant networks (DTNs). Large-scale future space exploration will offer complex communication challenges that may be best addressed by establishing a network infrastructure. However, current internet techniques for congestion control are not well suited for operation of a network over interplanetary distances. An alternative, delay-tolerant technique for congestion control in a delay-tolerant network is presented. A simple DTN was constructed and an experimental congestion control mechanism was applied. The mechanism appeared to be effective and each router was able to make its bundle acceptance decisions autonomously. Future research will examine more complex topologies and alternative bundle acceptance rules that might enhance performance.
Hierarchical-control-based output synchronization of coexisting attractor networks
International Nuclear Information System (INIS)
Yun-Zhong, Song; Yi-Fa, Tang
2010-01-01
This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the Tightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme. (general)
Passivity-based control and estimation in networked robotics
Hatanaka, Takeshi; Fujita, Masayuki; Spong, Mark W
2015-01-01
Highlighting the control of networked robotic systems, this book synthesizes a unified passivity-based approach to an emerging cross-disciplinary subject. Thanks to this unified approach, readers can access various state-of-the-art research fields by studying only the background foundations associated with passivity. In addition to the theoretical results and techniques, the authors provide experimental case studies on testbeds of robotic systems including networked haptic devices, visual robotic systems, robotic network systems and visual sensor network systems. The text begins with an introduction to passivity and passivity-based control together with the other foundations needed in this book. The main body of the book consists of three parts. The first examines how passivity can be utilized for bilateral teleoperation and demonstrates the inherent robustness of the passivity-based controller against communication delays. The second part emphasizes passivity’s usefulness for visual feedback control ...
Structure-based control of complex networks with nonlinear dynamics
Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka
What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.
STIMULUS: End-System Network Interface Controller for 100 Gb/s Wide Area Networks
Energy Technology Data Exchange (ETDEWEB)
Zarkesh-Ha, Payman [University of New Mexico
2014-09-12
The main goal of this research grant is to develop a system-level solution leveraging novel technologies that enable network communications at 100 Gb/s or beyond. University of New Mexico in collaboration with Acadia Optronics LLC has been working on this project to develop the 100 Gb/s Network Interface Controller (NIC) under this Department of Energy (DOE) grant.
The Vital Network: An Algorithmic Milieu of Communication and Control
Directory of Open Access Journals (Sweden)
Sandra Robinson
2016-09-01
Full Text Available The biological turn in computing has influenced the development of algorithmic control and what I call the vital network: a dynamic, relational, and generative assemblage that is self-organizing in response to the heterogeneity of contemporary network processes, connections, and communication. I discuss this biological turn in computation and control for communication alongside historically significant developments in cybernetics that set out the foundation for the development of self-regulating computer systems. Control is shifting away from models that historically relied on the human-animal model of cognition to govern communication and control, as in early cybernetics and computer science, to a decentred, nonhuman model of control by algorithm for communication and networks. To illustrate the rise of contemporary algorithmic control, I outline a particular example, that of the biologically-inspired routing algorithm known as a ‘quorum sensing’ algorithm. The increasing expansion of algorithms as a sense-making apparatus is important in the context of social media, but also in the subsystems that coordinate networked flows of information. In that domain, algorithms are not inferring categories of identity, sociality, and practice associated with Internet consumers, rather, these algorithms are designed to act on information flows as they are transmitted along the network. The development of autonomous control realized through the power of the algorithm to monitor, sort, organize, determine, and transmit communication is the form of control emerging as a postscript to Gilles Deleuze’s ‘postscript on societies of control.’
Deng, Lei; Li, Guoqi; Pei, Jing; Huang, Jiangshuai
2018-03-13
Locating a pre-given number of key nodes that are connected to external control sources so as to minimize the cost of controlling a directed network ẋ(t)=Ax(t)+Bu(t), known as the minimum cost control problem, is of critical importance. Considering a network consisting of N nodes with M external control sources, the state of art techniques employ iterative searching to determine the input matrix B that characterizes how nodes are connected to external control sources, in a matrix space R N×M . The nodes having M largest values of a defined importance index are selected as key nodes. However, such techniques may suffer from large performance penalty in some networks due to the diversity of real-life networks. To address this outstanding issue, we propose an iterative method, termed "L 0 -norm constraint based projected gradient method" (LPGM). We probabilistically search the input matrix in each iteration by restricting its L 0 norm as a fixed value M, which implies that each control source is always only connected to a single key node during the whole searching process. Simulation results show that the solution always efficiently approaches a suboptimal key node set in a few iterations. These results provide a new point of view regarding the key nodes selection in the minimum cost control of directed networks. Copyright © 2018. Published by Elsevier Ltd.
Application of Artificial Neural Networks for Process Identification and Control
Bolf, N.; Jerbić, I.
2006-01-01
During the development of intelligent systems inspired by biological neural system, in the last two decades the researchers from various scientific fields have created neural networks for solving a series of problems from pattern recognition, prediction, diagnostic, software sensor, modelling and identification, control and optimization. In this paper a review of neural network application in the field of chemical engineering with emphasis on identification and process control is given. T...
Graphs for information security control in software defined networks
Grusho, Alexander A.; Abaev, Pavel O.; Shorgin, Sergey Ya.; Timonina, Elena E.
2017-07-01
Information security control in software defined networks (SDN) is connected with execution of the security policy rules regulating information accesses and protection against distribution of the malicious code and harmful influences. The paper offers a representation of a security policy in the form of hierarchical structure which in case of distribution of resources for the solution of tasks defines graphs of admissible interactions in a networks. These graphs define commutation tables of switches via the SDN controller.
Mitigating the controller performance bottlenecks in Software Defined Networks
DEFF Research Database (Denmark)
Caba, Cosmin Marius; Soler, José
2016-01-01
The centralization of the control plane decision logic in Software Defined Networking (SDN) has raised concerns regarding the performance of the SDN Controller (SDNC) when the network scales up. A number of solutions have been proposed in the literature to address these concerns. This paper......, etc.). A series of tests have been performed, and results confirm that by careful configurations, the computational overhead at the SDNC can be reduced without significantly affecting the efficiency of its components....
Brookhaven Reactor Experiment Control Facility, a distributed function computer network
International Nuclear Information System (INIS)
Dimmler, D.G.; Greenlaw, N.; Kelley, M.A.; Potter, D.W.; Rankowitz, S.; Stubblefield, F.W.
1975-11-01
A computer network for real-time data acquisition, monitoring and control of a series of experiments at the Brookhaven High Flux Beam Reactor has been developed and has been set into routine operation. This reactor experiment control facility presently services nine neutron spectrometers and one x-ray diffractometer. Several additional experiment connections are in progress. The architecture of the facility is based on a distributed function network concept. A statement of implementation and results is presented
A graph clustering method for community detection in complex networks
Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn
2017-03-01
Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.
Brain and Cognitive Reserve: Translation via Network Control Theory
Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H.; Thompson-Schill, Sharon L.; Bassett, Danielle S.
2017-01-01
Traditional approaches to understanding the brain’s resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive “reserve,” associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention. PMID:28104411
Ideomotor feedback control in a recurrent neural network.
Galtier, Mathieu
2015-06-01
The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.
Multi-level Control Framework for Enhanced Flexibility of Active Distribution Network
DEFF Research Database (Denmark)
Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna
2017-01-01
In this paper, the control objectives of future active distribution networks with high penetration of renewables and flexible loads are analyzed and reviewed. From a state of the art review, the important control objectives seen from the perspective of a distribution system operator are identified...... the selected control objectives and provides enhanced flexibility. The control architecture is supported by generation/load forecasting and distribution state estimation techniques to improve the controllability of the network. The multi-level control architecture consists of three levels of hierarchical...... control and an improved interface to the transmission system operator. The functions and the appropriate control methods to be used in each control level are described based on the state of the art review. A combined control action of these control layers is aimed for efficient co...
New tuning method for PID controller.
Shen, Jing-Chung
2002-10-01
In this paper, a tuning method for proportional-integral-derivative (PID) controller and the performance assessment formulas for this method are proposed. This tuning method is based on a genetic algorithm based PID controller design method. For deriving the tuning formula, the genetic algorithm based design method is applied to design PID controllers for a variety of processes. The relationship between the controller parameters and the parameters that characterize the process dynamics are determined and the tuning formula is then derived. Using simulation studies, the rules for assessing the performance of a PID controller tuned by the proposed method are also given. This makes it possible to incorporate the capability to determine if the PID controller is well tuned or not into an autotuner. An autotuner based on this new tuning method and the corresponding performance assessment rules is also established. Simulations and real-time experimental results are given to demonstrate the effectiveness and usefulness of these formulas.
Adaptive neural network controller for the molten steel level control of strip casting processes
International Nuclear Information System (INIS)
Chen, Hung Yi; Huang, Shiuh Jer
2010-01-01
The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system's nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semi experimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional Pid controller
Neural network control of mobile robot formations using RISE feedback.
Dierks, Travis; Jagannathan, S
2009-04-01
In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are AS and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most NN controllers. Additionally, the stability of the formation in the presence of obstacles is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation do not occur. The asymptotic stability of the follower robots as well as the entire formation during an obstacle avoidance maneuver is demonstrated using Lyapunov methods, and numerical results are provided to verify the theoretical conjectures.
Multi-agent model predictive control for transportation networks : Serial versus parallel schemes
Negenborn, R.R.; De Schutter, B.; Hellendoorn, J.
2006-01-01
We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed
Research in Neural Network Based Adaptive Control
National Research Council Canada - National Science Library
Calise, Anthony
2000-01-01
.... We regard this as a major step towards flight certification of adaptive controllers. The approach is more general in that it permits a broad class of input nonlinearities, including such effects as discrete and bang/bang control...
SDN control of optical nodes in metro networks for high capacity inter-datacentre links
Magalhães, Eduardo; Perry, Philip; Barry, Liam
2017-11-01
Worldwide demand for bandwidth has been growing fast for some years and continues to do so. To cover this, mega datacentres need scalable connectivity to provide rich connectivity to handle the heavy traffic across them. Therefore, hardware infrastructures must be able to play different roles according to service and traffic requirements. In this context, software defined networking (SDN) decouples the network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstracted for applications and network services. In addition, elastic optical networking (EON) technologies enable efficient spectrum utilization by allocating variable bandwidth to each user according to their actual needs. In particular, flexible transponders and reconfigurable optical add/drop multiplexers (ROADMs) are key elements since they can offer degrees of freedom to self adapt accordingly. Thus, it is crucial to design control methods in order to optimize the hardware utilization and offer high reconfigurability, flexibility and adaptability. In this paper, we propose and analyze, using a simulation framework, a method of capacity maximization through optical power profile manipulation for inter datacentre links that use existing metropolitan optical networks by exploiting the global network view afforded by SDN. Results show that manipulating the loss profiles of the ROADMs in the metro-network can yield optical signal-to-noise ratio (OSNR) improvements up to 10 dB leading to an increase in 112% in total capacity.
Hierarchically structured distributed microprocessor network for control
International Nuclear Information System (INIS)
Greenwood, J.R.; Holloway, F.W.; Rupert, P.R.; Ozarski, R.G.; Suski, G.J.
1979-01-01
To satisfy a broad range of control-analysis and data-acquisition requirements for Shiva, a hierarchical, computer-based, modular-distributed control system was designed. This system handles the more than 3000 control elements and 1000 data acquisition units in a severe high-voltage, high-current environment. The control system design gives one a flexible and reliable configuration to meet the development milestones for Shiva within critical time limits
Controlling localized spatiotemporal chaos using feedback control method
International Nuclear Information System (INIS)
Li Yan; Zhang Xu
2006-01-01
Suppression of localized spatiotemporal chaos observed in one-dimensional coupled map lattice system is achieved using feedback control [P. Parmananda, Yu. Jiang, Phys. Lett. A 231 (1997) 159; P. Parmananda, M. Hildebrand, M. Eiswirth, Phys. Rev. E 56 (1997) 239]. The control is successful both for the frozen random pattern and defect chaotic diffusion pattern. This Letter introduces a new improved feedback control method. To compare with other methods of feedback control, this method can achieve stable state with less iteration steps and more simple calculation process. We prove the stability of the controlled result by calculating maximal Lyapunov exponent. And we also find that this method is robust to small disturbance
Comparisons of topological properties in autism for the brain network construction methods
Lee, Min-Hee; Kim, Dong Youn; Lee, Sang Hyeon; Kim, Jin Uk; Chung, Moo K.
2015-03-01
Structural brain networks can be constructed from the white matter fiber tractography of diffusion tensor imaging (DTI), and the structural characteristics of the brain can be analyzed from its networks. When brain networks are constructed by the parcellation method, their network structures change according to the parcellation scale selection and arbitrary thresholding. To overcome these issues, we modified the Ɛ -neighbor construction method proposed by Chung et al. (2011). The purpose of this study was to construct brain networks for 14 control subjects and 16 subjects with autism using both the parcellation and the Ɛ-neighbor construction method and to compare their topological properties between two methods. As the number of nodes increased, connectedness decreased in the parcellation method. However in the Ɛ-neighbor construction method, connectedness remained at a high level even with the rising number of nodes. In addition, statistical analysis for the parcellation method showed significant difference only in the path length. However, statistical analysis for the Ɛ-neighbor construction method showed significant difference with the path length, the degree and the density.
Action selection in growing state spaces: control of network structure growth
International Nuclear Information System (INIS)
Thalmeier, Dominik; Kappen, Hilbert J; Gómez, Vicenç
2017-01-01
The dynamical processes taking place on a network depend on its topology. Influencing the growth process of a network therefore has important implications on such dynamical processes. We formulate the problem of influencing the growth of a network as a stochastic optimal control problem in which a structural cost function penalizes undesired topologies. We approximate this control problem with a restricted class of control problems that can be solved using probabilistic inference methods. To deal with the increasing problem dimensionality, we introduce an adaptive importance sampling method for approximating the optimal control. We illustrate this methodology in the context of formation of information cascades, considering the task of influencing the structure of a growing conversation thread, as in Internet forums. Using a realistic model of growing trees, we show that our approach can yield conversation threads with better structural properties than the ones observed without control. (paper)
Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft
Directory of Open Access Journals (Sweden)
Yanchao Yin
2017-01-01
Full Text Available A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA. Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.
Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft
Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.
2006-01-01
Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.
Vibro-acoustic control with a distributed sensor network.
Frampton, Kenneth D
2006-04-01
The purpose of this work is to demonstrate the ability of a distributed control system, based on a smart sensor network, to reduce acoustic radiation from a vibrating structure. The platform from which control is effected consists of a network of smart sensors, each referred to as a node. Each node possesses its own computational capability, sensor, actuator and the ability to communicate with other nodes via a wired or wireless network. The primary focus of this work is to employ existing group management middleware concepts to enable vibro-acoustic control with such a distributed network. Group management middleware is distributed software that provides for the establishment and maintenance of groups of distributed nodes and that provides for the network communication among such groups. The control objective is met by designing distributed feedback compensators that take advantage of node groups in order to effect their control. The node groups are formed based on physical proximity. The global control objective is to minimize the radiated sound power from a rectangular plate. Results of this investigation demonstrate that such a distributed control system can achieve attenuations comparable to those achieved by a centralized controller.
Baker-Doyle, Kira J.
2015-01-01
Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…
Outlier Detection Method Use for the Network Flow Anomaly Detection
Directory of Open Access Journals (Sweden)
Rimas Ciplinskas
2016-06-01
Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.
Applying Trusted Network Technology To Process Control Systems
Okhravi, Hamed; Nicol, David
Interconnections between process control networks and enterprise networks expose instrumentation and control systems and the critical infrastructure components they operate to a variety of cyber attacks. Several architectural standards and security best practices have been proposed for industrial control systems. However, they are based on older architectures and do not leverage the latest hardware and software technologies. This paper describes new technologies that can be applied to the design of next generation security architectures for industrial control systems. The technologies are discussed along with their security benefits and design trade-offs.
Distributed Ship Navigation Control System Based on Dual Network
Yao, Ying; Lv, Wu
2017-10-01
Navigation system is very important for ship’s normal running. There are a lot of devices and sensors in the navigation system to guarantee ship’s regular work. In the past, these devices and sensors were usually connected via CAN bus for high performance and reliability. However, as the development of related devices and sensors, the navigation system also needs the ability of high information throughput and remote data sharing. To meet these new requirements, we propose the communication method based on dual network which contains CAN bus and industrial Ethernet. Also, we import multiple distributed control terminals with cooperative strategy based on the idea of synchronizing the status by multicasting UDP message contained operation timestamp to make the system more efficient and reliable.
Approximation methods for efficient learning of Bayesian networks
Riggelsen, C
2008-01-01
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
Reverse Engineering Cellular Networks with Information Theoretic Methods
Directory of Open Access Journals (Sweden)
Julio R. Banga
2013-05-01
Full Text Available Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.
Spectral shift reactor control method
International Nuclear Information System (INIS)
Impink, A.J. Jr.
1981-01-01
A method of operating a nuclear reactor having a core and coolant displacer elements arranged in the core wherein is established a reator coolant temperature set point at which it is desired to operate said reactor and first reactor coolant temperature band limits are provided within which said set point is located and it is desired to operate said reactor charactrized in that said reactor coolant displacer elements are moved relative to the reactor core for adjusting the volume of reactor coolant in said core as said reactor coolant temperature approaches said first band limits thereby to maintain said reactor coolant temperature near said set point and within said first band limits
Modelling, Estimation and Control of Networked Complex Systems
Chiuso, Alessandro; Frasca, Mattia; Rizzo, Alessandro; Schenato, Luca; Zampieri, Sandro
2009-01-01
The paradigm of complexity is pervading both science and engineering, leading to the emergence of novel approaches oriented at the development of a systemic view of the phenomena under study; the definition of powerful tools for modelling, estimation, and control; and the cross-fertilization of different disciplines and approaches. This book is devoted to networked systems which are one of the most promising paradigms of complexity. It is demonstrated that complex, dynamical networks are powerful tools to model, estimate, and control many interesting phenomena, like agent coordination, synchronization, social and economics events, networks of critical infrastructures, resources allocation, information processing, or control over communication networks. Moreover, it is shown how the recent technological advances in wireless communication and decreasing in cost and size of electronic devices are promoting the appearance of large inexpensive interconnected systems, each with computational, sensing and mobile cap...
Systematic construction and control of stereo nerve vision network in intelligent manufacturing
Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei
2017-10-01
A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.
Neural network based adaptive output feedback control: Applications and improvements
Kutay, Ali Turker
Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in
Goal-congruent default network activity facilitates cognitive control.
Spreng, R Nathan; DuPre, Elizabeth; Selarka, Dhawal; Garcia, Juliana; Gojkovic, Stefan; Mildner, Judith; Luh, Wen-Ming; Turner, Gary R
2014-10-15
Substantial neuroimaging evidence suggests that spontaneous engagement of the default network impairs performance on tasks requiring executive control. We investigated whether this impairment depends on the congruence between executive control demands and internal mentation. We hypothesized that activation of the default network might enhance performance on an executive control task if control processes engage long-term memory representations that are supported by the default network. Using fMRI, we scanned 36 healthy young adult humans on a novel two-back task requiring working memory for famous and anonymous faces. In this task, participants (1) matched anonymous faces interleaved with anonymous face, (2) matched anonymous faces interleaved with a famous face, or (3) matched a famous faces interleaved with an anonymous face. As predicted, we observed a facilitation effect when matching famous faces, compared with anonymous faces. We also observed greater activation of the default network during these famous face-matching trials. The results suggest that activation of the default network can contribute to task performance during an externally directed executive control task. Our findings provide evidence that successful activation of the default network in a contextually relevant manner facilitates goal-directed cognition. Copyright © 2014 the authors 0270-6474/14/3414108-07$15.00/0.
Epidemic Propagation of Control Plane Failures in GMPLS Controlled Optical Transport Networks
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
In this paper, we investigate the behaviour of a dataplane-decoupled GMPLS control plane, when it is affected by failures that spread in the network in an epidemic manner. In particular, we consider network nodes to be either fully functional, or having a failed control plane, or having both...... of the infection spreading rate and probability together with the infection start location in five network topologies. Our results indicate that the general availability of the network and the impact on its operation depend on a set of parameters, such as the network size and connectivity, the duration...
Adaptive Control Methods for Soft Robots
National Aeronautics and Space Administration — I propose to develop methods for soft and inflatable robots that will allow the control system to adapt and change control parameters based on changing conditions...
Utilization of Selected Data Mining Methods for Communication Network Analysis
Directory of Open Access Journals (Sweden)
V. Ondryhal
2011-06-01
Full Text Available The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture nonstandard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.
Quantitative Method for Network Security Situation Based on Attack Prediction
Directory of Open Access Journals (Sweden)
Hao Hu
2017-01-01
Full Text Available Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication.
Generation of clusters in complex dynamical networks via pinning control
International Nuclear Information System (INIS)
Li Kezan; Fu Xinchu; Small, Michael
2008-01-01
Many real-world networks show community structure, i.e., groups (or clusters) of nodes that have a high density of links within them but with a lower density of links between them. In this paper, by applying feedback injections to a fraction of network nodes, various clusters are synchronized independently according to the community structure generated by the group partition of the network (cluster synchronization). This control is achieved by pinning (i.e. applying linear feedback control) to a subset of the network nodes. Those pinned nodes are selected not randomly but according to the topological structure of communities of a given network. Specifically, for a given group partition of a network, those nodes with direct connections between groups must be pinned in order to achieve cluster synchronization. Both the local stability and global stability of cluster synchronization are investigated. Taking the tree-shaped network and the most modular network as two particular examples, we illustrate in detail how the pinning strategy influences the generation of clusters. The simulations verify the efficiency of the pinning schemes used in this paper
Operator splitting method for simulation of dynamic flows in natural gas pipeline networks
Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; Chertkov, Michael
2017-12-01
We develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.
FIPA agent based network distributed control system
Energy Technology Data Exchange (ETDEWEB)
D. Abbott; V. Gyurjyan; G. Heyes; E. Jastrzembski; C. Timmer; E. Wolin
2003-03-01
A control system with the capabilities to combine heterogeneous control systems or processes into a uniform homogeneous environment is discussed. This dynamically extensible system is an example of the software system at the agent level of abstraction. This level of abstraction considers agents as atomic entities that communicate to implement the functionality of the control system. Agents' engineering aspects are addressed by adopting the domain independent software standard, formulated by FIPA. Jade core Java classes are used as a FIPA specification implementation. A special, lightweight, XML RDFS based, control oriented, ontology markup language is developed to standardize the description of the arbitrary control system data processor. Control processes, described in this language, are integrated into the global system at runtime, without actual programming. Fault tolerance and recovery issues are also addressed.
Real-Time Helicopter Flight Control: Modelling and Control by Linearization and Neural Networks
Pallett, Tobias J.; Ahmad, Shaheen
1991-01-01
In this report we determine the dynamic model of a miniature helicopter in hovering flight. Identification procedures for the nonlinear terms are also described. The model is then used to design several linearized control laws and a neural network controller. The controllers were then flight tested on a miniature helicopter flight control test bed the details of which are also presented in this report. Experimental performance of the linearized and neural network controllers are discussed. It...
A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks
Directory of Open Access Journals (Sweden)
Ke-yan Liu
2017-05-01
Full Text Available This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO. Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA. In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.
A biologically inspired neural network controller for ballistic arm movements
Directory of Open Access Journals (Sweden)
Schmid Maurizio
2007-09-01
Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of
Improvement on the Performance of Canal Network and Method of ...
African Journals Online (AJOL)
This paper presents the required improvement on the performance of canal network and method of on-farm water application systems at Tunga-Kawo irrigation scheme, Wushishi, Niger state. The problems of poor delivery of water to the farmland were identified to include erosion of canal embarkment, lack of water ...
A hierarchical network modeling method for railway tunnels safety assessment
Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin
2017-02-01
Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.
The harmonics detection method based on neural network applied ...
African Journals Online (AJOL)
user
with MATLAB Simulink Power System Toolbox. The simulation study results of this novel technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability. Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic ...
Fuzzy logic control to be conventional method
International Nuclear Information System (INIS)
Eker, Ilyas; Torun, Yunis
2006-01-01
Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system
Fatemeh. Dehghani; Shahram. Darooei
2016-01-01
Network on chip has emerged as a long-term and effective method in Multiprocessor System-on-Chip communications in order to overcome the bottleneck in bus based communication architectures. Efficiency and performance of network on chip is so dependent on the architecture and structure of the network. In this paper a new structure and architecture for adaptive traffic control in network on chip using Code Division Multiple Access technique is presented. To solve the problem of synchronous acce...
Quality Control Guidelines for SAM Chemical Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the chemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Pathogen Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the biotoxin methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Biotoxin Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the pathogen methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Radiochemical Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the radiochemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
A Network Scheduling Model for Distributed Control Simulation
Culley, Dennis; Thomas, George; Aretskin-Hariton, Eliot
2016-01-01
Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.
NETWORK-CENTRIC TECHNOLOGIES FOR CONTROL OF THREE-PHASE NETWORK OPERATION MODES
Directory of Open Access Journals (Sweden)
Ye. I. Sokol
2017-06-01
Full Text Available Purpose. The development of the control system for three-phase network is based on intelligent technologies of network-centric control of heterogeneous objects. The introduction of unmanned aerial vehicles for monitoring of three-phase network increases the efficiency of management. Methodology. The case of decomposition of the instantaneous capacities of the fixed and variable components for 3-wire system. The features of power balance for the different modes of its functioning. It should be noted that symmetric sinusoidal mode is balanced and good, but really unbalanced, if the standard reactive power is not zero. To solve the problem of compensation is sufficient knowledge of the total value of the inactive components of full power (value of the inactive power without detail. The creation of a methodology of measurement and assessment will require knowledge of the magnitudes of each inactive component separately, which leads to the development of a unified approach to the measurement and compensation of inactive components of full power and the development of a generalized theory of power. Results. Procedure for the compensation of the current of zero sequence excludes from circuit the source, as the active component of instantaneous power of zero sequence, and a vector due to a current of zero sequence. This procedure is performed without time delay as it does not require integration. Only a 3–wire system with symmetrical voltage eliminates pulsations and symmetrization of the equivalent conductances of the phases of the task. Under asymmetric voltage, the power is different, its analysis requires the creation of a vector mathematical model of the energy processes of asymmetrical modes of 3–phase systems. Originality. The proposed method extends the basis of the vector method for any zero sequence voltages and shows that the various theories of instantaneous power three wired scheme due to the choice of a basis in a two
Active Noise Feedback Control Using a Neural Network
Qizhi, Zhang; Yongle, Jia
2001-01-01
The active noise control (ANC) is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with ...
Estimating Population Size Using the Network Scale Up Method.
Maltiel, Rachael; Raftery, Adrian E; McCormick, Tyler H; Baraff, Aaron J
2015-09-01
We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents' networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents' degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people's propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts' group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.
Quartet-based methods to reconstruct phylogenetic networks.
Yang, Jialiang; Grünewald, Stefan; Xu, Yifei; Wan, Xiu-Feng
2014-02-20
Phylogenetic networks are employed to visualize evolutionary relationships among a group of nucleotide sequences, genes or species when reticulate events like hybridization, recombination, reassortant and horizontal gene transfer are believed to be involved. In comparison to traditional distance-based methods, quartet-based methods consider more information in the reconstruction process and thus have the potential to be more accurate. We introduce QuartetSuite, which includes a set of new quartet-based methods, namely QuartetS, QuartetA, and QuartetM, to reconstruct phylogenetic networks from nucleotide sequences. We tested their performances and compared them with other popular methods on two simulated nucleotide sequence data sets: one generated from a tree topology and the other from a complicated evolutionary history containing three reticulate events. We further validated these methods to two real data sets: a bacterial data set consisting of seven concatenated genes of 36 bacterial species and an influenza data set related to recently emerging H7N9 low pathogenic avian influenza viruses in China. QuartetS, QuartetA, and QuartetM have the potential to accurately reconstruct evolutionary scenarios from simple branching trees to complicated networks containing many reticulate events. These methods could provide insights into the understanding of complicated biological evolutionary processes such as bacterial taxonomy and reassortant of influenza viruses.
A Network Centrality Method for the Rating Problem
2015-01-01
We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity. PMID:25830502
Constructing financial network based on PMFG and threshold method
Nie, Chun-Xiao; Song, Fu-Tie
2018-04-01
Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.
Stability analysis and control models for rumor spreading in online social networks
Jiang, Ping; Yan, Xiangbin
This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs.
Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei
2018-02-01
This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.