Loop Problem Research Articles

A Novel Route Deviation and Optimization Algorithm Using Simulation-Optimization and Energy Efficient Constraints for Minimizing Link-Loss and Loop Problems in Wireless Sensor Network

Wireless and mobile devices and networks are an integral part of today’s life. With the advancement of technology, a large number of smart devices with advanced computational and communicational types of equipment are designed and developed. With these advancements, the availability of services over these devices is required to be of high quality for ensuring better experiences day by day. Thus, network performance improvement with minimum path loss and optimized packet delivery without loops is a major concern to integrate the existing network infrastructure with advanced and future networks. This work has designed a novel route deviation and optimization algorithm with simulation optimization and energy efficiency constraints. The simulation-optimization approach is considered to have minimum link losses and avoid the loop problem. Whereas, local and global simulation optimization processes are followed to identify the best possibilities in route identification in consideration with local and network environment settings. The proposed approach is statistically found to have a minimum error rate and optimizes the performance with computational and communicational overheads. In results, a minimum of 10 seconds and a maximum of 150 seconds are observed before a device using the proposed approach gets utilized fully. Thus, the proposed approach is very effective for link loss and loop problems in wireless sensor networks. Further, an improvement of 135 (approx.) is observed in the proposed approach.

Event-Triggered Output-Feedback Control for Large-Scale Systems With Unknown Hysteresis.

This article focuses on the event-triggered-based adaptive neural-network (NN) control problem for nonlinear large-scale systems (LSSs) in the presence of full-state constraints and unknown hysteresis. The characteristic of radial basis function NNs is utilized to construct a state observer and address the algebraic loop problem. To reduce the communication burden and the signal transmission frequency, the event-triggered mechanism and the encoding-decoding strategy are proposed with the help of a backstepping control technique. To encode and decode the event-triggering control signal, a one-bit signal transmission strategy is adopted to consume less communication bandwidth. Then, by estimating the unknown constants in the differential equation of unknown hysteresis, the effect caused by unknown backlash-like hysteresis is compensated for nonlinear LSSs. Moreover, the violation of full-state constraints is prevented based on the barrier Lyapunov functions and all signals of the closed-loop system are proven to be semiglobally ultimately uniformly bounded. Finally, two simulation examples are given to illustrate the effectiveness of the developed strategy.

A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments

Pneumatic artificial muscle (PAM) actuators are a kind of biomimetic actuators, which are being widely used in the applications of biomimetic robots and medical auxiliary devices. However, PAM systems usually have high nonlinearities, uncertainties, and time-varying characteristics, which bring challenges for accurate dynamic modeling and controller design. To deal with the above issues, in this paper, a neuroadaptive control method is proposed to handle the system uncertainties and achieve satisfactory tracking performance. First, in order to compensate the unknown nonlinear term involved in the dynamic model of the PAM system online, a three-layer neural network is utilized. Next, by means of the filtered signal, the algebraic loop problem can be solved effectively. Then, based on a sliding mode surface, a nonlinear robust controller is designed. By using the proposed method, the asymptotic convergence of tracking errors of the PAM system is guaranteed, and the tracking errors are always restricted within preset bounds during the control process. Moreover, the stability of the closed-loop system is proven theoretically by utilizing Lyapunov techniques. Finally, a series of hardware experiments are implemented on a self-built PAM testbed to validate the effectiveness and robustness of the proposed neuroadaptive control method.

Bridging the Architecture Gap: Abstracting Performance-Relevant Properties of Modern Server Processors

We describe a universal modeling approach for predicting single- and multicore runtime of steady-state loops on server processors. To this end we strictly differentiate between application and machine models: An application model comprises the loop code, problem sizes, and other runtime parameters, while a machine model is an abstraction of all performance-relevant properties of a CPU. We introduce a generic method for determining machine models and present results for relevant server-processor architectures by Intel, AMD, IBM, and Marvell/Cavium. Considering this wide range of architectures, the set of features required for adequate performance modeling is surprisingly small. To validate our approach, we compare performance predictions to empirical data for an OpenMP-parallel preconditioned CG algorithm, which includes compute- and memory-bound kernels. Both single- and multicore analysis shows that the model exhibits average and maximum relative errors of 5% and 10%. Deviations from the model and insights gained are discussed in detail.

Neural-Network-Based Adaptive Event-Triggered Consensus Control of Nonstrict-Feedback Nonlinear Systems.

The event-triggered consensus control problem is studied for nonstrict-feedback nonlinear systems with a dynamic leader. Neural networks (NNs) are utilized to approximate the unknown dynamics of each follower and its neighbors. A novel adaptive event-trigger condition is constructed, which depends on the relative output measurement, the NN weights estimations, and the states of each follower. Based on the designed event-trigger condition, an adaptive NN controller is developed by using the backstepping control design technique. In the control design process, the algebraic loop problem is overcome by utilizing the property of NN basis functions and by designing novel adaptive parameter laws of the NN weights. The proposed adaptive NN event-triggered controller does not need continuous communication among neighboring agents, and it can substantially reduce the data communication and the frequency of the controller updates. It is proven that ultimately bounded leader-following consensus is achieved without exhibiting the Zeno behavior. The effectiveness of the theoretical results is verified through simulation studies.

Cooperative Adaptive Dynamic Surface Control for a Class of High-Order Stochastic Nonlinear Multiagent Systems.

This article investigates the consensus tracking problem for high-order stochastic pure-feedback nonlinear multiagent systems (MASs) with dead zones. It should be pointed out that each follower's virtual and actual control items are the power-exponential functions with positive odd numbers instead of linear items. Because of the structural characteristics of the followers' dynamics, a technique called adding a power integrator is used, which effectively overcomes the difficulties of states and dead zone with power-exponential functions. Furthermore, radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback structure of MASs. Meanwhile, the problems of "explosion of complexity" caused by repeated differentiations of the virtual controller are solved by using the tracking differentiators. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobally uniformly ultimately bounded in probability, and the tracking errors can converge to a small neighborhood of the origin. Finally, simulation results are presented to verify the effectiveness of the proposed approach.

A novel versatile window function for memristor model with application in spiking neural network

The memristor is a nano-size electrical device that is widely investigated in memory, logic circuits, and neuromorphic systems. This paper puts forward a general window function for Hewlett-Packard (HP) TiO2 memristor model, which covers almost all typical window functions and resolves the boundary lock problem, the inflexible parameter problem, and the distorted pinched hysteresis loop problem. Several adjustable parameters are introduced in the proposed window function in order to make it more flexible and scalable. A comparison with some classic window functions is given. Meanwhile, the range of these parameters is deduced with the aim of finding suitable parameters to simulate memristor devices. This TiO2 memristor model based on the novel window function is further applied to the spiking neural network and tested with the MNIST database of handwritten digits. The result demonstrates that the memristor-based neural network has higher recognition accuracy and better classification effect, indicating the availability of the entire scheme.

Pembelajaran Double Loop Problem Solving di Kelas VII-A MTs Negeri 3 Banyumas untuk Meningkatkan Kemampuan Komunikasi Matematis dan Self-Efficacy

This study aimed to enhance mathematical communication ability and self-efficacy on seventh-grade students through Double Loop Problem Solving learning at MTs Negeri 3 Banyumas. The subjects was 26 seventh-grade students from MTs Negeri 3 Banyumas in academic year of 2018/2019. This research was a class action research consisting of 3 cycles. The instrument was the test to measure mathematical communication ability and the questionnaire to measure self-efficacy. The results are the average score of mathematical communication ability in cycle I, cycle II, and cycle III respectively are 52,03; 74,02; dan 76,58. In addition, the average score of self-efficacy in cycle I, cycle II, and cycle III respectively are 68,59; 72,80; and 75,64.

Adaptive finite-time output-feedback control design for switched pure-feedback nonlinear systems with average dwell time

In this paper, the issue of adaptive fuzzy tracking control is addressed for a class of completely non-affine uncertain switched pure-feedback nonlinear systems with unmeasurable states. In such a system, the average dwell time switching rule is adopted, and its stability is discussed for the first time in the sense of finite time. A fuzzy logic system-based switched observer is constructed to approximate unmeasurable states, which reduces the conservativeness stemming from utilizing a common observer for all subsystems. In addition, filter signals are applied to avoid algebraic loop problem existing in adoption of the controller. Then, a novel adaptive finite-time control strategy is developed on the basis of multiple Lyapunov functions approach, dynamic surface control and backstepping technique. It is proved that the designed controllers of subsystems can ensure that all the closed-loop signals are bounded under a class of switching signals with average dwell time, while the tracking error can converge to a small area of the origin in a finite time. Finally, two simulation examples are given to demonstrate the validity of the proposed control scheme.

Vessel Structure Extraction using Constrained Minimal Path Propagation.

Minimal path method has been widely recognized as an efficient tool for extracting vascular structures in medical imaging. In a previous paper, a method termed minimal path propagation with backtracking (MPP-BT) was derived to deal with curve-like structures such as vessel centerlines. A robust approach termed CMPP (constrained minimal path propagation) is here proposed to extend this work. The proposed method utilizes another minimal path propagation procedure to extract the complete vessel lumen after the centerlines have been found. Moreover, a process named local MPP-BT is applied to handle structure missing caused by the so-called close loop problems. This approach is fast and unsupervised with only one roughly set start point required in the whole process to get the entire vascular structure. A variety of datasets, including 2D cardiac angiography, 2D retinal images and 3D kidney CT angiography, are used for validation. A quantitative evaluation, together with a comparison to recently reported methods, is performed on retinal images for which a ground truth is available. The proposed method leads to specificity (Sp) and sensitivity (Se) values equal to 0.9750 and 0.6591. This evaluation is also extended to 3D synthetic vascular datasets and shows that the specificity (Sp) and sensitivity (Se) values are higher than 0.99. Parameter setting and computation cost are analyzed in this paper.

Time-varying formation tracking control for high-order nonlinear multi-agent systems in fixed-time framework

In this paper, we investigate the time-varying formation tracking control (TVFTC) problem for high-order uncertain nonlinear multi-agent systems (MASs) with a dynamic leader. Both unknown disturbances and uncertain nonlinear functions are considered. A novel fixed-time TVFTC strategy is proposed with two steps, which can avoid the communication loop problem. Firstly, a distributed fixed-time observer (DFTO) for the directed graph is introduced, such that the leader’s information can be accurately obtained by each agent within a bounded time. Secondly, by using fixed-time technique, a Lyapunov-function-based design process has been schemed to avoid the singularity problem. The developed TVFTC protocols can ensure all the followers achieve the desired time-varying formation and track the leader simultaneously. Furthermore, according to practical requirements, the configuration of formation can be chosen as arbitrary shape and the corresponding convergence time is allowed to be pre-estimated or preset. Stability analysis and some simulation examples for the control strategy are given, which further verify the validity.

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

SummaryIn this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non‐affine nonlinearities with the help of Butterworth low‐pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first‐order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back‐stepping approach. The boundedness and convergence of the closed‐loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.

Geometry of the Rabi Problem and Duality of Loops

Abstract We investigate the motion of a classical spin processing around a periodic magnetic field using Floquet theory, as well as elementary differential geometry and considering a couple of examples. Under certain conditions, the role of spin and magnetic field can be interchanged, leading to the notion of “duality of loops” on the Bloch sphere.

Study on Master Slave Interaction Model Based on Stackelberg Game in Distributed Environment

In view of the problems such as low efficiency, difficulty in resolving local conflicts and lack of practical application scenarios, existing in the interaction model of multi-agent systems in a distributed environment, a multi-master multi-slave interaction model was designed based on the Stackelberg game, which is applied to the interaction game problem between the controller and the participant in the command and control process. Through optimizing the Stackelberg game model and multi-attribute decision-making, the multi-master, multi-slave, multi-agent system of the Stackelberg game was designed, and the closed loop problem under the Stackelberg game is solved for dimension reduction and optimal function value. Finally, through the numerical derivation simulation and the training results of related system data, the high efficiency and strong robustness of the model were verified from multiple perspectives, and this model algorithm was proved to be true and highly efficient.

The effect of Double Loop Problem Solving (DLPS) on critical thinking skills and mathematical problem solving abilities

This research aimed to identify the effect of DLPS model on students’ critical thinking skills and mathematical problem-solving abilities. The variables involved in this research were thinking skills and mathematical problem-solving abilities as independent variables, and DLPS model as a dependent variable. One of the best learning strategies to encourage active thinking and problem-solving is the Double Loop Problem Solving (DLPS) model. DPLS model is a learning model of mathematical problem-solving approach that emphasizes students to find the main cause of the existence of a problem. This is in line with students’ mathematical problem-solving abilities that require the student to understand problems, plan or design problem-solving strategies, carry out calculations and verify the truth of the outcome or solution. The population of this study was the students at seventh grade in Junior High School State 1 Rembang, Purbalingga Regency, even semester in the school year 2017/2018. There was selected seventh grade of class B as experimental with 34 students and seventh grade of class E as control class with 34 students. The summary of the hypothesis testing, with the help of the SPSS program, is presented. Using Kolmogorov-Smirnov test for normality test and Levene’s test for equality of variances for homogeneity test, obtained significant value are 0.057; 0.145 and 0.168; 0.062 0.063; 0.681 and 0.244; 0.171 and 1.306; 2.359 more than alpha 0.05. It means that the students’ critical thinking skills and mathematical problem-solving abilities fulfilled assumption of normality test and homogeneity test. After conducting a series of normality and homogeneity tests, it was concluded that t-test for equality of means was used for hypothesis testing. The significant value is 0.019; 0.012 less than alpha 0.05. So that H0 is rejected, it means that students’ critical thinking skills and mathematical problem-solving ability taught by using DLPS model are better than students’ critical thinking skills and mathematical problem-solving ability taught by conventional learning. In other words, the DLPS model influences or effects on students’ critical thinking skills and mathematical problem-solving abilities.

Adaptive Dynamic Surface Control Based on Observer for Switched Non-Strict Feedback Systems With Full State Constraints

As we all know, it is very difficult to design the controller and prove the stability for switched nonlinear systems. Therefore, the engineering application of switching system and the development of switching control theory are limited. In order to solve the control problem for constrained switched system, an adaptive output feedback control scheme based on backstepping technology is studied in this paper for switched non-strict feedback nonlinear systems with asymmetric time-varying full state constraints and unknown external disturbances. A switched state observer based on fuzzy logic system is designed to estimate the unmeasurable states of the uncertain switched system. Asymmetric time-varying barrier Lyapunov functions are adopted to keep the full states of the system satisfying their asymmetric time-varying constraints. A variable separation approach is used to address the algebraic loop problem of non-strict feedback structure. The stability of the closed-loop system and the semi-globally uniformly ultimately bounds of the signals are proved by the Lyapunov method and average dwell time theory. Finally, simulation results are given to show the effectiveness of the proposed control scheme. Different from the existing results, this paper is the first to investigate adaptive control for switched non-strict feedback systems with full state time-varying constraints, unmeasurable states and unknown disturbances, which is a more general case in real systems.

Detection of Dataflow Modeling Anomalies in Workflow Systems: Toward an Active Help Verification Using Model Checking

Business process modeling is a standard activity in the new technology of many organizations. Any information system needs to exchange data between activities by transit inputs and outputs. Workflow-net with data (WFD-net) has been used to verify the data anomalies such as missing data, lost data and redundant, with a focus on read, write and destroy operations’ approaches controls and verifies the data at the end modeling. This verification starts until the system has finished modeling, and begun the correction begins after a passive verification is completed. In this sense, the system uses these approaches follow a certain dysfunction, produce a significant streaming over time during modelling. In addition, after each end of correction the modeler should return in beginning to make another correction for each time for other errors detected, that causes an infinite loop. The objective of this paper is to reduce this problem of infinite loop with an identify data flow anomalies and to correct the failure of each data state in an activity using the network ad-hoc methods have been used to detect the dataflow modleing anomalies. Also, the verification made by the side of applying the combining active-help verifications and Temporal logic CTL* in a model checking with a data operations guard for each process fragment. As part of this verification in a workflow-net with a data operation, Data-State concept are used to register the status of each last data operation with its activity.

Fuzzy reduced-order observer-based adaptive tracking control for a class of switched non-affine systems

This paper investigates an adaptive fuzzy tracking control problem under arbitrary switching for a class of switched non-affine systems with completely unknown nonlinear functions and unmeasurable states. Combining with dynamic surface control (DSC) method and fuzzy approximation technique, an adaptive output-feedback common control approach is presented based on a new fuzzy reduced-order observer which is independent of any switching signal. The given design method does not rely on the boundness assumption about the control gain functions raised by the mean value theorem for non-affine systems, which contributes to the less conservation of the common controller. Meanwhile, the algebraic loop problem caused by the nonstrict-feedback structure and the repeated approximation problem in existing results are also circumvented in the given common control design process. Based on Lyapunov stability theory, the designed common controller can guarantee all the signals in the resulting closed-loop switched systems are uniformly bounded and the tracking error can converge to a small neighborhood of the origin. Two examples are provided to verify the feasibility and practicability of the proposed method.

Design and analysation of DC voltage synchronisation control for a VSC‐MTDC based on virtual synchronous generator

Voltage source converter (VSC)-based high-voltage dc (HVDC) transmission is widely utilised nowadays. However, a VSC-HVDC connected to a weak ac system still faces the problem of phase-locked loop (PLL) synchronisation. To tackle this problem, this study proposes a dc voltage synchronisation control (DCSC) based on the power synchronisation control and the power balance equation of the virtual synchronous generator. To analyse the dynamic stability of DCSC, voltage sensitivity indicator (VSI) is used to reflect the voltage fluctuations. Considering the deviations of dc voltage and converter losses, a closed-loop transfer function of dc voltage is designed. Three important parameters including VSI, virtual damping coefficient and inertia time constant are investigated. The dynamic stability analysis shows DCSC has strong controllability. Then, a three terminals VSC-MTDC model is simulated in PSCAD/EMTDC. Herein, DCSC is used in one inverter and PLL is used in another inverter. The waveforms obtained by using DCSC are compared with that by using PLL. Simulation results clearly depict that the inverter with DCSC has a normal steady state and a better transient response than using PLL when connected to a strong ac system, also provides better power transfer capability when connected to a weak ac system.

A simple void node and loop‐free three‐dimensional routing protocol for multi‐hop wireless networks

SummaryWireless networks are now very essential part for modern ubiquitous communication systems. The design of efficient routing and scheduling techniques for such networks have gained importance to ensure reliable communication. Most of the currently proposed geographic routing protocols are designed for 2D spatial distribution of user nodes, although in many practical scenarios user nodes may be deployed in 3D space also. In this paper, we propose 3D routing protocols for multihop wireless networks that may be implemented in two different ways depending on how the routing paths are computed. When the routing paths to different user nodes from the base station in the wireless network are computed by the base station, we call it centralized protocol (3DMA‐CS). A distributed routing (3DMA‐DS) protocol is implemented when respective routing path of each user node to the base station is computed by the user node. In both of these protocols, the user (base station) selects the relay node to forward packets in the direction of destination, from the set of its neighbours, which makes minimum angle with the reference line drawn from user (base station) to the base station (user), within its transmission range. The proposed protocols are free from looping problem and can solve the void node problem (VNP) of multihop wireless networks. Performance analysis of the proposed protocol is shown by calculating end‐to‐end throughput, average path length, end‐to‐end delay, and energy consumption of each routing path through extensive simulation under different network densities and transmission ranges.