In this paper, the Hamilton method is used to establish a distributed parameter model for a single-link flexible robotic arm based on the form of partial differential equations. The distributed parameter system is use...
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ISBN:
(数字)9781728182889
ISBN:
(纸本)9781728182896
In this paper, the Hamilton method is used to establish a distributed parameter model for a single-link flexible robotic arm based on the form of partial differential equations. The distributed parameter system is used to control the model. The boundary control method is used to add a boundary control input to the end boundary of the manipulator. The PD boundary controller is designed, and the distributed parameter boundary control based on the exponential convergence method is performed on the end boundary of the manipulator to adjust the vibration of the robotic arm. The Lyapunov function is designed to prove the stability of the system through calculation.
Unified Power Quality Conditioner system used on the distribution side of power system can be improvised by integrating with renewable energy resources like the photovoltaic system. Photo Voltaic (PV) integrated UPQC ...
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ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
Unified Power Quality Conditioner system used on the distribution side of power system can be improvised by integrating with renewable energy resources like the photovoltaic system. Photo Voltaic (PV) integrated UPQC compensates for real (P) and reactive power (Q). PV-UPQC system consists of back to back series and shunt compensators interlinked by a common DC link capacitor powered by the photovoltaic system. The reactive power compensation is achieved by shunt compensator of PV-UPQC system. The voltage fluctuations like voltage sag/swell are compensated by the series compensator. The nonlinear sensitive loads like induction motor loads can introduce harmonics and distort the stability of the power system. The harmonics introduced by the induction motor load can be mitigated by PV-UPQC system. This paper involves the modelling of the PV-UPQC system in MATLAB. The synchronous reference frame control algorithm is used for the control of sensitive load. The PV system is coupled with an MPPT control algorithm and the boost converter is designed to provide power to the DC-Link. The induction motor load characteristics are analysed under various voltage fluctuating conditions.
According to the actual needs of marine resource development and marine environmental protection, in order to solve the problem of cooperative operation of multiple Unmanned Surface Vehicles (USVs), a multi-USVs coope...
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ISBN:
(数字)9781728182889
ISBN:
(纸本)9781728182896
According to the actual needs of marine resource development and marine environmental protection, in order to solve the problem of cooperative operation of multiple Unmanned Surface Vehicles (USVs), a multi-USVs cooperative path tracking algorithm based on Sigmoid function is proposed. The characteristic model for multi-USVs synchronous navigation is established. The path tracking algorithm is studied for the leader USV based on the inverse trigonometric function method. Considering the turning speed, the follower USV collaborative control algorithm is discussed. The control algorithm is used for the cooperative navigation control of multi-USVs. In this paper, simulation experiments on cooperative navigation of multi-USVs are carried out. The experimental data shows the feasibility and reliability of the leader-follower cooperative mechanism and cooperative control algorithm.
Unlike the existing loss-based and delay-based congestion control algorithms, the Bottleneck Bandwidth Round-trip propagation time (BBR) congestion control algorithm determines the amount of sending data to be transfe...
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ISBN:
(数字)9781728138930
ISBN:
(纸本)9781728138947
Unlike the existing loss-based and delay-based congestion control algorithms, the Bottleneck Bandwidth Round-trip propagation time (BBR) congestion control algorithm determines the amount of sending data to be transferred at a specific time by calculting BDP (Bandwidth Delay Product). However, when BBR competes with loss-based congestion control algorithms, such as Reno and CUBIC, most of the bottleneck bandwidth is occupied by specific flows or excessive packet loss occurs. In previous work, we proposed an improved loss recovery mechanism in order to address above problems. Through the previous proposal, the fairness between BBR and loss-based algorithms was improved if the buffer size was larger than 2 BDP. However, if the buffer size was smaller than 2 BDP, the performance imbalance and continuous packet retransmission still occurred. In this paper, we propose a congestion window upper scaling method that complements the previously proposed loss recovery mechanism. In addition, we conduct an experiment and evalute its enhanced performance in a Mininet emulator.
This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors ...
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This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew's Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.
Evaluation of subjective examinations using computerized tools has been a topic of research for more than four decades. Several statistical and mathematical techniques have been proposed by various researchers. In thi...
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Evaluation of subjective examinations using computerized tools has been a topic of research for more than four decades. Several statistical and mathematical techniques have been proposed by various researchers. In this research work, the several methods proposed earlier like Latent Semantic Analysis (LSA), Generalized Latent Semantic Analysis (GLSA), Bilingual Evaluation Understudy (BLEU), and Maximum Entropy (MaxEnt) are compared on common input data. The techniques are implemented using Java programming language, MatLab, and other open source tools. Experiments have been conducted and developed prototypes are tested using a database of 4500 answers with approximately 50 questions of computer science. Comparison of these techniques on a common database is not available in the literature as far as the authors' review is concerned. The database used for testing is collected by conducting tests of students of graduate level in the field of computer science. The pros and cons of each technique on the basis of experiments are discussed in the paper.
Real-time logics are popular specification languages for reasoning about systems intended to meet timing constraints. Numerous formalisms have been proposed with different underlying time models that can be characteri...
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Real-time logics are popular specification languages for reasoning about systems intended to meet timing constraints. Numerous formalisms have been proposed with different underlying time models that can be characterized along two dimensions: dense versus discrete time and point-based versus interval-based. We present monitoring algorithms for the past-only fragment of metric temporal logics that differ along these two dimensions, analyze their complexity, and compare them on a class of formulas for which the point-based and the interval-based settings coincide. Our comparison reveals similarities and differences between the monitoring algorithms and highlights key concepts underlying our and prior monitoring algorithms. For example, point-based algorithms are conceptually simpler and more efficient than interval-based ones as they are invoked only at time points occurring in the monitored trace and their reasoning is limited to just those time points.
Smart indoor lighting systems use occupancy and light sensor data to adapt artificial lighting in accordance with changing occupancy and daylight conditions. Such systems can be designed to reduce lighting energy cons...
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Smart indoor lighting systems use occupancy and light sensor data to adapt artificial lighting in accordance with changing occupancy and daylight conditions. Such systems can be designed to reduce lighting energy consumption significantly. However, these systems cannot account for individual user preferences at the workplace in real time. We propose a sensor-driven, human-in-the-loop lighting system that incorporates user feedback in addition to occupancy and light sensor inputs. In this system, luminaires transmit unique visible light communication identifier signals. By processing the image captured by a smartphone camera, a user obtains two pieces of information: visible light communication identifiers of luminaires in the vicinity and average image pixel value. A control algorithm is designed that incorporates these user inputs along with occupancy and light sensor inputs to determine the dimming levels of the luminaires to achieve illumination levels acceptable to users. We compare the performance of the proposed lighting control system with a sensor-driven lighting control system in an office test bed.
We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We sp...
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We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We specifically derive upper and lower bounds on the optimal rates under a smoothness condition on the correct prior, with the number of samples per data set equal the VC dimension. These results have implications for the improvements achievable via transfer learning. We additionally extend this setting to real valued function, where we establish consistency of an estimator for the prior, and discuss an additional application to a preference elicitation problem in algorithmic economics. (C) 2017 Published by Elsevier B.V.
The numerical algorithm for tracing exact frequency-dependent rays, which makes it possible to trace these rays in arbitrary media is proposed. In addition, a numerical investigation of the properties of rays under co...
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The numerical algorithm for tracing exact frequency-dependent rays, which makes it possible to trace these rays in arbitrary media is proposed. In addition, a numerical investigation of the properties of rays under consideration and comparison of them with the standard ray theory and finite-difference simulation was performed. The results of numerical experiments for a number of models, including the Sigsbee model, containing a salt body of complex shape are presented.
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