The paper presents a rule-based stabilising controller for electrical power systems. The proposed rule-based stabiliser is suitable for on-line computer control because heavy calculation is not involved. The control s...
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The paper presents a rule-based stabilising controller for electrical power systems. The proposed rule-based stabiliser is suitable for on-line computer control because heavy calculation is not involved. The control scheme utilised is a modified, time-optimal, bang-bang control scheme. Several rules are prepared to improve the dynamic behaviour of generator. From both the rules and the generator condition, measured at each sampling time, the supplementary stabilising signal is determined and applied to the excitation control loop at each sampling time. There exist several adjustable parameters; these parameters are set to their optimal values by investigating the values of discrete-type, quadratic, performance indices. To investigate the efficiency of the proposed rule-based stabiliser, several simulations were performed. Very good system performance was obtained by applying the rule-based stabiliser throughout all the simulations. The implementation of the stabiliser may be easy because of its simple control rules and required data.
RTAG is a system for automated implementation of communication protocols from formal specifications. The RTAG specification language is based on attribute grammars, and allows complex protocols to be specified concise...
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RTAG is a system for automated implementation of communication protocols from formal specifications. The RTAG specification language is based on attribute grammars, and allows complex protocols to be specified concisely and with minimal need for additional program code. This paper describes a set of techniques for efficient automated implementation of protocols from RTAG specifications, and compares the performance to that of hand-coded protocol implementations. We conclude that in many cases the performance of RTAG-based protocol implementations is acceptable for experimental or production uses.
In lossy compression, Wang et al. [1] recently introduced the rate-distortion-perception-classification function, which supports multi-task learning by jointly optimizing perceptual quality, classification accuracy, a...
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Internet of Vehicles（IoV） is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle（V2V） communications by forming a vehicular ad hoc net...
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Internet of Vehicles（IoV） is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle（V2V） communications by forming a vehicular ad hoc networks（VANETs）, with roadside units using vehicle-to-roadside（V2R） communications. IoV offers several benefits such as road safety, traffic efficiency, and infotainment by forwarding up-to-date traffic information about upcoming traffic. For instance, IoV is regarded as a technology that could help reduce the number of deaths caused by road accidents, and reduce fuel costs and travel time on the road. Vehicles could rapidly learn about the road condition and promptly respond and notify drivers for making informed decisions. However, malicious users in IoV may mislead the whole communications and create chaos on the road. Data falsification attack is one of the main security issues in IoV where vehicles rely on information received from other peers/vehicles. In this paper,we present data falsification attack detection using hashes for enhancing network security and performance by adapting contention window size to forward accurate information to the neighboring vehicles in a timely manner（to improve throughput while reducing end-to-end delay）. We also present clustering approach to reduce travel time in case of traffic congestion. Performance of the proposed approach is evaluated using numerical results obtained from simulations. We found that the proposed adaptive approach prevents IoV from data falsification attacks and provides higher throughput with lower delay.
This paper investigates the robustness of fuzzy logic stabilizers using the information of speed and acceleration states of a study unit. The input signals are the real power output and/or the speed of the study unit....
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This paper investigates the robustness of fuzzy logic stabilizers using the information of speed and acceleration states of a study unit. The input signals are the real power output and/or the speed of the study unit. Non-linear simulations show the robustness of the fuzzy logic power system stabilizers. Experiments are also performed by using a micro-machine system. The results show the feasibility of proposed fuzzy logic stabilizer.
A feed forward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was back-propagation, assuming initially a sigmoid tra...
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A feed forward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was back-propagation, assuming initially a sigmoid transfer function for the network's processing units (''neurons''), Once the network was trained the units' transfer function was changed to hard limiters with thresholds equal to the biases obtained for the sigmoids during training. The off-line experimental results presented in this paper show that a FFNN may be considered as an alternative method to make the discrimination between inrush and fault currents in a digital relay implementation.
The Thevenin theorem, one of the most celebrated results of electric circuit theory, provides a two-parameter characterization of the behavior of an arbitrarily large circuit, as seen from two of its terminals. We int...
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The Thevenin theorem, one of the most celebrated results of electric circuit theory, provides a two-parameter characterization of the behavior of an arbitrarily large circuit, as seen from two of its terminals. We interpret the theorem as a sensitivity result in an associated minimum energy/network flow problem, and we abstract its main idea to develop a decomposition method for convex quadratic programming problems with linear equality constraints, of the type arising in a variety of contexts such as the Newton method, interior point methods, and least squares estimation. Like the Thevenin theorem, our method is particularly useful in problems involving a system consisting of several subsystems, connected to each other with a small number of coupling variables.
Carbon Nano-Tube Field Effect Transistors(CNTFETs) are being widely studied as possible successors to silicon *** current mode has many advantages such as performing sum operation by means of a simple wired ***,direct...
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Carbon Nano-Tube Field Effect Transistors(CNTFETs) are being widely studied as possible successors to silicon *** current mode has many advantages such as performing sum operation by means of a simple wired ***,direction of the current can be used to exhibit the sign of *** is expected that the advantages of current mode approaches will become even more important with increased speed requirements and decreased supply *** this paper,we present five new circuit designs for differential absolute value in current mode logic which have been simulated by CNTFET *** considered base current for this model is 2 μA and supply voltage is 0.9 *** all of our designs we used N-type CNTFET current mirrors which operate as truncated difference *** operation of Differential Absolute Value circuit calculates the difference between two input currents and our circuit designs are operate in 8 logic levels.
Mitotic figure counting plays a critical role in glioma grading and prognostication, yet manual counting remains time-consuming and subject to variability. The Glioma-MDC 2025 Challenge, hosted at ISBI 2025, aims to a...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Mitotic figure counting plays a critical role in glioma grading and prognostication, yet manual counting remains time-consuming and subject to variability. The Glioma-MDC 2025 Challenge, hosted at ISBI 2025, aims to address this challenge by advancing automated solutions. In response, we present SEMA, a semi-supervised learning framework that incorporates a cross-magnification attention mechanism. SEMA mimics the multi-magnification workflow used by pathologists, employing attention to model interactions between cells and neighboring regions, effectively replicating how pathologists analyze whole slide images. Additionally, SEMA leverages the abundance of unlabeled cells in raw tissue images from competition datasets through a semi-supervised learning approach, progressively enhancing its robustness and performance. Extensive experiments demonstrate that SEMA outperforms other baselines by up to 40% in FI score, with each component contributing significantly to its success. Notably, SEMA achieves a perfect F1 score, securing top performance on the public leaderboard.
The transition between the transient and the stationary regimes of stimulated Raman scattering is examined computationally for pulses with a sharp initial rise. We find that the transient-regime predictions are useful...
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The transition between the transient and the stationary regimes of stimulated Raman scattering is examined computationally for pulses with a sharp initial rise. We find that the transient-regime predictions are useful for times less than T-2 regardless of the pulse duration and that the stationary-regime predictions become useful only for times much greater than T-2. (C) 1996 Optical Society of America.
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