State estimation problem is considered for a kind of wireless network control system with stochastic uncertainty and time delay. A sliding mode observer is designed for the system under the situation that no missing measurement occurs and system uncertainty happens in a stochastic way. The observer designed for the system can guarantee the system states will be driven onto the sliding surface under control law, and the sliding motion of system states on sliding surface will be stable. By constructing proper Lyapunov-Krasovskii functional, sufficient conditions are acquired via linear matrix inequality. Finally, simulation result is employed to show the effectiveness of the proposed method.
This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth; to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.
This study investigates the output tracking problem for a class of Takagi–Sugeno fuzzy systems subject to periodic signals and actuator saturation via equivalent-input-disturbance (EID) technique. In particular, to ensure the periodic signals tracking, a state-space repetitive control structure is considered. Further, the EID technique is utilised to improve the disturbance rejection performance without any prior knowledge of the disturbance and inverse dynamics of the plant. By constructing a suitable Lyapunov–Krasovskii functional and using the Wirtinger-based integral inequality, a new set of sufficient conditions is derived in terms of linear matrix inequalities (LMIs) which ensures the stability of the addressed system. In addition to that by using a Lyapunov level set, saturation-dependent Lyapunov function captures the real-time information on the severity of actuator saturation and leads to less conservative estimate of the domain of attraction, which is based on the solution of an LMI optimisation problem. Moreover, the designed fuzzy repetitive controller is reliable in the sense that the stability and the satisfactory performance of the closed-loop system are achieved not only under normal operation, but also in the presence of any actuator faults, saturation and dead zone. Finally, the proposed method is validated through two numerical examples to illustrate the effectiveness and superiority of the developed controller design.
Water heating applications consume a considerable portion of electricity demand in most of countries. Small wind turbines are one of attractive alternatives for grid electricity based water heating systems. Wind energy can be converted to heat energy in a high efficient manner. However it is essential that wind turbine based water heating systems should be economical and reliable. Maximum power point tracking algorithm of most of available wind turbines requires information from a wind speed sensor and a rotor speed sensor which reduces the reliability of the system. In this paper, the proposed 5 kW wind turbine does not require external wind speed sensors and rotor speed sensors. The system is consistent with sensorless maximum power point tracking algorithm, which eliminates the need for both wind speed and rotor speed sensors and gives a highly reliable solution for water heating applications. Internal voltage and current sensors are used to measure the output voltage, the output current and the power of the generator. Using those measurements, the sliding-mode observer can accurately estimate the rotor speed and position and which is used in the maximum power point tracking algorithm. To calculate the optimum rotor speed, the generator output power measurements are used with power signal feedback method. The effectiveness of the proposed system is verified using a simulation model by comparing the performances with a sensor based model.
In this paper we present the application of regressive models to simulation of a full-scale vehicle-to-pole impact. The capability of an ARMAX model to reproduce vehicle kinematics was examined. Regressive model parameters were established by minimizing a weighted sum of squares of prediction errors. The prediction horizon was assigned to evaluate model's robustness and verify its time series data forecasting performance. It was found that the ARMAX model reproduces the signal which was used for its establishment (i.e. real vehicle's acceleration). Moreover, such estimation technique preserves all characteristic information relevant for a given collision, since integration of the estimated acceleration pulse yields plots of velocity and displacement which closely follow the reference ones.
This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT
No abstract is provided for this article.
No abstract is provided for this article.
Superresolution array processing, and in particular signal subspace-type techniques, are based effectively on the searching of the array manifold in order to estimate signal parameters. Although the array manifold concept has been used extensively to solve parameter estimation problems, only very recently has research been directed toward identifying the shape of the manifold. Knowledge of the manifold shape is not only essential for the investigation of ambiguities and assessment of the detection-resolution capabilities of an array, but it may also prove useful in developing new and more effective methods for its search process. Concepts related to two array manifold properties, namely arc length and first curvature, are used to assess the accuracy and resolution capabilities offered by a general array in the presence of near-field emitters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
