In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied ...
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In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.
Based on today’s modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote He...
Based on today’s modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors’ health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside “Non-invasive Monitoring system and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)” research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved.
The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux ...
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ISBN:
(数字)9781665467261
ISBN:
(纸本)9781665467278
The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments.
In recent years, wind energy is gaining importance, with the aim of achieving a clean and sustainable energy model. However, an enhancement in wind turbine performance is still required to compete with traditional ene...
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In this paper, we propose a Secure Energy Management system (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning netw...
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ISBN:
(纸本)9781665480468
In this paper, we propose a Secure Energy Management system (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems.
High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applicat...
High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R 2 ) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R 2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during *** Relevance— This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.
The use of Agriculture Intelligent systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternativ...
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ISBN:
(纸本)9781665460156
The use of Agriculture Intelligent systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy.
In response to the urgent need for renewable energy development and variability management due to escalating population growth, rising energy demands, and diminishing natural reserves, this research focuses on optimiz...
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ISBN:
(数字)9798350349351
ISBN:
(纸本)9798350349368
In response to the urgent need for renewable energy development and variability management due to escalating population growth, rising energy demands, and diminishing natural reserves, this research focuses on optimizing energy management within a grid-connected microgrid. The study uniquely employs advanced heuristic algorithms to define system choices and constraints, achieving significant improvements in energy efficiency and cost reduction. Utilizing Matlab Simulink and Stateflow environments, various simulation scenarios are explored to demonstrate the benefits of the proposed approach, including enhanced stability and reliability of the energy supply. The findings highlight the potential for significant advancements in microgrid energy management through innovative algorithmic strategies.
In this paper we propose an original distributed control framework for DC microgrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on d...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
In this paper we propose an original distributed control framework for DC microgrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on duality, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC microgrid and the designed distributed controlsystem in a power preserving way, we steer the DC microgrid’s state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework.
The increasing volume of online reviews has made the development of sentiment analysis models possible for determining customers’ opinions regarding different products and services. Until now, sentiment analysis has ...
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The increasing volume of online reviews has made the development of sentiment analysis models possible for determining customers’ opinions regarding different products and services. Until now, sentiment analysis has proven to be an effective tool for determining the overall polarity of reviews. To improve the granularity at the aspect level for a better understanding of the service or product, the task of aspect-based sentiment analysis aims to first identify aspects and then determine the user’s opinion about them. The complexity of this task lies in the fact that the same review can present multiple aspects, each with its own polarity. Current solutions have poor performance on such data. We address this problem by proposing ATESA-BÆRT, a heterogeneous ensemble learning model for Aspect-Based Sentiment Analysis. Firstly, we divide our problem into two sub-tasks, i.e., Aspect Term Extraction and Aspect Term Sentiment Analysis. Secondly, we use the argmax multi-class classification on six transformers-based learners for each sub-task. The proposed ensemble integrates representations from pre-trained and fine-tuned BERT and BART models in order to capture diverse linguistic features. By combining these with Linear , BiLSTM , and CNN-BiLSTM models, we aim to achieve enhanced performance on the aspect-based sentiment analysis task. Initial experiments on two publicly available real-world English online review datasets prove that ATESA-BÆRT outperforms current state-of-the-art solutions while tackling the sentiment analysis many aspects problem.
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