作者:
Rajarshi BardhanDebasish GhoseGuidance
Control and Decision Systems Laboratory (GCDSL) Dept. of Aerospace Engineering Indian Institute of Science Bangalore India GCDSL
the Department of Aerospace Engineering Indian Institute of Science Bangalore India
A guidance law derived by modifying state dependent Riccati equation technique, to enable the imposition of a predetermined terminal intercept angle to a maneuvering target, is presented in this paper. The interceptor...
详细信息
ISBN:
(纸本)9781457710957
A guidance law derived by modifying state dependent Riccati equation technique, to enable the imposition of a predetermined terminal intercept angle to a maneuvering target, is presented in this paper. The interceptor is assumed to have no knowledge about the type of maneuver the target is executing. The problem is cast in a non-cooperative game theoretic form. The guidance law obtained is dependent on the LOS angular rotational rate and on the impact angle error. Theoretical conditions which guarantee existence of solutions under this method have been derived. It is shown that imposing the impact angle constraint calls for an increase in the gains of the guidance law considerably, subsequently requiring a higher maneuverability advantage of the interceptor. The performance of the proposed guidance law is studied using a non-linear two dimensional simulation of the relative kinematics, assuming first order dynamics for the interceptor and target.
The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constr...
详细信息
ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.
Cardiovascular diseases (CVDs) pose a significant threat to global public health, affecting individuals across various age groups. Factors such as cholesterol levels, smoking, alcohol consumption, and physical inactiv...
详细信息
作者:
NITSCH, Ais presently Manager
Systems Engineering Unit Navy and Marine Engineering Industry Control Dept. General Electric Company. He has been with G.E. since receiving a B.S. (EE) in 1951 from Iowa State University working in engineering associated with testing heavy equipment control materials handling equipment and central operations systems.
The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply t...
详细信息
ISBN:
(数字)9798350372939
ISBN:
(纸本)9798350372946
The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply the latest developed quantum machine learning method to the hierarchical classification models of texts. This approach avoids the text corpus necessity and supports the online learning due to the very design. The model proposed is tested in the experiment. The target content was given in the experiment and the model is used to measure the correspondence between the target topics and the test messages. The result of the experiment shows that the proposed method allows to distinguish the target content in the test text.
We present a framework to support design refinement during the virtual prototyping of microelectromechanical systems (MEMS). By instantiating MEMS components and connecting them to each other via ports, the designer c...
详细信息
Electrical Impedance Tomography (EIT) has been the subject of intensive research since its development in the early 1980s by Barber and Brown at the Department of Medical Physics and Clinical engineering, Hallamshire ...
详细信息
ISBN:
(纸本)9789898425355
Electrical Impedance Tomography (EIT) has been the subject of intensive research since its development in the early 1980s by Barber and Brown at the Department of Medical Physics and Clinical engineering, Hallamshire Hospital in Sheffield (UK). In particular, pulmonary measurement has been the focus of most EIT related research. One of the relatively recent advances in EIT is the development of an absolute EIT system (aEIT) which can estimate absolute values of lung resistivity and lung volumes. However, there is still active research in the area of validating and improving the accuracy and consistency of the aEIT estimation of lung volumes towards characterising the system as suitable for clinical use. In this paper we present a new approach based on Computational Intelligence (CI) modelling to model the 'Resistivity - Lung Volume' relationship that will allow more accurate lung volume predictions. Eight (8) healthy volunteers were measured simultaneously by the Sheffield aEIT system and a Spirometer and the recorded results were used to develop subject-specific Neural-Fuzzy models able to predict absolute values of lung volume based only on absolute lung resistivity data. The developed models show improved accuracy in the prediction of lung volumes, as compared with the original Sheffield aEIT system. However the interindividual differences observed in the subject-specific modelling behaviour of the 'Resistivity-Lung Volume' curves suggest that a model extension is needed, whereby the modelling structure auto-calibrates to account for subject (or patient-specific) inter-parameter variability.
In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reachi...
In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reaching implications, affecting individuals, communities, and even entire countries. To address this issue, a low-cost, self-regulating, and forward-thinking rumor detection technique is required. This research performs an intensive analysis of the performance of three robust machine learning algorithms, including XGBoost, SVM, and Random Forest, as well as two deep learning-based transformers, namely BERT and DistilBert. In this research, the models are trained and evaluated on a combined dataset comprising data from Twitter15 and Twitter16 datasets. Support Vector Machine (SVM) and Random Forest exhibit the best accuracy among classical machine learning models, reaching 89.05%. In comparison, among the transformer-based deep learning models, BERT achieves the best accuracy of 90.20%. In conclusion, the learning-based transformers beat its competitors in terms of accuracy, recall, precision, and F-measure, proving its efficacy in minimizing the detrimental impact of rumors on people, communities, and society as a whole.
In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertaintie...
详细信息
In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in the LPV scheduling variables. The proposed method employs SVM and takes advantage of the so-called “kernel trick” to allow for the identification of the LPV-ARX model structure solely based on the input-output data. The objective function, as defined in this paper, allows to consider uncertainties related to the LPV scheduling parameters, and hence results in a new formulation that provides a more accurate estimation of the LPV model in the presence of scheduling uncertainties. We further demonstrate the viability of the proposed LPV identification method through numerical examples, where we show that higher best fit rate (BFR) can be achieved under realistic noise conditions using the proposed method compared to the method initially proposed in [6].
D-Grid is a German national grid initiative founded by the German Federal Ministry of Education and Research. It provides a frame for the collaboration of researchers across Germany and offers access to distributed se...
暂无评论