Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third ...
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In a wireless passive sensor network, radio frequency (RF) sources wirelessly supply energy to sensor nodes. Against theoretical expectations of an abundance of energy, a wireless passive sensor network suffers from a...
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Prior studies of human communication have demonstrated that prosocial outcomes occur when facets of communication converge between interlocutors - for example, social likeability and perceived competence increase when...
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Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These mod...
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The widespread integration of Distributed Generation (DG) and new loads such as Electric Vehicles (EVs) into power distribution networks presents substantial technical challenges for network operators, such as voltage...
The widespread integration of Distributed Generation (DG) and new loads such as Electric Vehicles (EVs) into power distribution networks presents substantial technical challenges for network operators, such as voltage fluctuations and phase unbalance. These can be addressed by many power electronic devices with advanced control strategies, including the Hybrid Transformer (HT). This paper presents a new three-phase HT concept which can perform multiple functions, including voltage regulation and voltage balancing, to address power quality problems in distribution networks. Theory and control strategy are presented, along with simulated results incorporating an unbalance-tolerant control scheme and 3D Space Vector Modulation, to demonstrate the validity of the proposed HT topology. Its advantages include being retrofittable to existing distribution transformers, not requiring bulky low-frequency converter magnetics, having a low semiconductor component count, and allowing reduced DC-link capacitances. This makes it cost-effective, compact and light weight in comparison to the existing topologies that provide similar functionality.
The proliferation of the internet of things (IoT) has led to the emergence of a wide range of intelligent devices, creating a broad domain with significant security concerns. These concerns impose a high level of secu...
The proliferation of the internet of things (IoT) has led to the emergence of a wide range of intelligent devices, creating a broad domain with significant security concerns. These concerns impose a high level of security; unfortunately, IoT devices usually have limited resources in terms of little memory, low computing power, and a short battery life. Therefore, IoT application developers must use lightweight cryptographic tools to achieve a trade-off between performance and security. The storage and high computation capacity of cloud computing is often exploited to manage the vast amount of data produced by such gadgets. Some methods still suffer from attacks, and others cannot achieve low complexity. We propose a secure and low-complexity system for smart buildings in transferring data between the local server, the cloud, and users authorized by the owner. The LED encryption algorithm, which is lightweight and requires limited resources and less energy, was used to create a mobile application system characterized by confidentiality, authentication, and privacy. For further security, the owner's biometrics were used and derived as the key to decrypt data from the cloud. We have leveraged Dragonfly authentication technology to transfer data from the local server to the users. The owner can add authorized persons in the cloud database and local server to enjoy using the application. Moreover, we successfully balance security complexity and performance in our work. As a result, we achieve good results with a computation cost of 0.281 s and a communication cost of 1472 $bit$ .
Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural langu...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI’s ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements.
The deployment of microservices presents a challenge in striking a balance between application performance and cost, which is one of the critical considerations in microservice deployment. This study proposes a micros...
The deployment of microservices presents a challenge in striking a balance between application performance and cost, which is one of the critical considerations in microservice deployment. This study proposes a microservice deployment algorithm based on ant colony optimization. It conducts experimental cases to analyze the performance and deployment cost of applications deployed using Kubernetes deployment and the deployment solution proposed in this study. The experiments demonstrate that the deployment solution designed using the proposed algorithm achieves higher performance and lower deployment cost during the deployment phase.
Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the success of DoReMi in Large Language Mod...
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Multi-objective decision making systems based on Spatial-Temporal logics presents a class of computational systems based on Artificial Intelligence in which spatial location and time evolution of processes (events) ar...
Multi-objective decision making systems based on Spatial-Temporal logics presents a class of computational systems based on Artificial Intelligence in which spatial location and time evolution of processes (events) are taken into account. Multi-objective decision making systems showcase a distributed computing architecture based on Agents (Computational Nodes) that form a network for data processing. The functionality of the decision making system is based on events that are spatially and temporally localized. The set of Agents, based on the events, calculates its influence coefficient on the decision taken by the Agent. An Agent can generate both decisions, which lead to personal qualitative evolution, and events that will be processed by other Agents. This work elaborates the following: the multi-objective decision system diagram, the communication model between Agents, the set of operators for spatial-temporal logic, and the model for optimal solution search in event space.
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