Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveill...
Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.
Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classifica...
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Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.
The problems of high voltage equipment usually come from the imperfection of solid insulation, which may be caused by poor workmanship during cable installation, leading to multi-void inside solid insulation and subse...
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In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested:...
In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.
This paper presents the Partial Discharge (PD) investigation of defective medium voltage (3.6/6(7.2) kV) cable terminations and the impact of using different voltage sources namely AC 50 Hz and Very Low Frequency (VLF...
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Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires ...
Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.
This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm.
Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Cont...
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An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of ...
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An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.
Objective: Glucose homeostasis is the only way to manage diabetic progression as all medications used do not cure diabetes. This study was aimed at verifying the feasibility of lowering glucose with non-invasive ultra...
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