This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar ...
ISBN:
(纸本)9798400709449
This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. The trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. These challenges may provide directions for future research.
Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture)...
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Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal *** thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 *** dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and *** prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for *** performance assessme
The integration of real-world clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database into the Open Electronic Medical Records (OpenEMR) system presents a unique opportunity to develo...
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ISBN:
(数字)9798350384727
ISBN:
(纸本)9798350384734
The integration of real-world clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database into the Open Electronic Medical Records (OpenEMR) system presents a unique opportunity to develop and implement innovative Extract, Transform, and Load (ETL) methodologies. This study focuses on the complex process of migrating 40,000 patient records from 33 tables in MIMIC-IV to the intricate schema of OpenEMR, which comprises 264 tables. The primary objective is to outline the novel approaches employed in the ETL process to ensure data fidelity, integrity, and usability within OpenEMR’s frontend interface. The methodology involves a three-stage process: extraction, transformation, and loading. During the extraction phase, relevant data is carefully selected from MIMIC-IV, taking into account data privacy considerations. The transformation phase involves intricate data mapping and manipulation to align the extracted data with OpenEMR’s schema, addressing challenges such as schema mismatches and data format inconsistencies. Finally, the loading phase ensures the accurate and efficient population of OpenEMR with the transformed data. The ETL process is designed to maintain data quality and integrity throughout the migration, employing robust validation and error-handling mechanisms. This study contributes to the advancement of ETL methodologies in the context of integrating real-world clinical data into electronic medical record systems. The detailed description of the innovative approaches employed in this project might serve as a valuable resource for researchers and practitioners working on similar data migration tasks.
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution. In t...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution. In this work, we consider the inpainting of semantic Bird’s-Eye-View maps. We propose SePaint, an inpainting model for semantic data based on generative multinomial diffusion. To maintain semantic consistency, we need to condition the prediction for the missing regions on the known regions. We propose a novel and efficient condition strategy, Look-Back Condition (LB-Con), which performs one-step look-back operations during the reverse diffusion process. By doing so, we are able to strengthen the harmonization between unknown and known parts, leading to better completion performance. We have conducted extensive experiments on different datasets, showing our proposed model outperforms commonly used interpolation methods in various robotic applications.
Autonomous navigation in unknown environments is challenging and requires the consideration of both geometric and semantic information to assess the navigability of the environment. In this work, we propose a novel sp...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Autonomous navigation in unknown environments is challenging and requires the consideration of both geometric and semantic information to assess the navigability of the environment. In this work, we propose a novel space modeling framework, Visual-Geometry Sparse Gaussian Process (VG-SGP), that simultaneously considers the semantics and geometry of the scene. Our proposed approach can overcome the limitation of visual planners that fail to recognize geometry associated with the semantic and the geometric planners that completely overlook the semantic information which is very critical in real-world navigation. The proposed method leverages dual Sparse Gaussian Processes in an integrated manner; the first is trained to forecast geometrically navigable spaces while the second predicts the semantically navigable areas. This integrated model is able to pinpoint the overlapping (geometric and semantic) navigable space. The simulation and real-world experiments demonstrate that the proposed VG-SGP model, coupled with our innovative navigation strategy, outperforms models solely reliant on visual or geometric navigation algorithms, highlighting a superior adaptive behavior. We provided a demonstration video
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and open-sourced our code
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.
We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (p...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (pointcloud) to learn the terrain elevation profile and extract the feasible navigation subgoals around the robot. Subsequently, a cost function, which prioritizes the safety of the robot in terms of keeping the robot’s roll and pitch angles bounded within a specified range, is used to select a safety-aware subgoal that leads the robot to its final destination. The algorithm is designed to run in real-time and is intensively evaluated in simulation and real-world experiments. The results compellingly demonstrate that our proposed algorithm consistently navigates uneven terrains with high efficiency and surpasses the performance of other planners. The implementation of our method, including the supplementary video showing the experimental and real-world results, is available at https://***/3ov2r8.
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the ‘default policy,’ based on previous experience. However, the inherent rig...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the ‘default policy,’ based on previous experience. However, the inherent rigidity of the static default policy presents significant challenges for agents when operating in unknown environment, that are not included in agent’s prior knowledge. In this work, we introduce a context-generative default policy that leverages the region observed by the robot to predict unobserved part of the environment, thereby enabling the robot to adaptively adjust its default policy based on both the actual observed map and the imagined unobserved map. Furthermore, the adaptive nature of the bounded rationality framework enables the robot to manage unreliable or incorrect imaginations by selectively sampling a few trajectories in the vicinity of the default policy. Our approach utilizes a diffusion model for map prediction and a sampling-based planning with B-spline trajectory optimization to generate the default policy. Extensive evaluations reveal that the context-generative policy outperforms the baseline methods in identifying and avoiding unseen obstacles. Additionally, real-world experiments conducted with the Crazyflie drones demonstrate the adaptability of our proposed method, even when acting in environments outside the domain of the training distribution.
The gradual guarantee is an important litmus test for gradually typed languages, that is, languages that enable a mixture of static and dynamic typing. The gradual guarantee states that changing the precision of a typ...
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The increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring s...
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Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models ...
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