Abstract.Aiming at the scheduling problem of UAV smoke screen cooperative masking of incoming missiles, this paper proposes a hierarchical solution framework that combines geometric analysis and intelligent optimization. Firstly, based on kinematics and line of sight occlusion geometric analysis, the three-dimensional occlusion judgment is simplified to discrete point judgment, and the effective occlusion time of the benchmark strategy is calculated to be 1.3920 seconds. Then, an optimization model with the maximum occlusion time is established, and the particle swarm algorithm with dynamically … Read more →

Abstract.Rock masses in underground engineering are generally subjected to complex three-dimensional stress conditions rather than uniaxial loading. Therefore, triaxial compression testing has become a fundamental approach for investigating the strength, deformation, and failure behavior of rocks. In recent decades, extensive studies have been conducted on rock failure laws under triaxial compression, with particular attention to the effects of confining pressure, crack initiation and propagation, damage evolution, and macroscopic failure modes. Existing studies indicate that confining pressure not … Read more →

Abstract.Aiming at the challenges of insufficient illumination, heavy dust interference, large scale variation of targets, and limited computational resources of edge devices in underground coal mine drilling scenarios, this paper proposes a lightweight object detection model named YOLO-Drill. The proposed model is developed based on the YOLO framework with several improvements. First, a Lightweight Drill Feature Block (LDFB) is designed by integrating depthwise separable convolution and directional perception mechanisms, which reduces computational complexity while enhancing the representation ability … Read more →

Abstract.This project uses LLMs to perform sentiment analysis on financial news headlines to predict Bitcoin price trends. First we replicated FinBERT’s performance and retrained it on GDELT subset to improve its accuracy from 64.8% to 73.8%. Next, three sentiment scores were extracted from GDELT news dataset using retranied FinBERT model and the results were aggregated to develop multiple sentiment signals. Then we calculate Bitcoin returns from Bitcoin price dataset and construct multiple return signals. By calculating Pearson correlation coefficient, we find that the continuous sum sigmoid sentiment … Read more →

Abstract.With the continuous development of the gaming industry, game characters are an important medium for users to interact with games, which to some extent determines the quality of the gaming user experience. This article refers to Donald Norman's three-level emotional design theory model and explores the relationship and optimization strategies between game character modeling and game user experience from three aspects: instinctive layer, behavioral layer, and reflective layer. This article explores the relationship between emotional design theory and game characters, and proposes a visual … Read more →

Abstract.As the scenarios of human-computer interaction in China's social development have gradually expanded and giant artificial intelligences (GIIs) have become more frequent, making users feel better through emotional identification and empathic responses has become increasingly significant. Although the current generation of models can achieve precise semantic encoding, they usually face more prominent issues such as emotion-recognition bias and rigid responses; In terms of multi-turn dialogues, there is also a lack of emotional consistency. Due to the above limitations, they are difficult to be … Read more →

Abstract.In recent years, a large amount of data resources has been accumulated in the fields of medical imaging, genetic information, and electronic medical records. Therefore, the effective integration of these different sources of data to improve disease diagnosis has received increasing attention. Therefore, this section proposes a sparse Bayesian learning method to achieve joint analysis between multiple medical data and apply it to assist doctors in judging patients' conditions. Due to its ability to simultaneously select variables and estimate their uncertainty, sparse Bayesian learning has good … Read more →

Abstract.To address the limitations of traditional infusion management models, such as reliance on manual monitoring, low efficiency, high safety risks, and poor patient comfort, this paper designs and implements an intelligent infusion monitoring system based on the Internet of Things (IoT). The system uses an STC89C52 microcontroller as the core controller, integrates dual U-shaped photoelectric sensors for drip rate detection and infusion status recognition, and employs a DS18B20 temperature sensor with a graphene heating pad to establish a closed-loop temperature control system for the medication … Read more →

Abstract.High-dimensional medical data are often characterized by extremely high feature dimensionality, limited sample sizes, substantial redundant information, and strong noise interference. To address these challenges, this paper proposes a filter-based feature selection method, termed ARMR (Adaptive Redundancy-based Minimum Redundancy Maximum Relevance Feature Selection). The proposed method first employs mutual information to select initial features from the original feature set. It then uses joint mutual information to characterize the joint discriminative capability of candidate features and … Read more →

Abstract.Drug-target interaction prediction plays an important role in drug discovery and drug repositioning. Traditional experimental screening methods are expensive and time-consuming, while existing computational approaches still have limitations in molecular representation and feature fusion. To address these problems, this paper proposes a drug-target interaction prediction model based on deep feature fusion. In the GCT-DTI, a Graph Isomorphism Network is used to capture the topological structure information of drug molecules, and Morgan fingerprints are introduced to supplement global chemical … Read more →

Abstract.With the rapid advancement of neuroimaging technologies and deep learning methods, multimodal brain imaging analysis has emerged as an important research direction for the early diagnosis and progression assessment of neurological disorders. Different neuroimaging modalities, such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI), provide complementary information from anatomical, functional, and structural connectivity perspectives. Compared with single-modality analysis, multimodal learning can offer a more … Read more →

Abstract.Brain tumor segmentation from multimodal MRI is a challenging task due to large inter-patient heterogeneity, irregular lesion geometry, and ambiguous boundaries between pathological and normal tissues. Although existing 3D segmentation networks have achieved promising performance, most of them primarily operate in the spatial domain and often rely on coarse feature fusion, which limits their ability to explicitly disentangle global structural information from fine-grained boundary details. To address this issue, we propose TriFNet, a Tri-domain Frequency-aware network for 3D brain tumor … Read more →

Abstract.Road pit detection is one of the important means to maintain road and traffic safety. Road pit detection includes target detection of pit holes in the video based on the driving recorder, and judging the location and size of road pit holes. This study aims to accurately judge the pit holes on the road, the size of the pits and the pit levels of the pits based on the driving recorder. Provide valuable pit repair advice for road inspectors, thereby reducing their workload and improving patrol efficiency. This study collected more than 2,000 images of real road pits containing complex conditions … Read more →

Abstract.As a critical branch of multi-modal image processing, infrared and visible image fusion boasts high application value in intelligent security and has drawn widespread global research attention. Enhancing model feature extraction is a core scientific challenge in this field. This paper presents a dual-branch infrared and visible image fusion algorithm based on feature decomposition. In fusion tasks, shared features characterize global information while private features focus on local details. To boost feature representation, we design a feature decomposition module that splits shallow features … Read more →