Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computing, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
A Visual SLAM-Based Framework for Indoor Hovering Robots in 3D Rescue Missions with Dynamic Obstacle Avoidance
This paper presents an intelligent and energy-efficient UAV framework for indoor rescue missions in GPS-denied environments. The proposed system integrates visual-inertial SLAM (Simultaneous Localization and Mapping), dynamic obstacle avoidance, and adaptive path planning using lightweight embedded sensors. At its core, the design fuses monocular camera data with inertial measurements through ORB-SLAM3, enhanced by 3D depth sensing from the STMicroelectronics VL53L9 dToF LiDAR. A deep learning module based on YOLOv5 and semantic mapping enables the UAV to detect and navigate around dynamic obstacles, while a novel risk-based path planner evaluates cost maps in real time to optimize safety and energy consumption. The architecture is evaluated in simulated disaster environments, demonstrating improved localization accuracy, reduced power usage, and superior obstacle avoidance over conventional SLAM-based and planning baselines. The results support the framework’s viability for deployment in real-world rescue missions requiring intelligent aerial autonomy.
Copyright (c) 2026 Journal of Computing and Electronic Information Management https://creativecommons.org/licenses/by-nc/4.0
Structure Enhancement and Cross-Modal Alignment for Open-Vocabulary Semantic Segmentation
This paper proposes a structure-enhanced cross-modal alignment method for open-vocabulary semantic segmentation. Existing methods mostly rely on CLIP’s image-level vision-language alignment capability, but CLIP visual features remain insufficient for modeling fine-grained spatial information such as boundaries, textures, and region structures. Moreover, relying solely on semantic alignment between images and text categories makes it difficult to model fine-grained correspondences between visual and textual information. To address these issues, we design a DINO Structure Enhancement Module and a Cross-Modal Alignment Module (CCMA). The DINO Structure Enhancement Module introduces a parameter-frozen DINO model to extract structural priors and adaptively enhance CLIP visual features, thereby producing structure-aware visual features. CCMA jointly models global visual semantics, local region features, and text semantic prototypes to mine fine-grained vision-language consistency at the region level, thereby strengthening the correspondence between image regions and textual semantics. Experimental results demonstrate that the proposed method effectively improves open-vocabulary semantic segmentation performance.
A weighted average temperature model for the Yunnan-Guizhou region considering multiple factors
Addressing the issue of complex terrain in the Yunnan-Guizhou region and the insufficient applicability of existing weighted mean temperature (Tm) models, this paper constructs a multi-factor, multivariate nonlinear Tm empirical model (YGTm) based on data from six sounding stations spanning 2010 to 2019. Internal consistency, external consistency, and leave-one-out cross-validation were employed to compare the model with Bevis, GPT2w, and HGPT2 models. The results indicate that the YGTm model exhibits a root mean square error (RMS) of 2.52 K, representing a 15%–39% improvement in accuracy compared to the comparative models; the relative error of atmospheric precipitable water vapor (PWV) retrieved based on YGTm is only 0.85% on average. This model can provide high-precision Tm support for GNSS water vapor retrieval in the Yunnan-Guizhou region.
An Enhanced CTNet for Motor Imagery EEG Classification
To address the limited feature discriminability, substantial training fluctuations, and limited decision stability during inference in four-class motor imagery electroencephalogram (MI-EEG) classification, this paper proposes an enhanced CTNet method that integrates lightweight structural enhancement, stability-oriented training, and complementary fusion inference. Built upon CTNet, the proposed method introduces a lightweight token-channel gate to adaptively recalibrate high-level token features on top of the joint modeling of a convolutional front-end and a Transformer encoder, and further adopts a two-layer classification head to enhance final discriminative capability. During training, label smoothing, center loss, exponential moving average of parameters, Top-5 checkpoint averaging, composite-score-based model selection, and lightweight input augmentation are incorporated to reduce model fluctuations under small-sample conditions. During inference, the logits of the CTNet-Repro baseline model and the enhanced model are fused in a weighted manner to exploit their decision complementarity. Under the subject-dependent protocol on the BCI Competition IV 2a dataset, the proposed method achieves average Accuracy, Macro-F1, and Balanced Accuracy of 80.63%, 80.17%, and 80.41%, respectively, across 9 subjects, representing improvements of 4.52, 4.69, and 4.49 percentage points over CTNet-Repro. The results demonstrate that the proposed method can effectively improve discriminative performance, model-selection stability, and test-stage robustness for MI-EEG classification while largely preserving the original CTNet backbone. Highlights: (1) An enhanced CTNet is proposed for four-class MI-EEG classification.(2)A lightweight token-channel gate improves discriminative high-level token features. (3) Stabilized training combines label smoothing, center loss, EMA, and Top-5 averaging. (4) Composite-score-based model selection improves robustness under small-sample settings. (5) Logits-level post-fusion further boosts Accuracy, Macro-F1, and Balanced Accuracy.
Remote Sensing Identification and Spatiotemporal Evolution of Cultivated Land Non-Agriculturalization in Qixian County Based on Google Earth Engine
Cultivated land non-agriculturalization is an important manifestation of county-level land use transition. Timely identification of its spatial distribution and phased evolution is essential for farmland protection and rational land resource allocation. Taking Qixian County in Henan Province, China, as the study area, this paper used Sentinel-2 imagery from 2020, 2023, and 2025 on the Google Earth Engine platform. Percentile compositing was employed to generate multi-temporal feature images, and a random forest classifier was used to extract land use information. A unified cultivated land mask for 2020 was then used as a constraint, and post-classification comparison was applied to identify the conversion of cultivated land to non-agricultural uses. The spatiotemporal evolution of non-agriculturalization was analyzed in terms of area, transition type, and spatial distribution. The results show that the overall accuracy of land use classification in the three periods was higher than 83%, and the Kappa coefficient was above 0.77. The areas of cultivated land non-agriculturalization in 2020–2023, 2023–2025, and 2020–2025 were 54.5241 km², 10.3185 km², and 40.6306 km², respectively. Conversion to water dominated in 2020–2023, whereas conversion to orchard/woodland became dominant later. Spatially, non-agriculturalization was concentrated around the county seat, township centers, and transport corridors.
A concept drift-oriented active learning method
The continuous growth and evolving internal distribution of streaming data were long challenged by the phenomenon of concept drift in classification tasks. In practical applications, labeling costs were prohibitively high, and active learning was frequently employed to mitigate the scarcity of labels. However, in drifting environments, a single sampling strategy was prone to induce selection bias and information redundancy. In this paper, a concept drift-oriented active learning method, termed CDAL, was proposed, which integrated drift detection with a clustering-based representative sampling mechanism. Upon detecting a distribution change, a candidate set was constructed from neighboring samples and structurally partitioned using online clustering. A global average uncertainty was then calculated as a dynamic reference, and the overall uncertainty level within each cluster was compared. Based on this comparison, either a centroid sample or a random sample was adaptively selected from the cluster for labeling. This design eliminated the need for preset fixed thresholds and balanced the information content of samples with their distribution coverage while controlling annotation costs. Experiments conducted on multiple real-world and synthetic datasets demonstrated that CDAL achieved superior cumulative accuracy and related evaluation metrics compared to baseline methods and was capable of rapidly restoring classification performance for new concepts after drift occurred, thereby validating the effectiveness of the proposed strategy.
Optimization and Comprehensive Application of ORB Algorithm and CNN Combined in Feature Extraction
In recent years, an increasing number of studies have attempted to combine ORB with CNN to achieve both efficiency and high accuracy. This survey, based on existing literature, systematically organizes typical fusion methods, application cases, optimization strategies, and challenges of ORB+CNN, and discusses future research trends in this direction. All literature used in this survey comes from published research results, including representative studies of ORB and CNN in fields such as iris recognition, clothing retrieval, vehicle classification, and SDN traffic classification.
Research on Weak Formation Interval Identification in Oil Drilling Based on CNN-BiLSTM-MHA Model
Weak formation intervals in oil drilling are critical sections that may induce complex downhole problems, such as wellbore instability and borehole enlargement. Their accurate identification is of great significance for improving drilling safety and operational efficiency. To address the limitations of traditional weak formation identification methods, including strong dependence on expert experience, high subjectivity, and insufficient utilization of local logging responses and depth-wise sequential dependencies, this paper proposes a weak formation identification method based on a CNN-BiLSTM-MHA model. First, weak formation identification samples are constructed using logging curves and well trajectory parameters, and data preprocessing is performed through missing value handling, outlier correction, standardization, and sliding-window sampling. Then, a convolutional neural network is used to extract local response features, a bidirectional long short-term memory network is employed to model forward and backward dependencies along the depth direction, and a multi-head attention mechanism is introduced to enhance the representation of key intervals and critical features. Finally, comparative experiments and ablation experiments are conducted to verify the effectiveness of the proposed model. Experimental results show that the proposed model achieves an Accuracy, Precision, Recall, and F1-score of 91.52%, 90.28%, 90.15%, and 90.21%, respectively, outperforming comparative models such as SVM, XGBoost, TCN, and Informer. The proposed method can provide methodological support for intelligent identification of weak formation intervals in oil drilling.
Weak formation intervals in oil drilling
Local Elite-Guided Collaborative Optimization Algorithm for Continuous Distributed Constraint Optimization Problems
To address the issues of premature convergence to local optima and slow convergence speed in Continuous Distributed Constraint Optimization Problems (C-DCOPs), this paper proposes a Local Elite-Guided Collaborative Optimization (EGA-LC) algorithm. Utilizing a Breadth-First Search (BFS) pseudo-tree, the algorithm constructs a distributed population structure partitioned into ten sub-populations for parallel search, each further classified into dominant, non-dominant, and local dominant sub-populations based on fitness. Subsequently, a differential evolution strategy is designed to guide individual updates through the synergistic influence of global elites and local dominant solutions, enabling deep mining of high-quality local constraint information. Furthermore, a periodic cross-population recombination and reassignment mechanism is introduced, which leverages historical global optimal solutions to accelerate convergence and prevents evolution stagnation by reallocating sub-populations. Experimental results on four benchmark problem sets demonstrate that EGA-LC significantly outperforms five state-of-the-art algorithms in terms of both solution quality and convergence speed.
Continuous Distributed Constraint Optimization Problems
A Real-Time PCB Defect Detection Framework on Raspberry Pi Based on YOLOv5n
PCB defect detection requires both high accuracy and real-time performance, while small defect sizes and complex backgrounds pose significant challenges for deployment on edge devices. This paper proposes a real-time PCB defect detection method based on the YOLOv5n model and constructs an edge-side detection system on Raspberry Pi 5. To enhance small defect detection capability, dataset integration, image slicing, and data augmentation strategies are employed. The trained model is deployed using the NCNN framework for lightweight inference. Experimental results demonstrate that the proposed method achieves 97.0% mAP50 on the test set and maintains a stable real-time detection speed of approximately 9 FPS on Raspberry Pi 5, showing strong performance on small-scale and sparse defects. The proposed approach provides a feasible solution for low-cost PCB defect detection on edge devices.
A Multimodal Video Anomaly Detection Method Based on Vision-Language Alignment
Existing weakly supervised anomaly detection methods suffer from insufficient semantic alignment of features and a lack of fine-grained localisation capabilities. Furthermore, purely visual models have limitations in understanding semantic information, making it difficult to effectively integrate visual features with semantic information, which results in poor discrimination of multi-class anomalies. To address these issues, this paper proposes a vision-language aligned video anomaly detection model. By introducing a vision-language alignment branch and utilising a pre-trained CLIP model to achieve fine-grained semantic alignment between video features and category text prompts, this model overcomes the limitations of purely visual models in understanding semantic information.It designs an adaptive gated fusion mechanism to dynamically fuse the global anomaly scores from the original visual branch with the semantically guided scores from the alignment branch, combining the complementary strengths of visual pattern recognition and semantic understanding to enhance the model’s ability to distinguish between multiple anomaly categories.And it constructs a multi-task loss function to jointly optimise temporal localisation and fine-grained classification tasks, making full use of video-level weak supervision signals and cross-modal alignment information. Experimental results on the UCF-Crime and XD-Violence datasets demonstrate that this method effectively improves fine-grained anomaly localisation and classification performance, exhibiting significant advantages.
Tuckman Optimization Algorithm: A novel metaheuristic inspired by Tuckman’s Stages of Group Development for solving benchmark and engineering problems
This paper proposes a novel Tuckman Optimization Algorithm (TOA) based on Tuckman’s Stages of Group Development for solving optimization and engineering design problems. TOA is the first to embed this theory into an intelligent optimization framework, simulating team dynamics to guide population search behavior. Its core innovations include a team control parameter and a team structure reconfiguration mechanism. The control parameter dynamically adjusts stage weights and evolutionary strategies based on iteration progress, enhancing adaptability and convergence stability. The team structure reconfiguration mechanism, based on a logistic growth model, replaces inferior individuals to maintain population vitality and avoid premature convergence. Experimental results on the 10-dimensional CEC2017, CEC2020, and CEC2022 benchmark suites show that TOA outperforms three baseline algorithms, ten advanced metaheuristics, and the CEC2017 winner LSHADE SPACMA in both accuracy and robustness. Additional tests on engineering design problems further validate TOA’s effectiveness and broad applicability in real-world optimization scenarios.
Data-driven and algorithm-enabled: The logical evolution, paradigm reconfiguration and practical dimension research of artificial intelligence in sports event prediction
With the exponential growth of sensing technology and computing power, artificial intelligence (AI) is increasingly deeply involved in the field of sports prediction, driving a paradigm shift in sports scientific research from "experience-driven" to "data-driven". This article systematically reviews the evolution logic of AI in sports prediction and analyzes the underlying mechanism of the migration from traditional statistical models to deep learning and reinforcement learning algorithms. The study finds that AI, through the deep mining of multi-source heterogeneous sports data, not only demonstrates remarkable accuracy in predicting competitive performance and assessing the risk of sports injuries, but also achieves functional reconfiguration in the dimensions of sports industry decision support and enhanced spectator experience. This article constructs a technical path model for AI sports prediction and conducts a deep examination of its value spillover and technical limitations in practical applications. The research aims to provide theoretical support for the digital transformation of China's sports industry and offer path references for the optimization and ethical governance of intelligent sports prediction algorithms.
SDAFormer: A Semantic-Guided and Detail-Aware Transformer for Apple Counting in Complex Orchards
Accurate apple counting is crucial for orchard yield estimation and automated management. However, in complex natural agricultural settings, issues such as scale variations, fruit occlusion, and background interference pose significant challenges to existing counting methods. Current mainstream models often struggle to balance global contextual information with local fine-grained features, resulting in inaccurate counts in these areas and difficulty in effectively distinguishing fruits from complex backgrounds. To address the issues of easily disturbed shallow-level details and insufficient coordination between high-level semantics and local structure that apple targets face under varying scales and occlusion conditions in real orchard scenarios, this paper proposes a semantic-guided and detail-aware Transformer-based apple counting method, Named SDAFormer. This method uses the Semantic-Aware Detail Refinement Module (SADRM) to explicitly inject deep semantic information into shallow-level edge, texture, and local structural features, thereby enhancing the feature completeness and discriminative power of occluded apple regions; Through the Coordinate-Aware Multi-scale Module (CAMM), it enhances the position-aware capabilities and multi-scale context modeling during the density map regression stage, thereby improving the model’s counting stability under varying scales and in partially occluded scenarios. Experimental results demonstrate that this method achieves superior counting performance on a self-built apple dataset, with a Mean Absolute Error (MAE) of 3.61 and a Mean Squared Error (MSE) 4.76.
Real Time Fraud Detection at Scale in High Volume Enterprise Payment Ecosystems
The rapid proliferation of digital payment channels has created unprecedented opportunities for fraudulent activity, compelling enterprise organizations to deploy increasingly sophisticated detection mechanisms capable of operating at high throughput with minimal latency. This paper presents a comprehensive review of real-time fraud detection (RTFD) methodologies applied within high-volume enterprise payment ecosystems, encompassing machine learning (ML), deep learning (DL), graph neural networks (GNN), and streaming data architectures. We examine how ensemble methods, anomaly detection frameworks, and feature engineering pipelines converge to form robust, production-grade fraud detection systems (FDS). The paper further discusses the tension between detection accuracy and operational latency, the challenge of class imbalance in transactional datasets, and the evolving regulatory landscape that shapes deployment constraints. By synthesizing findings from recent literature, this review identifies key trends including federated learning (FL) for privacy-preserving fraud detection, transformer-based sequence models for behavioral analysis, and adaptive threshold mechanisms for dynamic fraud pattern recognition. Our analysis reveals that no single algorithmic approach suffices in isolation; rather, layered architectures combining rule-based systems with data-driven models consistently achieve superior performance across precision, recall, and throughput metrics in enterprise-scale deployments.
Achieving Resource Isolation in Multi-Tenant Cloud Platforms Without Sacrificing Performance
Multi-tenant cloud (MTC) platforms have become the cornerstone of modern distributed computing infrastructure, enabling diverse workloads from independent clients to coexist on shared physical hardware while each tenant perceives a logically dedicated environment. The central engineering challenge in these environments is achieving robust resource isolation (RI) without incurring performance penalties that undermine the economic and operational advantages of consolidation. This paper provides a comprehensive review of mechanisms, scheduling policies, and architectural innovations that address the isolation-performance trade-off across compute, memory, network, and storage domains. We survey hardware-assisted virtualization techniques, container-based sandboxing, software-defined networking (SDN) overlays, and disaggregated storage architectures, analyzing how each approach positions itself on the spectrum between isolation fidelity and execution efficiency. We further examine the growing role of machine learning (ML) in dynamic resource management, the unique demands imposed by emerging workload classes such as serverless functions and artificial intelligence (AI) training pipelines, and the microarchitectural threat landscape shaped by speculative execution vulnerabilities.
Autoscaling Stateful Microservices Under Variable Load and Traffic Uncertainty
Modern cloud-native applications increasingly adopt microservice architecture (MSA) to achieve modularity, independent deployment, and scalability. While stateless microservices have been extensively studied in the context of autoscaling, stateful microservices (SMS) present unique challenges due to their dependency on persistent state, session continuity, and data locality. Autoscaling SMS under variable load and traffic uncertainty requires sophisticated mechanisms that transcend conventional reactive approaches. This paper provides a comprehensive review of existing methodologies for autoscaling SMS, encompassing reactive, proactive, and hybrid scaling strategies. We examine the role of machine learning (ML) and deep learning (DL) in traffic forecasting and workload prediction, the management of distributed state during scaling events, and the integration of service mesh technologies to mitigate traffic uncertainty. Key challenges including cold-start latency, state migration overhead, and quality of service (QoS) degradation during scaling transitions are discussed in depth. We further review benchmark frameworks and evaluation methodologies used to assess autoscaling systems. The paper concludes by identifying critical open research problems and future directions in this rapidly evolving domain.
A Review of Colorectal Polyp Segmentation Methods
Colorectal cancer is a malignant tumor with a persistently high incidence worldwide, and the majority of cases arise from the malignant transformation of polyps. However, polyps in colonoscopic images exhibit significant variations in morphology and scale, and the boundaries between polyps and surrounding tissues are often indistinct, which makes manual detection particularly challenging. On the one hand, the diagnostic process relies heavily on physicians’ experience, which may lead to missed diagnoses and misdiagnoses. On the other hand, the limited number of experienced clinicians makes it difficult to meet patients’ needs in a timely manner. This diagnostic uncertainty, combined with the scarcity of medical resources, highlights the critical value of AI-assisted diagnostic systems. Developing high-performance polyp segmentation algorithms that accurately locate and delineate polyp regions can improve diagnostic efficiency and provide effective decision support for clinicians. Recent advances in deep learning have significantly improved colorectal polyp segmentation. Regarding network architectures, models have evolved from early U-shaped designs to Transformer-based architectures that capture global context. More recently, Mamba-like networks and dual-branch architectures have also been proposed. In terms of research paradigms, this paper focuses on the application of methods such as multi-scale feature fusion, attention mechanisms, and diffusion models. In addition, this study summarizes commonly used datasets in the field of polyp segmentation and provides a detailed description of evaluation metrics closely related to segmentation performance. Finally, this paper systematically analyzes the limitations of current colorectal polyp segmentation methods in clinical practice. Based on this analysis, it discusses potential directions and trends for future algorithm development, providing a reference for further research.
Neural Methods for Adaptive Voltage and Frequency Scaling in Energy Aware Processors
Dynamic Voltage and Frequency Scaling (DVFS) represents one of the most effective runtime mechanisms for balancing computational performance against energy consumption in modern processors. Traditional rule-based and heuristic-driven DVFS governors struggle to adapt to increasingly heterogeneous and unpredictable workload patterns characteristic of contemporary computing environments, including mobile devices, data centers, and edge deployments. This review examines the growing body of research applying neural methods—including recurrent neural networks, convolutional neural networks, deep reinforcement learning (DRL), and federated learning—to the adaptive DVFS problem. We survey the evolution from classical prediction-based approaches to policy-learning frameworks capable of jointly optimizing energy efficiency and Quality of Service (QoS) constraints. Our analysis reveals that DRL-based approaches consistently achieve energy reductions of 25–35% over fixed-frequency baselines while maintaining QoS violation rates below 4%, outperforming both traditional governors and supervised prediction models. We conclude by identifying open research directions in multi-objective optimization, on-device continual learning, and heterogeneous processor support.
Optimization of precast Component Production Scheduling: A Comparative Study of Genetic Algorithm and Simulated Annealing
The production of precast components is a core link in the industrialization of construction, and the rationality of production scheduling directly determines project costs, construction progress, and resource utilization efficiency. Faced with challenges such as intricate production processes, multiple resource constraints, and the need for flexible response to market demands, traditional scheduling methods are increasingly incompetent. To address these issues, this study explores the application of two intelligent optimization algorithms—Genetic Algorithm (GA) and Simulated Annealing (SA)—in precast component production scheduling. Based on a systematic analysis of the production process and constraints of precast components, corresponding mathematical models are established for each algorithm. Through case studies using real production data from precast component manufacturers, the effectiveness of the two algorithms is verified. The results show that both GA and SA can significantly optimize the production sequence, shorten the maximum makespan, and improve equipment utilization. Specifically, GA achieves a makespan of 45 hours with a more efficient convergence rate, while SA effectively avoids local optima and obtains a makespan of 46.8 hours. This research provides a scientific and flexible scheduling solution for precast component manufacturers and enriches the application of intelligent algorithms in the construction industry.
A lightweight bauxite detection and identification algorithm
To address the issues of low efficiency and poor accuracy of manual methods in bauxite sorting, this paper proposes a lightweight ore identification and detection algorithm, CL-YOLOv11, based on an improved YOLOv11. This algorithm incorporates a C3k2-GD module integrating GhostModule and dynamic convolution in the backbone network to enhance adaptive feature extraction while reducing redundant parameters. Furthermore, a lightweight shared detail enhancement detection head (LSDECD) is constructed in the detection head, improving the accuracy of capturing small targets and texture edges by introducing detail enhancement convolution and learnable dynamic factors. Experimental results show that compared to the original model, CL-YOLOv11 reduces the number of parameters by approximately 33%, decreases floating-point computation by 23%, and significantly improves inference speed. It achieves a balance between high accuracy and high efficiency while maintaining a high detection accuracy of 93.60%, providing an effective technical solution for automated intelligent bauxite sorting.
Unstructured Road Obstacle Avoidance Path Planning Algorithm Based on BEV Grid Map
In view of the narrow, unstructured road surfaces, irregular distribution of obstacles, and the large body and steering lag characteristics of mining trucks, traditional path planning algorithms tend to generate edge-hugging paths or infeasible broken paths, posing collision risks. Based on high-precision BEV grid maps generated by LiDAR-camera multi-sensor fusion, this paper proposes an improved hybrid A* obstacle avoidance path planning algorithm that combines safety potential fields with kinematic constraints. The algorithm constructs an environmental safety potential field through Euclidean distance transformation to quantify collision risks in the grid space; it expands the A* algorithm's state space and improves the cost function by introducing steering penalties and maximum steering angle limits to ensure kinematic feasibility of the path; finally, cubic B-spline curves are used to smooth the discrete path, producing a curvature-continuous trajectory. Simulation results show that, compared with the traditional A* algorithm, the proposed algorithm increases the minimum safety distance between the path and obstacles by more than 17 times, effectively eliminates broken paths, and its planning time meets the real-time control requirements for low-speed operation of mining trucks, providing safe and smooth local navigation trajectories for autonomous mining vehicles on unstructured roads.
Random Forest Landslide Susceptibility Assessment Based on Weighted Factor and Bayesian Mean Particle Swarm Optimization
To address the overfitting problem of the random forest (RF) model in landslide susceptibility assessment, and to improve both the model's prediction accuracy and hyperparameter optimization efficiency, a random forest model combining weighted factors and Bayesian mean particle swarm optimization (BW-MPSO-RF) is proposed. First, the contribution of landslide influencing factors is quantified by random forest feature importance and SHAP value, and a weighted evaluation index system is constructed, with higher contribution factors given higher weights. Second, mean particle swarm optimization (MPSO) and Bayesian optimization algorithm are integrated. MPSO is used to complete the global coarse search of hyperparameters, and Bayesian optimization is used to achieve the local fine search, forming a two-layer hyperparameter adaptive optimization strategy. Finally, taking Lanzhou as the study area, the research is carried out from four aspects: model construction, construction of weighted index system, accuracy verification, and evaluation result analysis. The results show that the AUC value of the BW-MPSO-RF model reaches 0.9427, and the prediction accuracy is 94.16%. Compared with MPSO-RF, PSO-RF, GA-RF and traditional RF models, the prediction accuracy and stability are significantly improved. This model can effectively solve the overfitting problem of RF, and the hyperparameter optimization efficiency is 32.6% higher than that of MPSO alone, providing more efficient and accurate technical support for landslide disaster risk management.
Based on Non-Inertial Frame Modeling for High-Dynamic Companion Flight Control Research
This paper investigates the intelligent companion flight control problem for mobile platforms (UGVs) and unmanned aerial vehicles (UAVs) under highly dynamic conditions. To address the Coriolis, Euler, and centrifugal fictitious forces introduced by a non-inertial reference frame and rapidly varying disturbances, an integrated approach of "non-inertial relative dynamics modeling + parameter preview estimation based on the Unscented Kalman Filter (UKF) + Continuous Gain Scheduling (CGS) MPC" is proposed. First, a complete and reproducible set of relative kinematics and dynamics under platform-fixed connections is presented, unifying gravity and thrust modeling. Subsequently, a unified UKF for platform angular velocity/angular acceleration and apparent acceleration is constructed, with its output fed into the MPC as time-varying parameters and tightening radii. Finally, a continuously varying weight scheduling law dependent on non-inertia intensity and uncertainty is proposed, balancing tracking accuracy and robustness. Simulation results indicate that, compared to fixed-weight MPC and simplified models that ignore fictitious forces, the proposed method significantly reduces peak errors and constraint violations under high-maneuvering and strong-noise conditions while maintaining control smoothness and real-time performance.
MSDA-SparseInst: Real-Time Instance Segmentation via Multi-Scale Feature Fusion and Dual-Branch Attention
Real-time instance segmentation is crucial for urban road-scene understanding, where accurate pixel-level perception is required under complex backgrounds, occlusion, and large scale variation. However, existing efficient methods often struggle to balance segmentation accuracy and inference speed, especially for small distant objects and densely distributed instances. To address this issue, this paper proposes MSDA-SparseInst, a real-time instance segmentation framework based on SparseInst. Specifically, an improved backbone is adopted to enhance feature extraction, a Multi-scale Dilated Feature Aggregation (MDFA) module is introduced to strengthen cross-scale contextual modeling, and a lightweight dual-branch attention strategy composed of GCSA and GGCA is designed to refine decoder features. Experimental results on the Cityscapes validation set show that the proposed method achieves 21.8 AP, 43.4 AP50, and 18.1 AP75 at 30.7 FPS, improving the baseline SparseInst by 3.0 AP while maintaining real-time performance. The results demonstrate that MSDA-SparseInst provides a better trade-off between segmentation accuracy and efficiency for urban road-scene instance segmentation.
A Happiness Prediction Model Based on Performance-Diversity Dual-Constraint Dynamic Weighted Ensemble
To address the problems of insufficient generalization ability of traditional single machine learning models in happiness prediction tasks and the failure of fixed-weight ensemble models to adapt to sample-level prediction differences, this paper proposes a Performance-Diversity Dual-constraint Dynamic Weighting Ensemble (PD-DWE) model. First, a diversified base model pool including Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting Tree, XGBoost, Multilayer Perceptron, and LightGBM is constructed, and hyperparameter optimization of the base models is completed through a two-stage random search. Second, based on the 10-fold stratified cross-validation framework, high-quality model selection is performed using an F1-score threshold in each fold, and diversity constraint is achieved by combining predictive correlation filtering. Finally, a dynamic weight calculation strategy integrating the global performance of models and sample-level prediction confidence is designed, and the optimal classification threshold is adaptively searched to complete the final prediction. Experimental results show that the proposed PD-DWE model achieves an F1-score of 79.1% on the test set, representing a 12.43% relative improvement compared with the optimal single model, and an AUC value of 69.61%. Its comprehensive prediction performance is significantly superior to that of single machine learning models, providing a more stable and efficient technical solution for the quantitative prediction of happiness.
Collision-Free Trajectory Optimization of Manipulator Based on Black-Winged Kite Algorithm
Aiming at the requirement of global obstacle avoidance for each link of the manipulator, an obstacle avoidance trajectory planning method based on the Black-winged Kite Algorithm (BKA) was proposed. Firstly, the envelope model of the obstacle and the manipulator was simplified, and the collision detection problem was transformed into the problem of calculating whether the distance between the center of the obstacle envelope model and each joint was too large. Secondly, the trajectory planning was carried out by using cubic polynomial, and the trajectory curve was adjusted by adjusting the cubic coefficient to meet the obstacle avoidance demand. Finally, the BKA was used to optimize the time of the motion trajectory, and the running time of the manipulator was reduced under the premise of meeting the working demand. The simulation results of MATLAB showed that this method could plan a trajectory that could meet the collision-free requirements of each link of the robot arm, and the movement time of the robot arm was greatly shortened, which could ensure that the robot arm could complete the task smoothly and efficiently in actual production.
A Method for Localization and Dense Mapping in Indoor Dynamic Environments Based on an Improved ORB-SLAM2 Algorithm
To address the difficulty of ORB-SLAM2 in constructing dense maps in complex indoor dynamic environments, this paper proposes an improved method for localization and dense mapping in dynamic indoor scenes. The proposed approach integrates a semantic segmentation module based on DeepLabV3+ and introduces an additional semantic thread to obtain scene semantic information. In the tracking process, semantic segmentation results are combined with a dual depth-consistency checking strategy to identify and remove dynamic feature points while preserving reliable static features. Furthermore, the original keyframe selection mechanism is enhanced by incorporating relative pose variations between consecutive frames as an additional criterion, which helps reduce redundant keyframes and improve localization accuracy. A dense mapping thread is also introduced to fuse multi-frame point cloud data and generate a dense three-dimensional map of indoor environments. Experiments conducted on the TUM RGB-D Dataset demonstrate that the proposed method significantly improves trajectory accuracy. The average localization error is reduced by more than 95% in highly dynamic sequences and by 39.60% in low-dynamic sequences. The results show that the proposed approach achieves more accurate pose estimation and dense map reconstruction, providing effective support for mobile robot localization and navigation in indoor dynamic environments.
Research on Physically-Constrained Continuous-Time Dynamical Models and Precise Smartphone Battery Life Prediction
Addressing the challenge of balancing prediction accuracy and interpretability in smartphone battery life forecasting, this study constructs a physically-driven continuous-time dynamical model and a dynamically calibrated Time-to-End-of-Life (TTE) prediction system. The study first decomposes battery output power into external loads and internal losses based on the law of energy conservation, coupling it with a first-order lumped-parameter thermal model. To address user behavior randomness, a continuous-time Markov chain simulation mode switching is introduced, utilizing an Unscented Kalman Filter (UKF) for online parameter calibration. Empirical results demonstrate that this model accurately describes the evolution trajectory of the state of charge (SOC) under various load and temperature conditions. Specifically, the framework achieves precise runtime estimation across diverse scenarios—predicting TTE from 10.69 hours in compute-heavy modes to 23.46 hours in light-load modes—and identifies CPU operations as the primary energy consumption driver, while revealing GPS impact is negligible. This study provides a systematic quantitative solution for energy efficiency management and high-precision runtime prediction in mobile devices.
Band-Focused EdgeNeXt: A Lightweight Architecture for Tibetan Dialect Classification via Spectral Attention and Dual-Pooling Fusion
This study enhances Tibetan dialect classification in low-resource scenarios by proposing a Frequency Band-Focused SE Block and GAP+GMP dual-pooling fusion. The SE Block dynamically weights critical features (e.g., Ü-Tsang F2 formant) while resisting noise. Dual-pooling resolves feature smoothing/loss issues, with progressive stochastic depth boosting generalization. On a 26,762-spectrogram dataset (Ü-Tsang/Amdo/Khams), the 5.8M-parameter model achieves 99.4% accuracy, surpassing EdgeNeXt/RepViT/DilatedFormer by 0.6%/4.0%/0.5%, halving misclassification rates. Signal-to-noise ratio =5dB tests confirm robustness for edge-computing deployment.