Reviewerly/exHarmony huggingface dataset:
A Method of UAV Navigation Planning Based on ROS and Improved A-star Algorithm

The traditional A-star algorithm searches numerous nodes, paths are more costly, and the obtained result is too close to the obstacle at the inflection point, which may easily cause flight safety hazards. Thus, an improved method is proposed to change the above situations. Firstly, the evaluation function is revised by using dynamic weighting; secondly, use azimuth to change the search neighborhood, and adjust the search method adaptively according to different map areas; then, considering the influence of the actual size of the UAV, set the UAV and the safety radius of obstacles; Finally, inflection points of the generated path are smoothed using Bezier curve. The efficiency and path security are improved while ensuring optimality. The MATPLOT simulation results show that the cost is effectively shortened, both search amount and inflection points are reduced, the path is smoother and safer. Based on ROS experiments, the revised algorithm has excellent effects on obstacle avoidance and smoothness, facilitates UAV operations in indoor scenarios.
Conceptual Approach in Structuring the Semantic Core Author's Text

Proposed conceptual approach for the linguistic analysis author's text by structuring its semantic core with homogeneous and heterogeneous semantic networks, with a final synthesis based on the removal of semantic contradictions. Used semantic search and method for highlighting text topics of intellectual monitoring.
Satellite Power System Anomaly Detection based on Spatio-Temporal Uncertainty Fusion

Satellites have been widely used for military and civil applications. The power system is critical for the satellite’s operating safety. However, satellite power system anomaly monitoring is faced with challenges like large amounts of data, insufficient anomaly samples, and complex operating conditions. To address these challenging issues, this paper proposes a data-driven anomaly detection method that fuses the time-related historical data and spatial-associated sub-system monitor data. Firstly, the probabilistic prediction model is used to establish the prediction model based on time and space The corresponding prediction uncertainties of the temporal and spatial models are quantitatively represented. Then, the stochastic distribution model is introduced to fuse the two uncertainties to obtain the predicted mean value and fusion uncertainty of the current power system state. The mean value is taken as the expected result of the state, and the fused uncertainty interval is taken as the threshold of abnormal judgment to realize the detection of the abnormal condition. The telemetry data of the actual satellite power system are used for experimental and comparative analysis. The results show that the false negative rate of anomaly detection results can be effectively reduced, and the detection accuracy can be improved through the fusion of spatio-temporal uncertainty.
Research on Knowledge Graph Construction for Operational Safety of Cryogenic Loading System

Complex industrial systems often have characteristics such as large scale, complex structure, and equipment coupling, making it difficult to describe the evolution process of system accidents using mechanism models or data models. This article takes the cryogenic loading system of a liquid rocket as an example to study the construction method of the operational safety knowledge graph for complex industrial systems. By extracting and integrating the relationships between key elements in the system operation process, knowledge modeling of the "Dangers-Faults" evolution process is achieved. In order to study the potential relationships between variables, faults, equipment, systems, and accidents in the knowledge graph of system operational safety, this paper proposes a new relation mining model. The model redefines the relation mining task as the link prediction task in the knowledge graph, uses T-GAT as the encoder and ConvKB as the decoder to learn the hierarchical category information of entities and the structural information of the knowledge graph, so that the vector representation of entities and relationships contain more semantic information. The experimental results demonstrate that the proposed model can effectively mine implicit relation between entities.
Data Augmentation Based on Active Virtual Sample Selection for Bearing Fault Diagnosis

With the development of the industrial Internet, data-driven methods have been widely used in industrial fault diagnosis scenarios. However, due to the scarcity of abnormal samples in industrial data and the high cost of manual labeling, labeled data is relatively scarce and often faces the problem of a small sample size. One approach to address the small sample problem is using data augmentation methods to generate virtual samples to alleviate the problem of insufficient data. Feature-based data augmentation is a method that allows better handling of complex industrial data by directly augmenting the features. However, it still suffers from sample quality problems common to generated data, which affects the effectiveness of the data augmentation method. In this paper, we propose an improvement to the method for local linear data augmentation and put forward a strategy for selecting generated data. The proposed method is varified through experiments, which show that our method can solve the problem of small sample learning better.
Multi-class criterion information fusion based on evidential reasoning: Case study on quality evaluation of LIMU with meters redundancy

In order to improve the reliability and accuracy of inertial navigation system, multi-meter redundant Laser inertial measurement unit emerged under this demand. This type of Laser inertial measurement unit has more components, more complex structure and simpler criteria. To adapt to the quality evaluation of laser inertial units under multi-meter redundancy design, this article constructs a quality evaluation index system that meets practical requirements through known data forms. Adopting data bias as a health indicator and adding resume information as an availability indicator makes the indicator system more scientific and complete. In this premise, the multi-index criteria under different data requirements are used reasonably, and the quality is evaluated by the information fusion method of evidence reasoning. The case analysis results show that the multi-criteria information fusion based on evidence reasoning is more in line with the practical requirements of quality evaluation of five-table redundant laser IMU, and can better provide decision support for the next equipment use and maintenance.
Research on Spacecraft Anomaly Detection Method Based on Transformer

Spacecraft anomaly detection is a critically important task in the modern aerospace field, essential for ensuring spacecraft safety. This paper presented a method for spacecraft anomaly detection. The method utilized one-dimensional time series of spacecraft parameters as raw data to create a dataset. Subsequently, a Transformer-based regression model was employed to analyze the dataset. The difference between predicted values and actual values was used to determine a threshold range for identifying anomalies in the samples. Experimental results indicated that this method outperformed traditional algorithms such as random forests, decision trees, and support vector machines in anomaly detection.
State Fusion Estimation for Asynchronous Multirate Multisensor Systems with Heavy–tailed Noises

This study investigates fusion estimation towards an asynchronous in-multisensor multirate dynamic system with unreliable observations and heavy-tailed noises. Our dynamic system at most detailed level is known. Numerous sensors independently observe a same target, each having distinct sample frequencies, while the observations are unreliable and asynchronously acquired. Those measurement noises and system noise are both non–Gaussian yet heavy–tailed. The sequential fusion estimation approach is formulated. As an efficient extension of traditionary Kalman filter-grounded sequential fusion method with Gaussian noises, both theoretical analysis and experimental evaluations demonstrate the efficacy of our derived approach.
Communication and Self-Learning Strategies Incorporated State Transition Algorithm for Optimization of Complex Systems

The optimization of complex industrial systems is the key to improving the efficiency and safety of system operation. With the increasing scale of modern industry and the complexity of the production process, traditional optimization methods such as state transition algorithm (STA) are difficult to apply to the optimization problems of complex systems. Insufficient individual diversity of candidate solutions and the susceptibility of the algorithm to local optima are two main challenges that need to be addressed. This paper proposes a novel heuristic optimization method named CSL-STA, which integrates communication and self-learning strategies with STA. The communication strategy increases diversity during algorithm iteration, while the self-learning strategy increases the probability of escaping local optima. A series of comprehensive experiments have been conducted to verify the performance of the proposed method. These experimental results reveal that CSL-STA surpasses several existing optimization algorithms regarding global search capability and solution accuracy.
Anomaly Sound Detection of Industrial Equipment Based on Incremental Learning

The objective of the anomaly sound detection task is to monitor for sounds coming from the target object and analyze whether they are coming from it normally or in an anomalous status. Existing anomaly sound detection models have limitations and are difficult to learn effective knowledge from continuous data streams, which makes them unsuitable for general applications. Incremental learning allows a model to gradually improve its performance and accuracy by utilizing new data and knowledge without requiring to retrain the entire model. This paper proposes an incremental learning-based anomaly sound detection model that enhances the model’s capacity to learn from continuous data streams, reduces knowledge forgetting, and improves the model’s stability in anomaly sound detection task. The amalgamation of knowledge distillation loss and crossentropy loss in the process of model incremental learning causes the model to remember the old knowledge while learning new devices. Experiments using Task 2 data from the DCASE 2020 challenge shows that the proposed method effectively improves the average AUC and average pAUC by 7% to 10% when compared to the fine-tuning strategy.
Fitting Weights up to the Second Order in Multi-Valued Cognitive Maps. Aircraft Control Mode Selection

The report presents an algorithm for selecting weights in a multi-valued cognitive map up to the second order. In such a map, variables (concepts and weights) are elements of a linguistic variable lattice, and there is no transition from them to numbers. This map is used to decide about the aircraft control mode. The decision results are compared with the decision using multi-valued associative memory.
Improved U-Net for Industrial Image Denoising

Many cameras are now used in industrial settings to monitor production processes. The cameras inevitably bring in noise while transmitting the images, which hinders the understanding of the images for subsequent vision tasks, such as worker detection. Researchers usually use methods based on deep learning to denoise industrial images, which tend to corrupt the texture and edge information of the images. To solve this problem, this paper proposes a U-Net algorithm based on feature fusion and adaptive fusion interpolation. First, a dense convolution module is added to the feature extraction process to improve the utilization of different levels of features, and then the images of different sizes are decoded using up-sampling based on adaptive fusion interpolation to recover clean images. The experimental results show that the proposed algorithm in this paper is more effective compared with existing algorithms and can better preserve texture and edge information while denoising.
Infrared video behavior recognition algorithm based on AGX Xavier environment

Analyzing human behavior recognition for infrared video is important since crimes are more likely to occur in nighttime environments. Therefore, this paper proposes an infrared video behavior recognition algorithm based on AGX Xavier, an edge device, using a TSM module embedded in a 2D neural network with temporal modeling capability, which can significantly reduce the number of model parameters and computational effort to achieve deployment on a lightweight edge device with cameras. Online behavior recognition is achieved. The method's accuracy is 91% on the dataset captured by the subject's active infrared camera. It is tested in real application scenarios, and the online recognition mode has low latency and high speed to meet the real-time behavior recognition requirements.
LSTM-DPPO based deep reinforcement learning controller for path following optimization of unmanned surface vehicle

To solve the path following control problem for unmanned surface vehicles (USVs), a control method based on deep reinforcement learning (DRL) with long short-term memory (LSTM) networks is proposed. A distributed proximal policy optimization (DPPO) algorithm, which is a modified actorcritic-based type of reinforcement learning algorithm, is adapted to improve the controller performance in repeated trials. The LSTM network structure is introduced to solve the strong temporal correlation USV control problem. In addition, a specially designed path dataset, including straight and curved paths, is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible. Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.
TEEVseL4: Trusted Execution Environment for Virtualized seL4-Based Systems

The growing computing power of embedded systems has led to an increase in the use of general-purpose Operating Systems (OSs) such as Linux. However, the substantial attack surface arising from their complexity makes them unsuitable for safety and security-critical use cases. Addressing this issue requires isolating the security-critical functionalities into separate execution environments and protecting them from the untrusted OS. Arm TrustZone applies this approach by providing hardware-based partitioning of the system into a secure and non-secure world, facilitating a Trusted Execution environment for the protection of security-critical functionality in the secure world. TrustZone, however, falls short when dealing with systems that virtualize multiple operating systems. Another approach to isolate functionality is employing a microkernel, such as the formally proven correct seL4 kernel, especially if it also offers virtualization functions. While current seL4-based virtualization systems offer good security and safety properties, they do not provide TrustZone-compatible security services to their virtualized guests. In this paper, we propose TEEVseL4, a TrustZone-compatible virtualization system leveraging the strengths of the seL4 microkernel, that can provide security services to the Linux guests based on the dynamic, scalable and flexible Trusted Computing Base of an seL4 system. A high-level performance benchmarking shows that TEEVseL4 can provide security services with acceptable overheads (less than 20%) when compared to a native TrustZone system, making it an attractive option for platforms with multiple, mutually-distrustful virtualized guests.
Text Labels Classification Model based on BERT Algorithm

Text labels are extracted from the content of texts and is a issue of natural language process, which contains multiple labels. The text labels classification aims to divide the multiple labels into only one correct category. Traditionally, researchers utilize machine learning algorithms to train an one classifier to solve the multiple labels, which may cause wrong classifier due to the machine learning can not process the relationship between texts. In this paper, we utilize pre-training of deep bidirectional transformers (BERT) to dispose text understanding issues. Initially, the pre-train process is established to capture the text representations and the initial classification is executed. Subsequently, the fine-tunning function is to optimize the parameters of BERT model including learning rate, training epochs and batch sizes. From our extensive experimental results, we can significantly conclude that our proposed method can achieve the text labels classification with reasonable computation costs and training process.
Controllable and One-to-many Generate QA pairs from Contexts for Semi-Supervised Question Answering

"Building a question-answering (QA) model typically requires labeling numerous question-answer pairs, which can be costly. Current popular solutions to this challenge include using question generative (QG) models or question-answer generative (QAG) models to generate question answer pairs (QAPs). The latter is more time-saving and cost-effective. Although it is currently possible to directly generate question-answer pairs using large generative models such as ChatGPT, large models have been unable to generate question-answer pairs in customized domains and and large-scale language models always generate questions beyond the given text. Therefore, this study presents a Conditional Variational Autoencoder (CVAE) model to generate QAPs. The model employs a 'Question Style Discriminator' to maintain desired question styles and addresses the potential 'posterior collapse' issue associated with CVAE usage. The results of our experiments showcase that the state-of-the-art performance when assessed using question-answering evaluation metrics (QAE) across multiple benchmark datasets. Furthermore, after apply the generated QAPs and group-truth QAPs for semi-supervised learning, our generated data greatly enhances question answering model performance.
Designing a 3D Human Pose Estimation-Based VR Tennis Training System

We propose a Virtual Reality (VR) tennis serve training system based on the machine learning-based 3D skeleton estimation method. Our approach utilizes a single-view 3D skeleton estimation machine learning model to predict the serve motion of a coach from professional tennis players' videos. User movements are acquired from motion capture sensors and compared to the motions of the professional tennis players in real time, and feedback is provided based on the user's body motion. Compared to existing works, our approach can capture and generate a serve motion based on one video, which paves the way for utilizing existing videos of expert tennis players for tennis training in VR. We believe our system can provide a new method to motivate users to practice tennis by directly imitating their favorite expert tennis players. Moreover, we expect our approach to have performance advantages when compared to existing approaches, as it focuses on training that involves motion and posture of the user's entire body.
An unbalanced data classification method based on improved SMOTE

With the continuous development of modern industry, product quality control standards have become increasingly stringent. The classification of product quality characteristics is of paramount importance to product quality control. It is necessary to improve classification methods specifically for imbalanced data to solve the issue of data imbalance in the classification of product quality characteristics. The Synthetic Minority Over-Sampling Technique (SMOTE) is a mature algorithm capable of effectively classifying imbalanced data and is widely applied to various classification problems. However, its performance in handling class overlap and noise issues leaves room for improvement, and these issues are often prevalent in the classification of product quality characteristics. This paper proposes an improved data sampling classification algorithm based on KNN under-sampling and DBSCAN methods to enhance the SMOTE algorithm. We first employ the KNN algorithm to remove part of the majority class data in class overlap regions, then introduce DBSCAN to eliminate noise within the sample data. Lastly, we apply SMOTE for over-sampling to balance the data. We compare common imbalanced data classification methods and our improved KD-SMOTE algorithm using a decision tree classifier on five groups of public UCI datasets. Experimental results indicate that our method improves AUC and other metrics, providing a significant enhancement over existing algorithms. This method better caters to the needs of product quality characteristic classification.
Leverage Subsampling for Joint Mean and Variance Models with Massive Data

In the era of massive data, how to obtain valuable information from large-scale data has become an important research direction. Subsampling is a common method to reduce the dimension of such data and improve the efficiency of model calculation. The subsampling based on the Joint Mean and Variance Models (JMVM) are proposed. Numerical simulation is used to evaluate uniform sampling (UNIF), leverage subsampling (LEV) and shrinkage leverage subsampling (SLEV). The results show that the models based on LEV and SLEV exhibit higher accuracy in the estimation of mean parameters.
A Knowledge Graph Reasoning Approach Integrating Attention-based LSTM and Multi-Agent Reinforcement Learning

Knowledge reasoning methods play a pivotal role in various applications, including knowledge graph completion, knowledge-based question answering, and knowledge recommendation. Among these methods, path-based multi-hop reasoning techniques have the ability to leverage the rich graph information in knowledge graphs beyond triplets, but they still encounter certain challenges. Existing multi-hop knowledge reasoning methods heavily rely on data and lack interpretability. Additionally, the vast exploration space of paths, composed of numerous entities and relations in large knowledge graphs, often leads to irrelevant and redundant exploration. To address these challenges, this paper proposes a novel knowledge reasoning method named ALMARL (Attention-based LSTM and Multi-Agent Reinforcement Learning for Knowledge Graph Reasoning). It utilizes Attention-based LSTM in conjunction with multi-agent reinforcement learning. The method first employs clustering techniques to group entities. Based on the clustering results, multi-agents at different levels are established to selectively explore certain clusters or limit the search to specific clusters, effectively reducing the exploration of irrelevant entities and minimizing redundant exploration. Subsequently, the agents efficiently explore paths and effectively mine deep semantic information in entity relationships through the integration of Attention-based LSTM. Finally, the model produces inference results and extracts interpretable paths. We evaluate our proposed model on two types of tasks: link prediction and fact prediction. Experimental results demonstrate significant performance improvements compared to several competitive baselines.