Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Isolation Forest Algorithm Based on Feature Weights and Local Data Relationships

Aiming at the problem of declining accuracy of isolation forest algorithm in high-dimensional data and misclassification caused by the considering only global data relations, this paper proposes an improved isolation forest algorithm based on feature weight and local data (FLiForest). Firstly, the method calculates the mutual information of feature and density for subsamples, and sets the feature weights according to the mutual information. Secondly, the nearest neighbor method is used to find the anomaly score of the local neighborhood of the test object, and the local score matrix is formed. Finally, the abnormal scores in the local score matrix are combined to determine the abnormal scores of the test data. Based on eight public anomaly detection data sets, the proposed method is compared with eight advanced anomaly detection algorithms. The area under the receiver operating characteristic curve (AUC) and the average accuracy (AP) were higher by 8%-30% and 18%-43% on average.
High-precision Doppler frequency estimation based positioning using OTFS modulations by red and blue frequency shift discriminator

Orthogonal Time Frequency and Space (OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio (SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator (RBFSD) based on the pseudo-noise (PN) sequence. The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than −15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.
Water level detection based on ghost convolution and CA attention mechanism

Water level monitoring is the key issue of water conservancy construction, timely grasp of water conditions, flood prevention is important for social development, for this reason we propose an intelligent image water level recognition algorithm: the introduction of ghost convolutional backbone network optimization, adding GA attention mechanism of yolov5's deep learning water level recognition algorithm, the training of the collected images, to achieve the automatic identification of water level. Experimental results show that the improved yolov5 deep learning water level recognition algorithm can effectively is to recognize the water level, the mAP value reaches 97.6%, compared with the unimproved version to improve 5.2%, in line with the engineering water level monitoring error requirements.
Enhancing Cloud-Native System Robustness: Time-Series GAN-Powered Fault Injection Model

In the realm of cloud-native infrastructures, ascertaining system robustness is crucial for uninterrupted functionality. Cloud-native systems’ dynamic and complex nature often hinders accurate resilience assessments in varied fault scenarios. This research presents an advanced method that leverages Time-Series Generative Adversarial Networks (Time-Series GANs) infused with chaos theory to enhance system resilience through an innovative fault injection model. The experimental results validate our approach, revealing a significant 19.4% improvement in injection accuracy and a 28.6% reduction in training time compared to existing benchmarks.
Research on Obstacle Avoidance Algorithm of UAVs Reinforcement Learning based on Vision

In recent years, the combination of deep reinforcement learning and unmanned aerial vehicle (UAV) to achieve autonomous flight has been a hot research field. In this paper, an obstacle avoidance navigation algorithm (PA-SAC) based on priority experience buffer pool (PEBP), attention, and Soft-Actor-Critic (SAC) is proposed to solve the continuous space obstacle avoidance navigation problem of UAVs by using a deep reinforcement learning algorithm. In the two simulation experiments, the success rate of the PA-SAC algorithm was 95.6% in the known environment and 73% in the unknown environment. These results demonstrate that the PA-SAC algorithm can achieve autonomous obstacle avoidance for UAVs with deep image input.
Lightweight Face Detection Method Based on YOLOv7

Face detection is one of the important tasks of object detection. Typically, instrumentation is the first stage of model recognition and authentication. In recent years, object detection algorithms based on deep learning have developed rapidly, and these algorithms are divided into single-stage and two-stage methods. Among them, YOLO as a single-stage algorithm is not ideal for the detection of small targets such as faces. Aiming at the scale change of face, a lightweight face detection method based on YOLOv7 is proposed to improve the performance of face detection on embedded devices and mobile devices. Experiments on WiderFace show that the improved detection method significantly improves the detection accuracy while maintaining a fast detection speed.
A Named Entity Recognition Model Based on BERT Model and Lexical Fusion in the Financial Regulation Field

Based on the needs of financial regulation, in view of the features of high proportion of long entities and rich professional vocabulary information in the text of this field, we proposes model Lexical-Fusion BERT(LF-BERT), which is a named entity recognition (NER) model suitable for this field. LF-BERT obtains character vectors based on BERT, integrates relevant lexical information and inter-lexical correlation information with attention mechanism and bidirectional long and short-term memory network (BiLSTM), and adopts the entity head-end label prediction to realize named entity recognition. Compared with other baseline named entity recognition models, experimental results indicate that LF-BERT obtains the best F1 value on both public and financial regulation field NER datasets, verifying the effectiveness of our method.
An Adaptive Local Joint-Weighted Method for Continuous Stripping Intensity Correction of Airborne Bathymetric LiDAR

Alongside geometric information accepted widely, airborne laser bathymetry (ALB) typically records the radiometric properties of sensed targets and assists with strip registration, ground (sediment)-type classification, and geometric modeling. Continuous intensity stripping frequently occurs in the ALB-recorded intensity images due to automatic gain control (AGC), an established circuit to compensate for echo power variations caused by distance changes. This situation is exacerbated by the delayed gain control response and inadequate adaptability to target. To address this issue, an adaptive local joint-weighted method for continuous intensity stripping correction is designed. In this method, an index-sharing mechanism is established between intensity, point cloud, emission angle, and return number for rapid localization and intervention of intensity. Unordered intensities are divided into scan line units based on the signal emission period, and subsequent stripped intensity range identification is achieved by assessing the intensity difference between adjacent scan lines. Neighboring scan lines and neighborhood intensities are weighted to jointly reset the stripped intensity values. In the new method, a bidirectional shifting strategy is implemented to attenuate the degradation of the intensity correction accuracy due to the gradual accumulation of correction defects. From the two measurement missions using the ALB, it is concluded that the stripping intensity is mainly distributed in the region of maximum water depth detectable by the sensor and areas with mixed high and low returns. Compared with the original intensity, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the corrected intensity decreased by 35% and 40%, respectively, and the variation coefficient of the stripped intensity and the neighboring intensity decreased from 0.4 to 0.18.
Optimization Strategy of Wind Farm-Energy Storage Operation Considering Wind Power Output Assessment

At present, due to its flexibility, energy storage system (ESS) has been used in the joint operation of wind power generation, as a regulating unit to compensate and absorb the output of wind power generation. In this paper, by using the characteristics of battery charging and discharging, on the basis of historical data, the error value between the day-ahead reported value of the forecast and the actual output value is corrected, and the wind farm-energy storage operation optimization model is established with the objective function of reducing the day-ahead predicted value error, and the power generation operation strategy of the wind farm is optimized. Under the comprehensive consideration of charging and discharging cost, assessment cost and other factors of energy storage system, the daily income model of wind farm is established. The results of the example show that the strategy effectively improves the operational economic benefits of the wind farm, and at the same time improves the accuracy of the day-ahead forecast value, which confirms the effectiveness of the strategy.
Artificial Electromagnetic Medium Usages in the Energy Sector for the Purposes of Transmission

We provide a thorough investigation of the field of metamaterials in order and meta surfaces with particular attention to its uses in wireless energy extraction and wireless power transfer (WPT). In WPT, the electromagnetic field path from source to receiver is divided by radiative technologies, which include both near- and far-field methods, and nonradiative near-field technologies. We evaluate a wide range of designs, comparing and contrasting their many aspects. Our research shows that meta material and meta surfaces are essential for increasing the effectiveness and operating diversity of WPT systems. These novel materials may reduce sensitivity to incoming wave angles and polarizations, increasing the energy conversion effectiveness in wireless energy harvesters. A vital component of both WPT und WEH is the rectenna. Our results demonstrate the advantages of rectennas based on metamaterials, demonstrating their capacity to attain better conversion efficiencies from RF to DC. These prospects highlight the dynamic character of this area, ranging from researching innovative applications to enhancing current solutions. With the advent of wireless energy collecting and transmission, metamaterials play an increasingly important role in influencing the development of efficient and sustainable technologies.
Semi-supervised Projected Subspace Clustering

In high-dimensional data, clusters lay on subspaces of the original space. Projected clustering is challenging because the circular dependence on the subspaces searching and the cluster assignment. Without extra information, it seems impossible to break the circular dependence. In this paper, we propose Semi-supervised Projected Subspace Clustering to utilize pairwise constraints to break the circular dependence. The algorithm consists of two phases: subspace finding and cluster forming. In the first phase, constraints are mobilized to find subspaces based on the feature correlation. In the second phase, an adaptive dimension voting is employed to refine the subspaces and a Semi-supervised k-medoid-like assign method is adopted to detect clusters. We emphasize the distance divergence in the process of adaptive dimension voting guided by constraints. Experiments on real world datasets show that the three variants of SSPSC outperform baseline algorithms in accuracies and scalability.
Design and Development of Smart Charging Infrastructure for Electric Vehicles

Electric vehicles (EVs) are rising as one of the favourable strategies to overcome the increasing environmental concerns due to over-dependence on fossil fuels, emissions, and energy insufficiency. This work aims to design and develop a software framework to contribute to the EV charging infrastructure. The proposed work includes (1) creating front-end software for charging station operators and owners for managing and operating charging points, (2) developing cloud-based intelligent models for region prediction, price prediction, station filtering, and slot booking, (3) designing slot scheduling algorithms for optimal utilization of the charging station. Python and software tools like Streamlit, SQL, and Flask facilitate the implementation of the front-end software, database, and computational algorithms. The web application, built using the Streamlit Python framework, acts as the user interface for all stakeholders. The work utilizes the Linear Search Algorithm and the Binary Search Algorithm to filter stations based on the user requirements, such as distance and price range, which users indicate through the web application. The Gradient Boosting approach gives good results for region prediction for the chosen dataset of EV charging stations. In this work, a priority scheduling approach considering the time-of-use price achieves EV charging slot allocation. The software framework provides a comprehensive electric vehicle charging infrastructure where users can easily find the best charging station and reserve a spot for charging. The work's future extension can include the addition of functionalities for Vehicle-to-vehicle and Vehicle-to-grid.
Advancements in InGaN-Based Red Micro-LEDs

Micro-LEDs have emerged as a promising fourth generation display technology with many advantages over traditional displays. However, the mass production of micro-LEDs still faces challenges, particularly in the production of high-quality red micro-LEDs. Low efficiency red micro-LEDs are a major challenge that needs to be overcome. In this paper, we focus on recent advancements in improving the device performance of InGaN-based red micro-LEDs. We summarize the various approaches taken by research groups in recent years to improve the efficiency and performance of red micro-LEDs at both the epitaxial and process levels. These include the use of InGaNOS substrates, increasing the density of V-shaped pits and other methods to improve crystal quality, the incorporation of nanostructures to enhance light extraction efficiency, and the optimization of fabrication processes to reduce defects and improve uniformity. These approaches have resulted in significant improvements in the efficiency and performance of red micro-LEDs. Despite these efforts, the development of efficient and reliable red micro-LEDs remains a critical challenge. Further research is needed to address the fundamental material and device physics underlying the performance limitations of red micro-LEDs.
Chinese Food Safety Entity Recognition Based on BERT Model

Food safety has always been a hot topic of concern for humans. Online news, Weibo, and other online media platforms are generating a large amount of text data in real-time. The availability of this data can help us to promptly detect and address food safety issues, reducing the impact of such events while safeguarding human health. In the field of food safety, there is a lack of publicly available named entity datasets, especially in Chinese text. This paper collected news headlines from online media and inspection announcements from government departments to construct a corpus. It also designed a dual-task BERT model to identify food safety risks and extract food risk-related entities, leveraging the mutual relationship between the two to enhance the model’s performance. Experimental results show an F1 score of 0.86 for sequence classification and an F1 score of 0.77 for entity recognition, validating the effectiveness and soundness of the model proposed in this paper.
A Neural Network Model for Identifying Abnormal Online Learning States

For the monitoring of learning status and the assessment of learning effects in online education, it is of great significance to accurately discover learners with abnormal learning states in a timely manner. On the basis of analyzing the triggers that lead to abnormal learning state of learners, a neural network model is proposed, which models and discovers potential abnormal learners based on the behavioral data of learners’ historical learning process. Experiments on two publicly available datasets show that the method is not only able to more accurately identify whether a learner has an abnormal learning state or not, but also has good robustness compared with existing methods.
Crowd OS Powered Active 3D Mapping Leveraging Heterogeneous Crowd Robots

This paper presents a method for active 3D mapping leveraging heterogeneous crowd robots. By incorporating a UAV with its aerial perspective and powerful perception capabilities, the blind spots present in ground robot mapping are successfully eliminated, resulting in more comprehensive and accurate mapping outcomes. Furthermore, our heterogeneous crowd robots exhibits high scalability, allowing for the addition of a specified number of robots based on the mapping requirements of different indoor environments, while also supporting dynamic addition and removal. This scalability enables the system to cater to diverse applications and establishes a foundation for a more robust, comprehensive, and sustainable collective robot system. Through this research, we fully demonstrate the advantages of integrating heterogeneous robotic systems and how different types of robots with complementary capabilities collaborate to improve indoor 3D mapping processes. In particular, the introduced CrowdOS plays a significant role in task assignment and coordination, offering powerful functionalities for intelligent task allocation and coordination among different robots.
A Technical Bas System Through Cetralised System for Grid Systems in Trnsmission of Data

Industrial communication protocols are used by SCADA (supervising control and data acquisition) networks and Industrial Control Systems (ICS) to enhance their functionality. Modbus is an example of an application protocol that facilitates communication between millions of automation units. Sadly, Modbus lacks essential security features, which makes it vulnerable to errors in both design and implementation. These flaws leave systems open to eavesdropping, replay, and man-in-the-middle assaults, among other types of attacks. Taking advantage of these vulnerabilities may have serious repercussions for businesses as well as the general public, especially if important infrastructure assets like power stations, water distribution systems, and train transportation networks are attacked. The Modbus association has published security requirements in an effort to fix the security flaws in the protocol. Through the integration of TLS (Transport Layer Security) with the conventional Modbus protocol, these standards seek to improve security. Modbus packets are encapsulated by TLS in order to safeguard message integrity and offer authentication. Digital certificates based on X.509v3 are used by the security features to authenticate the client and server. This paper proposes a new secure version of the role-based access control architecture (RBAC) in order to solve security vulnerabilities with the Modbus protocol. This approach uses a message authorized via unit ID, a unique identifier for approving the Modbus frame, and an authorization procedure via roles, introduced as an arbitrary extensions within the X.509v3 certificate, to approve only the client & the Modbus frame. To summarise, the incorporation of TLS into Modbus, in conjunction with the suggested RBAC framework, effectively tackles security issues and fortifies the protocol's general resilience against any intrusions, guaranteeing the safe and effective functioning of industrial systems.
Hybrid CNN-Transformer and Multi-Region Selective Features for No-Reference Screen Content Image Quality Assessment

Visual quality assessment of screen content images (SCIs) has emerged as a significant research field in digital image processing. Current methods based on convolutional neural networks (CNN) and Transformer have exhibited promising performance. However, most of the existing methods only extract local features or global contextual features from distorted images for quality perception, lacking the information interaction between local and global features. Furthermore, due to the limited training data, most methods use image chunking for data enhancement, ignoring the visual quality differences among different image patches. Aiming at the above problems, our paper proposes an effective no-reference image quality assessment model for SCIs based on hybrid CNN-transformer and multi-region selective features. Firstly, we extract the local and global contextual features of multiple regions separately. Then, we use the bilateral feature interaction module proposed to enhance the information exchange between local and global features, thereby refining the image features with different granularities. Secondly, to fully consider the visual quality differences among different regional image patches, we propose a regional feature selection module to adaptively learn the correlation between them, assigning higher learning weights to important regional features. The experimental results show that the model proposed in this paper outperforms the state-of-the-art no-reference and full-reference SCI quality assessment methods, and achieves higher consistency with the subjective visual perception.
Feature Selection Using Binary Particle Swarm Optimization Based on Conditional Independence

Feature selection is an important step of data preprocessing in data mining, so selecting the optimal subset of features can effectively reduce the data dimension and computational cost of learning algorithms. In this paper, we focus on the use of a Binary Particle Swarm Optimization (BPSO) based on conditional independence (CIBPSO) for feature selection, and to reduce the number of features and improve classification accuracy. CIBPSO adaptively adjusts particles for local search to prevent premature convergence. In a comparative analysis with BPSO and two BPSO-based feature selection algorithms at the same computational cost on 10 datasets of different sizes, the results show that CIBPSO consistently excels in reducing the number of features and significantly improves classification accuracy across a range of datasets.
Optical Based Real Time Vehicle to Vehicle Communication System for Self Driving Vehicles

Current work introduces an optical-based real-time vehicle-to-vehicle (V2V) communication system tailored for self-driving vehicles. This system relies on LED transmitters and camera receivers equipped with a specialized optical communication image sensor (OCI). Such OCI features a “communication pixel” for swift response to changes in light intensity and an “LED flag image” output circuit, effectively highlighting high-intensity light sources, including LEDs. This advanced technology enables the camera receiver to achieve a remarkable 10 Mb/s optical signal reception as well as real-time LED detection. Through rigorous testing under actual driving conditions and varying outdoor lighting, the system proves its reliability, ensuring robust LED detection using the flag image method, even in challenging scenarios.