Miracle-dz/UniFAR-FTUs huggingface dataset:
Calamity classification in satellite and drone images using CNN and Transfer Learning

Every year, natural disasters strike, wreaking havoc on lives, property, and the environment forever. They are unstoppable phenomena. In recent years, remote sensing imaging analysis has become increasingly important for the purpose of identifying and monitoring natural disasters in the context of climate change and environmental surveillance. The ability to convey a large amount of information in a single piece while still capturing the status of the underlying ground is one advantage of aerial or satellite imaging. Remotely sensed imagery has become increasingly important for environmental and climate monitoring in recent years, particularly for the detection and management of natural disasters. Using a convolutional neural network (CNN) to better extract the catastrophe characteristic, we propose autonomous natural disaster identification in this paper. CNN is resistant to shadows and is able to accurately determine the characteristics of a disaster. It is also able to avoid operator error, which can have an impact on the efficiency of disaster relief efforts. The images used as the model’s training data are divided into four categories: floods, cyclones, wildfires, and earthquakes. The input for disaster detection is either a live video stream or a video that has already been recorded. The study demonstrated that transfer learning can be used to accurately recognize natural disasters. We hope that the findings of this study will lead to the creation of monitoring or surveillance systems that are capable of accurately recognizing natural disasters in real-time. Based on the encouraging results, the suggested method may contribute to our understanding of deep learning’s role in catastrophe detection.
Flower classification Model based on improved attention

At present, the flower classification method is mainly based on the deep learning method. The deep learning model is used to classify flowers. However, most of the models are based on their own structure to build a complex structure, which requires high training requirements for the model and carries a large network burden. Attention model is widely used in the field of computer vision. Adding attention model to convolutional neural network can significantly improve the performance of the network without increasing the burden of model training. Therefore, this paper improves on BAMxs’s attention and integrates it into the deep learning model, using the self-built data set of 7 flower categories for experiments. The experimental results show that the accuracy rate is improved by 2.81% after integration into the network, and compared with common attention, the network performance can be greatly improved by introducing a few parameters into the attention model.
VRCK: A Virtual Reality Teaching System based on Generation and Editing of Constrained Keyframes

Virtual reality (VR) demonstration is safe, convenient, low-cost, and has easy access to learning data. Keyframes have been extensively used in learning from demonstration (LfD) to teach robots skills, especially when the skills need to be further updated and adapted. However, few works use keyframe-based methods in VR demonstrations to combine the advantages of both. Besides, human annotations and multiple demonstrations are always required to generate keyframe models, which causes problems of large demonstration workload, poor interpretability, and low generalization performance. To tackle the issues mentioned above, we propose VRCK: a VR teaching system based on generation and editing of constrained keyframes. In this system, only one demonstration is needed to generate the skill keyframes, path keyframes and additional keyframes. Constraints can be generated automatically using object detection and binocular localization algorithms. During the VR teaching process, users can visualize keyframes and constraints, and use UI to bind and edit them at any time. Finally, three task cases and a physical experiment are provided to validate our system and illustrate how it enables intratask generalization.
Detection Approach of Malicious JavaScript Code Based on deep learning

Traditional machine learning methods for detecting JavaScript malicious code have the problems of complex feature extraction process, extensive computation, and difficult detection due to malicious code confusion, which are not conducive to the current requirements of JavaScript malicious code detection accuracy and real-time. This paper proposes a bi-directional long and short-time neural network (BiLSTM) based on an attention mechanism for JavaScript malicious code detection. Firstly, the obtained sample data will be deconfused, disambiguated, and vectorized to obtain the normalized data adapted to the neural network input. Second, the proposed algorithm is used to train the vectorized data and learn the abstract features of the JavaScript malicious code. Finally, the learned features are used to classify the code. The method is compared with deep learning methods and mainstream machine learning methods, and the results show that the method has a high accuracy and low false alarm rate.
Self-Distilled Depth From Single-Shot Structured Light With Intensity Reconstruction

Depth from structured light (SL) is a mainstream approach for 3D acquisition. In this article, we propose a unique depth reconstruction method for single-shot SL systems, which reconstructs the intensity image of the scene simultaneously. The merits of our method are twofold. First, the intensity image can be used to extract scene textures under ambient light from the captured image, parsing out the projected SL pattern and thus improving depth reconstruction performance. Second, the intensity information of the scene can be useful in many applications when additional RGB cameras are not available along with the SL system. The proposed method is realized by a dual-branch deep neural network for recovering depth and intensity, respectively, where the intermediate output of the intensity branch is fed into the depth branch. Specifically, we introduce a self-distillation strategy to facilitate training the network in an unsupervised manner.
Towards Contactless Remote Health Monitoring using Ambient RF Sensing

In this paper, a passive (bi-static) radar-based health monitoring system called, HBR-Radio, is introduced. HBR-Radio utilizes ambient radio frequency disturbance sensing to achieve remote health monitoring. HBR-Radio is a vital signs monitoring system that detects heartbeat and breathing rates without the need for any body mounted contact based devices. A Wi-Fi access point, using a standard 802.11 protocol, is utilized as the transmitter (illuminator) of the system. A Software-Defined Radio (SDR) platform is used to capture the signal transmitted directly from the source (reference signal), and the reflected signals from the surroundings including the one reflected from the monitored person (scatter signal). To extract the desired vital signs, several signal processing techniques are utilized. The importance of the introduced system stems from the need for developing integrated smart remote ubiquitous health monitoring systems that are convenient to users in modern society. The proposed system serves as an example application of the potential of ambient RF sensing as an enabling technology for better quality of life.
Hydrogen energy management system based on blockchain technology and convolutional neural network

This paper introduces blockchain technology to record data in the hydrogen energy management system, which enables the recording and distributed storage of system data, clarifies personnel permissions and responsibilities, and deploys convolutional neural networks on one of the nodes to diagnose faults in the hydrogen energy management system equipment. Different rights are given to the operators in the system through smart contracts. The communication network of the hydrogen energy management system is divided into VLAN(Virtual Local Area Network) according to photovoltaic, wind power and hydrogen energy networks and classifies the data streams and smart terminals in the system to improve the efficiency of the communication network transmission. The role of the system is to coordinate the various devices in the system and to convert excess electricity generated by clean energy into hydrogen for storage using electrolysis of water to produce hydrogen. When clean energy is not producing, the system deploys fuel cells to convert the hydrogen energy into electricity.
Reinforcement Learning Driving Strategy based on Auxiliary Task for Multi-Scenarios Autonomous Driving

Reinforcement learning (RL) has made great progress in autonomous driving applications. However, using one RL based driving policy for multi-scenarios autonomous driving is still challenging for RL in autonomous driving. There are different observations and reward measurements in different scenarios. At the same time, there is also the problem of multi-source heterogeneous observation in autonomous driving. To address the problems above, we propose a reinforcement learning framework based on the auxiliary task. Firstly, we designed a reward function to enable vehicles to learn safe and efficient strategies. Further, an auxiliary task is designed to learn the characteristics of different scenarios so that the ego agent can adopt different strategies for different scenarios. Finally, in order to handle the driving problem in multiple scenarios, we propose a representation network based on Multi-layer perceptron (MLP), Convolutional neural network (CNN), and Transformer networks to learn multi-source heterogeneous observation. The multi-source heterogeneous observation consists of the ego vehicle state, the bird's eye view (BEV) state and neighbour vehicle states. Experiments show that our method achieves a higher success rate compared to a popular reinforcement learning algorithm.
Conductive FDM Filament: Electrical Resistivity Assessment and Sensor Applications

A conductive filament proposed for Fused Deposition Modeling was produced and evaluated in terms of electrical resistivity. Thermoplastic polyester polylactic acid (PLA) was selected as a pristine polymer. Carbon black (CB) was added to PLA to improve the electrical conductivity. The incorporation ratio of the CB in PLA was set to 30 wt. %. Neat polymer and another commercially available filament with CB filling were included in the measurements and comparison. Volume and surface resistivity was measured perpendicularly to the printing direction. Further, the resistivity evaluation of the materials was performed in the same direction as the filament deposition. This resistivity was determined using a four-wire measuring method and purposefully designed specimens. A decrease of twelve orders of volume resistivity was observed for filled PLA. Mechanical properties were determined via tensile testing. The behavior of the material under thermomechanical loading was observed by dynamic mechanical analysis (DMA). Glass transition temperature was determined from DMA diagrams. A touch sensor in the form of circular electrodes was prepared for the purpose of practical use assessment. The functionality of the sensor was verified by switching the transistor, which controlled the LED. Magnitudes of electric current flowing through the sensor at various supplied voltage levels were monitored. The second practical utilization was demonstrated by the strain gauge. The sensor for bending detection was designed to exhibit resistivity in hundreds of kiloohms. The strain sensing behavior of the strain gauge was determined by conducting tensile loading.
Detection of Breast Tumor Using Microstrip Ring Resonator

In this paper a new method will be used to detect breast cancer without affecting the human body. This will use a microstrip ring resonator operated at 2.4 GHz with supporting material for detecting of tumor based on measuring the resonance frequency, effective dielectric constant and then calculated dielectric constant.The breast phantom was designed and simulated in the form of a rectangle shape containing, Skin, Fat and Glandular, whereas the tumor is shaped as spherical with different radiuses.Simulation results was carried out using CST software, were the tumor was detected by resonance frequency and return loss by comparing between the healthy and affected breast. The results showed that, the tumor with size of 0.5mm radius was detected.Finally, this method was simplified by removing the support material and the simulation process is repeated again and it gave a close simulated results as with it.
Proportionate Exponential Hyperbolic Sine Adaptive Filter for Sparse System Identification

The hyperbolic group of algorithms for adaptive filters has been extensively exploited in recent years to tackle the non-Gaussian noise in a system. A recently developed exponential hyperbolic sine adaptive filter (EHSAF) algorithm provided better performance compared to other algorithms developed for non-Gaussian noise. In this paper, a novel proportionate exponential hyperbolic sine adaptive filter (PEHSAF) algorithm is proposed to provide enhanced behaviour in the presence of impulsive noise for a system having sparsity in the impulse response. The number of active components compared to inactive components defines the sparsity of a system. The enhanced behaviour of the proposed PEHSAF algorithm is demonstrated by the experimental results for a sparse system identification scenario where the physical system is affected by impulsive noise and alpha-stable noise.
Neural network-based variable impedance control of flexible joint robots

In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.
Drug Repurposing for Type 2 Diabetes Using Combined Textual and Structural Graph Representation Based on Transformer

Type 2 diabetes has been predominant for years. There is still no cure to help patients completely recover from the sickness. Drug repurposing can help to discover a new drug by repositioning existing drugs for new medications. Prior attempts, however, to resolve this issue are found to be limited. Performance relied on a single representation without any improvement being achieved. To surmount this task, a deep learning approach is applied. Link prediction using graph neural networks is used. Our model’s architecture is based on a “transformer”: a recent and popular deep learning network. For our link prediction in the graph, each entity is embedded by using both: (1) structural information sourced from a node and its neighbor’s nodes, and (2) semantic information extracted from its name and description. The experiment is conducted via type 2 diabetes data that we extracted and prepared from PubMed and UMLS Metathesaurus. Results demonstrate that our combined model, by showing an increase of 77.17% on the mean reciprocal rank (MRR) score for the drug discovery task, can outperform other models that only have a single module i.e. StAR and HittER. Finally, after deploying the model for drug repurposing, we can discover some drugs that have the potential to be used for type 2 diabetes treatment.
Economic dispatch of power system based on neural network algorithm

In order to improve the absorption of new energy and quickly solve the economic dispatching problem of the power system, this paper uses neural network algorithm to build a neural network model framework to solve the unit power generation plan, cluster the load forecasting data and photovoltaic power generation data, use the improved six-node system to construct a data set, train the neural network algorithm, and finally verify the accuracy of the algorithm, providing a basis for the framework requirements in the actual project.
The symmetry structures of curved manifolds and wave equations

Killing vectors are widely used to study conservation laws admitted by spacetime metrics or to determine exact solutions of Einstein field equations (EFE) via Killing’s equation. Its solutions on a manifold are in one-to-one correspondence with continuous symmetries of the metric on that manifold. Two well known spherically symmetric static spacetime metrics in Relativity that admit maximal symmetry are given by Minkowski and de-Sitter metrics. Some other spherically symmetric metrics forming interesting solutions of the EFE are known as Schwarzschild, Kerr, Bertotti-Robinson and Einstein metrics. We study the symmetry properties and conservation laws of the geodesic equations following these metrics as well as the wave and Klein-Gordon (KG) type equations constructed using the covariant d’Alembertian operator on these manifolds. As expected, properties of reduction procedures using symmetries are more involved than on the well known flat (Minkowski) manifold.
AI Processor based Data Correction for Enhancing Accuracy of Ultrasonic Sensor

The usage of various sensors in vehicles has increased with the generalization of advanced driver assistance systems (ADAS). To ensure the safety of drivers and pedestrians, considering the accuracy of measured sensor data is essential. In this paper, we propose a data correction system for enhancing the accuracy of distance data from an ultrasonic sensor utilizing an AI processor. The proposed system detects the motion of an object and adjusts the obtained distance data to align with an ideal gradient of sequential data. Experimental results of the proposed system show an error detection rate of 90.6%.
Noninvasive Wearable Device for Monitoring and Assisting Asthma Patients

Asthma is a chronic condition that affects the air passages in the lungs, causing symptoms such as cough, wheeze, shortness of breath, and chest tightness, which can be triggered by various factors including viral infections, dust, smoke, pollen, and soaps. It can affect patients’ daily lives in many harsh, debilitating ways, severe cases can lead to emergency health care, hospitalization, and even death. Although asthma can’t be cured, good management with inhaled medications can control the disease and enable people with asthma to lead a normal, active life. One of the ways in which asthma management becomes easier is the prediction of severity of asthma exacerbations in a patient. This model utilizes sensors and data collected from IoT devices and smartphones to predict asthma risk and severity. The model is trained on a dataset of asthma patients and takes into account various factors such as symptoms, triggers, and objective test results. The model is integrated with a non-invasive wearable device through bluetooth. The device itself adopts the latest IoT technologies to collect data about the patient’s whereabouts, their triggers as well as the condition of their disease. As the wearable device collects information from the sensor, this data is stored in the web application, where it can be compared to the previously collected readings to predict the severity of the asthma patient. The web application provides an interface between the patient and the data collected for prediction. This system significantly benefits asthma patients by providing a way to manage their condition better.
Gait Recognition Algorithm GCGait based on Skeleton

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method.
Dual Band Circular Patch Antenna for GPS and WiMAX Applications

This paper presents the design of a dual-band microstrip circular patch antenna for GPS and WiMAX applications. The dual-band circular patch antenna was created from a single-band conventional circular patch antenna after introducing slots to the circular patch and using defected partial ground plane structure. Simulation results show that the antenna radiates at 1.228 GHz , 3.32529 GHz and 3.553GHz with a return loss lower than -20 dB at these frequencies. Antenna gain at the resonating frequencies is 4.917 dBi 5.5 dBi, and 6.145 dBi respectively. The antenna is designed over Rogers RO4003C substrate and simulated using CST microwave studio.
Utilization of Direct-Write for the Realization of a Microstrip Resonator on a Flexible Substrate

In this paper, a new method with possible challenges for the development of microstrip resonators using Printed Electronics (PE) methods as an alternative technology to fabricate Radio Frequency (RF) circuits is presented. To validate the utilization of PE technology, two microstrip resonators were designed and simulated. Designed resonators are then realized with a dispensing (or so-called direct-write) printing system on a DuPont Teijin flexible polyethylene terephthalate (PET) foil with a thickness of 175 $\mu$m and a dielectric constatnt of 3. Simulated and measured frequency responses are compared, the process of printing microstrip structures is examined and results are discussed.
Anomaly Root Cause Analysis for Wind Turbines Based on Denoising Autoencoder and Sparse Estimation

Wind turbine condition monitoring has been extensively studied to reduce maintenance costs. Although there exist a vast amount of literature on anomaly detection for wind turbine, anomaly root cause analysis has not been fully addressed so far. To cope with this problem, we propose a denoising autoencoder (DAE) based anomaly detector and performs anomaly root cause analysis using sparse estimation. For anomaly detection, deep denoising autoencoder is learned with normal history data, with enhanced robustness compared to the conventional autoencoder. The reconstruction error from the DAE is further evaluated by the exponentially weighted moving average control chart (EWMA) to reduce the false positive rate. After anomaly detection, root cause analysis performs sparse fault estimation, with the assumption that a small number of observed variables are affected under the abnormal condition. The fault estimates are then leveraged to identify the variables most relevant to the underlying anomaly root causes. Real cases on a public dataset demonstrate the effectiveness of the proposed method.